A Crash Course in R
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Introduction
About R and RStudio
R is a flexible, convenient and powerful tool for managing and analysing data, and well as for creating publication materials such as visualisations, reports and dashboards. RStudio is an IDE (Integrated Development Environment) for using R. R is free to install, as is the open source edition of RStudio (there are professional and hosted versions of RStudio which are not free). In my opinion, R and RStudio provide at least as much of the functionality you would see in other quite expensive statistical computing platforms.
About this guide
This guide will be just enough to get you going. It does not show the full range of features for every function. It uses tidyverse and isn’t very concerned with technical stuff going on under the hood.
Getting help
The help function in R provides great documentation and examples for
all functions. Use it by running ? followed by the name of
the function. For example, to find out more about the function
select just run ?select.
There are very good cheatsheets for different packages on the RStudio website. One cheatsheet that you won’t find on that page but which I think is really useful for beginners is the one on data wrangling.
Finally there are a couple of good online forums where users post their coding problems and other users try to provide solutions. One of the most active of these is Stack Overflow. Try searching or posting your problem there if you are getting stuck. Users are more likely to be able to help if you provide a reproducible example, so try to provide enough code so that they can recreate the problem themselves.
A word on web restrictions
R needs to access the web in order to download packages from CRAN. You may also want to import data from the web, e.g. through the csodata package for importing data from the CSO website. If you are having trouble with these steps then it may be because your organisation has not allowed R to access the web, so you might need to contact your IT department. If you are in the same organisation as me and you are experiencing difficulties, let me know, there are some settings that I can pass on that will help.
Packages and Tidyverse
About packages
The capabilities of R are extended through a wide array of packages. As a beginner with R, it is likely that all of the extra packages that you use will be downloaded from an online repository of packages called CRAN, which stands for the Comprehensive R Archive Network. Packages are downloaded from CRAN within the R environment, you rarely have to visit the CRAN website at all. There is a stringent process to getting your package included on CRAN which includes a series of checks and testing, so there is a very low likelihood of you ever downloading malicious code from CRAN.
Tidyverse and Base R
Tidyverse is a very widely used collection of packages which makes it easier to write and read code in R. It is so commonly used that it is almost ubiquitous, so I wouldn’t hesitate to recommend that you begin learning R through tidyverse. If you are not using tidyverse and you are only using the handful of default packages in R then you would say that you are using ‘base R’, where ‘base’ is the main default package (I think there are seven default packages). This is not an ‘either-or’ decision, when you load tidyverse it is perfectly fine to write code using the ‘base’ set of functions. It’s good to understand base R, but I think it’s ok to pick this up as you go along rather than starting out with exclusive use of base R.
Installing and loading
To install a package you use the function
install.packages, so to install the tidyverse packages you
run install.packages("tidyverse"). Then to use the package
you have to load it into your environment using the function
library, so to use the tidyverse functions and features you
run library(tidyverse) (note you need inverted commas for
install.packages but not for library).
You only have to install a package once, or again if you want to
update the package, but you have to load the package every time you
start a new session in R. Note that the default packages that
come with R do not have to be installed or loaded using
install.packages and library.
Core tidyverse and other tidyverse
Within tidyverse there are eight core packages, and then a couple of
extra packages and some back-end packages. All of these are installed
when you run install.packages("tidyverse"), but only the
core packages are loaded when you run library(tidyverse).
This is not something that you have to worry too much about, it just
means that you have to run an extra library line when you
want to use one of those extra packages that come with tidyverse but are
beyond the core tidyverse packages.
The RStudio environment
A screenshot of RStudio is shown below. There are four main panels. This guide will not go through every single feature in RStudio, but will hopefully be enough to get you started.
- The top left panel is where you write and run code and where you
view your datasets. You can open up a new scripting window by clicking
File > New File > R Script (or ‘Ctrl Shift n’). You can save these
as ‘.R’ files. To run code in this panel you have a couple of options.
My preferred way is to select the block of code that I want to run and
hit ‘Ctrl Enter’. If you don’t select a block of code then it will run
whatever line your cursor is on, and helpfully, the cursor automatically
jumps on to the next line. Equivalent to hitting ‘Ctrl Enter’ is hitting
the ‘Run’ button with the green arrow to the top. To run all of the code
in the script window, you can press ‘Ctrl Alt r’. To view datasets in
this window you can click them in the ‘Environment’ panel (top right) or
run
View(dataset_name). - The bottom left panel is the console, which does two things. The first is that it outputs useful information after you have run some code, such as warnings or any print statements. The second thing is that you can also write code here. Generally I would write code in the Console if I didn’t care about not having access to that code again, so I might run quick spot checks in the console, and it is also where I would run code for installing packages. There are tabs for ‘Terminal’, ‘R Markdown’ and ‘Jobs’ which you don’t need to worry about for now.
- I would call the top right panel the environment panel, even though
‘Environment’ is only one of the six tabs in that panel, because I have
never used the other five tabs. The environment panel shows you all the
datasets and other variables that you have created. There is a blue
arrow next to the datasets which allows you to browse their structure.
Note that there are some datasets which exist and which you can use, but
they don’t appear in the environment panel. These are usually associated
with training or as demo datasets to play with a package. One of these
is
mtcars, which contains information on 32 cars. In the code in the screenshot, I have usedmtcarsto make a new duplicate dataset calledmy_datawhich does appear in the environment panel. - The bottom right panel shows plots and help, and also allows you to browse files and installed packages. I don’t think it is too difficult to find your way around this panel. If you create a plot or run a help command then the appropriate tab will become active.
A screenshot of the RStudio environment
Data objects in R
Vectors
Vectors are one-dimensional objects containing the same type of data,
so all entries are either numerical, character (strings) or logical
(TRUE or FALSE). These are created using the function c(),
with the elements separated by commas. A vector of three names would
look like: c("Andy", "Betty" , "Carol"). A vector of three
numbers would look like: c(71, 8.5 , 0.13). You can make a
vector of consecutive integers by putting a colon (:)
between the low and high integer, so 5:8 is equivalent to
c(5, 6, 7, 8). Finally, you can make a logical vector like
so: c(TRUE, FALSE, FALSE).
Dataframes
Most of the data objects you’ll encounter will be dataframes, which can be thought of as tables with rows and columns. Each column has a particular type which can be numerical, character (strings) or logical (TRUE or FALSE). The columns of dataframes have names. The rows of dataframes can have names too, but this is a stupid feature, it makes more sense to just use another column for whatever information is stored as the row name.
The columns of dataframes are vectors. So what?
It is useful to know that the columns of dataframes are themselves vectors. Why is that useful to know? Two reasons. One is that you can extract a column from a dataframe and manipulate it as a vector. The second reason is that programming in R is generally geared towards working with vectors as the basic unit. It is easier to program in R with “long” datasets, where you have data for different groups stacked vertically, so you might have a column for X, a column for Y, and a column for group. If you were working in Excel you would probably arrange things differently, maybe with a “wide” dataset, having a column for X, then a column for Y_1 (representing group 1), a column for Y_2 (representing group 2), and so on. You will find that row-wise operations in R are not as straightforward as column-wise operations. Writing for loops over a dataframe is pretty slow and discouraged. You might notice other effects like this as you become more proficient.In tidyverse, there is an improved version of a dataframe called a tibble. The differences between a tibble and a regular dataframe are quite subtle from the beginner’s perspective and they can more-or-less be handled in the same way, so I wouldn’t worry about it for now. Just be aware that if you see a reference to a ‘tibble’ then it’s a special type of a dataframe.
Other structures
There are other objects in R including matrices, arrays and lists. You can skip this section if you like as it is unlikely you will need to deal with them as a beginner.
Click for info on other structures
A matrix is a 2D table with only one type of data (usually all numeric). An array is more general than a matrix, it can have any number of dimensions. I don’t think I have ever had to use matrices or arrays.
A list is a 1D structure where the elements don’t have to be of the same type, i.e. the first element could be a number and the second element could be a string. But moreover, the elements of lists can be vectors, dataframes, or even more lists. Dealing with lists would be for an intermediate or advanced course, but even as a beginner you might find yourself working with a package which uses lists. Often in these cases, the lists have a specific structure, or ‘class’, which is recognised by the package. Don’t panic, there will usually be a helpful vignette for you to follow.Fundamentals: Running Code and Creating Objects
This section covers just a couple of fundamental tools from Base R. . I prefer to do all my data filtering, manipulation and aggregation using tidyverse functions, so those kinds of processes are not covered here.
How do I run code?
After you write some code in the scripting window you will want to run it. My preferred way to run code is to place the cursor somewhere in the line of code and press ‘Ctrl Enter’. As well as running the line of code, the cursor will then jump to the next line of code, and this is useful as it helps you to run several concurrent lines of code by holding ‘Ctrl’ and tapping ‘Enter’.
You can also write code directly in the console. After you write your code there, simply hit ‘Enter’ and it will run.
Click to see other ways of running code
There are other ways of running code from your scripting window. Pressing the ‘Run’ button at the top of the scripting window has the same effect as hitting ‘Ctrl Enter’. Instead of placing the cursor in the line of code you can select the whole line of code. You can select several lines of code and then hit ‘Ctrl Enter’ or ‘Run’, and they will all run in the order they appear.
Finally, you can run all of the code in the script by pressing ‘Ctrl Alt r’. Note that this also saves the script.The assignment operator <-
To create objects, we use the assignment operator: <-
You can quickly write this operator by hitting ‘Alt -’. Let’s make an
object x which has a value of 5.
x <- 5After you run this line of code you will see the code appear in the
Console, and x appear in the Environment panel along with
its value, 5.
To output the value of x, we can write x in
a line on its own and run that line. The value 5 will appear in the
Console. We can also run x + 2 to return
7.
x
## [1] 5
x + 2
## [1] 7Let’s make a vector containing a couple of numeric values using the
function c, and output it as well.
numbers_vec <- c(5,6,7,8,9)
numbers_vec## [1] 5 6 7 8 9Click to example of string and logical vectors
We can make a vector of strings by putting each string in inverted commas. We can use single or double inverted commas.
names_vec <- c("Mary", "Louise", "Tom")
names_vec## [1] "Mary" "Louise" "Tom"And we can make a logical vector like so. Note that I am writing
T and F which are abbreviations of
TRUE and FALSE and work in exactly the same
way.
logical_vec <- c(T, F, F, T)
logical_vec## [1] TRUE FALSE FALSE TRUEI find that I very rarely need to make dataframes from scratch. As a beginner you usually work with built-in sample dataframes, and as a regular user you usually work with dataframes created from data from files or from the web.
Although the built-in datasets don’t appear in the Environment panel,
they are all there waiting to be used. We will make our own copy of a
dataset from a study on oesophageal cancer which is called
esoph. Our copy of this dataset will be called
cancer. Note that cancer will appear in the
Environment panel.
cancer <- esoph| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 40-79 | 10-19 | 0 | 7 |
| 25-34 | 40-79 | 20-29 | 0 | 4 |
| 25-34 | 40-79 | 30+ | 0 | 7 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 |
| 25-34 | 80-119 | 10-19 | 0 | 1 |
| 25-34 | 80-119 | 30+ | 0 | 2 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 25-34 | 120+ | 20-29 | 0 | 1 |
| 25-34 | 120+ | 30+ | 0 | 2 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 |
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 35-44 | 40-79 | 20-29 | 1 | 13 |
| 35-44 | 40-79 | 30+ | 0 | 8 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 |
| 35-44 | 80-119 | 10-19 | 0 | 6 |
| 35-44 | 80-119 | 20-29 | 0 | 2 |
| 35-44 | 80-119 | 30+ | 0 | 1 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 |
| 35-44 | 120+ | 10-19 | 0 | 3 |
| 35-44 | 120+ | 20-29 | 2 | 2 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30+ | 5 | 2 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 45-54 | 80-119 | 20-29 | 1 | 4 |
| 45-54 | 80-119 | 30+ | 2 | 2 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 45-54 | 120+ | 20-29 | 2 | 1 |
| 45-54 | 120+ | 30+ | 4 | 0 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 55-64 | 40-79 | 30+ | 3 | 3 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 |
| 55-64 | 80-119 | 10-19 | 8 | 7 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30+ | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
| 55-64 | 120+ | 20-29 | 2 | 1 |
| 55-64 | 120+ | 30+ | 5 | 1 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 65-74 | 80-119 | 20-29 | 2 | 1 |
| 65-74 | 80-119 | 30+ | 1 | 0 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
| 65-74 | 120+ | 10-19 | 1 | 1 |
| 65-74 | 120+ | 20-29 | 1 | 0 |
| 65-74 | 120+ | 30+ | 1 | 0 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 75+ | 0-39g/day | 30+ | 1 | 2 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 20-29 | 0 | 3 |
| 75+ | 40-79 | 30+ | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
Click to see how to make a dataframe from scratch
Like I say, this is something you rarely need to do. Dataframes are
created using a bunch of named vectors separated by commas, within the
function data.frame(). Here I am making a character
variable called name, a numerical variable called
age, and a logical variable called
married.
some_dataframe <- data.frame(name = c("Mary", "Louise", "Tom"),
age = c(30, 40, 50),
married = c(FALSE, TRUE, TRUE))Accessing elements with [] and $
We can use the square brackets [] to access specific
elements within vectors and dataframes. Let’s create the vector
numbers_vec as before and access the second element using
numbers_vec[2].
numbers_vec <- c(5,6,7,8,9)
numbers_vec[2]## [1] 6Similarly we can access the ith row and the jth
column of a dataframe using [i,j]. Let’s go to the fifth
row of the second column of the cancer dataframe using
cancer[5,2].
cancer <- esoph
cancer[5,2]## [1] 40-79
## Levels: 0-39g/day < 40-79 < 80-119 < 120+You’ll see in the output above that as well as giving the contents of
that cell (which is 40-79), it shows:
Levels: 0-39g/day < 40-79 < 80-119 < 120+. This is
because this particular variable (alcgp) is stored as a
factor. A full discussion of factors would be better
placed in an intermediate guide to R, but in this context a factor is a
variable where each element can have one of several possible values or
levels. The allowed levels of the variable
alcgp are 0-39g/day, 40-79,
80-119 and 120+. The levels of a factor can be
given an order, in which case we would say that the variable is an
‘ordinal variable’. Factors have many uses. In particular they can be
used for sorting string variables in an order that is not alphabetical,
for example Low, Medium,
High.
We can access a whole row or a whole column of a dataframe by using
square brackets with either i or j left blank.
For example, we can access the whole fifth row of cancer
using cancer[5,]:
cancer[5,]## agegp alcgp tobgp ncases ncontrols
## 5 25-34 40-79 0-9g/day 0 27To access a whole column we can leave i blank and
include the value for j (like cancer[,2]).
Alternatively, and perhaps more practically, we can refer to the column
by name. Here we will output the column agegp using
cancer$agegp, and this will print all 88 elements (along
with the list of levels, which are ordered).
cancer$agegp## [1] 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34 25-34
## [13] 25-34 25-34 25-34 35-44 35-44 35-44 35-44 35-44 35-44 35-44 35-44 35-44
## [25] 35-44 35-44 35-44 35-44 35-44 35-44 45-54 45-54 45-54 45-54 45-54 45-54
## [37] 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54 45-54 55-64 55-64
## [49] 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64 55-64
## [61] 55-64 55-64 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74 65-74
## [73] 65-74 65-74 65-74 65-74 65-74 75+ 75+ 75+ 75+ 75+ 75+ 75+
## [85] 75+ 75+ 75+ 75+
## Levels: 25-34 < 35-44 < 45-54 < 55-64 < 65-74 < 75+As mentioned in one of the collapsible sections above, the columns of
dataframes are themselves vectors. So we can access the fifth element of
the column agegp using square brackets, e.g.:
cancer$agegp[5]## [1] 25-34
## Levels: 25-34 < 35-44 < 45-54 < 55-64 < 65-74 < 75+Viewing dataframes
The easiest way to view a dataframe is by clicking on it in the
Environment panel. The dataframe will then appear in its own window in
the top-left panel, and you can scroll and search within that window.
Note that View(dataset name) will
appear in the console. Running that line of code would have the same
effect.
Beside the name of each dataframe in the environment panel is a blue
arrow which allows you to expand the dataset to look at the variable
names and the first couple of values. This view also tells you the type
of each value (logical (logi), numeric (num) or character (chr)). It
also tells you if a variable is stored as a factor, as is the case for
three of the variables in the cancer dataset.
A very similar view to that produced by expanding the values in the
environment panel can be achieved using the glimpse
function (e.g. run glimpse(cancer)). This is output to
console.
You can output the first or last couple of lines of a dataframe to
console using the head and tail functions
(e.g. run head(cancer)). By default it prints the first (or
last) 6 rows, but you can change that using the extra argument
n (e.g. run head(cancer , n=10)).
You can print an entire dataframe to console by simply running the name of the dataframe on its own.
The pipe and five key functions
We’ll go through five of the most important functions in R. These are all from tidyverse, so you will need to load the tidyverse package before progressing.
library(tidyverse)Most of the examples below start with the dataset cancer
which was created as a copy of esoph as shown earlier. In
each case the dataset cancer is pushed into a function and
the result is shown below. In practice what you might do is create a new
dataset to capture the output, by writing the name of your dataset and
the assignment operator <- at the top, so instead
of…
cancer %>%
some_function()…you might have…
my_new_dataset <- cancer %>%
some_function()The pipe: %>%
The pipe is a tool which allows you to pass an object such as a
dataframe through a series of operations. It makes code much easier to
read and write. The way it works is that it takes whatever is on the
left hand side and pushes it through the function on the right hand
side. The shortcut for writing a pipe is ‘Ctrl Shift m’. Suppose we have
a dataset called my_dataset and we want to apply a function
called some_function, the following two lines of code would
be equivalent:
my_dataset %>% some_function()
# the following line is equivalent:
some_function(my_dataset)It is clearer why the pipe makes code easier to read and write when
you consider a series of functions which may each have additional
arguments. Suppose we want to subsequently apply another function called
another_function, and furthermore that the two functions
require argument_A and argument_B. Compare the
following two equivalent lines of code.
my_dataset %>%
some_function(argument_A = 10) %>%
another_function(argument_B = 20)
# the following line is equivalent, but much harder to read!
another_function( some_function( my_dataset , argument_A = 10), argument_B = 20 )The pipe always passes the left-hand side as the first argument to the function on the right hand side. What does this mean? It means that functions that work with the pipe have to be set up so that their first argument is the object that is being manipulated (usually a dataframe). All of the tidyverse functions are set up in this way, so it’s not something that you need to worry too much about. If you were creating your own function for manipulating a dataframe in some way (something for an intermediate course) and wanted it to be compatible with the pipe, you would set it up so that the first argument to your function was the incoming dataframe.
select()
The select function allows you to select specific
columns from a dataframe. Like other tidyverse
functions, the first argument is the dataframe, so it can be used with
the pipe. Then you can pass the names of variables that you want to
keep. Here we’ll take the cancer dataframe and keep just
the variables agegp and alcgp.
cancer %>%
select(agegp , alcgp)| agegp | alcgp |
|---|---|
| 25-34 | 0-39g/day |
| 25-34 | 0-39g/day |
| 25-34 | 0-39g/day |
| 25-34 | 0-39g/day |
| 25-34 | 40-79 |
| 25-34 | 40-79 |
| 25-34 | 40-79 |
| 25-34 | 40-79 |
| 25-34 | 80-119 |
| 25-34 | 80-119 |
| 25-34 | 80-119 |
| 25-34 | 120+ |
| 25-34 | 120+ |
| 25-34 | 120+ |
| 25-34 | 120+ |
| 35-44 | 0-39g/day |
| 35-44 | 0-39g/day |
| 35-44 | 0-39g/day |
| 35-44 | 0-39g/day |
| 35-44 | 40-79 |
| 35-44 | 40-79 |
| 35-44 | 40-79 |
| 35-44 | 40-79 |
| 35-44 | 80-119 |
| 35-44 | 80-119 |
| 35-44 | 80-119 |
| 35-44 | 80-119 |
| 35-44 | 120+ |
| 35-44 | 120+ |
| 35-44 | 120+ |
| 45-54 | 0-39g/day |
| 45-54 | 0-39g/day |
| 45-54 | 0-39g/day |
| 45-54 | 0-39g/day |
| 45-54 | 40-79 |
| 45-54 | 40-79 |
| 45-54 | 40-79 |
| 45-54 | 40-79 |
| 45-54 | 80-119 |
| 45-54 | 80-119 |
| 45-54 | 80-119 |
| 45-54 | 80-119 |
| 45-54 | 120+ |
| 45-54 | 120+ |
| 45-54 | 120+ |
| 45-54 | 120+ |
| 55-64 | 0-39g/day |
| 55-64 | 0-39g/day |
| 55-64 | 0-39g/day |
| 55-64 | 0-39g/day |
| 55-64 | 40-79 |
| 55-64 | 40-79 |
| 55-64 | 40-79 |
| 55-64 | 40-79 |
| 55-64 | 80-119 |
| 55-64 | 80-119 |
| 55-64 | 80-119 |
| 55-64 | 80-119 |
| 55-64 | 120+ |
| 55-64 | 120+ |
| 55-64 | 120+ |
| 55-64 | 120+ |
| 65-74 | 0-39g/day |
| 65-74 | 0-39g/day |
| 65-74 | 0-39g/day |
| 65-74 | 0-39g/day |
| 65-74 | 40-79 |
| 65-74 | 40-79 |
| 65-74 | 40-79 |
| 65-74 | 80-119 |
| 65-74 | 80-119 |
| 65-74 | 80-119 |
| 65-74 | 80-119 |
| 65-74 | 120+ |
| 65-74 | 120+ |
| 65-74 | 120+ |
| 65-74 | 120+ |
| 75+ | 0-39g/day |
| 75+ | 0-39g/day |
| 75+ | 0-39g/day |
| 75+ | 40-79 |
| 75+ | 40-79 |
| 75+ | 40-79 |
| 75+ | 40-79 |
| 75+ | 80-119 |
| 75+ | 80-119 |
| 75+ | 120+ |
| 75+ | 120+ |
We can also drop variables by putting a minus sign in front of them.
Here we’ll drop the variables alcgp and
tobgp.
cancer %>%
select(-alcgp , -tobgp)| agegp | ncases | ncontrols |
|---|---|---|
| 25-34 | 0 | 40 |
| 25-34 | 0 | 10 |
| 25-34 | 0 | 6 |
| 25-34 | 0 | 5 |
| 25-34 | 0 | 27 |
| 25-34 | 0 | 7 |
| 25-34 | 0 | 4 |
| 25-34 | 0 | 7 |
| 25-34 | 0 | 2 |
| 25-34 | 0 | 1 |
| 25-34 | 0 | 2 |
| 25-34 | 0 | 1 |
| 25-34 | 1 | 0 |
| 25-34 | 0 | 1 |
| 25-34 | 0 | 2 |
| 35-44 | 0 | 60 |
| 35-44 | 1 | 13 |
| 35-44 | 0 | 7 |
| 35-44 | 0 | 8 |
| 35-44 | 0 | 35 |
| 35-44 | 3 | 20 |
| 35-44 | 1 | 13 |
| 35-44 | 0 | 8 |
| 35-44 | 0 | 11 |
| 35-44 | 0 | 6 |
| 35-44 | 0 | 2 |
| 35-44 | 0 | 1 |
| 35-44 | 2 | 1 |
| 35-44 | 0 | 3 |
| 35-44 | 2 | 2 |
| 45-54 | 1 | 45 |
| 45-54 | 0 | 18 |
| 45-54 | 0 | 10 |
| 45-54 | 0 | 4 |
| 45-54 | 6 | 32 |
| 45-54 | 4 | 17 |
| 45-54 | 5 | 10 |
| 45-54 | 5 | 2 |
| 45-54 | 3 | 13 |
| 45-54 | 6 | 8 |
| 45-54 | 1 | 4 |
| 45-54 | 2 | 2 |
| 45-54 | 4 | 0 |
| 45-54 | 3 | 1 |
| 45-54 | 2 | 1 |
| 45-54 | 4 | 0 |
| 55-64 | 2 | 47 |
| 55-64 | 3 | 19 |
| 55-64 | 3 | 9 |
| 55-64 | 4 | 2 |
| 55-64 | 9 | 31 |
| 55-64 | 6 | 15 |
| 55-64 | 4 | 13 |
| 55-64 | 3 | 3 |
| 55-64 | 9 | 9 |
| 55-64 | 8 | 7 |
| 55-64 | 3 | 3 |
| 55-64 | 4 | 0 |
| 55-64 | 5 | 5 |
| 55-64 | 6 | 1 |
| 55-64 | 2 | 1 |
| 55-64 | 5 | 1 |
| 65-74 | 5 | 43 |
| 65-74 | 4 | 10 |
| 65-74 | 2 | 5 |
| 65-74 | 0 | 2 |
| 65-74 | 17 | 17 |
| 65-74 | 3 | 7 |
| 65-74 | 5 | 4 |
| 65-74 | 6 | 7 |
| 65-74 | 4 | 8 |
| 65-74 | 2 | 1 |
| 65-74 | 1 | 0 |
| 65-74 | 3 | 1 |
| 65-74 | 1 | 1 |
| 65-74 | 1 | 0 |
| 65-74 | 1 | 0 |
| 75+ | 1 | 17 |
| 75+ | 2 | 4 |
| 75+ | 1 | 2 |
| 75+ | 2 | 3 |
| 75+ | 1 | 2 |
| 75+ | 0 | 3 |
| 75+ | 1 | 0 |
| 75+ | 1 | 0 |
| 75+ | 1 | 0 |
| 75+ | 2 | 0 |
| 75+ | 1 | 0 |
There are a couple of really useful ‘selection helper’ functions that
help you to keep or drop variables which contain certain string
patterns. For example, we can use select with the selection
helper function contains with the argument
"gp" to select only variables whose names contain the
string pattern “gp” (so agegp, alcgp and
tobgp). The functions starts_with and
ends_width are similar but the variable name has to start
or end with the string pattern.
Here we will put a minus in front of contains to
drop variables containing the string pattern “gp”.
cancer %>%
select(-contains("gp"))| ncases | ncontrols |
|---|---|
| 0 | 40 |
| 0 | 10 |
| 0 | 6 |
| 0 | 5 |
| 0 | 27 |
| 0 | 7 |
| 0 | 4 |
| 0 | 7 |
| 0 | 2 |
| 0 | 1 |
| 0 | 2 |
| 0 | 1 |
| 1 | 0 |
| 0 | 1 |
| 0 | 2 |
| 0 | 60 |
| 1 | 13 |
| 0 | 7 |
| 0 | 8 |
| 0 | 35 |
| 3 | 20 |
| 1 | 13 |
| 0 | 8 |
| 0 | 11 |
| 0 | 6 |
| 0 | 2 |
| 0 | 1 |
| 2 | 1 |
| 0 | 3 |
| 2 | 2 |
| 1 | 45 |
| 0 | 18 |
| 0 | 10 |
| 0 | 4 |
| 6 | 32 |
| 4 | 17 |
| 5 | 10 |
| 5 | 2 |
| 3 | 13 |
| 6 | 8 |
| 1 | 4 |
| 2 | 2 |
| 4 | 0 |
| 3 | 1 |
| 2 | 1 |
| 4 | 0 |
| 2 | 47 |
| 3 | 19 |
| 3 | 9 |
| 4 | 2 |
| 9 | 31 |
| 6 | 15 |
| 4 | 13 |
| 3 | 3 |
| 9 | 9 |
| 8 | 7 |
| 3 | 3 |
| 4 | 0 |
| 5 | 5 |
| 6 | 1 |
| 2 | 1 |
| 5 | 1 |
| 5 | 43 |
| 4 | 10 |
| 2 | 5 |
| 0 | 2 |
| 17 | 17 |
| 3 | 7 |
| 5 | 4 |
| 6 | 7 |
| 4 | 8 |
| 2 | 1 |
| 1 | 0 |
| 3 | 1 |
| 1 | 1 |
| 1 | 0 |
| 1 | 0 |
| 1 | 17 |
| 2 | 4 |
| 1 | 2 |
| 2 | 3 |
| 1 | 2 |
| 0 | 3 |
| 1 | 0 |
| 1 | 0 |
| 1 | 0 |
| 2 | 0 |
| 1 | 0 |
filter()
The filter function allows you to select specific
rows from a dataframe. Again the first argument is the
dataframe so that it can be used by the pipe. Then you provide some
logical expression based on the variables in the dataframe, and cases
where that expression is true are retained in the output. Let’s take the
cancer dataframe again and filter out the rows where
tobgp is equal to "30+". Note the use of the
double equals == which is the binary comparison operator
for ‘equals’.
cancer %>%
filter(togbp == "30+")| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 30+ | 0 | 5 |
| 25-34 | 40-79 | 30+ | 0 | 7 |
| 25-34 | 80-119 | 30+ | 0 | 2 |
| 25-34 | 120+ | 30+ | 0 | 2 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 |
| 35-44 | 40-79 | 30+ | 0 | 8 |
| 35-44 | 80-119 | 30+ | 0 | 1 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 |
| 45-54 | 40-79 | 30+ | 5 | 2 |
| 45-54 | 80-119 | 30+ | 2 | 2 |
| 45-54 | 120+ | 30+ | 4 | 0 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 |
| 55-64 | 40-79 | 30+ | 3 | 3 |
| 55-64 | 80-119 | 30+ | 4 | 0 |
| 55-64 | 120+ | 30+ | 5 | 1 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 |
| 65-74 | 80-119 | 30+ | 1 | 0 |
| 65-74 | 120+ | 30+ | 1 | 0 |
| 75+ | 0-39g/day | 30+ | 1 | 2 |
| 75+ | 40-79 | 30+ | 1 | 0 |
You can specify several conditions within a single
filter function. If you have two conditions and want
both of them to apply, you can separate them using a
comma or the boolean ‘AND’ symbol which is the ampersand
&.
If you have two conditions and want either of them
to apply you separate them using the boolean ‘OR’ symbol which is the
pipe | (unfortunately this character has the same name as
the %>% tool in R, but it would be read as ‘OR’).
Let’s filter the cancer dataset keeping cases where
ncases is not equal to zero or
ncontrols is greater than 20.
cancer %>%
filter(ncases != 0 | ncontrols > 20)| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 |
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 35-44 | 40-79 | 20-29 | 1 | 13 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 |
| 35-44 | 120+ | 20-29 | 2 | 2 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30+ | 5 | 2 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 45-54 | 80-119 | 20-29 | 1 | 4 |
| 45-54 | 80-119 | 30+ | 2 | 2 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 45-54 | 120+ | 20-29 | 2 | 1 |
| 45-54 | 120+ | 30+ | 4 | 0 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 55-64 | 40-79 | 30+ | 3 | 3 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 |
| 55-64 | 80-119 | 10-19 | 8 | 7 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30+ | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
| 55-64 | 120+ | 20-29 | 2 | 1 |
| 55-64 | 120+ | 30+ | 5 | 1 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 65-74 | 80-119 | 20-29 | 2 | 1 |
| 65-74 | 80-119 | 30+ | 1 | 0 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
| 65-74 | 120+ | 10-19 | 1 | 1 |
| 65-74 | 120+ | 20-29 | 1 | 0 |
| 65-74 | 120+ | 30+ | 1 | 0 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 75+ | 0-39g/day | 30+ | 1 | 2 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 30+ | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
What if we want to filter cases where a variable matches one of a
selection of different values? Using multiple OR statements would become
messy after about 3 options. A better way is to use the value matching
tool %in%. The format is
x %in% c(a, b, c, ... ) where c(a, b, c, ... )
is a vector of options which are the same type as the variable
x. Let’s filter the cancer dataset keeping only the rows
where ncases in equal to 3, 4, 5 or 6.
cancer %>%
filter(ncases %in% c(3,4,5,6))| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30+ | 5 | 2 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 45-54 | 120+ | 30+ | 4 | 0 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 55-64 | 40-79 | 30+ | 3 | 3 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30+ | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
| 55-64 | 120+ | 30+ | 5 | 1 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
Note that since these four options are consecutive integers, we could
have written the vector simply as 3:6, so an equivalent
piece of code would be:
cancer %>%
filter(ncases %in% 3:6)mutate()
Simple variable creation
The mutate function is used to edit variables or to
create new ones. Let’s take the cancer dataset and create a
new variable called new_var which is just equal to
ncases plus 5.
cancer %>%
mutate(new_var = ncases + 5)| agegp | alcgp | tobgp | ncases | ncontrols | new_var |
|---|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 | 5 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 | 5 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 | 5 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 | 5 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 | 5 |
| 25-34 | 40-79 | 10-19 | 0 | 7 | 5 |
| 25-34 | 40-79 | 20-29 | 0 | 4 | 5 |
| 25-34 | 40-79 | 30+ | 0 | 7 | 5 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 | 5 |
| 25-34 | 80-119 | 10-19 | 0 | 1 | 5 |
| 25-34 | 80-119 | 30+ | 0 | 2 | 5 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 | 5 |
| 25-34 | 120+ | 10-19 | 1 | 0 | 6 |
| 25-34 | 120+ | 20-29 | 0 | 1 | 5 |
| 25-34 | 120+ | 30+ | 0 | 2 | 5 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 | 5 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 | 6 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 | 5 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 | 5 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 | 5 |
| 35-44 | 40-79 | 10-19 | 3 | 20 | 8 |
| 35-44 | 40-79 | 20-29 | 1 | 13 | 6 |
| 35-44 | 40-79 | 30+ | 0 | 8 | 5 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 | 5 |
| 35-44 | 80-119 | 10-19 | 0 | 6 | 5 |
| 35-44 | 80-119 | 20-29 | 0 | 2 | 5 |
| 35-44 | 80-119 | 30+ | 0 | 1 | 5 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 | 7 |
| 35-44 | 120+ | 10-19 | 0 | 3 | 5 |
| 35-44 | 120+ | 20-29 | 2 | 2 | 7 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 | 6 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 | 5 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 | 5 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 | 5 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 | 11 |
| 45-54 | 40-79 | 10-19 | 4 | 17 | 9 |
| 45-54 | 40-79 | 20-29 | 5 | 10 | 10 |
| 45-54 | 40-79 | 30+ | 5 | 2 | 10 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 | 8 |
| 45-54 | 80-119 | 10-19 | 6 | 8 | 11 |
| 45-54 | 80-119 | 20-29 | 1 | 4 | 6 |
| 45-54 | 80-119 | 30+ | 2 | 2 | 7 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 | 9 |
| 45-54 | 120+ | 10-19 | 3 | 1 | 8 |
| 45-54 | 120+ | 20-29 | 2 | 1 | 7 |
| 45-54 | 120+ | 30+ | 4 | 0 | 9 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 | 7 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 | 8 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 | 8 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 | 9 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 | 14 |
| 55-64 | 40-79 | 10-19 | 6 | 15 | 11 |
| 55-64 | 40-79 | 20-29 | 4 | 13 | 9 |
| 55-64 | 40-79 | 30+ | 3 | 3 | 8 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 | 14 |
| 55-64 | 80-119 | 10-19 | 8 | 7 | 13 |
| 55-64 | 80-119 | 20-29 | 3 | 3 | 8 |
| 55-64 | 80-119 | 30+ | 4 | 0 | 9 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 | 10 |
| 55-64 | 120+ | 10-19 | 6 | 1 | 11 |
| 55-64 | 120+ | 20-29 | 2 | 1 | 7 |
| 55-64 | 120+ | 30+ | 5 | 1 | 10 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 | 10 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 | 9 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 | 7 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 | 5 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 | 22 |
| 65-74 | 40-79 | 10-19 | 3 | 7 | 8 |
| 65-74 | 40-79 | 20-29 | 5 | 4 | 10 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 | 11 |
| 65-74 | 80-119 | 10-19 | 4 | 8 | 9 |
| 65-74 | 80-119 | 20-29 | 2 | 1 | 7 |
| 65-74 | 80-119 | 30+ | 1 | 0 | 6 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 | 8 |
| 65-74 | 120+ | 10-19 | 1 | 1 | 6 |
| 65-74 | 120+ | 20-29 | 1 | 0 | 6 |
| 65-74 | 120+ | 30+ | 1 | 0 | 6 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 | 6 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 | 7 |
| 75+ | 0-39g/day | 30+ | 1 | 2 | 6 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 | 7 |
| 75+ | 40-79 | 10-19 | 1 | 2 | 6 |
| 75+ | 40-79 | 20-29 | 0 | 3 | 5 |
| 75+ | 40-79 | 30+ | 1 | 0 | 6 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 | 6 |
| 75+ | 80-119 | 10-19 | 1 | 0 | 6 |
| 75+ | 120+ | 0-9g/day | 2 | 0 | 7 |
| 75+ | 120+ | 10-19 | 1 | 0 | 6 |
Conditional variables
We can create a variable whose value is conditional on another
variable using if_else. This function takes a logical
expression as its first argument, and then its second and third
arguments provide the result if the expression is true or false
respectively. Let’s make a variable called lots_of_controls
which is "Y" if ncontrols is greater than 15,
and "N" otherwise. We’ll begin by selecting just the column
ncontrols to make the output easier to read.
cancer %>%
select(ncontrols) %>%
mutate(lots_of_controls = if_else(ncontrols > 15 , "Y" , "N"))| ncontrols | lots_of_controls |
|---|---|
| 40 | Y |
| 10 | N |
| 6 | N |
| 5 | N |
| 27 | Y |
| 7 | N |
| 4 | N |
| 7 | N |
| 2 | N |
| 1 | N |
| 2 | N |
| 1 | N |
| 0 | N |
| 1 | N |
| 2 | N |
| 60 | Y |
| 13 | N |
| 7 | N |
| 8 | N |
| 35 | Y |
| 20 | Y |
| 13 | N |
| 8 | N |
| 11 | N |
| 6 | N |
| 2 | N |
| 1 | N |
| 1 | N |
| 3 | N |
| 2 | N |
| 45 | Y |
| 18 | Y |
| 10 | N |
| 4 | N |
| 32 | Y |
| 17 | Y |
| 10 | N |
| 2 | N |
| 13 | N |
| 8 | N |
| 4 | N |
| 2 | N |
| 0 | N |
| 1 | N |
| 1 | N |
| 0 | N |
| 47 | Y |
| 19 | Y |
| 9 | N |
| 2 | N |
| 31 | Y |
| 15 | N |
| 13 | N |
| 3 | N |
| 9 | N |
| 7 | N |
| 3 | N |
| 0 | N |
| 5 | N |
| 1 | N |
| 1 | N |
| 1 | N |
| 43 | Y |
| 10 | N |
| 5 | N |
| 2 | N |
| 17 | Y |
| 7 | N |
| 4 | N |
| 7 | N |
| 8 | N |
| 1 | N |
| 0 | N |
| 1 | N |
| 1 | N |
| 0 | N |
| 0 | N |
| 17 | Y |
| 4 | N |
| 2 | N |
| 3 | N |
| 2 | N |
| 3 | N |
| 0 | N |
| 0 | N |
| 0 | N |
| 0 | N |
| 0 | N |
Let’s say I wanted to do some conditional editing on one of those factor variables…
Suppose you wanted to change the variable tobgp so that
instead of having 30+ it would read
30 or more. If tobgp was a regular string
variable, you could use the following. Note that the ‘false’ option (the
third argument to if_else) is simply tobgp,
meaning that tobgp is left as-is if the logical expression
is false. This is a common structure, at least for me.
cancer %>%
mutate(tobgp = if_else(tobgp == "30+" , "30 or more" , tobgp))However, if you try to run that piece of code you’ll get an error,
and this is because tobgp is a factor with defined levels,
and "30 or more" is not one of those levels.
There are two options here. The first is to change the variable
tobgp into a regular character variable using
mutate with as.character, and then do the
switcheroo.
cancer %>%
mutate(tobgp = as.character(tobgp)) %>%
mutate(tobgp = if_else(tobgp == "30+" , "30 or more" , tobgp))| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 |
| 25-34 | 0-39g/day | 30 or more | 0 | 5 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 40-79 | 10-19 | 0 | 7 |
| 25-34 | 40-79 | 20-29 | 0 | 4 |
| 25-34 | 40-79 | 30 or more | 0 | 7 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 |
| 25-34 | 80-119 | 10-19 | 0 | 1 |
| 25-34 | 80-119 | 30 or more | 0 | 2 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 25-34 | 120+ | 20-29 | 0 | 1 |
| 25-34 | 120+ | 30 or more | 0 | 2 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 |
| 35-44 | 0-39g/day | 30 or more | 0 | 8 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 |
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 35-44 | 40-79 | 20-29 | 1 | 13 |
| 35-44 | 40-79 | 30 or more | 0 | 8 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 |
| 35-44 | 80-119 | 10-19 | 0 | 6 |
| 35-44 | 80-119 | 20-29 | 0 | 2 |
| 35-44 | 80-119 | 30 or more | 0 | 1 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 |
| 35-44 | 120+ | 10-19 | 0 | 3 |
| 35-44 | 120+ | 20-29 | 2 | 2 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 |
| 45-54 | 0-39g/day | 30 or more | 0 | 4 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30 or more | 5 | 2 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 45-54 | 80-119 | 20-29 | 1 | 4 |
| 45-54 | 80-119 | 30 or more | 2 | 2 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 45-54 | 120+ | 20-29 | 2 | 1 |
| 45-54 | 120+ | 30 or more | 4 | 0 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30 or more | 4 | 2 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 55-64 | 40-79 | 30 or more | 3 | 3 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 |
| 55-64 | 80-119 | 10-19 | 8 | 7 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30 or more | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
| 55-64 | 120+ | 20-29 | 2 | 1 |
| 55-64 | 120+ | 30 or more | 5 | 1 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 |
| 65-74 | 0-39g/day | 30 or more | 0 | 2 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 65-74 | 80-119 | 20-29 | 2 | 1 |
| 65-74 | 80-119 | 30 or more | 1 | 0 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
| 65-74 | 120+ | 10-19 | 1 | 1 |
| 65-74 | 120+ | 20-29 | 1 | 0 |
| 65-74 | 120+ | 30 or more | 1 | 0 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 75+ | 0-39g/day | 30 or more | 1 | 2 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 20-29 | 0 | 3 |
| 75+ | 40-79 | 30 or more | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
That would work, but you would lose the ordered factor levels of
tobgp which is useful for sorting. You could re-create the
factor again (not hard but beyond the scope of a crash course). A better
way might be to recode the level "30+" in
the original factor using the function fct_recode. Here’s
how you would do that:
cancer %>%
mutate(tobgp = fct_recode(tobgp , "30 or more" = "30+"))| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 |
| 25-34 | 0-39g/day | 30 or more | 0 | 5 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 40-79 | 10-19 | 0 | 7 |
| 25-34 | 40-79 | 20-29 | 0 | 4 |
| 25-34 | 40-79 | 30 or more | 0 | 7 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 |
| 25-34 | 80-119 | 10-19 | 0 | 1 |
| 25-34 | 80-119 | 30 or more | 0 | 2 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 25-34 | 120+ | 20-29 | 0 | 1 |
| 25-34 | 120+ | 30 or more | 0 | 2 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 |
| 35-44 | 0-39g/day | 30 or more | 0 | 8 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 |
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 35-44 | 40-79 | 20-29 | 1 | 13 |
| 35-44 | 40-79 | 30 or more | 0 | 8 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 |
| 35-44 | 80-119 | 10-19 | 0 | 6 |
| 35-44 | 80-119 | 20-29 | 0 | 2 |
| 35-44 | 80-119 | 30 or more | 0 | 1 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 |
| 35-44 | 120+ | 10-19 | 0 | 3 |
| 35-44 | 120+ | 20-29 | 2 | 2 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 |
| 45-54 | 0-39g/day | 30 or more | 0 | 4 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30 or more | 5 | 2 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 45-54 | 80-119 | 20-29 | 1 | 4 |
| 45-54 | 80-119 | 30 or more | 2 | 2 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 45-54 | 120+ | 20-29 | 2 | 1 |
| 45-54 | 120+ | 30 or more | 4 | 0 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30 or more | 4 | 2 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 55-64 | 40-79 | 30 or more | 3 | 3 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 |
| 55-64 | 80-119 | 10-19 | 8 | 7 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30 or more | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
| 55-64 | 120+ | 20-29 | 2 | 1 |
| 55-64 | 120+ | 30 or more | 5 | 1 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 |
| 65-74 | 0-39g/day | 30 or more | 0 | 2 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 65-74 | 80-119 | 20-29 | 2 | 1 |
| 65-74 | 80-119 | 30 or more | 1 | 0 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
| 65-74 | 120+ | 10-19 | 1 | 1 |
| 65-74 | 120+ | 20-29 | 1 | 0 |
| 65-74 | 120+ | 30 or more | 1 | 0 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 75+ | 0-39g/day | 30 or more | 1 | 2 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 20-29 | 0 | 3 |
| 75+ | 40-79 | 30 or more | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
The function case_when provides even greater
(potentially unlimited) options for conditionally defining a value. The
format for this function is a logical expression (condition) followed by
a tilde (~) followed by the value to be assigned in the
event of that expression being true. This is repeated for further
conditions, with each option separated by a comma, and usually written
on separate lines for ease of reading. Below is an example where
amount_of_controls has a value of "very few"
if ncontrols is less than 10, "a couple" if
ncontrols is between 10 and 19, and "lots" for
ncontrols equal to 20 or more.
cancer %>%
select(ncontrols) %>%
mutate(amount_of_controls = case_when(
ncontrols < 10 ~ "very few",
ncontrols < 20 ~ "a couple",
ncontrols >= 20 ~ "lots"
))| ncontrols | amount_of_controls |
|---|---|
| 40 | lots |
| 10 | a couple |
| 6 | very few |
| 5 | very few |
| 27 | lots |
| 7 | very few |
| 4 | very few |
| 7 | very few |
| 2 | very few |
| 1 | very few |
| 2 | very few |
| 1 | very few |
| 0 | very few |
| 1 | very few |
| 2 | very few |
| 60 | lots |
| 13 | a couple |
| 7 | very few |
| 8 | very few |
| 35 | lots |
| 20 | lots |
| 13 | a couple |
| 8 | very few |
| 11 | a couple |
| 6 | very few |
| 2 | very few |
| 1 | very few |
| 1 | very few |
| 3 | very few |
| 2 | very few |
| 45 | lots |
| 18 | a couple |
| 10 | a couple |
| 4 | very few |
| 32 | lots |
| 17 | a couple |
| 10 | a couple |
| 2 | very few |
| 13 | a couple |
| 8 | very few |
| 4 | very few |
| 2 | very few |
| 0 | very few |
| 1 | very few |
| 1 | very few |
| 0 | very few |
| 47 | lots |
| 19 | a couple |
| 9 | very few |
| 2 | very few |
| 31 | lots |
| 15 | a couple |
| 13 | a couple |
| 3 | very few |
| 9 | very few |
| 7 | very few |
| 3 | very few |
| 0 | very few |
| 5 | very few |
| 1 | very few |
| 1 | very few |
| 1 | very few |
| 43 | lots |
| 10 | a couple |
| 5 | very few |
| 2 | very few |
| 17 | a couple |
| 7 | very few |
| 4 | very few |
| 7 | very few |
| 8 | very few |
| 1 | very few |
| 0 | very few |
| 1 | very few |
| 1 | very few |
| 0 | very few |
| 0 | very few |
| 17 | a couple |
| 4 | very few |
| 2 | very few |
| 3 | very few |
| 2 | very few |
| 3 | very few |
| 0 | very few |
| 0 | very few |
| 0 | very few |
| 0 | very few |
| 0 | very few |
Note that the value returned by case_when is determined
by the first true expression. This means that for the
second condition we don’t have to specify that ncontrols is
greater or equal to 10, we write ncontrols < 20 rather
than ncontrols >=10 & ncontrols < 20. This is
because the possibility that is ncontrols is less than 10
is already covered off by the first expression. If
ncontrols was equal to 5, for example, it would never make
it past the first condition into the second condition.
For the third expression (ncontrols >= 20) it
shouldn’t have been necessary to specify any logical expression at all,
since all cases remaining after the first two
expressions would be greater than 20 by default and should be
categorised as "lots". We can therefore replace
ncontrols >= 20 with the word TRUE and get
the same result (output not shown but identical to the above).
cancer %>%
select(ncontrols) %>%
mutate(amount_of_controls = case_when(
ncontrols < 10 ~ "very few",
ncontrols < 20 ~ "a couple",
TRUE ~ "lots"
))Summary, size and row number functions
Before we move on from mutate I want to mention a couple
of other useful functions because these will be useful later. We can
create a variable equal to the minimum, maximum or sum of another
variable using the functions min, max and
sum. Let’s calculate the sum of ncases as a
separate variable.
cancer %>%
mutate(total_cases = sum(ncases))| agegp | alcgp | tobgp | ncases | ncontrols | total_cases |
|---|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 | 200 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 | 200 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 | 200 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 | 200 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 | 200 |
| 25-34 | 40-79 | 10-19 | 0 | 7 | 200 |
| 25-34 | 40-79 | 20-29 | 0 | 4 | 200 |
| 25-34 | 40-79 | 30+ | 0 | 7 | 200 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 | 200 |
| 25-34 | 80-119 | 10-19 | 0 | 1 | 200 |
| 25-34 | 80-119 | 30+ | 0 | 2 | 200 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 | 200 |
| 25-34 | 120+ | 10-19 | 1 | 0 | 200 |
| 25-34 | 120+ | 20-29 | 0 | 1 | 200 |
| 25-34 | 120+ | 30+ | 0 | 2 | 200 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 | 200 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 | 200 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 | 200 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 | 200 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 | 200 |
| 35-44 | 40-79 | 10-19 | 3 | 20 | 200 |
| 35-44 | 40-79 | 20-29 | 1 | 13 | 200 |
| 35-44 | 40-79 | 30+ | 0 | 8 | 200 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 | 200 |
| 35-44 | 80-119 | 10-19 | 0 | 6 | 200 |
| 35-44 | 80-119 | 20-29 | 0 | 2 | 200 |
| 35-44 | 80-119 | 30+ | 0 | 1 | 200 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 | 200 |
| 35-44 | 120+ | 10-19 | 0 | 3 | 200 |
| 35-44 | 120+ | 20-29 | 2 | 2 | 200 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 | 200 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 | 200 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 | 200 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 | 200 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 | 200 |
| 45-54 | 40-79 | 10-19 | 4 | 17 | 200 |
| 45-54 | 40-79 | 20-29 | 5 | 10 | 200 |
| 45-54 | 40-79 | 30+ | 5 | 2 | 200 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 | 200 |
| 45-54 | 80-119 | 10-19 | 6 | 8 | 200 |
| 45-54 | 80-119 | 20-29 | 1 | 4 | 200 |
| 45-54 | 80-119 | 30+ | 2 | 2 | 200 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 | 200 |
| 45-54 | 120+ | 10-19 | 3 | 1 | 200 |
| 45-54 | 120+ | 20-29 | 2 | 1 | 200 |
| 45-54 | 120+ | 30+ | 4 | 0 | 200 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 | 200 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 | 200 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 | 200 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 | 200 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 | 200 |
| 55-64 | 40-79 | 10-19 | 6 | 15 | 200 |
| 55-64 | 40-79 | 20-29 | 4 | 13 | 200 |
| 55-64 | 40-79 | 30+ | 3 | 3 | 200 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 | 200 |
| 55-64 | 80-119 | 10-19 | 8 | 7 | 200 |
| 55-64 | 80-119 | 20-29 | 3 | 3 | 200 |
| 55-64 | 80-119 | 30+ | 4 | 0 | 200 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 | 200 |
| 55-64 | 120+ | 10-19 | 6 | 1 | 200 |
| 55-64 | 120+ | 20-29 | 2 | 1 | 200 |
| 55-64 | 120+ | 30+ | 5 | 1 | 200 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 | 200 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 | 200 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 | 200 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 | 200 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 | 200 |
| 65-74 | 40-79 | 10-19 | 3 | 7 | 200 |
| 65-74 | 40-79 | 20-29 | 5 | 4 | 200 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 | 200 |
| 65-74 | 80-119 | 10-19 | 4 | 8 | 200 |
| 65-74 | 80-119 | 20-29 | 2 | 1 | 200 |
| 65-74 | 80-119 | 30+ | 1 | 0 | 200 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 | 200 |
| 65-74 | 120+ | 10-19 | 1 | 1 | 200 |
| 65-74 | 120+ | 20-29 | 1 | 0 | 200 |
| 65-74 | 120+ | 30+ | 1 | 0 | 200 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 | 200 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 | 200 |
| 75+ | 0-39g/day | 30+ | 1 | 2 | 200 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 | 200 |
| 75+ | 40-79 | 10-19 | 1 | 2 | 200 |
| 75+ | 40-79 | 20-29 | 0 | 3 | 200 |
| 75+ | 40-79 | 30+ | 1 | 0 | 200 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 | 200 |
| 75+ | 80-119 | 10-19 | 1 | 0 | 200 |
| 75+ | 120+ | 0-9g/day | 2 | 0 | 200 |
| 75+ | 120+ | 10-19 | 1 | 0 | 200 |
We can also create a variable equal to the row number using
row_number. We’ll create my_id using this
function. Finally we can get the number of entries in the group using
n(). Let’s call this variable number_of_rows.
We’ll create both of these variables at the same time. Note that you can
create multiple variables in a single mutate function
(separated by commas). You can even create a new variable in a
mutate function and create another variable which depends
on the first one within the same mutate function.
We will see later that row_number and n are
pretty versatile functions.
cancer %>%
mutate(my_id = row_number() , number_of_rows = n())| agegp | alcgp | tobgp | ncases | ncontrols | my_id | number_of_rows |
|---|---|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 | 1 | 88 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 | 2 | 88 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 | 3 | 88 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 | 4 | 88 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 | 5 | 88 |
| 25-34 | 40-79 | 10-19 | 0 | 7 | 6 | 88 |
| 25-34 | 40-79 | 20-29 | 0 | 4 | 7 | 88 |
| 25-34 | 40-79 | 30+ | 0 | 7 | 8 | 88 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 | 9 | 88 |
| 25-34 | 80-119 | 10-19 | 0 | 1 | 10 | 88 |
| 25-34 | 80-119 | 30+ | 0 | 2 | 11 | 88 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 | 12 | 88 |
| 25-34 | 120+ | 10-19 | 1 | 0 | 13 | 88 |
| 25-34 | 120+ | 20-29 | 0 | 1 | 14 | 88 |
| 25-34 | 120+ | 30+ | 0 | 2 | 15 | 88 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 | 16 | 88 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 | 17 | 88 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 | 18 | 88 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 | 19 | 88 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 | 20 | 88 |
| 35-44 | 40-79 | 10-19 | 3 | 20 | 21 | 88 |
| 35-44 | 40-79 | 20-29 | 1 | 13 | 22 | 88 |
| 35-44 | 40-79 | 30+ | 0 | 8 | 23 | 88 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 | 24 | 88 |
| 35-44 | 80-119 | 10-19 | 0 | 6 | 25 | 88 |
| 35-44 | 80-119 | 20-29 | 0 | 2 | 26 | 88 |
| 35-44 | 80-119 | 30+ | 0 | 1 | 27 | 88 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 | 28 | 88 |
| 35-44 | 120+ | 10-19 | 0 | 3 | 29 | 88 |
| 35-44 | 120+ | 20-29 | 2 | 2 | 30 | 88 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 | 31 | 88 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 | 32 | 88 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 | 33 | 88 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 | 34 | 88 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 | 35 | 88 |
| 45-54 | 40-79 | 10-19 | 4 | 17 | 36 | 88 |
| 45-54 | 40-79 | 20-29 | 5 | 10 | 37 | 88 |
| 45-54 | 40-79 | 30+ | 5 | 2 | 38 | 88 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 | 39 | 88 |
| 45-54 | 80-119 | 10-19 | 6 | 8 | 40 | 88 |
| 45-54 | 80-119 | 20-29 | 1 | 4 | 41 | 88 |
| 45-54 | 80-119 | 30+ | 2 | 2 | 42 | 88 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 | 43 | 88 |
| 45-54 | 120+ | 10-19 | 3 | 1 | 44 | 88 |
| 45-54 | 120+ | 20-29 | 2 | 1 | 45 | 88 |
| 45-54 | 120+ | 30+ | 4 | 0 | 46 | 88 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 | 47 | 88 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 | 48 | 88 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 | 49 | 88 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 | 50 | 88 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 | 51 | 88 |
| 55-64 | 40-79 | 10-19 | 6 | 15 | 52 | 88 |
| 55-64 | 40-79 | 20-29 | 4 | 13 | 53 | 88 |
| 55-64 | 40-79 | 30+ | 3 | 3 | 54 | 88 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 | 55 | 88 |
| 55-64 | 80-119 | 10-19 | 8 | 7 | 56 | 88 |
| 55-64 | 80-119 | 20-29 | 3 | 3 | 57 | 88 |
| 55-64 | 80-119 | 30+ | 4 | 0 | 58 | 88 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 | 59 | 88 |
| 55-64 | 120+ | 10-19 | 6 | 1 | 60 | 88 |
| 55-64 | 120+ | 20-29 | 2 | 1 | 61 | 88 |
| 55-64 | 120+ | 30+ | 5 | 1 | 62 | 88 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 | 63 | 88 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 | 64 | 88 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 | 65 | 88 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 | 66 | 88 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 | 67 | 88 |
| 65-74 | 40-79 | 10-19 | 3 | 7 | 68 | 88 |
| 65-74 | 40-79 | 20-29 | 5 | 4 | 69 | 88 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 | 70 | 88 |
| 65-74 | 80-119 | 10-19 | 4 | 8 | 71 | 88 |
| 65-74 | 80-119 | 20-29 | 2 | 1 | 72 | 88 |
| 65-74 | 80-119 | 30+ | 1 | 0 | 73 | 88 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 | 74 | 88 |
| 65-74 | 120+ | 10-19 | 1 | 1 | 75 | 88 |
| 65-74 | 120+ | 20-29 | 1 | 0 | 76 | 88 |
| 65-74 | 120+ | 30+ | 1 | 0 | 77 | 88 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 | 78 | 88 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 | 79 | 88 |
| 75+ | 0-39g/day | 30+ | 1 | 2 | 80 | 88 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 | 81 | 88 |
| 75+ | 40-79 | 10-19 | 1 | 2 | 82 | 88 |
| 75+ | 40-79 | 20-29 | 0 | 3 | 83 | 88 |
| 75+ | 40-79 | 30+ | 1 | 0 | 84 | 88 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 | 85 | 88 |
| 75+ | 80-119 | 10-19 | 1 | 0 | 86 | 88 |
| 75+ | 120+ | 0-9g/day | 2 | 0 | 87 | 88 |
| 75+ | 120+ | 10-19 | 1 | 0 | 88 | 88 |
summarise()
The function summarise is used to produce aggregated
data from a dataframe. In practice it is almost always used in
combination with summary functions such as max,
sum, etc.
Let’s calculate the sum of ncases and
ncontrols from the cancer dataset. We will also calculate
the number of rows in the dataset using the function
n().
cancer %>%
summarise(total_cases = sum(ncases) ,
total_controls = sum(ncontrols),
number_of_rows = n())| total_cases | total_controls | number_of_rows |
|---|---|---|
| 200 | 775 | 88 |
Note that whereas mutate added the new calculations as
additional rows, summarise has done away with the original
data.
group_by()
The real power of mutate and summarise
becomes clear when you start to use them in combination with
group_by. This function allows you to and calculate counts
and summary functions over groups within the data. With
mutate the new grouped aggregated data is added to the
dataset, and with summarise only the aggregated data for
each group remains.
group_by with mutate
Let’s calculate the total number of controls for each of the four
bands of tobacco intake (tobgp) and add that as a new
column called total_controls.
cancer %>%
group_by(tobgp) %>%
mutate(total_controls = sum(ncases))| agegp | alcgp | tobgp | ncases | ncontrols | total_controls |
|---|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 | 78 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 | 58 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 | 33 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 | 31 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 | 78 |
| 25-34 | 40-79 | 10-19 | 0 | 7 | 58 |
| 25-34 | 40-79 | 20-29 | 0 | 4 | 33 |
| 25-34 | 40-79 | 30+ | 0 | 7 | 31 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 | 78 |
| 25-34 | 80-119 | 10-19 | 0 | 1 | 58 |
| 25-34 | 80-119 | 30+ | 0 | 2 | 31 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 | 78 |
| 25-34 | 120+ | 10-19 | 1 | 0 | 58 |
| 25-34 | 120+ | 20-29 | 0 | 1 | 33 |
| 25-34 | 120+ | 30+ | 0 | 2 | 31 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 | 78 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 | 58 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 | 33 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 | 31 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 | 78 |
| 35-44 | 40-79 | 10-19 | 3 | 20 | 58 |
| 35-44 | 40-79 | 20-29 | 1 | 13 | 33 |
| 35-44 | 40-79 | 30+ | 0 | 8 | 31 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 | 78 |
| 35-44 | 80-119 | 10-19 | 0 | 6 | 58 |
| 35-44 | 80-119 | 20-29 | 0 | 2 | 33 |
| 35-44 | 80-119 | 30+ | 0 | 1 | 31 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 | 78 |
| 35-44 | 120+ | 10-19 | 0 | 3 | 58 |
| 35-44 | 120+ | 20-29 | 2 | 2 | 33 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 | 78 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 | 58 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 | 33 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 | 31 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 | 78 |
| 45-54 | 40-79 | 10-19 | 4 | 17 | 58 |
| 45-54 | 40-79 | 20-29 | 5 | 10 | 33 |
| 45-54 | 40-79 | 30+ | 5 | 2 | 31 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 | 78 |
| 45-54 | 80-119 | 10-19 | 6 | 8 | 58 |
| 45-54 | 80-119 | 20-29 | 1 | 4 | 33 |
| 45-54 | 80-119 | 30+ | 2 | 2 | 31 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 | 78 |
| 45-54 | 120+ | 10-19 | 3 | 1 | 58 |
| 45-54 | 120+ | 20-29 | 2 | 1 | 33 |
| 45-54 | 120+ | 30+ | 4 | 0 | 31 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 | 78 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 | 58 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 | 33 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 | 31 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 | 78 |
| 55-64 | 40-79 | 10-19 | 6 | 15 | 58 |
| 55-64 | 40-79 | 20-29 | 4 | 13 | 33 |
| 55-64 | 40-79 | 30+ | 3 | 3 | 31 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 | 78 |
| 55-64 | 80-119 | 10-19 | 8 | 7 | 58 |
| 55-64 | 80-119 | 20-29 | 3 | 3 | 33 |
| 55-64 | 80-119 | 30+ | 4 | 0 | 31 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 | 78 |
| 55-64 | 120+ | 10-19 | 6 | 1 | 58 |
| 55-64 | 120+ | 20-29 | 2 | 1 | 33 |
| 55-64 | 120+ | 30+ | 5 | 1 | 31 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 | 78 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 | 58 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 | 33 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 | 31 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 | 78 |
| 65-74 | 40-79 | 10-19 | 3 | 7 | 58 |
| 65-74 | 40-79 | 20-29 | 5 | 4 | 33 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 | 78 |
| 65-74 | 80-119 | 10-19 | 4 | 8 | 58 |
| 65-74 | 80-119 | 20-29 | 2 | 1 | 33 |
| 65-74 | 80-119 | 30+ | 1 | 0 | 31 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 | 78 |
| 65-74 | 120+ | 10-19 | 1 | 1 | 58 |
| 65-74 | 120+ | 20-29 | 1 | 0 | 33 |
| 65-74 | 120+ | 30+ | 1 | 0 | 31 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 | 78 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 | 58 |
| 75+ | 0-39g/day | 30+ | 1 | 2 | 31 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 | 78 |
| 75+ | 40-79 | 10-19 | 1 | 2 | 58 |
| 75+ | 40-79 | 20-29 | 0 | 3 | 33 |
| 75+ | 40-79 | 30+ | 1 | 0 | 31 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 | 78 |
| 75+ | 80-119 | 10-19 | 1 | 0 | 58 |
| 75+ | 120+ | 0-9g/day | 2 | 0 | 78 |
| 75+ | 120+ | 10-19 | 1 | 0 | 58 |
The row number function can be used with group_by to
produce a counter or unique number for each group. Here we’ll create a
counter called age_group_counter for each value of
agegp.
cancer %>%
group_by(agegp) %>%
mutate(age_group_counter = row_number())| agegp | alcgp | tobgp | ncases | ncontrols | age_group_counter |
|---|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 | 1 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 | 2 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 | 3 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 | 4 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 | 5 |
| 25-34 | 40-79 | 10-19 | 0 | 7 | 6 |
| 25-34 | 40-79 | 20-29 | 0 | 4 | 7 |
| 25-34 | 40-79 | 30+ | 0 | 7 | 8 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 | 9 |
| 25-34 | 80-119 | 10-19 | 0 | 1 | 10 |
| 25-34 | 80-119 | 30+ | 0 | 2 | 11 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 | 12 |
| 25-34 | 120+ | 10-19 | 1 | 0 | 13 |
| 25-34 | 120+ | 20-29 | 0 | 1 | 14 |
| 25-34 | 120+ | 30+ | 0 | 2 | 15 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 | 1 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 | 2 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 | 3 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 | 4 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 | 5 |
| 35-44 | 40-79 | 10-19 | 3 | 20 | 6 |
| 35-44 | 40-79 | 20-29 | 1 | 13 | 7 |
| 35-44 | 40-79 | 30+ | 0 | 8 | 8 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 | 9 |
| 35-44 | 80-119 | 10-19 | 0 | 6 | 10 |
| 35-44 | 80-119 | 20-29 | 0 | 2 | 11 |
| 35-44 | 80-119 | 30+ | 0 | 1 | 12 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 | 13 |
| 35-44 | 120+ | 10-19 | 0 | 3 | 14 |
| 35-44 | 120+ | 20-29 | 2 | 2 | 15 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 | 1 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 | 2 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 | 3 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 | 4 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 | 5 |
| 45-54 | 40-79 | 10-19 | 4 | 17 | 6 |
| 45-54 | 40-79 | 20-29 | 5 | 10 | 7 |
| 45-54 | 40-79 | 30+ | 5 | 2 | 8 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 | 9 |
| 45-54 | 80-119 | 10-19 | 6 | 8 | 10 |
| 45-54 | 80-119 | 20-29 | 1 | 4 | 11 |
| 45-54 | 80-119 | 30+ | 2 | 2 | 12 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 | 13 |
| 45-54 | 120+ | 10-19 | 3 | 1 | 14 |
| 45-54 | 120+ | 20-29 | 2 | 1 | 15 |
| 45-54 | 120+ | 30+ | 4 | 0 | 16 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 | 1 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 | 2 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 | 3 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 | 4 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 | 5 |
| 55-64 | 40-79 | 10-19 | 6 | 15 | 6 |
| 55-64 | 40-79 | 20-29 | 4 | 13 | 7 |
| 55-64 | 40-79 | 30+ | 3 | 3 | 8 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 | 9 |
| 55-64 | 80-119 | 10-19 | 8 | 7 | 10 |
| 55-64 | 80-119 | 20-29 | 3 | 3 | 11 |
| 55-64 | 80-119 | 30+ | 4 | 0 | 12 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 | 13 |
| 55-64 | 120+ | 10-19 | 6 | 1 | 14 |
| 55-64 | 120+ | 20-29 | 2 | 1 | 15 |
| 55-64 | 120+ | 30+ | 5 | 1 | 16 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 | 1 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 | 2 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 | 3 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 | 4 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 | 5 |
| 65-74 | 40-79 | 10-19 | 3 | 7 | 6 |
| 65-74 | 40-79 | 20-29 | 5 | 4 | 7 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 | 8 |
| 65-74 | 80-119 | 10-19 | 4 | 8 | 9 |
| 65-74 | 80-119 | 20-29 | 2 | 1 | 10 |
| 65-74 | 80-119 | 30+ | 1 | 0 | 11 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 | 12 |
| 65-74 | 120+ | 10-19 | 1 | 1 | 13 |
| 65-74 | 120+ | 20-29 | 1 | 0 | 14 |
| 65-74 | 120+ | 30+ | 1 | 0 | 15 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 | 1 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 | 2 |
| 75+ | 0-39g/day | 30+ | 1 | 2 | 3 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 | 4 |
| 75+ | 40-79 | 10-19 | 1 | 2 | 5 |
| 75+ | 40-79 | 20-29 | 0 | 3 | 6 |
| 75+ | 40-79 | 30+ | 1 | 0 | 7 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 | 8 |
| 75+ | 80-119 | 10-19 | 1 | 0 | 9 |
| 75+ | 120+ | 0-9g/day | 2 | 0 | 10 |
| 75+ | 120+ | 10-19 | 1 | 0 | 11 |
group_by with summarise
Let’s get the total number of cases by age group (agegp)
and tobacco intake group (tobgp).
Note that we group by two variables using a single
group_by statement. If we use a second
group_by it will overwrite the previous grouping, the
function is not ‘additive’ in that way.
cancer %>%
group_by(agegp, tobgp) %>%
summarise(total_cases = sum(ncases))| agegp | tobgp | total_cases |
|---|---|---|
| 25-34 | 0-9g/day | 0 |
| 25-34 | 10-19 | 1 |
| 25-34 | 20-29 | 0 |
| 25-34 | 30+ | 0 |
| 35-44 | 0-9g/day | 2 |
| 35-44 | 10-19 | 4 |
| 35-44 | 20-29 | 3 |
| 35-44 | 30+ | 0 |
| 45-54 | 0-9g/day | 14 |
| 45-54 | 10-19 | 13 |
| 45-54 | 20-29 | 8 |
| 45-54 | 30+ | 11 |
| 55-64 | 0-9g/day | 25 |
| 55-64 | 10-19 | 23 |
| 55-64 | 20-29 | 12 |
| 55-64 | 30+ | 16 |
| 65-74 | 0-9g/day | 31 |
| 65-74 | 10-19 | 12 |
| 65-74 | 20-29 | 10 |
| 65-74 | 30+ | 2 |
| 75+ | 0-9g/day | 6 |
| 75+ | 10-19 | 5 |
| 75+ | 20-29 | 0 |
| 75+ | 30+ | 2 |
ungroup
After using a group_by you may wish to apply the
function ungroup(). This could avoid unexpected outcomes
later, for example if you were using a summary function on a dataset
that you had grouped for the purposes if you are using a summary
function later, for example. Here’s how you would use it (results not
shown):
cancer %>%
group_by(tobgp) %>%
mutate(total_controls = sum(ncases)) %>%
ungroup()Other useful tools
Renaming variables
You will probably need to rename variables at some stage. We use the
rename function to do that. This is another tidyverse
function, so its first argument is the dataframe to be edited and
therefore it can be used with the pipe. You can rename more than one
variable in a single rename function. Let’s take the cancer
dataset and rename the variable agegp as
age_group, and alcgp as
alcohol_group. Note that the order of the variable names in
this function is new_name = old_name.
cancer %>%
rename(age_group = agegp , alcohol_group = alcgp)| age_group | alcohol_group | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 40-79 | 10-19 | 0 | 7 |
| 25-34 | 40-79 | 20-29 | 0 | 4 |
| 25-34 | 40-79 | 30+ | 0 | 7 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 |
| 25-34 | 80-119 | 10-19 | 0 | 1 |
| 25-34 | 80-119 | 30+ | 0 | 2 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 25-34 | 120+ | 20-29 | 0 | 1 |
| 25-34 | 120+ | 30+ | 0 | 2 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 |
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 35-44 | 40-79 | 20-29 | 1 | 13 |
| 35-44 | 40-79 | 30+ | 0 | 8 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 |
| 35-44 | 80-119 | 10-19 | 0 | 6 |
| 35-44 | 80-119 | 20-29 | 0 | 2 |
| 35-44 | 80-119 | 30+ | 0 | 1 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 |
| 35-44 | 120+ | 10-19 | 0 | 3 |
| 35-44 | 120+ | 20-29 | 2 | 2 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30+ | 5 | 2 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 45-54 | 80-119 | 20-29 | 1 | 4 |
| 45-54 | 80-119 | 30+ | 2 | 2 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 45-54 | 120+ | 20-29 | 2 | 1 |
| 45-54 | 120+ | 30+ | 4 | 0 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 55-64 | 40-79 | 30+ | 3 | 3 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 |
| 55-64 | 80-119 | 10-19 | 8 | 7 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30+ | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
| 55-64 | 120+ | 20-29 | 2 | 1 |
| 55-64 | 120+ | 30+ | 5 | 1 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 65-74 | 80-119 | 20-29 | 2 | 1 |
| 65-74 | 80-119 | 30+ | 1 | 0 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
| 65-74 | 120+ | 10-19 | 1 | 1 |
| 65-74 | 120+ | 20-29 | 1 | 0 |
| 65-74 | 120+ | 30+ | 1 | 0 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 75+ | 0-39g/day | 30+ | 1 | 2 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 20-29 | 0 | 3 |
| 75+ | 40-79 | 30+ | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
Sorting datasets
You can sort datasets using the function arrange. This
is another tidyverse function so has the dataframe as the first argument
and is pipe-friendly. You can sort by as many variables as you like.
Let’s sort the cancer dataset by alcgp and then by
ncases. Note that since alcgp is an ordered
factor (ordinal variable), it is sorted according to the order of the
levels rather than alphabetically.
cancer %>%
arrange(alcgp, ncases)| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 |
| 35-44 | 0-39g/day | 30+ | 0 | 8 |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 |
| 45-54 | 0-39g/day | 30+ | 0 | 4 |
| 65-74 | 0-39g/day | 30+ | 0 | 2 |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 30+ | 1 | 2 |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 |
| 55-64 | 0-39g/day | 30+ | 4 | 2 |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 40-79 | 10-19 | 0 | 7 |
| 25-34 | 40-79 | 20-29 | 0 | 4 |
| 25-34 | 40-79 | 30+ | 0 | 7 |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 |
| 35-44 | 40-79 | 30+ | 0 | 8 |
| 75+ | 40-79 | 20-29 | 0 | 3 |
| 35-44 | 40-79 | 20-29 | 1 | 13 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 30+ | 1 | 0 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 35-44 | 40-79 | 10-19 | 3 | 20 |
| 55-64 | 40-79 | 30+ | 3 | 3 |
| 65-74 | 40-79 | 10-19 | 3 | 7 |
| 45-54 | 40-79 | 10-19 | 4 | 17 |
| 55-64 | 40-79 | 20-29 | 4 | 13 |
| 45-54 | 40-79 | 20-29 | 5 | 10 |
| 45-54 | 40-79 | 30+ | 5 | 2 |
| 65-74 | 40-79 | 20-29 | 5 | 4 |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 |
| 55-64 | 40-79 | 10-19 | 6 | 15 |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 |
| 25-34 | 80-119 | 10-19 | 0 | 1 |
| 25-34 | 80-119 | 30+ | 0 | 2 |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 |
| 35-44 | 80-119 | 10-19 | 0 | 6 |
| 35-44 | 80-119 | 20-29 | 0 | 2 |
| 35-44 | 80-119 | 30+ | 0 | 1 |
| 45-54 | 80-119 | 20-29 | 1 | 4 |
| 65-74 | 80-119 | 30+ | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 45-54 | 80-119 | 30+ | 2 | 2 |
| 65-74 | 80-119 | 20-29 | 2 | 1 |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 |
| 55-64 | 80-119 | 20-29 | 3 | 3 |
| 55-64 | 80-119 | 30+ | 4 | 0 |
| 65-74 | 80-119 | 10-19 | 4 | 8 |
| 45-54 | 80-119 | 10-19 | 6 | 8 |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 |
| 55-64 | 80-119 | 10-19 | 8 | 7 |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 |
| 25-34 | 120+ | 20-29 | 0 | 1 |
| 25-34 | 120+ | 30+ | 0 | 2 |
| 35-44 | 120+ | 10-19 | 0 | 3 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 65-74 | 120+ | 10-19 | 1 | 1 |
| 65-74 | 120+ | 20-29 | 1 | 0 |
| 65-74 | 120+ | 30+ | 1 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
| 35-44 | 120+ | 0-9g/day | 2 | 1 |
| 35-44 | 120+ | 20-29 | 2 | 2 |
| 45-54 | 120+ | 20-29 | 2 | 1 |
| 55-64 | 120+ | 20-29 | 2 | 1 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 45-54 | 120+ | 10-19 | 3 | 1 |
| 65-74 | 120+ | 0-9g/day | 3 | 1 |
| 45-54 | 120+ | 0-9g/day | 4 | 0 |
| 45-54 | 120+ | 30+ | 4 | 0 |
| 55-64 | 120+ | 0-9g/day | 5 | 5 |
| 55-64 | 120+ | 30+ | 5 | 1 |
| 55-64 | 120+ | 10-19 | 6 | 1 |
Combining datasets
Joining
You will probably need to join datasets together by some common id or
other variable. We don’t have anything to join onto the cancer dataset
yet, so let’s make a dataframe called ‘data_set_to_join’. It has one
variable in common with the cancer dataset which is tobgp,
however it has one extra value of tobgp which is
"All amounts". Then it has another variable called
tobacco_code.
data_set_to_join <- data.frame(
tobgp = c("0-9g/day", "10-19", "20-29", "30+", "All amounts"),
tobacco_code = c("A", "B", "C", "D", "X")
)| tobgp | tobacco_code |
|---|---|
| 0-9g/day | A |
| 10-19 | B |
| 20-29 | C |
| 30+ | D |
| All amounts | X |
The join functions from tidyverse are inner_join,
left_join, right_join and
full_join. You can probably guess what each of these does
(if you’re not sure check the help, e.g. ?left_join). Let’s
do a left join of the cancer dataset onto data_set_to_join. The first
two arguments to the join function are the two datasets to be joined,
and then there is a by argument which is the joining
variable wrapped in quotes. If there is more than one joining variable
then they are passed to by as a vector
(e.g. by = c("var_1", "var_2")). You can actually leave out
the by argument and R will join by whatever common
variables are present.
We’ll output the resulting dataframe into a new object called
‘combined_dataset’. The value of "All amounts" for
tobgp does not appear in combined_dataset since this is a
left join, but it would appear if you did a full_join or a
right_join instead.
combined_dataset <- left_join(cancer, data_set_to_join , by = "tobgp")| agegp | alcgp | tobgp | ncases | ncontrols | tobacco_code |
|---|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 | A |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 | B |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 | C |
| 25-34 | 0-39g/day | 30+ | 0 | 5 | D |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 | A |
| 25-34 | 40-79 | 10-19 | 0 | 7 | B |
| 25-34 | 40-79 | 20-29 | 0 | 4 | C |
| 25-34 | 40-79 | 30+ | 0 | 7 | D |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 | A |
| 25-34 | 80-119 | 10-19 | 0 | 1 | B |
| 25-34 | 80-119 | 30+ | 0 | 2 | D |
| 25-34 | 120+ | 0-9g/day | 0 | 1 | A |
| 25-34 | 120+ | 10-19 | 1 | 0 | B |
| 25-34 | 120+ | 20-29 | 0 | 1 | C |
| 25-34 | 120+ | 30+ | 0 | 2 | D |
| 35-44 | 0-39g/day | 0-9g/day | 0 | 60 | A |
| 35-44 | 0-39g/day | 10-19 | 1 | 13 | B |
| 35-44 | 0-39g/day | 20-29 | 0 | 7 | C |
| 35-44 | 0-39g/day | 30+ | 0 | 8 | D |
| 35-44 | 40-79 | 0-9g/day | 0 | 35 | A |
| 35-44 | 40-79 | 10-19 | 3 | 20 | B |
| 35-44 | 40-79 | 20-29 | 1 | 13 | C |
| 35-44 | 40-79 | 30+ | 0 | 8 | D |
| 35-44 | 80-119 | 0-9g/day | 0 | 11 | A |
| 35-44 | 80-119 | 10-19 | 0 | 6 | B |
| 35-44 | 80-119 | 20-29 | 0 | 2 | C |
| 35-44 | 80-119 | 30+ | 0 | 1 | D |
| 35-44 | 120+ | 0-9g/day | 2 | 1 | A |
| 35-44 | 120+ | 10-19 | 0 | 3 | B |
| 35-44 | 120+ | 20-29 | 2 | 2 | C |
| 45-54 | 0-39g/day | 0-9g/day | 1 | 45 | A |
| 45-54 | 0-39g/day | 10-19 | 0 | 18 | B |
| 45-54 | 0-39g/day | 20-29 | 0 | 10 | C |
| 45-54 | 0-39g/day | 30+ | 0 | 4 | D |
| 45-54 | 40-79 | 0-9g/day | 6 | 32 | A |
| 45-54 | 40-79 | 10-19 | 4 | 17 | B |
| 45-54 | 40-79 | 20-29 | 5 | 10 | C |
| 45-54 | 40-79 | 30+ | 5 | 2 | D |
| 45-54 | 80-119 | 0-9g/day | 3 | 13 | A |
| 45-54 | 80-119 | 10-19 | 6 | 8 | B |
| 45-54 | 80-119 | 20-29 | 1 | 4 | C |
| 45-54 | 80-119 | 30+ | 2 | 2 | D |
| 45-54 | 120+ | 0-9g/day | 4 | 0 | A |
| 45-54 | 120+ | 10-19 | 3 | 1 | B |
| 45-54 | 120+ | 20-29 | 2 | 1 | C |
| 45-54 | 120+ | 30+ | 4 | 0 | D |
| 55-64 | 0-39g/day | 0-9g/day | 2 | 47 | A |
| 55-64 | 0-39g/day | 10-19 | 3 | 19 | B |
| 55-64 | 0-39g/day | 20-29 | 3 | 9 | C |
| 55-64 | 0-39g/day | 30+ | 4 | 2 | D |
| 55-64 | 40-79 | 0-9g/day | 9 | 31 | A |
| 55-64 | 40-79 | 10-19 | 6 | 15 | B |
| 55-64 | 40-79 | 20-29 | 4 | 13 | C |
| 55-64 | 40-79 | 30+ | 3 | 3 | D |
| 55-64 | 80-119 | 0-9g/day | 9 | 9 | A |
| 55-64 | 80-119 | 10-19 | 8 | 7 | B |
| 55-64 | 80-119 | 20-29 | 3 | 3 | C |
| 55-64 | 80-119 | 30+ | 4 | 0 | D |
| 55-64 | 120+ | 0-9g/day | 5 | 5 | A |
| 55-64 | 120+ | 10-19 | 6 | 1 | B |
| 55-64 | 120+ | 20-29 | 2 | 1 | C |
| 55-64 | 120+ | 30+ | 5 | 1 | D |
| 65-74 | 0-39g/day | 0-9g/day | 5 | 43 | A |
| 65-74 | 0-39g/day | 10-19 | 4 | 10 | B |
| 65-74 | 0-39g/day | 20-29 | 2 | 5 | C |
| 65-74 | 0-39g/day | 30+ | 0 | 2 | D |
| 65-74 | 40-79 | 0-9g/day | 17 | 17 | A |
| 65-74 | 40-79 | 10-19 | 3 | 7 | B |
| 65-74 | 40-79 | 20-29 | 5 | 4 | C |
| 65-74 | 80-119 | 0-9g/day | 6 | 7 | A |
| 65-74 | 80-119 | 10-19 | 4 | 8 | B |
| 65-74 | 80-119 | 20-29 | 2 | 1 | C |
| 65-74 | 80-119 | 30+ | 1 | 0 | D |
| 65-74 | 120+ | 0-9g/day | 3 | 1 | A |
| 65-74 | 120+ | 10-19 | 1 | 1 | B |
| 65-74 | 120+ | 20-29 | 1 | 0 | C |
| 65-74 | 120+ | 30+ | 1 | 0 | D |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 | A |
| 75+ | 0-39g/day | 10-19 | 2 | 4 | B |
| 75+ | 0-39g/day | 30+ | 1 | 2 | D |
| 75+ | 40-79 | 0-9g/day | 2 | 3 | A |
| 75+ | 40-79 | 10-19 | 1 | 2 | B |
| 75+ | 40-79 | 20-29 | 0 | 3 | C |
| 75+ | 40-79 | 30+ | 1 | 0 | D |
| 75+ | 80-119 | 0-9g/day | 1 | 0 | A |
| 75+ | 80-119 | 10-19 | 1 | 0 | B |
| 75+ | 120+ | 0-9g/day | 2 | 0 | A |
| 75+ | 120+ | 10-19 | 1 | 0 | B |
Binding
We can also combine datasets by binding them vertically using
bind_rows and horizontally using bind_cols.
These functions take the datasets to be bound together as arguments. We
don’t have any datasets to bind ready to hand, so let’s produce
‘cancer_young’ containing the rows from ‘cancer’ where the age is
“25-34” and ‘cancer_old’ where the age is
"75+".
cancer_young <- cancer %>%
filter(agegp == "25-34")
cancer_old <- cancer %>%
filter(agegp == "75+")Now we can bind these together vertically using
bind_rows (sorry that this example is a bit
artificial).
combined_dataset <- bind_rows(cancer_young, cancer_old)| agegp | alcgp | tobgp | ncases | ncontrols |
|---|---|---|---|---|
| 25-34 | 0-39g/day | 0-9g/day | 0 | 40 |
| 25-34 | 0-39g/day | 10-19 | 0 | 10 |
| 25-34 | 0-39g/day | 20-29 | 0 | 6 |
| 25-34 | 0-39g/day | 30+ | 0 | 5 |
| 25-34 | 40-79 | 0-9g/day | 0 | 27 |
| 25-34 | 40-79 | 10-19 | 0 | 7 |
| 25-34 | 40-79 | 20-29 | 0 | 4 |
| 25-34 | 40-79 | 30+ | 0 | 7 |
| 25-34 | 80-119 | 0-9g/day | 0 | 2 |
| 25-34 | 80-119 | 10-19 | 0 | 1 |
| 25-34 | 80-119 | 30+ | 0 | 2 |
| 25-34 | 120+ | 0-9g/day | 0 | 1 |
| 25-34 | 120+ | 10-19 | 1 | 0 |
| 25-34 | 120+ | 20-29 | 0 | 1 |
| 25-34 | 120+ | 30+ | 0 | 2 |
| 75+ | 0-39g/day | 0-9g/day | 1 | 17 |
| 75+ | 0-39g/day | 10-19 | 2 | 4 |
| 75+ | 0-39g/day | 30+ | 1 | 2 |
| 75+ | 40-79 | 0-9g/day | 2 | 3 |
| 75+ | 40-79 | 10-19 | 1 | 2 |
| 75+ | 40-79 | 20-29 | 0 | 3 |
| 75+ | 40-79 | 30+ | 1 | 0 |
| 75+ | 80-119 | 0-9g/day | 1 | 0 |
| 75+ | 80-119 | 10-19 | 1 | 0 |
| 75+ | 120+ | 0-9g/day | 2 | 0 |
| 75+ | 120+ | 10-19 | 1 | 0 |
If there was a variable in one dataset but not in the other, then it
would appear in the resulting dataset with values of NA for
the rows coming from the dataset where it did not exist.
The function bind_cols works in a similar fashion. One
or more of arguments can be a vector. It produces an error if the
dataframes/vectors have varying numbers of rows/elements.
File input and output
As well as data stored in R’s own file format (.rds) and fairly
common data file types like .csv and .txt, you can import a very wide
range of data files from other programs through different packages. The
package readxl provides functions for reading in Excel files, and the
package haven has functions read_sas for SAS files,
read_stata for Stata files, and read_sav for
SPSS files. There are ‘write’ versions of these functions that allow you
to output these types of files from R. Both readxl and haven are
installed as part of the tidyverse collection but they are not loaded
with library(tidyverse), so you would have to load them
seperately to use these functions
(e.g. library(readxl)).
Here we’ll just look at CSVs and the native data file format for R (.rds).
CSVs
We can read in a CSV using read_csv. The only argument
it really needs is the filename, so you could use it like:
my_dataframe <- read_csv("my_folder/my_csv_file.csv")By default it assumes that there is a header row in your CSV which
becomes the column names for the dataframe. If that is not the case then
use the additional argument col_names = FALSE, and it will
come up with some default column names on its own.
To write a dataframe to CSV you use write_csv, which
takes a dataframe as the first argument and the filename as the second
argument, so you could use it like:
write_csv(my_dataframe, "my_folder/my_csv_file.csv")One thing with read_csv and read_excel:
When these functions are used to read a file, they guess the type of a
column by looking at the first number of rows. The number of rows that
it examines before reading the file is determined by the argument
guess_max, which by default is set to 1,000. I had a case
where I had a very large Excel file and there were some columns with
more than 1,000 missing cells before it got to an actual non-missing
value. The function read_excel then mistook the type of the
column and it wasn’t read correctly. So I had to set
guess_max = Inf (Inf meaning infinity) so that
it checked all the rows to correctly determine column type.
rds
To save a single dataframe to file, use saveRDS. It
works the same way as write_csv, e.g.
saveRDS(my_dataframe, "my_folder/my_rds_file.rds")And to read such a file use readRDS, like so:
a_new_dataframe <- readRDS("my_folder/my_rds_file.rds")save and load functions
You may see functionssave and load. I don’t
use them so much. The key differences are that you can save and load as
many objects as you like with single save and
load functions, and the load function brings
back all of the objects with the same names that they were saved with,
you don’t use load with the assignment operator to make a
new object.
Plotting
Introduction to ggplot
Plotting is done through the ggplot2 package, which is a core
tidyverse package and is loaded with
library(tidyverse).
There are other handy functions for plotting like plot
and qplot, but I think it’s best to start with ggplot2
because the format is so general.
The way it works is something like the pseudocode shown below. It
starts out with the function ggplot which just takes the
dataframe as its argument. Then there is a geometry type function which
specifies the kind of plot that will be made. There is
geom_point for points (scatterplot), geom_line
for lines, geom_col which I use for both column and bar
charts, and lots more asides. I’ve just used geom_XXX as a
placeholder here, there is no function called geom_XXX.
Within the geometry function is the aesthetic mapping function
aes. This is where you specify what variables in the data
are associated with x and y as well as other
variables you might have like size, shape,
colour, fill etc.
You can have more than one geometry function,
e.g. geom_line and geom_points for a plot with
both points and lines. You only ever have one ggplot
function per plot though.
The other_functions alluded to below refers to functions
for altering the appearance of the plot. Don’t worry about that for
now.
You’ll notice all the separate parts are connected to by a plus sign.
I like to insert a new line after each + for ease of
reading. The indentation is automatic.
ggplot(data = the_dataframe) +
geom_XXX(mapping = aes(x = var_1 , y = var_2 , <other_variables>) , <options_for_this_geom> ) +
other_functions ...Just so you know, the dataset can be defined within the geometry
function (you can put data = within geom_XXX)
and the aesthetic mapping function can be defined within
ggplot (you can put mapping = aes(...) within
ggplot).
Here we’ll use the population dataset that comes loaded with
tidyverse, but doesn’t appear in the Environment panel. You can view it
by running View(population). We’ll make plots using subsets
of data from this, making a dataset called dataset_to_plot
each time.
| country | year | population |
|---|---|---|
| Afghanistan | 1995 | 17586073 |
| Afghanistan | 1996 | 18415307 |
| Afghanistan | 1997 | 19021226 |
| Afghanistan | 1998 | 19496836 |
| Afghanistan | 1999 | 19987071 |
| Afghanistan | 2000 | 20595360 |
| Afghanistan | 2001 | 21347782 |
| Afghanistan | 2002 | 22202806 |
| Afghanistan | 2003 | 23116142 |
| Afghanistan | 2004 | 24018682 |
| Afghanistan | 2005 | 24860855 |
| Afghanistan | 2006 | 25631282 |
| Afghanistan | 2007 | 26349243 |
| Afghanistan | 2008 | 27032197 |
| Afghanistan | 2009 | 27708187 |
| Afghanistan | 2010 | 28397812 |
| Afghanistan | 2011 | 29105480 |
| Afghanistan | 2012 | 29824536 |
| Afghanistan | 2013 | 30551674 |
| Albania | 1995 | 3357858 |
| Albania | 1996 | 3341043 |
| Albania | 1997 | 3331317 |
| Albania | 1998 | 3325456 |
| Albania | 1999 | 3317941 |
| Albania | 2000 | 3304948 |
| Albania | 2001 | 3286084 |
| Albania | 2002 | 3263596 |
| Albania | 2003 | 3239385 |
| Albania | 2004 | 3216197 |
| Albania | 2005 | 3196130 |
| Albania | 2006 | 3179573 |
| Albania | 2007 | 3166222 |
| Albania | 2008 | 3156608 |
| Albania | 2009 | 3151185 |
| Albania | 2010 | 3150143 |
| Albania | 2011 | 3153883 |
| Albania | 2012 | 3162083 |
| Albania | 2013 | 3173271 |
| Algeria | 1995 | 29315463 |
| Algeria | 1996 | 29845208 |
| Algeria | 1997 | 30345466 |
| Algeria | 1998 | 30820435 |
| Algeria | 1999 | 31276295 |
| Algeria | 2000 | 31719449 |
| Algeria | 2001 | 32150198 |
| Algeria | 2002 | 32572977 |
| Algeria | 2003 | 33003442 |
| Algeria | 2004 | 33461345 |
| Algeria | 2005 | 33960903 |
| Algeria | 2006 | 34507214 |
| Algeria | 2007 | 35097043 |
| Algeria | 2008 | 35725377 |
| Algeria | 2009 | 36383302 |
| Algeria | 2010 | 37062820 |
| Algeria | 2011 | 37762962 |
| Algeria | 2012 | 38481705 |
| Algeria | 2013 | 39208194 |
| American Samoa | 1995 | 52874 |
| American Samoa | 1996 | 53926 |
| American Samoa | 1997 | 54942 |
| American Samoa | 1998 | 55899 |
| American Samoa | 1999 | 56768 |
| American Samoa | 2000 | 57522 |
| American Samoa | 2001 | 58176 |
| American Samoa | 2002 | 58729 |
| American Samoa | 2003 | 59117 |
| American Samoa | 2004 | 59262 |
| American Samoa | 2005 | 59117 |
| American Samoa | 2006 | 58652 |
| American Samoa | 2007 | 57919 |
| American Samoa | 2008 | 57053 |
| American Samoa | 2009 | 56245 |
| American Samoa | 2010 | 55636 |
| American Samoa | 2011 | 55274 |
| American Samoa | 2012 | 55128 |
| American Samoa | 2013 | 55165 |
| Andorra | 1995 | 63854 |
| Andorra | 1996 | 64274 |
| Andorra | 1997 | 64090 |
| Andorra | 1998 | 63799 |
| Andorra | 1999 | 64084 |
| Andorra | 2000 | 65399 |
| Andorra | 2001 | 68000 |
| Andorra | 2002 | 71639 |
| Andorra | 2003 | 75643 |
| Andorra | 2004 | 79060 |
| Andorra | 2005 | 81223 |
| Andorra | 2006 | 81877 |
| Andorra | 2007 | 81292 |
| Andorra | 2008 | 79969 |
| Andorra | 2009 | 78659 |
| Andorra | 2010 | 77907 |
| Andorra | 2011 | 77865 |
| Andorra | 2012 | 78360 |
| Andorra | 2013 | 79218 |
| Angola | 1995 | 12104952 |
| Angola | 1996 | 12451945 |
| Angola | 1997 | 12791388 |
| Angola | 1998 | 13137542 |
| Angola | 1999 | 13510616 |
| Angola | 2000 | 13924930 |
| Angola | 2001 | 14385283 |
| Angola | 2002 | 14886574 |
| Angola | 2003 | 15421075 |
| Angola | 2004 | 15976715 |
| Angola | 2005 | 16544376 |
| Angola | 2006 | 17122409 |
| Angola | 2007 | 17712824 |
| Angola | 2008 | 18314441 |
| Angola | 2009 | 18926650 |
| Angola | 2010 | 19549124 |
| Angola | 2011 | 20180490 |
| Angola | 2012 | 20820525 |
| Angola | 2013 | 21471618 |
| Anguilla | 1995 | 9807 |
| Anguilla | 1996 | 10063 |
| Anguilla | 1997 | 10305 |
| Anguilla | 1998 | 10545 |
| Anguilla | 1999 | 10797 |
| Anguilla | 2000 | 11071 |
| Anguilla | 2001 | 11371 |
| Anguilla | 2002 | 11693 |
| Anguilla | 2003 | 12023 |
| Anguilla | 2004 | 12342 |
| Anguilla | 2005 | 12637 |
| Anguilla | 2006 | 12903 |
| Anguilla | 2007 | 13145 |
| Anguilla | 2008 | 13365 |
| Anguilla | 2009 | 13571 |
| Anguilla | 2010 | 13768 |
| Anguilla | 2011 | 13956 |
| Anguilla | 2012 | 14132 |
| Anguilla | 2013 | 14300 |
| Antigua and Barbuda | 1995 | 68349 |
| Antigua and Barbuda | 1996 | 70245 |
| Antigua and Barbuda | 1997 | 72232 |
| Antigua and Barbuda | 1998 | 74206 |
| Antigua and Barbuda | 1999 | 76041 |
| Antigua and Barbuda | 2000 | 77648 |
| Antigua and Barbuda | 2001 | 78972 |
| Antigua and Barbuda | 2002 | 80030 |
| Antigua and Barbuda | 2003 | 80904 |
| Antigua and Barbuda | 2004 | 81718 |
| Antigua and Barbuda | 2005 | 82565 |
| Antigua and Barbuda | 2006 | 83467 |
| Antigua and Barbuda | 2007 | 84397 |
| Antigua and Barbuda | 2008 | 85349 |
| Antigua and Barbuda | 2009 | 86300 |
| Antigua and Barbuda | 2010 | 87233 |
| Antigua and Barbuda | 2011 | 88152 |
| Antigua and Barbuda | 2012 | 89069 |
| Antigua and Barbuda | 2013 | 89985 |
| Argentina | 1995 | 34833168 |
| Argentina | 1996 | 35264070 |
| Argentina | 1997 | 35690778 |
| Argentina | 1998 | 36109342 |
| Argentina | 1999 | 36514558 |
| Argentina | 2000 | 36903067 |
| Argentina | 2001 | 37273361 |
| Argentina | 2002 | 37627545 |
| Argentina | 2003 | 37970411 |
| Argentina | 2004 | 38308779 |
| Argentina | 2005 | 38647854 |
| Argentina | 2006 | 38988923 |
| Argentina | 2007 | 39331357 |
| Argentina | 2008 | 39676083 |
| Argentina | 2009 | 40023641 |
| Argentina | 2010 | 40374224 |
| Argentina | 2011 | 40728738 |
| Argentina | 2012 | 41086927 |
| Argentina | 2013 | 41446246 |
| Armenia | 1995 | 3223173 |
| Armenia | 1996 | 3173425 |
| Armenia | 1997 | 3137652 |
| Armenia | 1998 | 3112958 |
| Armenia | 1999 | 3093820 |
| Armenia | 2000 | 3076098 |
| Armenia | 2001 | 3059960 |
| Armenia | 2002 | 3047002 |
| Armenia | 2003 | 3036032 |
| Armenia | 2004 | 3025652 |
| Armenia | 2005 | 3014917 |
| Armenia | 2006 | 3002911 |
| Armenia | 2007 | 2989882 |
| Armenia | 2008 | 2977488 |
| Armenia | 2009 | 2968154 |
| Armenia | 2010 | 2963496 |
| Armenia | 2011 | 2964120 |
| Armenia | 2012 | 2969081 |
| Armenia | 2013 | 2976566 |
| Aruba | 1995 | 80326 |
| Aruba | 1996 | 83195 |
| Aruba | 1997 | 85447 |
| Aruba | 1998 | 87276 |
| Aruba | 1999 | 89004 |
| Aruba | 2000 | 90858 |
| Aruba | 2001 | 92894 |
| Aruba | 2002 | 94995 |
| Aruba | 2003 | 97015 |
| Aruba | 2004 | 98742 |
| Aruba | 2005 | 100031 |
| Aruba | 2006 | 100830 |
| Aruba | 2007 | 101219 |
| Aruba | 2008 | 101344 |
| Aruba | 2009 | 101418 |
| Aruba | 2010 | 101597 |
| Aruba | 2011 | 101932 |
| Aruba | 2012 | 102384 |
| Aruba | 2013 | 102911 |
| Australia | 1995 | 18124234 |
| Australia | 1996 | 18339037 |
| Australia | 1997 | 18563442 |
| Australia | 1998 | 18794552 |
| Australia | 1999 | 19027438 |
| Australia | 2000 | 19259377 |
| Australia | 2001 | 19487257 |
| Australia | 2002 | 19714625 |
| Australia | 2003 | 19953121 |
| Australia | 2004 | 20218481 |
| Australia | 2005 | 20520736 |
| Australia | 2006 | 20865583 |
| Australia | 2007 | 21246274 |
| Australia | 2008 | 21645095 |
| Australia | 2009 | 22037143 |
| Australia | 2010 | 22404488 |
| Australia | 2011 | 22740536 |
| Australia | 2012 | 23050471 |
| Australia | 2013 | 23342553 |
| Austria | 1995 | 7985372 |
| Austria | 1996 | 8009345 |
| Austria | 1997 | 8014124 |
| Austria | 1998 | 8009051 |
| Austria | 1999 | 8007843 |
| Austria | 2000 | 8020262 |
| Austria | 2001 | 8049440 |
| Austria | 2002 | 8091804 |
| Austria | 2003 | 8142322 |
| Austria | 2004 | 8193157 |
| Austria | 2005 | 8238604 |
| Austria | 2006 | 8277393 |
| Austria | 2007 | 8311463 |
| Austria | 2008 | 8342129 |
| Austria | 2009 | 8371711 |
| Austria | 2010 | 8401924 |
| Austria | 2011 | 8432890 |
| Austria | 2012 | 8463948 |
| Austria | 2013 | 8495145 |
| Azerbaijan | 1995 | 7770806 |
| Azerbaijan | 1996 | 7852273 |
| Azerbaijan | 1997 | 7921745 |
| Azerbaijan | 1998 | 7984460 |
| Azerbaijan | 1999 | 8047936 |
| Azerbaijan | 2000 | 8117742 |
| Azerbaijan | 2001 | 8195427 |
| Azerbaijan | 2002 | 8279768 |
| Azerbaijan | 2003 | 8370169 |
| Azerbaijan | 2004 | 8465127 |
| Azerbaijan | 2005 | 8563398 |
| Azerbaijan | 2006 | 8665006 |
| Azerbaijan | 2007 | 8770122 |
| Azerbaijan | 2008 | 8877669 |
| Azerbaijan | 2009 | 8986266 |
| Azerbaijan | 2010 | 9094718 |
| Azerbaijan | 2011 | 9202432 |
| Azerbaijan | 2012 | 9308959 |
| Azerbaijan | 2013 | 9413420 |
| Bahamas | 1995 | 280050 |
| Bahamas | 1996 | 283678 |
| Bahamas | 1997 | 286845 |
| Bahamas | 1998 | 289926 |
| Bahamas | 1999 | 293442 |
| Bahamas | 2000 | 297759 |
| Bahamas | 2001 | 303005 |
| Bahamas | 2002 | 309039 |
| Bahamas | 2003 | 315624 |
| Bahamas | 2004 | 322400 |
| Bahamas | 2005 | 329088 |
| Bahamas | 2006 | 335622 |
| Bahamas | 2007 | 342049 |
| Bahamas | 2008 | 348340 |
| Bahamas | 2009 | 354492 |
| Bahamas | 2010 | 360498 |
| Bahamas | 2011 | 366331 |
| Bahamas | 2012 | 371960 |
| Bahamas | 2013 | 377374 |
| Bahrain | 1995 | 563730 |
| Bahrain | 1996 | 579697 |
| Bahrain | 1997 | 597400 |
| Bahrain | 1998 | 617537 |
| Bahrain | 1999 | 640913 |
| Bahrain | 2000 | 668239 |
| Bahrain | 2001 | 698749 |
| Bahrain | 2002 | 732541 |
| Bahrain | 2003 | 772058 |
| Bahrain | 2004 | 820505 |
| Bahrain | 2005 | 879534 |
| Bahrain | 2006 | 950951 |
| Bahrain | 2007 | 1032353 |
| Bahrain | 2008 | 1116038 |
| Bahrain | 2009 | 1191539 |
| Bahrain | 2010 | 1251513 |
| Bahrain | 2011 | 1292764 |
| Bahrain | 2012 | 1317827 |
| Bahrain | 2013 | 1332171 |
| Bangladesh | 1995 | 119869585 |
| Bangladesh | 1996 | 122400896 |
| Bangladesh | 1997 | 124945315 |
| Bangladesh | 1998 | 127478524 |
| Bangladesh | 1999 | 129966823 |
| Bangladesh | 2000 | 132383265 |
| Bangladesh | 2001 | 134729503 |
| Bangladesh | 2002 | 137006279 |
| Bangladesh | 2003 | 139185986 |
| Bangladesh | 2004 | 141235035 |
| Bangladesh | 2005 | 143135180 |
| Bangladesh | 2006 | 144868702 |
| Bangladesh | 2007 | 146457067 |
| Bangladesh | 2008 | 147969967 |
| Bangladesh | 2009 | 149503100 |
| Bangladesh | 2010 | 151125475 |
| Bangladesh | 2011 | 152862431 |
| Bangladesh | 2012 | 154695368 |
| Bangladesh | 2013 | 156594962 |
| Barbados | 1995 | 263165 |
| Barbados | 1996 | 263884 |
| Barbados | 1997 | 264606 |
| Barbados | 1998 | 265370 |
| Barbados | 1999 | 266220 |
| Barbados | 2000 | 267190 |
| Barbados | 2001 | 268296 |
| Barbados | 2002 | 269524 |
| Barbados | 2003 | 270844 |
| Barbados | 2004 | 272205 |
| Barbados | 2005 | 273568 |
| Barbados | 2006 | 274923 |
| Barbados | 2007 | 276277 |
| Barbados | 2008 | 277634 |
| Barbados | 2009 | 279006 |
| Barbados | 2010 | 280396 |
| Barbados | 2011 | 281804 |
| Barbados | 2012 | 283221 |
| Barbados | 2013 | 284644 |
| Belarus | 1995 | 10189075 |
| Belarus | 1996 | 10156258 |
| Belarus | 1997 | 10120556 |
| Belarus | 1998 | 10080772 |
| Belarus | 1999 | 10034775 |
| Belarus | 2000 | 9981460 |
| Belarus | 2001 | 9920173 |
| Belarus | 2002 | 9852866 |
| Belarus | 2003 | 9784111 |
| Belarus | 2004 | 9719991 |
| Belarus | 2005 | 9664714 |
| Belarus | 2006 | 9620359 |
| Belarus | 2007 | 9585368 |
| Belarus | 2008 | 9555801 |
| Belarus | 2009 | 9525785 |
| Belarus | 2010 | 9491070 |
| Belarus | 2011 | 9450391 |
| Belarus | 2012 | 9405097 |
| Belarus | 2013 | 9356678 |
| Belgium | 1995 | 10161914 |
| Belgium | 1996 | 10186767 |
| Belgium | 1997 | 10205970 |
| Belgium | 1998 | 10222896 |
| Belgium | 1999 | 10242449 |
| Belgium | 2000 | 10268380 |
| Belgium | 2001 | 10300775 |
| Belgium | 2002 | 10338969 |
| Belgium | 2003 | 10384953 |
| Belgium | 2004 | 10440977 |
| Belgium | 2005 | 10508080 |
| Belgium | 2006 | 10587731 |
| Belgium | 2007 | 10678058 |
| Belgium | 2008 | 10772572 |
| Belgium | 2009 | 10862441 |
| Belgium | 2010 | 10941288 |
| Belgium | 2011 | 11006632 |
| Belgium | 2012 | 11060095 |
| Belgium | 2013 | 11104476 |
| Belize | 1995 | 206962 |
| Belize | 1996 | 212375 |
| Belize | 1997 | 218484 |
| Belize | 1998 | 225083 |
| Belize | 1999 | 231860 |
| Belize | 2000 | 238586 |
| Belize | 2001 | 245198 |
| Belize | 2002 | 251766 |
| Belize | 2003 | 258346 |
| Belize | 2004 | 265040 |
| Belize | 2005 | 271920 |
| Belize | 2006 | 278985 |
| Belize | 2007 | 286196 |
| Belize | 2008 | 293544 |
| Belize | 2009 | 301016 |
| Belize | 2010 | 308595 |
| Belize | 2011 | 316280 |
| Belize | 2012 | 324060 |
| Belize | 2013 | 331900 |
| Benin | 1995 | 5985658 |
| Benin | 1996 | 6176318 |
| Benin | 1997 | 6361301 |
| Benin | 1998 | 6546493 |
| Benin | 1999 | 6740491 |
| Benin | 2000 | 6949366 |
| Benin | 2001 | 7174911 |
| Benin | 2002 | 7414744 |
| Benin | 2003 | 7665681 |
| Benin | 2004 | 7922796 |
| Benin | 2005 | 8182362 |
| Benin | 2006 | 8443671 |
| Benin | 2007 | 8707490 |
| Benin | 2008 | 8973293 |
| Benin | 2009 | 9240783 |
| Benin | 2010 | 9509798 |
| Benin | 2011 | 9779795 |
| Benin | 2012 | 10050702 |
| Benin | 2013 | 10323474 |
| Bermuda | 1995 | 61407 |
| Bermuda | 1996 | 61703 |
| Bermuda | 1997 | 61992 |
| Bermuda | 1998 | 62276 |
| Bermuda | 1999 | 62557 |
| Bermuda | 2000 | 62837 |
| Bermuda | 2001 | 63116 |
| Bermuda | 2002 | 63393 |
| Bermuda | 2003 | 63661 |
| Bermuda | 2004 | 63911 |
| Bermuda | 2005 | 64137 |
| Bermuda | 2006 | 64337 |
| Bermuda | 2007 | 64512 |
| Bermuda | 2008 | 64667 |
| Bermuda | 2009 | 64812 |
| Bermuda | 2010 | 64951 |
| Bermuda | 2011 | 65086 |
| Bermuda | 2012 | 65216 |
| Bermuda | 2013 | 65341 |
| Bhutan | 1995 | 509105 |
| Bhutan | 1996 | 512594 |
| Bhutan | 1997 | 521146 |
| Bhutan | 1998 | 533737 |
| Bhutan | 1999 | 548599 |
| Bhutan | 2000 | 564350 |
| Bhutan | 2001 | 580888 |
| Bhutan | 2002 | 598455 |
| Bhutan | 2003 | 616383 |
| Bhutan | 2004 | 633893 |
| Bhutan | 2005 | 650417 |
| Bhutan | 2006 | 665568 |
| Bhutan | 2007 | 679365 |
| Bhutan | 2008 | 692159 |
| Bhutan | 2009 | 704542 |
| Bhutan | 2010 | 716939 |
| Bhutan | 2011 | 729429 |
| Bhutan | 2012 | 741822 |
| Bhutan | 2013 | 753947 |
| Bolivia (Plurinational State of) | 1995 | 7635362 |
| Bolivia (Plurinational State of) | 1996 | 7806953 |
| Bolivia (Plurinational State of) | 1997 | 7978521 |
| Bolivia (Plurinational State of) | 1998 | 8150214 |
| Bolivia (Plurinational State of) | 1999 | 8322408 |
| Bolivia (Plurinational State of) | 2000 | 8495271 |
| Bolivia (Plurinational State of) | 2001 | 8669066 |
| Bolivia (Plurinational State of) | 2002 | 8843350 |
| Bolivia (Plurinational State of) | 2003 | 9016787 |
| Bolivia (Plurinational State of) | 2004 | 9187610 |
| Bolivia (Plurinational State of) | 2005 | 9354709 |
| Bolivia (Plurinational State of) | 2006 | 9517395 |
| Bolivia (Plurinational State of) | 2007 | 9676456 |
| Bolivia (Plurinational State of) | 2008 | 9834098 |
| Bolivia (Plurinational State of) | 2009 | 9993406 |
| Bolivia (Plurinational State of) | 2010 | 10156601 |
| Bolivia (Plurinational State of) | 2011 | 10324445 |
| Bolivia (Plurinational State of) | 2012 | 10496285 |
| Bolivia (Plurinational State of) | 2013 | 10671200 |
| Bonaire, Saint Eustatius and Saba | 2010 | 17616 |
| Bonaire, Saint Eustatius and Saba | 2011 | 18194 |
| Bonaire, Saint Eustatius and Saba | 2012 | 18694 |
| Bonaire, Saint Eustatius and Saba | 2013 | 19130 |
| Bosnia and Herzegovina | 1995 | 3520996 |
| Bosnia and Herzegovina | 1996 | 3485575 |
| Bosnia and Herzegovina | 1997 | 3535998 |
| Bosnia and Herzegovina | 1998 | 3640821 |
| Bosnia and Herzegovina | 1999 | 3752004 |
| Bosnia and Herzegovina | 2000 | 3834364 |
| Bosnia and Herzegovina | 2001 | 3879353 |
| Bosnia and Herzegovina | 2002 | 3897579 |
| Bosnia and Herzegovina | 2003 | 3895779 |
| Bosnia and Herzegovina | 2004 | 3886723 |
| Bosnia and Herzegovina | 2005 | 3879828 |
| Bosnia and Herzegovina | 2006 | 3875157 |
| Bosnia and Herzegovina | 2007 | 3868665 |
| Bosnia and Herzegovina | 2008 | 3861201 |
| Bosnia and Herzegovina | 2009 | 3853446 |
| Bosnia and Herzegovina | 2010 | 3845929 |
| Bosnia and Herzegovina | 2011 | 3839322 |
| Bosnia and Herzegovina | 2012 | 3833916 |
| Bosnia and Herzegovina | 2013 | 3829307 |
| Botswana | 1995 | 1583453 |
| Botswana | 1996 | 1620989 |
| Botswana | 1997 | 1657349 |
| Botswana | 1998 | 1692148 |
| Botswana | 1999 | 1724924 |
| Botswana | 2000 | 1755375 |
| Botswana | 2001 | 1783349 |
| Botswana | 2002 | 1808976 |
| Botswana | 2003 | 1832602 |
| Botswana | 2004 | 1854739 |
| Botswana | 2005 | 1875805 |
| Botswana | 2006 | 1895944 |
| Botswana | 2007 | 1915187 |
| Botswana | 2008 | 1933719 |
| Botswana | 2009 | 1951715 |
| Botswana | 2010 | 1969341 |
| Botswana | 2011 | 1986701 |
| Botswana | 2012 | 2003910 |
| Botswana | 2013 | 2021144 |
| Brazil | 1995 | 161890816 |
| Brazil | 1996 | 164392423 |
| Brazil | 1997 | 166925457 |
| Brazil | 1998 | 169472347 |
| Brazil | 1999 | 172006362 |
| Brazil | 2000 | 174504898 |
| Brazil | 2001 | 176968205 |
| Brazil | 2002 | 179393768 |
| Brazil | 2003 | 181752951 |
| Brazil | 2004 | 184010283 |
| Brazil | 2005 | 186142403 |
| Brazil | 2006 | 188134315 |
| Brazil | 2007 | 189996976 |
| Brazil | 2008 | 191765567 |
| Brazil | 2009 | 193490922 |
| Brazil | 2010 | 195210154 |
| Brazil | 2011 | 196935134 |
| Brazil | 2012 | 198656019 |
| Brazil | 2013 | 200361925 |
| British Virgin Islands | 1995 | 18425 |
| British Virgin Islands | 1996 | 18834 |
| British Virgin Islands | 1997 | 19270 |
| British Virgin Islands | 1998 | 19726 |
| British Virgin Islands | 1999 | 20186 |
| British Virgin Islands | 2000 | 20643 |
| British Virgin Islands | 2001 | 21084 |
| British Virgin Islands | 2002 | 21518 |
| British Virgin Islands | 2003 | 21982 |
| British Virgin Islands | 2004 | 22522 |
| British Virgin Islands | 2005 | 23169 |
| British Virgin Islands | 2006 | 23942 |
| British Virgin Islands | 2007 | 24815 |
| British Virgin Islands | 2008 | 25716 |
| British Virgin Islands | 2009 | 26542 |
| British Virgin Islands | 2010 | 27223 |
| British Virgin Islands | 2011 | 27730 |
| British Virgin Islands | 2012 | 28088 |
| British Virgin Islands | 2013 | 28341 |
| Brunei Darussalam | 1995 | 295003 |
| Brunei Darussalam | 1996 | 302511 |
| Brunei Darussalam | 1997 | 309904 |
| Brunei Darussalam | 1998 | 317214 |
| Brunei Darussalam | 1999 | 324501 |
| Brunei Darussalam | 2000 | 331801 |
| Brunei Darussalam | 2001 | 339114 |
| Brunei Darussalam | 2002 | 346407 |
| Brunei Darussalam | 2003 | 353649 |
| Brunei Darussalam | 2004 | 360797 |
| Brunei Darussalam | 2005 | 367815 |
| Brunei Darussalam | 2006 | 374697 |
| Brunei Darussalam | 2007 | 381440 |
| Brunei Darussalam | 2008 | 388017 |
| Brunei Darussalam | 2009 | 394400 |
| Brunei Darussalam | 2010 | 400569 |
| Brunei Darussalam | 2011 | 406512 |
| Brunei Darussalam | 2012 | 412238 |
| Brunei Darussalam | 2013 | 417784 |
| Bulgaria | 1995 | 8358116 |
| Bulgaria | 1996 | 8274592 |
| Bulgaria | 1997 | 8199715 |
| Bulgaria | 1998 | 8131424 |
| Bulgaria | 1999 | 8065947 |
| Bulgaria | 2000 | 8000510 |
| Bulgaria | 2001 | 7934973 |
| Bulgaria | 2002 | 7870488 |
| Bulgaria | 2003 | 7806995 |
| Bulgaria | 2004 | 7744575 |
| Bulgaria | 2005 | 7683264 |
| Bulgaria | 2006 | 7622852 |
| Bulgaria | 2007 | 7563141 |
| Bulgaria | 2008 | 7504206 |
| Bulgaria | 2009 | 7446197 |
| Bulgaria | 2010 | 7389175 |
| Bulgaria | 2011 | 7333122 |
| Bulgaria | 2012 | 7277831 |
| Bulgaria | 2013 | 7222943 |
| Burkina Faso | 1995 | 10089876 |
| Burkina Faso | 1996 | 10372562 |
| Burkina Faso | 1997 | 10664982 |
| Burkina Faso | 1998 | 10967836 |
| Burkina Faso | 1999 | 11281942 |
| Burkina Faso | 2000 | 11607944 |
| Burkina Faso | 2001 | 11946080 |
| Burkina Faso | 2002 | 12296399 |
| Burkina Faso | 2003 | 12659086 |
| Burkina Faso | 2004 | 13034258 |
| Burkina Faso | 2005 | 13421929 |
| Burkina Faso | 2006 | 13822257 |
| Burkina Faso | 2007 | 14235075 |
| Burkina Faso | 2008 | 14659646 |
| Burkina Faso | 2009 | 15094967 |
| Burkina Faso | 2010 | 15540284 |
| Burkina Faso | 2011 | 15995313 |
| Burkina Faso | 2012 | 16460141 |
| Burkina Faso | 2013 | 16934839 |
| Burundi | 1995 | 6209923 |
| Burundi | 1996 | 6294482 |
| Burundi | 1997 | 6369573 |
| Burundi | 1998 | 6447672 |
| Burundi | 1999 | 6545273 |
| Burundi | 2000 | 6674286 |
| Burundi | 2001 | 6839376 |
| Burundi | 2002 | 7037727 |
| Burundi | 2003 | 7264340 |
| Burundi | 2004 | 7510771 |
| Burundi | 2005 | 7770392 |
| Burundi | 2006 | 8042579 |
| Burundi | 2007 | 8328312 |
| Burundi | 2008 | 8624280 |
| Burundi | 2009 | 8926687 |
| Burundi | 2010 | 9232753 |
| Burundi | 2011 | 9540362 |
| Burundi | 2012 | 9849569 |
| Burundi | 2013 | 10162532 |
| Cabo Verde | 1995 | 399477 |
| Cabo Verde | 1996 | 408790 |
| Cabo Verde | 1997 | 417562 |
| Cabo Verde | 1998 | 425932 |
| Cabo Verde | 1999 | 434168 |
| Cabo Verde | 2000 | 442426 |
| Cabo Verde | 2001 | 450812 |
| Cabo Verde | 2002 | 459140 |
| Cabo Verde | 2003 | 466939 |
| Cabo Verde | 2004 | 473577 |
| Cabo Verde | 2005 | 478651 |
| Cabo Verde | 2006 | 481940 |
| Cabo Verde | 2007 | 483713 |
| Cabo Verde | 2008 | 484651 |
| Cabo Verde | 2009 | 485714 |
| Cabo Verde | 2010 | 487601 |
| Cabo Verde | 2011 | 490556 |
| Cabo Verde | 2012 | 494401 |
| Cabo Verde | 2013 | 498897 |
| Cambodia | 1995 | 10769198 |
| Cambodia | 1996 | 11090611 |
| Cambodia | 1997 | 11395958 |
| Cambodia | 1998 | 11685332 |
| Cambodia | 1999 | 11960467 |
| Cambodia | 2000 | 12222871 |
| Cambodia | 2001 | 12472586 |
| Cambodia | 2002 | 12709336 |
| Cambodia | 2003 | 12934369 |
| Cambodia | 2004 | 13149386 |
| Cambodia | 2005 | 13356424 |
| Cambodia | 2006 | 13555054 |
| Cambodia | 2007 | 13747288 |
| Cambodia | 2008 | 13940518 |
| Cambodia | 2009 | 14144225 |
| Cambodia | 2010 | 14364931 |
| Cambodia | 2011 | 14605862 |
| Cambodia | 2012 | 14864646 |
| Cambodia | 2013 | 15135169 |
| Cameroon | 1995 | 13929575 |
| Cameroon | 1996 | 14316949 |
| Cameroon | 1997 | 14709426 |
| Cameroon | 1998 | 15107909 |
| Cameroon | 1999 | 15513653 |
| Cameroon | 2000 | 15927713 |
| Cameroon | 2001 | 16350440 |
| Cameroon | 2002 | 16782044 |
| Cameroon | 2003 | 17223277 |
| Cameroon | 2004 | 17674960 |
| Cameroon | 2005 | 18137734 |
| Cameroon | 2006 | 18611937 |
| Cameroon | 2007 | 19097676 |
| Cameroon | 2008 | 19595026 |
| Cameroon | 2009 | 20103945 |
| Cameroon | 2010 | 20624343 |
| Cameroon | 2011 | 21156272 |
| Cameroon | 2012 | 21699631 |
| Cameroon | 2013 | 22253959 |
| Canada | 1995 | 29294899 |
| Canada | 1996 | 29586368 |
| Canada | 1997 | 29866502 |
| Canada | 1998 | 30140562 |
| Canada | 1999 | 30415664 |
| Canada | 2000 | 30697425 |
| Canada | 2001 | 30986788 |
| Canada | 2002 | 31283746 |
| Canada | 2003 | 31591631 |
| Canada | 2004 | 31914080 |
| Canada | 2005 | 32253092 |
| Canada | 2006 | 32610984 |
| Canada | 2007 | 32985806 |
| Canada | 2008 | 33369898 |
| Canada | 2009 | 33752618 |
| Canada | 2010 | 34126240 |
| Canada | 2011 | 34487414 |
| Canada | 2012 | 34837978 |
| Canada | 2013 | 35181704 |
| Cayman Islands | 1995 | 31672 |
| Cayman Islands | 1996 | 33535 |
| Cayman Islands | 1997 | 35596 |
| Cayman Islands | 1998 | 37742 |
| Cayman Islands | 1999 | 39810 |
| Cayman Islands | 2000 | 41685 |
| Cayman Islands | 2001 | 43317 |
| Cayman Islands | 2002 | 44742 |
| Cayman Islands | 2003 | 46032 |
| Cayman Islands | 2004 | 47299 |
| Cayman Islands | 2005 | 48623 |
| Cayman Islands | 2006 | 50026 |
| Cayman Islands | 2007 | 51472 |
| Cayman Islands | 2008 | 52912 |
| Cayman Islands | 2009 | 54275 |
| Cayman Islands | 2010 | 55509 |
| Cayman Islands | 2011 | 56601 |
| Cayman Islands | 2012 | 57570 |
| Cayman Islands | 2013 | 58435 |
| Central African Republic | 1995 | 3275695 |
| Central African Republic | 1996 | 3350299 |
| Central African Republic | 1997 | 3424733 |
| Central African Republic | 1998 | 3498140 |
| Central African Republic | 1999 | 3569523 |
| Central African Republic | 2000 | 3638316 |
| Central African Republic | 2001 | 3704045 |
| Central African Republic | 2002 | 3767248 |
| Central African Republic | 2003 | 3829636 |
| Central African Republic | 2004 | 3893595 |
| Central African Republic | 2005 | 3960897 |
| Central African Republic | 2006 | 4032102 |
| Central African Republic | 2007 | 4106897 |
| Central African Republic | 2008 | 4185106 |
| Central African Republic | 2009 | 4266247 |
| Central African Republic | 2010 | 4349921 |
| Central African Republic | 2011 | 4436217 |
| Central African Republic | 2012 | 4525209 |
| Central African Republic | 2013 | 4616417 |
| Chad | 1995 | 6980351 |
| Chad | 1996 | 7216321 |
| Chad | 1997 | 7463347 |
| Chad | 1998 | 7724316 |
| Chad | 1999 | 8002899 |
| Chad | 2000 | 8301151 |
| Chad | 2001 | 8620917 |
| Chad | 2002 | 8959964 |
| Chad | 2003 | 9311234 |
| Chad | 2004 | 9665024 |
| Chad | 2005 | 10014413 |
| Chad | 2006 | 10356822 |
| Chad | 2007 | 10694366 |
| Chad | 2008 | 11030628 |
| Chad | 2009 | 11371325 |
| Chad | 2010 | 11720781 |
| Chad | 2011 | 12080037 |
| Chad | 2012 | 12448175 |
| Chad | 2013 | 12825314 |
| Chile | 1995 | 14440103 |
| Chile | 1996 | 14662323 |
| Chile | 1997 | 14872458 |
| Chile | 1998 | 15072409 |
| Chile | 1999 | 15265536 |
| Chile | 2000 | 15454402 |
| Chile | 2001 | 15639289 |
| Chile | 2002 | 15819522 |
| Chile | 2003 | 15995658 |
| Chile | 2004 | 16168241 |
| Chile | 2005 | 16337749 |
| Chile | 2006 | 16504530 |
| Chile | 2007 | 16668892 |
| Chile | 2008 | 16831184 |
| Chile | 2009 | 16991729 |
| Chile | 2010 | 17150760 |
| Chile | 2011 | 17308449 |
| Chile | 2012 | 17464814 |
| Chile | 2013 | 17619708 |
| China | 1995 | 1237531429 |
| China | 1996 | 1247897092 |
| China | 1997 | 1257021784 |
| China | 1998 | 1265222536 |
| China | 1999 | 1272915272 |
| China | 2000 | 1280428583 |
| China | 2001 | 1287890449 |
| China | 2002 | 1295322020 |
| China | 2003 | 1302810258 |
| China | 2004 | 1310414386 |
| China | 2005 | 1318176835 |
| China | 2006 | 1326146433 |
| China | 2007 | 1334343509 |
| China | 2008 | 1342732604 |
| China | 2009 | 1351247555 |
| China | 2010 | 1359821465 |
| China | 2011 | 1368440300 |
| China | 2012 | 1377064907 |
| China | 2013 | 1385566537 |
| China, Hong Kong SAR | 1995 | 6144498 |
| China, Hong Kong SAR | 1996 | 6275363 |
| China, Hong Kong SAR | 1997 | 6430651 |
| China, Hong Kong SAR | 1998 | 6591717 |
| China, Hong Kong SAR | 1999 | 6732627 |
| China, Hong Kong SAR | 2000 | 6835301 |
| China, Hong Kong SAR | 2001 | 6892752 |
| China, Hong Kong SAR | 2002 | 6912079 |
| China, Hong Kong SAR | 2003 | 6906631 |
| China, Hong Kong SAR | 2004 | 6896523 |
| China, Hong Kong SAR | 2005 | 6896686 |
| China, Hong Kong SAR | 2006 | 6910671 |
| China, Hong Kong SAR | 2007 | 6934748 |
| China, Hong Kong SAR | 2008 | 6967866 |
| China, Hong Kong SAR | 2009 | 7006930 |
| China, Hong Kong SAR | 2010 | 7049514 |
| China, Hong Kong SAR | 2011 | 7096359 |
| China, Hong Kong SAR | 2012 | 7148493 |
| China, Hong Kong SAR | 2013 | 7203836 |
| China, Macao SAR | 1995 | 398459 |
| China, Macao SAR | 1996 | 405231 |
| China, Macao SAR | 1997 | 412031 |
| China, Macao SAR | 1998 | 418810 |
| China, Macao SAR | 1999 | 425448 |
| China, Macao SAR | 2000 | 431907 |
| China, Macao SAR | 2001 | 438080 |
| China, Macao SAR | 2002 | 444150 |
| China, Macao SAR | 2003 | 450711 |
| China, Macao SAR | 2004 | 458542 |
| China, Macao SAR | 2005 | 468149 |
| China, Macao SAR | 2006 | 479808 |
| China, Macao SAR | 2007 | 493206 |
| China, Macao SAR | 2008 | 507528 |
| China, Macao SAR | 2009 | 521617 |
| China, Macao SAR | 2010 | 534626 |
| China, Macao SAR | 2011 | 546278 |
| China, Macao SAR | 2012 | 556783 |
| China, Macao SAR | 2013 | 566375 |
| Colombia | 1995 | 36573895 |
| Colombia | 1996 | 37236124 |
| Colombia | 1997 | 37901358 |
| Colombia | 1998 | 38568056 |
| Colombia | 1999 | 39234115 |
| Colombia | 2000 | 39897984 |
| Colombia | 2001 | 40558648 |
| Colombia | 2002 | 41216304 |
| Colombia | 2003 | 41872051 |
| Colombia | 2004 | 42527623 |
| Colombia | 2005 | 43184026 |
| Colombia | 2006 | 43841370 |
| Colombia | 2007 | 44498390 |
| Colombia | 2008 | 45153037 |
| Colombia | 2009 | 45802561 |
| Colombia | 2010 | 46444798 |
| Colombia | 2011 | 47078792 |
| Colombia | 2012 | 47704427 |
| Colombia | 2013 | 48321405 |
| Comoros | 1995 | 465895 |
| Comoros | 1996 | 477548 |
| Comoros | 1997 | 489627 |
| Comoros | 1998 | 502128 |
| Comoros | 1999 | 515028 |
| Comoros | 2000 | 528312 |
| Comoros | 2001 | 541976 |
| Comoros | 2002 | 556028 |
| Comoros | 2003 | 570491 |
| Comoros | 2004 | 585389 |
| Comoros | 2005 | 600733 |
| Comoros | 2006 | 616526 |
| Comoros | 2007 | 632736 |
| Comoros | 2008 | 649291 |
| Comoros | 2009 | 666097 |
| Comoros | 2010 | 683081 |
| Comoros | 2011 | 700216 |
| Comoros | 2012 | 717503 |
| Comoros | 2013 | 734917 |
| Congo | 1995 | 2720633 |
| Congo | 1996 | 2797572 |
| Congo | 1997 | 2878333 |
| Congo | 1998 | 2961357 |
| Congo | 1999 | 3044444 |
| Congo | 2000 | 3126204 |
| Congo | 2001 | 3205636 |
| Congo | 2002 | 3283719 |
| Congo | 2003 | 3363418 |
| Congo | 2004 | 3448868 |
| Congo | 2005 | 3542867 |
| Congo | 2006 | 3646653 |
| Congo | 2007 | 3758858 |
| Congo | 2008 | 3876475 |
| Congo | 2009 | 3995146 |
| Congo | 2010 | 4111715 |
| Congo | 2011 | 4225359 |
| Congo | 2012 | 4337051 |
| Congo | 2013 | 4447632 |
| Cook Islands | 1995 | 18305 |
| Cook Islands | 1996 | 18244 |
| Cook Islands | 1997 | 18093 |
| Cook Islands | 1998 | 17915 |
| Cook Islands | 1999 | 17806 |
| Cook Islands | 2000 | 17826 |
| Cook Islands | 2001 | 18003 |
| Cook Islands | 2002 | 18308 |
| Cook Islands | 2003 | 18690 |
| Cook Islands | 2004 | 19072 |
| Cook Islands | 2005 | 19399 |
| Cook Islands | 2006 | 19656 |
| Cook Islands | 2007 | 19858 |
| Cook Islands | 2008 | 20016 |
| Cook Islands | 2009 | 20153 |
| Cook Islands | 2010 | 20284 |
| Cook Islands | 2011 | 20409 |
| Cook Islands | 2012 | 20523 |
| Cook Islands | 2013 | 20629 |
| Costa Rica | 1995 | 3478197 |
| Costa Rica | 1996 | 3566605 |
| Costa Rica | 1997 | 3658020 |
| Costa Rica | 1998 | 3750585 |
| Costa Rica | 1999 | 3841734 |
| Costa Rica | 2000 | 3929588 |
| Costa Rica | 2001 | 4013488 |
| Costa Rica | 2002 | 4093840 |
| Costa Rica | 2003 | 4171145 |
| Costa Rica | 2004 | 4246336 |
| Costa Rica | 2005 | 4320130 |
| Costa Rica | 2006 | 4392493 |
| Costa Rica | 2007 | 4463226 |
| Costa Rica | 2008 | 4532711 |
| Costa Rica | 2009 | 4601424 |
| Costa Rica | 2010 | 4669685 |
| Costa Rica | 2011 | 4737680 |
| Costa Rica | 2012 | 4805295 |
| Costa Rica | 2013 | 4872166 |
| Côte d’Ivoire | 1995 | 14217430 |
| Côte d’Ivoire | 1996 | 14632391 |
| Côte d’Ivoire | 1997 | 15042149 |
| Côte d’Ivoire | 1998 | 15436470 |
| Côte d’Ivoire | 1999 | 15802248 |
| Côte d’Ivoire | 2000 | 16131332 |
| Côte d’Ivoire | 2001 | 16420173 |
| Côte d’Ivoire | 2002 | 16674987 |
| Côte d’Ivoire | 2003 | 16909801 |
| Côte d’Ivoire | 2004 | 17144325 |
| Côte d’Ivoire | 2005 | 17393994 |
| Côte d’Ivoire | 2006 | 17662417 |
| Côte d’Ivoire | 2007 | 17949061 |
| Côte d’Ivoire | 2008 | 18260044 |
| Côte d’Ivoire | 2009 | 18601342 |
| Côte d’Ivoire | 2010 | 18976588 |
| Côte d’Ivoire | 2011 | 19389954 |
| Côte d’Ivoire | 2012 | 19839750 |
| Côte d’Ivoire | 2013 | 20316086 |
| Croatia | 1995 | 4690192 |
| Croatia | 1996 | 4647843 |
| Croatia | 1997 | 4601596 |
| Croatia | 1998 | 4554877 |
| Croatia | 1999 | 4511766 |
| Croatia | 2000 | 4475201 |
| Croatia | 2001 | 4446789 |
| Croatia | 2002 | 4426098 |
| Croatia | 2003 | 4411370 |
| Croatia | 2004 | 4399706 |
| Croatia | 2005 | 4388857 |
| Croatia | 2006 | 4378601 |
| Croatia | 2007 | 4369337 |
| Croatia | 2008 | 4360126 |
| Croatia | 2009 | 4349930 |
| Croatia | 2010 | 4338027 |
| Croatia | 2011 | 4323822 |
| Croatia | 2012 | 4307422 |
| Croatia | 2013 | 4289714 |
| Cuba | 1995 | 10932013 |
| Cuba | 1996 | 10980758 |
| Cuba | 1997 | 11024249 |
| Cuba | 1998 | 11063917 |
| Cuba | 1999 | 11101580 |
| Cuba | 2000 | 11138416 |
| Cuba | 2001 | 11175465 |
| Cuba | 2002 | 11212125 |
| Cuba | 2003 | 11245926 |
| Cuba | 2004 | 11273363 |
| Cuba | 2005 | 11292078 |
| Cuba | 2006 | 11301100 |
| Cuba | 2007 | 11301674 |
| Cuba | 2008 | 11296355 |
| Cuba | 2009 | 11288826 |
| Cuba | 2010 | 11281768 |
| Cuba | 2011 | 11276053 |
| Cuba | 2012 | 11270957 |
| Cuba | 2013 | 11265629 |
| Curaçao | 2010 | 147560 |
| Curaçao | 2011 | 151523 |
| Curaçao | 2012 | 155293 |
| Curaçao | 2013 | 158760 |
| Cyprus | 1995 | 855389 |
| Cyprus | 1996 | 873246 |
| Cyprus | 1997 | 890733 |
| Cyprus | 1998 | 908040 |
| Cyprus | 1999 | 925491 |
| Cyprus | 2000 | 943287 |
| Cyprus | 2001 | 961481 |
| Cyprus | 2002 | 979877 |
| Cyprus | 2003 | 998142 |
| Cyprus | 2004 | 1015820 |
| Cyprus | 2005 | 1032586 |
| Cyprus | 2006 | 1048314 |
| Cyprus | 2007 | 1063095 |
| Cyprus | 2008 | 1077089 |
| Cyprus | 2009 | 1090553 |
| Cyprus | 2010 | 1103685 |
| Cyprus | 2011 | 1116513 |
| Cyprus | 2012 | 1128994 |
| Cyprus | 2013 | 1141166 |
| Czech Republic | 1995 | 10339223 |
| Czech Republic | 1996 | 10327823 |
| Czech Republic | 1997 | 10311228 |
| Czech Republic | 1998 | 10291153 |
| Czech Republic | 1999 | 10270096 |
| Czech Republic | 2000 | 10250398 |
| Czech Republic | 2001 | 10231435 |
| Czech Republic | 2002 | 10214070 |
| Czech Republic | 2003 | 10203846 |
| Czech Republic | 2004 | 10207862 |
| Czech Republic | 2005 | 10230683 |
| Czech Republic | 2006 | 10274906 |
| Czech Republic | 2007 | 10337782 |
| Czech Republic | 2008 | 10411836 |
| Czech Republic | 2009 | 10486434 |
| Czech Republic | 2010 | 10553701 |
| Czech Republic | 2011 | 10611037 |
| Czech Republic | 2012 | 10660051 |
| Czech Republic | 2013 | 10702197 |
| Democratic People’s Republic of Korea | 1995 | 21763678 |
| Democratic People’s Republic of Korea | 1996 | 22016518 |
| Democratic People’s Republic of Korea | 1997 | 22240833 |
| Democratic People’s Republic of Korea | 1998 | 22444993 |
| Democratic People’s Republic of Korea | 1999 | 22641754 |
| Democratic People’s Republic of Korea | 2000 | 22840225 |
| Democratic People’s Republic of Korea | 2001 | 23043449 |
| Democratic People’s Republic of Korea | 2002 | 23248059 |
| Democratic People’s Republic of Korea | 2003 | 23449180 |
| Democratic People’s Republic of Korea | 2004 | 23639303 |
| Democratic People’s Republic of Korea | 2005 | 23813333 |
| Democratic People’s Republic of Korea | 2006 | 23969917 |
| Democratic People’s Republic of Korea | 2007 | 24111989 |
| Democratic People’s Republic of Korea | 2008 | 24243894 |
| Democratic People’s Republic of Korea | 2009 | 24371865 |
| Democratic People’s Republic of Korea | 2010 | 24500520 |
| Democratic People’s Republic of Korea | 2011 | 24631291 |
| Democratic People’s Republic of Korea | 2012 | 24763188 |
| Democratic People’s Republic of Korea | 2013 | 24895480 |
| Democratic Republic of the Congo | 1995 | 42012524 |
| Democratic Republic of the Congo | 1996 | 43122601 |
| Democratic Republic of the Congo | 1997 | 44078397 |
| Democratic Republic of the Congo | 1998 | 44960941 |
| Democratic Republic of the Congo | 1999 | 45889100 |
| Democratic Republic of the Congo | 2000 | 46949244 |
| Democratic Republic of the Congo | 2001 | 48167045 |
| Democratic Republic of the Congo | 2002 | 49516960 |
| Democratic Republic of the Congo | 2003 | 50972323 |
| Democratic Republic of the Congo | 2004 | 52487293 |
| Democratic Republic of the Congo | 2005 | 54028003 |
| Democratic Republic of the Congo | 2006 | 55590838 |
| Democratic Republic of the Congo | 2007 | 57187942 |
| Democratic Republic of the Congo | 2008 | 58819038 |
| Democratic Republic of the Congo | 2009 | 60486276 |
| Democratic Republic of the Congo | 2010 | 62191161 |
| Democratic Republic of the Congo | 2011 | 63931512 |
| Democratic Republic of the Congo | 2012 | 65705093 |
| Democratic Republic of the Congo | 2013 | 67513677 |
| Denmark | 1995 | 5232582 |
| Denmark | 1996 | 5254383 |
| Denmark | 1997 | 5276683 |
| Denmark | 1998 | 5298680 |
| Denmark | 1999 | 5319410 |
| Denmark | 2000 | 5338283 |
| Denmark | 2001 | 5354684 |
| Denmark | 2002 | 5368994 |
| Denmark | 2003 | 5382853 |
| Denmark | 2004 | 5398558 |
| Denmark | 2005 | 5417692 |
| Denmark | 2006 | 5440923 |
| Denmark | 2007 | 5467551 |
| Denmark | 2008 | 5496104 |
| Denmark | 2009 | 5524427 |
| Denmark | 2010 | 5550959 |
| Denmark | 2011 | 5575237 |
| Denmark | 2012 | 5597760 |
| Denmark | 2013 | 5619096 |
| Djibouti | 1995 | 663999 |
| Djibouti | 1996 | 675891 |
| Djibouti | 1997 | 687897 |
| Djibouti | 1998 | 699922 |
| Djibouti | 1999 | 711652 |
| Djibouti | 2000 | 722887 |
| Djibouti | 2001 | 733732 |
| Djibouti | 2002 | 744434 |
| Djibouti | 2003 | 755085 |
| Djibouti | 2004 | 765776 |
| Djibouti | 2005 | 776585 |
| Djibouti | 2006 | 787544 |
| Djibouti | 2007 | 798690 |
| Djibouti | 2008 | 810100 |
| Djibouti | 2009 | 821865 |
| Djibouti | 2010 | 834036 |
| Djibouti | 2011 | 846646 |
| Djibouti | 2012 | 859652 |
| Djibouti | 2013 | 872932 |
| Dominica | 1995 | 71367 |
| Dominica | 1996 | 71146 |
| Dominica | 1997 | 70756 |
| Dominica | 1998 | 70295 |
| Dominica | 1999 | 69902 |
| Dominica | 2000 | 69679 |
| Dominica | 2001 | 69660 |
| Dominica | 2002 | 69806 |
| Dominica | 2003 | 70058 |
| Dominica | 2004 | 70325 |
| Dominica | 2005 | 70542 |
| Dominica | 2006 | 70690 |
| Dominica | 2007 | 70795 |
| Dominica | 2008 | 70883 |
| Dominica | 2009 | 70996 |
| Dominica | 2010 | 71167 |
| Dominica | 2011 | 71401 |
| Dominica | 2012 | 71684 |
| Dominica | 2013 | 72003 |
| Dominican Republic | 1995 | 7977966 |
| Dominican Republic | 1996 | 8118570 |
| Dominican Republic | 1997 | 8256496 |
| Dominican Republic | 1998 | 8392534 |
| Dominican Republic | 1999 | 8527881 |
| Dominican Republic | 2000 | 8663421 |
| Dominican Republic | 2001 | 8799298 |
| Dominican Republic | 2002 | 8935261 |
| Dominican Republic | 2003 | 9071318 |
| Dominican Republic | 2004 | 9207389 |
| Dominican Republic | 2005 | 9343362 |
| Dominican Republic | 2006 | 9479269 |
| Dominican Republic | 2007 | 9615015 |
| Dominican Republic | 2008 | 9750195 |
| Dominican Republic | 2009 | 9884265 |
| Dominican Republic | 2010 | 10016797 |
| Dominican Republic | 2011 | 10147598 |
| Dominican Republic | 2012 | 10276621 |
| Dominican Republic | 2013 | 10403761 |
| Ecuador | 1995 | 11315800 |
| Ecuador | 1996 | 11557151 |
| Ecuador | 1997 | 11799289 |
| Ecuador | 1998 | 12042454 |
| Ecuador | 1999 | 12286995 |
| Ecuador | 2000 | 12533087 |
| Ecuador | 2001 | 12780869 |
| Ecuador | 2002 | 13030041 |
| Ecuador | 2003 | 13279806 |
| Ecuador | 2004 | 13529091 |
| Ecuador | 2005 | 13777131 |
| Ecuador | 2006 | 14023503 |
| Ecuador | 2007 | 14268397 |
| Ecuador | 2008 | 14512402 |
| Ecuador | 2009 | 14756424 |
| Ecuador | 2010 | 15001072 |
| Ecuador | 2011 | 15246481 |
| Ecuador | 2012 | 15492264 |
| Ecuador | 2013 | 15737878 |
| Egypt | 1995 | 61168397 |
| Egypt | 1996 | 62123592 |
| Egypt | 1997 | 63094069 |
| Egypt | 1998 | 64084443 |
| Egypt | 1999 | 65097777 |
| Egypt | 2000 | 66136590 |
| Egypt | 2001 | 67204189 |
| Egypt | 2002 | 68302914 |
| Egypt | 2003 | 69432477 |
| Egypt | 2004 | 70591288 |
| Egypt | 2005 | 71777678 |
| Egypt | 2006 | 72990754 |
| Egypt | 2007 | 74229577 |
| Egypt | 2008 | 75491922 |
| Egypt | 2009 | 76775023 |
| Egypt | 2010 | 78075705 |
| Egypt | 2011 | 79392466 |
| Egypt | 2012 | 80721874 |
| Egypt | 2013 | 82056378 |
| El Salvador | 1995 | 5748013 |
| El Salvador | 1996 | 5806750 |
| El Salvador | 1997 | 5855226 |
| El Salvador | 1998 | 5895018 |
| El Salvador | 1999 | 5928809 |
| El Salvador | 2000 | 5958794 |
| El Salvador | 2001 | 5985299 |
| El Salvador | 2002 | 6008308 |
| El Salvador | 2003 | 6029366 |
| El Salvador | 2004 | 6050297 |
| El Salvador | 2005 | 6072538 |
| El Salvador | 2006 | 6096692 |
| El Salvador | 2007 | 6122952 |
| El Salvador | 2008 | 6151776 |
| El Salvador | 2009 | 6183484 |
| El Salvador | 2010 | 6218195 |
| El Salvador | 2011 | 6256242 |
| El Salvador | 2012 | 6297394 |
| El Salvador | 2013 | 6340454 |
| Equatorial Guinea | 1995 | 441829 |
| Equatorial Guinea | 1996 | 456389 |
| Equatorial Guinea | 1997 | 471288 |
| Equatorial Guinea | 1998 | 486542 |
| Equatorial Guinea | 1999 | 502169 |
| Equatorial Guinea | 2000 | 518179 |
| Equatorial Guinea | 2001 | 534592 |
| Equatorial Guinea | 2002 | 551399 |
| Equatorial Guinea | 2003 | 568552 |
| Equatorial Guinea | 2004 | 585983 |
| Equatorial Guinea | 2005 | 603648 |
| Equatorial Guinea | 2006 | 621517 |
| Equatorial Guinea | 2007 | 639618 |
| Equatorial Guinea | 2008 | 658025 |
| Equatorial Guinea | 2009 | 676851 |
| Equatorial Guinea | 2010 | 696167 |
| Equatorial Guinea | 2011 | 715996 |
| Equatorial Guinea | 2012 | 736296 |
| Equatorial Guinea | 2013 | 757014 |
| Eritrea | 1995 | 3407812 |
| Eritrea | 1996 | 3473399 |
| Eritrea | 1997 | 3560353 |
| Eritrea | 1998 | 3668350 |
| Eritrea | 1999 | 3795384 |
| Eritrea | 2000 | 3939348 |
| Eritrea | 2001 | 4101609 |
| Eritrea | 2002 | 4281576 |
| Eritrea | 2003 | 4472533 |
| Eritrea | 2004 | 4665522 |
| Eritrea | 2005 | 4854066 |
| Eritrea | 2006 | 5035036 |
| Eritrea | 2007 | 5209846 |
| Eritrea | 2008 | 5382163 |
| Eritrea | 2009 | 5557889 |
| Eritrea | 2010 | 5741159 |
| Eritrea | 2011 | 5932852 |
| Eritrea | 2012 | 6130922 |
| Eritrea | 2013 | 6333135 |
| Estonia | 1995 | 1433076 |
| Estonia | 1996 | 1412522 |
| Estonia | 1997 | 1397071 |
| Estonia | 1998 | 1385531 |
| Estonia | 1999 | 1375744 |
| Estonia | 2000 | 1366117 |
| Estonia | 2001 | 1356549 |
| Estonia | 2002 | 1347674 |
| Estonia | 2003 | 1339453 |
| Estonia | 2004 | 1331928 |
| Estonia | 2005 | 1325112 |
| Estonia | 2006 | 1318893 |
| Estonia | 2007 | 1313138 |
| Estonia | 2008 | 1307827 |
| Estonia | 2009 | 1302965 |
| Estonia | 2010 | 1298533 |
| Estonia | 2011 | 1294498 |
| Estonia | 2012 | 1290778 |
| Estonia | 2013 | 1287251 |
| Ethiopia | 1995 | 57023519 |
| Ethiopia | 1996 | 58815116 |
| Ethiopia | 1997 | 60584273 |
| Ethiopia | 1998 | 62353942 |
| Ethiopia | 1999 | 64158887 |
| Ethiopia | 2000 | 66024199 |
| Ethiopia | 2001 | 67956866 |
| Ethiopia | 2002 | 69948344 |
| Ethiopia | 2003 | 71989666 |
| Ethiopia | 2004 | 74066147 |
| Ethiopia | 2005 | 76167240 |
| Ethiopia | 2006 | 78290649 |
| Ethiopia | 2007 | 80440708 |
| Ethiopia | 2008 | 82621190 |
| Ethiopia | 2009 | 84838032 |
| Ethiopia | 2010 | 87095281 |
| Ethiopia | 2011 | 89393063 |
| Ethiopia | 2012 | 91728849 |
| Ethiopia | 2013 | 94100756 |
| Fiji | 1995 | 775587 |
| Fiji | 1996 | 784647 |
| Fiji | 1997 | 793098 |
| Fiji | 1998 | 800616 |
| Fiji | 1999 | 806857 |
| Fiji | 2000 | 811647 |
| Fiji | 2001 | 814700 |
| Fiji | 2002 | 816237 |
| Fiji | 2003 | 817224 |
| Fiji | 2004 | 818995 |
| Fiji | 2005 | 822484 |
| Fiji | 2006 | 828060 |
| Fiji | 2007 | 835392 |
| Fiji | 2008 | 843851 |
| Fiji | 2009 | 852479 |
| Fiji | 2010 | 860559 |
| Fiji | 2011 | 867921 |
| Fiji | 2012 | 874742 |
| Fiji | 2013 | 881065 |
| Finland | 1995 | 5108176 |
| Finland | 1996 | 5126021 |
| Finland | 1997 | 5140755 |
| Finland | 1998 | 5153229 |
| Finland | 1999 | 5164780 |
| Finland | 2000 | 5176482 |
| Finland | 2001 | 5188330 |
| Finland | 2002 | 5200259 |
| Finland | 2003 | 5213213 |
| Finland | 2004 | 5228340 |
| Finland | 2005 | 5246368 |
| Finland | 2006 | 5267817 |
| Finland | 2007 | 5292261 |
| Finland | 2008 | 5318297 |
| Finland | 2009 | 5343927 |
| Finland | 2010 | 5367693 |
| Finland | 2011 | 5389084 |
| Finland | 2012 | 5408466 |
| Finland | 2013 | 5426323 |
| France | 1995 | 58008958 |
| France | 1996 | 58216225 |
| France | 1997 | 58418324 |
| France | 1998 | 58635564 |
| France | 1999 | 58894671 |
| France | 2000 | 59213096 |
| France | 2001 | 59600714 |
| France | 2002 | 60047743 |
| France | 2003 | 60527640 |
| France | 2004 | 61002537 |
| France | 2005 | 61444972 |
| France | 2006 | 61845239 |
| France | 2007 | 62210877 |
| France | 2008 | 62552614 |
| France | 2009 | 62888318 |
| France | 2010 | 63230866 |
| France | 2011 | 63582112 |
| France | 2012 | 63936575 |
| France | 2013 | 64291280 |
| French Polynesia | 1995 | 215200 |
| French Polynesia | 1996 | 219282 |
| French Polynesia | 1997 | 223734 |
| French Polynesia | 1998 | 228380 |
| French Polynesia | 1999 | 232956 |
| French Polynesia | 2000 | 237267 |
| French Polynesia | 2001 | 241276 |
| French Polynesia | 2002 | 245032 |
| French Polynesia | 2003 | 248536 |
| French Polynesia | 2004 | 251811 |
| French Polynesia | 2005 | 254884 |
| French Polynesia | 2006 | 257731 |
| French Polynesia | 2007 | 260361 |
| French Polynesia | 2008 | 262877 |
| French Polynesia | 2009 | 265412 |
| French Polynesia | 2010 | 268065 |
| French Polynesia | 2011 | 270874 |
| French Polynesia | 2012 | 273814 |
| French Polynesia | 2013 | 276831 |
| Gabon | 1995 | 1080477 |
| Gabon | 1996 | 1108698 |
| Gabon | 1997 | 1137412 |
| Gabon | 1998 | 1166525 |
| Gabon | 1999 | 1195919 |
| Gabon | 2000 | 1225527 |
| Gabon | 2001 | 1255299 |
| Gabon | 2002 | 1285318 |
| Gabon | 2003 | 1315820 |
| Gabon | 2004 | 1347125 |
| Gabon | 2005 | 1379465 |
| Gabon | 2006 | 1412907 |
| Gabon | 2007 | 1447388 |
| Gabon | 2008 | 1482843 |
| Gabon | 2009 | 1519155 |
| Gabon | 2010 | 1556222 |
| Gabon | 2011 | 1594034 |
| Gabon | 2012 | 1632572 |
| Gabon | 2013 | 1671711 |
| Gambia | 1995 | 1065746 |
| Gambia | 1996 | 1095930 |
| Gambia | 1997 | 1126986 |
| Gambia | 1998 | 1159271 |
| Gambia | 1999 | 1193143 |
| Gambia | 2000 | 1228863 |
| Gambia | 2001 | 1266691 |
| Gambia | 2002 | 1306667 |
| Gambia | 2003 | 1348548 |
| Gambia | 2004 | 1391934 |
| Gambia | 2005 | 1436549 |
| Gambia | 2006 | 1482324 |
| Gambia | 2007 | 1529406 |
| Gambia | 2008 | 1577984 |
| Gambia | 2009 | 1628332 |
| Gambia | 2010 | 1680640 |
| Gambia | 2011 | 1734966 |
| Gambia | 2012 | 1791225 |
| Gambia | 2013 | 1849285 |
| Georgia | 1995 | 5067143 |
| Georgia | 1996 | 4990097 |
| Georgia | 1997 | 4922489 |
| Georgia | 1998 | 4861848 |
| Georgia | 1999 | 4803312 |
| Georgia | 2000 | 4743591 |
| Georgia | 2001 | 4682290 |
| Georgia | 2002 | 4621837 |
| Georgia | 2003 | 4564938 |
| Georgia | 2004 | 4515280 |
| Georgia | 2005 | 4475306 |
| Georgia | 2006 | 4446032 |
| Georgia | 2007 | 4426077 |
| Georgia | 2008 | 4412502 |
| Georgia | 2009 | 4401093 |
| Georgia | 2010 | 4388674 |
| Georgia | 2011 | 4374226 |
| Georgia | 2012 | 4358242 |
| Georgia | 2013 | 4340895 |
| Germany | 1995 | 83147770 |
| Germany | 1996 | 83388930 |
| Germany | 1997 | 83490697 |
| Germany | 1998 | 83500716 |
| Germany | 1999 | 83490881 |
| Germany | 2000 | 83512459 |
| Germany | 2001 | 83583461 |
| Germany | 2002 | 83685160 |
| Germany | 2003 | 83788480 |
| Germany | 2004 | 83848844 |
| Germany | 2005 | 83835978 |
| Germany | 2006 | 83740302 |
| Germany | 2007 | 83578794 |
| Germany | 2008 | 83379538 |
| Germany | 2009 | 83182774 |
| Germany | 2010 | 83017404 |
| Germany | 2011 | 82892904 |
| Germany | 2012 | 82800121 |
| Germany | 2013 | 82726626 |
| Ghana | 1995 | 16760926 |
| Ghana | 1996 | 17169151 |
| Ghana | 1997 | 17568461 |
| Ghana | 1998 | 17968830 |
| Ghana | 1999 | 18384302 |
| Ghana | 2000 | 18825034 |
| Ghana | 2001 | 19293392 |
| Ghana | 2002 | 19786307 |
| Ghana | 2003 | 20301686 |
| Ghana | 2004 | 20835514 |
| Ghana | 2005 | 21384034 |
| Ghana | 2006 | 21947779 |
| Ghana | 2007 | 22525659 |
| Ghana | 2008 | 23110139 |
| Ghana | 2009 | 23691533 |
| Ghana | 2010 | 24262901 |
| Ghana | 2011 | 24820706 |
| Ghana | 2012 | 25366462 |
| Ghana | 2013 | 25904598 |
| Greece | 1995 | 10671809 |
| Greece | 1996 | 10758368 |
| Greece | 1997 | 10833632 |
| Greece | 1998 | 10896763 |
| Greece | 1999 | 10947724 |
| Greece | 2000 | 10986883 |
| Greece | 2001 | 11012931 |
| Greece | 2002 | 11026358 |
| Greece | 2003 | 11031728 |
| Greece | 2004 | 11035302 |
| Greece | 2005 | 11041642 |
| Greece | 2006 | 11052514 |
| Greece | 2007 | 11066884 |
| Greece | 2008 | 11083063 |
| Greece | 2009 | 11098149 |
| Greece | 2010 | 11109999 |
| Greece | 2011 | 11118556 |
| Greece | 2012 | 11124639 |
| Greece | 2013 | 11127990 |
| Greenland | 1995 | 55809 |
| Greenland | 1996 | 55832 |
| Greenland | 1997 | 55876 |
| Greenland | 1998 | 55948 |
| Greenland | 1999 | 56049 |
| Greenland | 2000 | 56174 |
| Greenland | 2001 | 56337 |
| Greenland | 2002 | 56534 |
| Greenland | 2003 | 56732 |
| Greenland | 2004 | 56881 |
| Greenland | 2005 | 56953 |
| Greenland | 2006 | 56925 |
| Greenland | 2007 | 56817 |
| Greenland | 2008 | 56675 |
| Greenland | 2009 | 56569 |
| Greenland | 2010 | 56546 |
| Greenland | 2011 | 56626 |
| Greenland | 2012 | 56787 |
| Greenland | 2013 | 56987 |
| Grenada | 1995 | 100253 |
| Grenada | 1996 | 100796 |
| Grenada | 1997 | 101125 |
| Grenada | 1998 | 101302 |
| Grenada | 1999 | 101441 |
| Grenada | 2000 | 101620 |
| Grenada | 2001 | 101849 |
| Grenada | 2002 | 102099 |
| Grenada | 2003 | 102369 |
| Grenada | 2004 | 102655 |
| Grenada | 2005 | 102951 |
| Grenada | 2006 | 103260 |
| Grenada | 2007 | 103586 |
| Grenada | 2008 | 103932 |
| Grenada | 2009 | 104296 |
| Grenada | 2010 | 104677 |
| Grenada | 2011 | 105074 |
| Grenada | 2012 | 105483 |
| Grenada | 2013 | 105897 |
| Guam | 1995 | 145562 |
| Guam | 1996 | 148060 |
| Guam | 1997 | 150306 |
| Guam | 1998 | 152275 |
| Guam | 1999 | 153951 |
| Guam | 2000 | 155328 |
| Guam | 2001 | 156417 |
| Guam | 2002 | 157241 |
| Guam | 2003 | 157823 |
| Guam | 2004 | 158194 |
| Guam | 2005 | 158401 |
| Guam | 2006 | 158429 |
| Guam | 2007 | 158331 |
| Guam | 2008 | 158310 |
| Guam | 2009 | 158621 |
| Guam | 2010 | 159440 |
| Guam | 2011 | 160858 |
| Guam | 2012 | 162810 |
| Guam | 2013 | 165124 |
| Guatemala | 1995 | 9983861 |
| Guatemala | 1996 | 10214623 |
| Guatemala | 1997 | 10449636 |
| Guatemala | 1998 | 10691090 |
| Guatemala | 1999 | 10941913 |
| Guatemala | 2000 | 11204183 |
| Guatemala | 2001 | 11478984 |
| Guatemala | 2002 | 11765738 |
| Guatemala | 2003 | 12062835 |
| Guatemala | 2004 | 12367800 |
| Guatemala | 2005 | 12678919 |
| Guatemala | 2006 | 12995374 |
| Guatemala | 2007 | 13317931 |
| Guatemala | 2008 | 13648307 |
| Guatemala | 2009 | 13988988 |
| Guatemala | 2010 | 14341576 |
| Guatemala | 2011 | 14706578 |
| Guatemala | 2012 | 15082831 |
| Guatemala | 2013 | 15468203 |
| Guinea | 1995 | 7837173 |
| Guinea | 1996 | 8094751 |
| Guinea | 1997 | 8296478 |
| Guinea | 1998 | 8457221 |
| Guinea | 1999 | 8600911 |
| Guinea | 2000 | 8746128 |
| Guinea | 2001 | 8895353 |
| Guinea | 2002 | 9045748 |
| Guinea | 2003 | 9204581 |
| Guinea | 2004 | 9379621 |
| Guinea | 2005 | 9576331 |
| Guinea | 2006 | 9798963 |
| Guinea | 2007 | 10046967 |
| Guinea | 2008 | 10314678 |
| Guinea | 2009 | 10593248 |
| Guinea | 2010 | 10876033 |
| Guinea | 2011 | 11161530 |
| Guinea | 2012 | 11451273 |
| Guinea | 2013 | 11745189 |
| Guinea-Bissau | 1995 | 1139667 |
| Guinea-Bissau | 1996 | 1165465 |
| Guinea-Bissau | 1997 | 1191672 |
| Guinea-Bissau | 1998 | 1218336 |
| Guinea-Bissau | 1999 | 1245530 |
| Guinea-Bissau | 2000 | 1273312 |
| Guinea-Bissau | 2001 | 1301748 |
| Guinea-Bissau | 2002 | 1330849 |
| Guinea-Bissau | 2003 | 1360559 |
| Guinea-Bissau | 2004 | 1390791 |
| Guinea-Bissau | 2005 | 1421515 |
| Guinea-Bissau | 2006 | 1452659 |
| Guinea-Bissau | 2007 | 1484337 |
| Guinea-Bissau | 2008 | 1516920 |
| Guinea-Bissau | 2009 | 1550905 |
| Guinea-Bissau | 2010 | 1586624 |
| Guinea-Bissau | 2011 | 1624228 |
| Guinea-Bissau | 2012 | 1663558 |
| Guinea-Bissau | 2013 | 1704255 |
| Guyana | 1995 | 728136 |
| Guyana | 1996 | 730865 |
| Guyana | 1997 | 734059 |
| Guyana | 1998 | 737526 |
| Guyana | 1999 | 741046 |
| Guyana | 2000 | 744471 |
| Guyana | 2001 | 747657 |
| Guyana | 2002 | 750629 |
| Guyana | 2003 | 753612 |
| Guyana | 2004 | 756939 |
| Guyana | 2005 | 760834 |
| Guyana | 2006 | 765367 |
| Guyana | 2007 | 770407 |
| Guyana | 2008 | 775739 |
| Guyana | 2009 | 781055 |
| Guyana | 2010 | 786126 |
| Guyana | 2011 | 790882 |
| Guyana | 2012 | 795369 |
| Guyana | 2013 | 799613 |
| Haiti | 1995 | 7838241 |
| Haiti | 1996 | 7986858 |
| Haiti | 1997 | 8136372 |
| Haiti | 1998 | 8285690 |
| Haiti | 1999 | 8433339 |
| Haiti | 2000 | 8578234 |
| Haiti | 2001 | 8720247 |
| Haiti | 2002 | 8859635 |
| Haiti | 2003 | 8996229 |
| Haiti | 2004 | 9129933 |
| Haiti | 2005 | 9260879 |
| Haiti | 2006 | 9388642 |
| Haiti | 2007 | 9513714 |
| Haiti | 2008 | 9638255 |
| Haiti | 2009 | 9765153 |
| Haiti | 2010 | 9896400 |
| Haiti | 2011 | 10032864 |
| Haiti | 2012 | 10173775 |
| Haiti | 2013 | 10317461 |
| Honduras | 1995 | 5591935 |
| Honduras | 1996 | 5723639 |
| Honduras | 1997 | 5852719 |
| Honduras | 1998 | 5980152 |
| Honduras | 1999 | 6107385 |
| Honduras | 2000 | 6235561 |
| Honduras | 2001 | 6365040 |
| Honduras | 2002 | 6495801 |
| Honduras | 2003 | 6628171 |
| Honduras | 2004 | 6762426 |
| Honduras | 2005 | 6898825 |
| Honduras | 2006 | 7037428 |
| Honduras | 2007 | 7178436 |
| Honduras | 2008 | 7322368 |
| Honduras | 2009 | 7469844 |
| Honduras | 2010 | 7621204 |
| Honduras | 2011 | 7776669 |
| Honduras | 2012 | 7935846 |
| Honduras | 2013 | 8097688 |
| Hungary | 1995 | 10351994 |
| Hungary | 1996 | 10334876 |
| Hungary | 1997 | 10311227 |
| Hungary | 1998 | 10282937 |
| Hungary | 1999 | 10253110 |
| Hungary | 2000 | 10224113 |
| Hungary | 2001 | 10196130 |
| Hungary | 2002 | 10168591 |
| Hungary | 2003 | 10142243 |
| Hungary | 2004 | 10117872 |
| Hungary | 2005 | 10095964 |
| Hungary | 2006 | 10077017 |
| Hungary | 2007 | 10060786 |
| Hungary | 2008 | 10046058 |
| Hungary | 2009 | 10031096 |
| Hungary | 2010 | 10014633 |
| Hungary | 2011 | 9996227 |
| Hungary | 2012 | 9976195 |
| Hungary | 2013 | 9954941 |
| Iceland | 1995 | 267454 |
| Iceland | 1996 | 270089 |
| Iceland | 1997 | 272798 |
| Iceland | 1998 | 275568 |
| Iceland | 1999 | 278376 |
| Iceland | 2000 | 281214 |
| Iceland | 2001 | 284056 |
| Iceland | 2002 | 286929 |
| Iceland | 2003 | 289925 |
| Iceland | 2004 | 293170 |
| Iceland | 2005 | 296745 |
| Iceland | 2006 | 300693 |
| Iceland | 2007 | 304957 |
| Iceland | 2008 | 309394 |
| Iceland | 2009 | 313804 |
| Iceland | 2010 | 318042 |
| Iceland | 2011 | 322052 |
| Iceland | 2012 | 325867 |
| Iceland | 2013 | 329535 |
| India | 1995 | 955804355 |
| India | 1996 | 973147577 |
| India | 1997 | 990460131 |
| India | 1998 | 1007746556 |
| India | 1999 | 1025014711 |
| India | 2000 | 1042261758 |
| India | 2001 | 1059500888 |
| India | 2002 | 1076705723 |
| India | 2003 | 1093786762 |
| India | 2004 | 1110626108 |
| India | 2005 | 1127143548 |
| India | 2006 | 1143289350 |
| India | 2007 | 1159095250 |
| India | 2008 | 1174662334 |
| India | 2009 | 1190138069 |
| India | 2010 | 1205624648 |
| India | 2011 | 1221156319 |
| India | 2012 | 1236686732 |
| India | 2013 | 1252139596 |
| Indonesia | 1995 | 194112556 |
| Indonesia | 1996 | 197097887 |
| Indonesia | 1997 | 200050444 |
| Indonesia | 1998 | 202990922 |
| Indonesia | 1999 | 205946831 |
| Indonesia | 2000 | 208938698 |
| Indonesia | 2001 | 211970371 |
| Indonesia | 2002 | 215038285 |
| Indonesia | 2003 | 218145617 |
| Indonesia | 2004 | 221293797 |
| Indonesia | 2005 | 224480901 |
| Indonesia | 2006 | 227709821 |
| Indonesia | 2007 | 230972808 |
| Indonesia | 2008 | 234243489 |
| Indonesia | 2009 | 237486894 |
| Indonesia | 2010 | 240676485 |
| Indonesia | 2011 | 243801639 |
| Indonesia | 2012 | 246864191 |
| Indonesia | 2013 | 249865631 |
| Iran (Islamic Republic of) | 1995 | 60468352 |
| Iran (Islamic Republic of) | 1996 | 61440887 |
| Iran (Islamic Republic of) | 1997 | 62542531 |
| Iran (Islamic Republic of) | 1998 | 63713397 |
| Iran (Islamic Republic of) | 1999 | 64858754 |
| Iran (Islamic Republic of) | 2000 | 65911052 |
| Iran (Islamic Republic of) | 2001 | 66857624 |
| Iran (Islamic Republic of) | 2002 | 67727274 |
| Iran (Islamic Republic of) | 2003 | 68543171 |
| Iran (Islamic Republic of) | 2004 | 69342126 |
| Iran (Islamic Republic of) | 2005 | 70152384 |
| Iran (Islamic Republic of) | 2006 | 70976584 |
| Iran (Islamic Republic of) | 2007 | 71809219 |
| Iran (Islamic Republic of) | 2008 | 72660887 |
| Iran (Islamic Republic of) | 2009 | 73542954 |
| Iran (Islamic Republic of) | 2010 | 74462314 |
| Iran (Islamic Republic of) | 2011 | 75424285 |
| Iran (Islamic Republic of) | 2012 | 76424443 |
| Iran (Islamic Republic of) | 2013 | 77447168 |
| Iraq | 1995 | 20363138 |
| Iraq | 1996 | 21017108 |
| Iraq | 1997 | 21693597 |
| Iraq | 1998 | 22387179 |
| Iraq | 1999 | 23091408 |
| Iraq | 2000 | 23801156 |
| Iraq | 2001 | 24516842 |
| Iraq | 2002 | 25238267 |
| Iraq | 2003 | 25959531 |
| Iraq | 2004 | 26673536 |
| Iraq | 2005 | 27377045 |
| Iraq | 2006 | 28064095 |
| Iraq | 2007 | 28740630 |
| Iraq | 2008 | 29429829 |
| Iraq | 2009 | 30163199 |
| Iraq | 2010 | 30962380 |
| Iraq | 2011 | 31837015 |
| Iraq | 2012 | 32778030 |
| Iraq | 2013 | 33765232 |
| Ireland | 1995 | 3610918 |
| Ireland | 1996 | 3638411 |
| Ireland | 1997 | 3669799 |
| Ireland | 1998 | 3706616 |
| Ireland | 1999 | 3750879 |
| Ireland | 2000 | 3803741 |
| Ireland | 2001 | 3865839 |
| Ireland | 2002 | 3935875 |
| Ireland | 2003 | 4010652 |
| Ireland | 2004 | 4085790 |
| Ireland | 2005 | 4158002 |
| Ireland | 2006 | 4226104 |
| Ireland | 2007 | 4290537 |
| Ireland | 2008 | 4351657 |
| Ireland | 2009 | 4410422 |
| Ireland | 2010 | 4467561 |
| Ireland | 2011 | 4522844 |
| Ireland | 2012 | 4575890 |
| Ireland | 2013 | 4627173 |
| Israel | 1995 | 5331622 |
| Israel | 1996 | 5486219 |
| Israel | 1997 | 5630066 |
| Israel | 1998 | 5764197 |
| Israel | 1999 | 5891263 |
| Israel | 2000 | 6013711 |
| Israel | 2001 | 6129590 |
| Israel | 2002 | 6238958 |
| Israel | 2003 | 6348782 |
| Israel | 2004 | 6468354 |
| Israel | 2005 | 6603677 |
| Israel | 2006 | 6758761 |
| Israel | 2007 | 6930068 |
| Israel | 2008 | 7106828 |
| Israel | 2009 | 7273804 |
| Israel | 2010 | 7420368 |
| Israel | 2011 | 7542327 |
| Israel | 2012 | 7643905 |
| Israel | 2013 | 7733144 |
| Italy | 1995 | 56967071 |
| Italy | 1996 | 56937309 |
| Italy | 1997 | 56887127 |
| Italy | 1998 | 56850303 |
| Italy | 1999 | 56872736 |
| Italy | 2000 | 56986085 |
| Italy | 2001 | 57200233 |
| Italy | 2002 | 57501450 |
| Italy | 2003 | 57868021 |
| Italy | 2004 | 58267068 |
| Italy | 2005 | 58671855 |
| Italy | 2006 | 59078271 |
| Italy | 2007 | 59485505 |
| Italy | 2008 | 59873820 |
| Italy | 2009 | 60220314 |
| Italy | 2010 | 60508978 |
| Italy | 2011 | 60729316 |
| Italy | 2012 | 60884593 |
| Italy | 2013 | 60990277 |
| Jamaica | 1995 | 2462051 |
| Jamaica | 1996 | 2485230 |
| Jamaica | 1997 | 2509439 |
| Jamaica | 1998 | 2534127 |
| Jamaica | 1999 | 2558579 |
| Jamaica | 2000 | 2582219 |
| Jamaica | 2001 | 2604946 |
| Jamaica | 2002 | 2626716 |
| Jamaica | 2003 | 2647116 |
| Jamaica | 2004 | 2665680 |
| Jamaica | 2005 | 2682149 |
| Jamaica | 2006 | 2696227 |
| Jamaica | 2007 | 2708152 |
| Jamaica | 2008 | 2718862 |
| Jamaica | 2009 | 2729660 |
| Jamaica | 2010 | 2741485 |
| Jamaica | 2011 | 2754669 |
| Jamaica | 2012 | 2768941 |
| Jamaica | 2013 | 2783888 |
| Japan | 1995 | 124483305 |
| Japan | 1996 | 124794906 |
| Japan | 1997 | 125048712 |
| Japan | 1998 | 125266855 |
| Japan | 1999 | 125481436 |
| Japan | 2000 | 125714674 |
| Japan | 2001 | 125973930 |
| Japan | 2002 | 126249090 |
| Japan | 2003 | 126523597 |
| Japan | 2004 | 126772899 |
| Japan | 2005 | 126978754 |
| Japan | 2006 | 127136020 |
| Japan | 2007 | 127248855 |
| Japan | 2008 | 127319434 |
| Japan | 2009 | 127352872 |
| Japan | 2010 | 127352833 |
| Japan | 2011 | 127319206 |
| Japan | 2012 | 127249704 |
| Japan | 2013 | 127143577 |
| Jordan | 1995 | 4320158 |
| Jordan | 1996 | 4450852 |
| Jordan | 1997 | 4551911 |
| Jordan | 1998 | 4630762 |
| Jordan | 1999 | 4699278 |
| Jordan | 2000 | 4767476 |
| Jordan | 2001 | 4835108 |
| Jordan | 2002 | 4902974 |
| Jordan | 2003 | 4983508 |
| Jordan | 2004 | 5092185 |
| Jordan | 2005 | 5239417 |
| Jordan | 2006 | 5428896 |
| Jordan | 2007 | 5655856 |
| Jordan | 2008 | 5911202 |
| Jordan | 2009 | 6181307 |
| Jordan | 2010 | 6454554 |
| Jordan | 2011 | 6731151 |
| Jordan | 2012 | 7009444 |
| Jordan | 2013 | 7273799 |
| Kazakhstan | 1995 | 15549632 |
| Kazakhstan | 1996 | 15326132 |
| Kazakhstan | 1997 | 15085965 |
| Kazakhstan | 1998 | 14858241 |
| Kazakhstan | 1999 | 14679568 |
| Kazakhstan | 2000 | 14575644 |
| Kazakhstan | 2001 | 14559255 |
| Kazakhstan | 2002 | 14623188 |
| Kazakhstan | 2003 | 14747754 |
| Kazakhstan | 2004 | 14902452 |
| Kazakhstan | 2005 | 15064088 |
| Kazakhstan | 2006 | 15227211 |
| Kazakhstan | 2007 | 15396045 |
| Kazakhstan | 2008 | 15568294 |
| Kazakhstan | 2009 | 15743552 |
| Kazakhstan | 2010 | 15921127 |
| Kazakhstan | 2011 | 16097998 |
| Kazakhstan | 2012 | 16271201 |
| Kazakhstan | 2013 | 16440586 |
| Kenya | 1995 | 27418077 |
| Kenya | 1996 | 28186224 |
| Kenya | 1997 | 28943647 |
| Kenya | 1998 | 29702246 |
| Kenya | 1999 | 30478597 |
| Kenya | 2000 | 31285050 |
| Kenya | 2001 | 32126351 |
| Kenya | 2002 | 33000524 |
| Kenya | 2003 | 33905011 |
| Kenya | 2004 | 34834606 |
| Kenya | 2005 | 35785718 |
| Kenya | 2006 | 36757498 |
| Kenya | 2007 | 37752304 |
| Kenya | 2008 | 38773277 |
| Kenya | 2009 | 39824734 |
| Kenya | 2010 | 40909194 |
| Kenya | 2011 | 42027891 |
| Kenya | 2012 | 43178141 |
| Kenya | 2013 | 44353691 |
| Kiribati | 1995 | 76378 |
| Kiribati | 1996 | 77511 |
| Kiribati | 1997 | 78725 |
| Kiribati | 1998 | 80018 |
| Kiribati | 1999 | 81377 |
| Kiribati | 2000 | 82788 |
| Kiribati | 2001 | 84261 |
| Kiribati | 2002 | 85799 |
| Kiribati | 2003 | 87371 |
| Kiribati | 2004 | 88936 |
| Kiribati | 2005 | 90468 |
| Kiribati | 2006 | 91953 |
| Kiribati | 2007 | 93401 |
| Kiribati | 2008 | 94832 |
| Kiribati | 2009 | 96272 |
| Kiribati | 2010 | 97743 |
| Kiribati | 2011 | 99250 |
| Kiribati | 2012 | 100786 |
| Kiribati | 2013 | 102351 |
| Kuwait | 1995 | 1586123 |
| Kuwait | 1996 | 1585244 |
| Kuwait | 1997 | 1635999 |
| Kuwait | 1998 | 1722208 |
| Kuwait | 1999 | 1818405 |
| Kuwait | 2000 | 1906231 |
| Kuwait | 2001 | 1980604 |
| Kuwait | 2002 | 2048232 |
| Kuwait | 2003 | 2116353 |
| Kuwait | 2004 | 2196466 |
| Kuwait | 2005 | 2296314 |
| Kuwait | 2006 | 2417445 |
| Kuwait | 2007 | 2554920 |
| Kuwait | 2008 | 2702221 |
| Kuwait | 2009 | 2850102 |
| Kuwait | 2010 | 2991580 |
| Kuwait | 2011 | 3124705 |
| Kuwait | 2012 | 3250496 |
| Kuwait | 2013 | 3368572 |
| Kyrgyzstan | 1995 | 4592135 |
| Kyrgyzstan | 1996 | 4658815 |
| Kyrgyzstan | 1997 | 4739263 |
| Kyrgyzstan | 1998 | 4823870 |
| Kyrgyzstan | 1999 | 4898865 |
| Kyrgyzstan | 2000 | 4954850 |
| Kyrgyzstan | 2001 | 4988162 |
| Kyrgyzstan | 2002 | 5003394 |
| Kyrgyzstan | 2003 | 5009453 |
| Kyrgyzstan | 2004 | 5019310 |
| Kyrgyzstan | 2005 | 5042381 |
| Kyrgyzstan | 2006 | 5081620 |
| Kyrgyzstan | 2007 | 5134297 |
| Kyrgyzstan | 2008 | 5197274 |
| Kyrgyzstan | 2009 | 5265218 |
| Kyrgyzstan | 2010 | 5334223 |
| Kyrgyzstan | 2011 | 5403419 |
| Kyrgyzstan | 2012 | 5474213 |
| Kyrgyzstan | 2013 | 5547548 |
| Lao People’s Democratic Republic | 1995 | 4871472 |
| Lao People’s Democratic Republic | 1996 | 4986592 |
| Lao People’s Democratic Republic | 1997 | 5096724 |
| Lao People’s Democratic Republic | 1998 | 5200898 |
| Lao People’s Democratic Republic | 1999 | 5298146 |
| Lao People’s Democratic Republic | 2000 | 5388281 |
| Lao People’s Democratic Republic | 2001 | 5470169 |
| Lao People’s Democratic Republic | 2002 | 5545245 |
| Lao People’s Democratic Republic | 2003 | 5619069 |
| Lao People’s Democratic Republic | 2004 | 5699112 |
| Lao People’s Democratic Republic | 2005 | 5790646 |
| Lao People’s Democratic Republic | 2006 | 5895930 |
| Lao People’s Democratic Republic | 2007 | 6013278 |
| Lao People’s Democratic Republic | 2008 | 6139127 |
| Lao People’s Democratic Republic | 2009 | 6267968 |
| Lao People’s Democratic Republic | 2010 | 6395713 |
| Lao People’s Democratic Republic | 2011 | 6521314 |
| Lao People’s Democratic Republic | 2012 | 6645827 |
| Lao People’s Democratic Republic | 2013 | 6769727 |
| Latvia | 1995 | 2487988 |
| Latvia | 1996 | 2458305 |
| Latvia | 1997 | 2434628 |
| Latvia | 1998 | 2414650 |
| Latvia | 1999 | 2394521 |
| Latvia | 2000 | 2371481 |
| Latvia | 2001 | 2345309 |
| Latvia | 2002 | 2317261 |
| Latvia | 2003 | 2287780 |
| Latvia | 2004 | 2257647 |
| Latvia | 2005 | 2227559 |
| Latvia | 2006 | 2197359 |
| Latvia | 2007 | 2167157 |
| Latvia | 2008 | 2138258 |
| Latvia | 2009 | 2112337 |
| Latvia | 2010 | 2090519 |
| Latvia | 2011 | 2073385 |
| Latvia | 2012 | 2060428 |
| Latvia | 2013 | 2050317 |
| Lebanon | 1995 | 3033406 |
| Lebanon | 1996 | 3070984 |
| Lebanon | 1997 | 3092718 |
| Lebanon | 1998 | 3114014 |
| Lebanon | 1999 | 3156706 |
| Lebanon | 2000 | 3235380 |
| Lebanon | 2001 | 3357600 |
| Lebanon | 2002 | 3515604 |
| Lebanon | 2003 | 3690110 |
| Lebanon | 2004 | 3853582 |
| Lebanon | 2005 | 3986865 |
| Lebanon | 2006 | 4079823 |
| Lebanon | 2007 | 4139813 |
| Lebanon | 2008 | 4186088 |
| Lebanon | 2009 | 4246924 |
| Lebanon | 2010 | 4341092 |
| Lebanon | 2011 | 4478105 |
| Lebanon | 2012 | 4647079 |
| Lebanon | 2013 | 4821971 |
| Lesotho | 1995 | 1753824 |
| Lesotho | 1996 | 1779197 |
| Lesotho | 1997 | 1801679 |
| Lesotho | 1998 | 1821609 |
| Lesotho | 1999 | 1839611 |
| Lesotho | 2000 | 1856225 |
| Lesotho | 2001 | 1871500 |
| Lesotho | 2002 | 1885487 |
| Lesotho | 2003 | 1898757 |
| Lesotho | 2004 | 1912022 |
| Lesotho | 2005 | 1925844 |
| Lesotho | 2006 | 1940413 |
| Lesotho | 2007 | 1955784 |
| Lesotho | 2008 | 1972199 |
| Lesotho | 2009 | 1989873 |
| Lesotho | 2010 | 2008921 |
| Lesotho | 2011 | 2029516 |
| Lesotho | 2012 | 2051545 |
| Lesotho | 2013 | 2074465 |
| Liberia | 1995 | 2079921 |
| Liberia | 1996 | 2197801 |
| Liberia | 1997 | 2365290 |
| Liberia | 1998 | 2558085 |
| Liberia | 1999 | 2741755 |
| Liberia | 2000 | 2891968 |
| Liberia | 2001 | 2998770 |
| Liberia | 2002 | 3070673 |
| Liberia | 2003 | 3124222 |
| Liberia | 2004 | 3184643 |
| Liberia | 2005 | 3269786 |
| Liberia | 2006 | 3384791 |
| Liberia | 2007 | 3522294 |
| Liberia | 2008 | 3672714 |
| Liberia | 2009 | 3821440 |
| Liberia | 2010 | 3957990 |
| Liberia | 2011 | 4079697 |
| Liberia | 2012 | 4190435 |
| Liberia | 2013 | 4294077 |
| Libya | 1995 | 4747619 |
| Libya | 1996 | 4837354 |
| Libya | 1997 | 4924347 |
| Libya | 1998 | 5009240 |
| Libya | 1999 | 5092939 |
| Libya | 2000 | 5176185 |
| Libya | 2001 | 5258677 |
| Libya | 2002 | 5340389 |
| Libya | 2003 | 5422612 |
| Libya | 2004 | 5507000 |
| Libya | 2005 | 5594450 |
| Libya | 2006 | 5686475 |
| Libya | 2007 | 5782108 |
| Libya | 2008 | 5876805 |
| Libya | 2009 | 5964325 |
| Libya | 2010 | 6040612 |
| Libya | 2011 | 6103233 |
| Libya | 2012 | 6154623 |
| Libya | 2013 | 6201521 |
| Lithuania | 1995 | 3628079 |
| Lithuania | 1996 | 3606012 |
| Lithuania | 1997 | 3583272 |
| Lithuania | 1998 | 3558827 |
| Lithuania | 1999 | 3530932 |
| Lithuania | 2000 | 3498446 |
| Lithuania | 2001 | 3461616 |
| Lithuania | 2002 | 3421430 |
| Lithuania | 2003 | 3378410 |
| Lithuania | 2004 | 3333259 |
| Lithuania | 2005 | 3286782 |
| Lithuania | 2006 | 3238819 |
| Lithuania | 2007 | 3190088 |
| Lithuania | 2008 | 3143310 |
| Lithuania | 2009 | 3101973 |
| Lithuania | 2010 | 3068457 |
| Lithuania | 2011 | 3043980 |
| Lithuania | 2012 | 3027621 |
| Lithuania | 2013 | 3016933 |
| Luxembourg | 1995 | 408152 |
| Luxembourg | 1996 | 414004 |
| Luxembourg | 1997 | 419979 |
| Luxembourg | 1998 | 425834 |
| Luxembourg | 1999 | 431268 |
| Luxembourg | 2000 | 436111 |
| Luxembourg | 2001 | 440137 |
| Luxembourg | 2002 | 443524 |
| Luxembourg | 2003 | 446996 |
| Luxembourg | 2004 | 451548 |
| Luxembourg | 2005 | 457847 |
| Luxembourg | 2006 | 466209 |
| Luxembourg | 2007 | 476294 |
| Luxembourg | 2008 | 487310 |
| Luxembourg | 2009 | 498116 |
| Luxembourg | 2010 | 507885 |
| Luxembourg | 2011 | 516348 |
| Luxembourg | 2012 | 523744 |
| Luxembourg | 2013 | 530380 |
| Madagascar | 1995 | 13452526 |
| Madagascar | 1996 | 13882646 |
| Madagascar | 1997 | 14329239 |
| Madagascar | 1998 | 14790245 |
| Madagascar | 1999 | 15262817 |
| Madagascar | 2000 | 15744811 |
| Madagascar | 2001 | 16235767 |
| Madagascar | 2002 | 16736029 |
| Madagascar | 2003 | 17245275 |
| Madagascar | 2004 | 17763367 |
| Madagascar | 2005 | 18290394 |
| Madagascar | 2006 | 18826126 |
| Madagascar | 2007 | 19371023 |
| Madagascar | 2008 | 19926785 |
| Madagascar | 2009 | 20495695 |
| Madagascar | 2010 | 21079532 |
| Madagascar | 2011 | 21678934 |
| Madagascar | 2012 | 22293914 |
| Madagascar | 2013 | 22924851 |
| Malawi | 1995 | 9964065 |
| Malawi | 1996 | 10153315 |
| Malawi | 1997 | 10404259 |
| Malawi | 1998 | 10700180 |
| Malawi | 1999 | 11012707 |
| Malawi | 2000 | 11321496 |
| Malawi | 2001 | 11623166 |
| Malawi | 2002 | 11926778 |
| Malawi | 2003 | 12238739 |
| Malawi | 2004 | 12569091 |
| Malawi | 2005 | 12924746 |
| Malawi | 2006 | 13307535 |
| Malawi | 2007 | 13713758 |
| Malawi | 2008 | 14138207 |
| Malawi | 2009 | 14573338 |
| Malawi | 2010 | 15013694 |
| Malawi | 2011 | 15457531 |
| Malawi | 2012 | 15906483 |
| Malawi | 2013 | 16362567 |
| Malaysia | 1995 | 20725374 |
| Malaysia | 1996 | 21259831 |
| Malaysia | 1997 | 21805835 |
| Malaysia | 1998 | 22355057 |
| Malaysia | 1999 | 22896048 |
| Malaysia | 2000 | 23420751 |
| Malaysia | 2001 | 23925742 |
| Malaysia | 2002 | 24413795 |
| Malaysia | 2003 | 24890654 |
| Malaysia | 2004 | 25365089 |
| Malaysia | 2005 | 25843466 |
| Malaysia | 2006 | 26327098 |
| Malaysia | 2007 | 26813819 |
| Malaysia | 2008 | 27302348 |
| Malaysia | 2009 | 27790324 |
| Malaysia | 2010 | 28275835 |
| Malaysia | 2011 | 28758968 |
| Malaysia | 2012 | 29239927 |
| Malaysia | 2013 | 29716965 |
| Maldives | 1995 | 244965 |
| Maldives | 1996 | 250659 |
| Maldives | 1997 | 256347 |
| Maldives | 1998 | 261973 |
| Maldives | 1999 | 267456 |
| Maldives | 2000 | 272745 |
| Maldives | 2001 | 277825 |
| Maldives | 2002 | 282743 |
| Maldives | 2003 | 287594 |
| Maldives | 2004 | 292505 |
| Maldives | 2005 | 297576 |
| Maldives | 2006 | 302825 |
| Maldives | 2007 | 308239 |
| Maldives | 2008 | 313843 |
| Maldives | 2009 | 319660 |
| Maldives | 2010 | 325694 |
| Maldives | 2011 | 331964 |
| Maldives | 2012 | 338442 |
| Maldives | 2013 | 345023 |
| Mali | 1995 | 8988853 |
| Mali | 1996 | 9222157 |
| Mali | 1997 | 9462256 |
| Mali | 1998 | 9712365 |
| Mali | 1999 | 9977308 |
| Mali | 2000 | 10260577 |
| Mali | 2001 | 10562768 |
| Mali | 2002 | 10882662 |
| Mali | 2003 | 11219737 |
| Mali | 2004 | 11572936 |
| Mali | 2005 | 11941258 |
| Mali | 2006 | 12325545 |
| Mali | 2007 | 12725629 |
| Mali | 2008 | 13138299 |
| Mali | 2009 | 13559296 |
| Mali | 2010 | 13985961 |
| Mali | 2011 | 14416737 |
| Mali | 2012 | 14853572 |
| Mali | 2013 | 15301650 |
| Malta | 1995 | 395615 |
| Malta | 1996 | 398698 |
| Malta | 1997 | 401373 |
| Malta | 1998 | 403688 |
| Malta | 1999 | 405716 |
| Malta | 2000 | 407529 |
| Malta | 2001 | 409116 |
| Malta | 2002 | 410493 |
| Malta | 2003 | 411790 |
| Malta | 2004 | 413170 |
| Malta | 2005 | 414747 |
| Malta | 2006 | 416584 |
| Malta | 2007 | 418640 |
| Malta | 2008 | 420796 |
| Malta | 2009 | 422872 |
| Malta | 2010 | 424738 |
| Malta | 2011 | 426356 |
| Malta | 2012 | 427764 |
| Malta | 2013 | 429004 |
| Marshall Islands | 1995 | 51020 |
| Marshall Islands | 1996 | 51397 |
| Marshall Islands | 1997 | 51697 |
| Marshall Islands | 1998 | 51922 |
| Marshall Islands | 1999 | 52076 |
| Marshall Islands | 2000 | 52161 |
| Marshall Islands | 2001 | 52184 |
| Marshall Islands | 2002 | 52161 |
| Marshall Islands | 2003 | 52115 |
| Marshall Islands | 2004 | 52074 |
| Marshall Islands | 2005 | 52058 |
| Marshall Islands | 2006 | 52084 |
| Marshall Islands | 2007 | 52150 |
| Marshall Islands | 2008 | 52245 |
| Marshall Islands | 2009 | 52341 |
| Marshall Islands | 2010 | 52428 |
| Marshall Islands | 2011 | 52495 |
| Marshall Islands | 2012 | 52555 |
| Marshall Islands | 2013 | 52634 |
| Mauritania | 1995 | 2334388 |
| Mauritania | 1996 | 2403805 |
| Mauritania | 1997 | 2475726 |
| Mauritania | 1998 | 2550307 |
| Mauritania | 1999 | 2627739 |
| Mauritania | 2000 | 2708095 |
| Mauritania | 2001 | 2791403 |
| Mauritania | 2002 | 2877431 |
| Mauritania | 2003 | 2965667 |
| Mauritania | 2004 | 3055425 |
| Mauritania | 2005 | 3146164 |
| Mauritania | 2006 | 3237713 |
| Mauritania | 2007 | 3330037 |
| Mauritania | 2008 | 3422901 |
| Mauritania | 2009 | 3516077 |
| Mauritania | 2010 | 3609420 |
| Mauritania | 2011 | 3702763 |
| Mauritania | 2012 | 3796141 |
| Mauritania | 2013 | 3889880 |
| Mauritius | 1995 | 1128676 |
| Mauritius | 1996 | 1142191 |
| Mauritius | 1997 | 1154701 |
| Mauritius | 1998 | 1166070 |
| Mauritius | 1999 | 1176227 |
| Mauritius | 2000 | 1185143 |
| Mauritius | 2001 | 1192719 |
| Mauritius | 2002 | 1198972 |
| Mauritius | 2003 | 1204161 |
| Mauritius | 2004 | 1208653 |
| Mauritius | 2005 | 1212752 |
| Mauritius | 2006 | 1216518 |
| Mauritius | 2007 | 1219976 |
| Mauritius | 2008 | 1223336 |
| Mauritius | 2009 | 1226841 |
| Mauritius | 2010 | 1230659 |
| Mauritius | 2011 | 1234910 |
| Mauritius | 2012 | 1239557 |
| Mauritius | 2013 | 1244403 |
| Mexico | 1995 | 95392647 |
| Mexico | 1996 | 97201533 |
| Mexico | 1997 | 98968558 |
| Mexico | 1998 | 100678867 |
| Mexico | 1999 | 102316781 |
| Mexico | 2000 | 103873607 |
| Mexico | 2001 | 105339877 |
| Mexico | 2002 | 106723661 |
| Mexico | 2003 | 108056312 |
| Mexico | 2004 | 109381550 |
| Mexico | 2005 | 110731826 |
| Mexico | 2006 | 112116694 |
| Mexico | 2007 | 113529819 |
| Mexico | 2008 | 114968039 |
| Mexico | 2009 | 116422752 |
| Mexico | 2010 | 117886404 |
| Mexico | 2011 | 119361233 |
| Mexico | 2012 | 120847477 |
| Mexico | 2013 | 122332399 |
| Micronesia (Federated States of) | 1995 | 107556 |
| Micronesia (Federated States of) | 1996 | 108342 |
| Micronesia (Federated States of) | 1997 | 108506 |
| Micronesia (Federated States of) | 1998 | 108236 |
| Micronesia (Federated States of) | 1999 | 107808 |
| Micronesia (Federated States of) | 2000 | 107430 |
| Micronesia (Federated States of) | 2001 | 107170 |
| Micronesia (Federated States of) | 2002 | 106983 |
| Micronesia (Federated States of) | 2003 | 106816 |
| Micronesia (Federated States of) | 2004 | 106575 |
| Micronesia (Federated States of) | 2005 | 106198 |
| Micronesia (Federated States of) | 2006 | 105686 |
| Micronesia (Federated States of) | 2007 | 105097 |
| Micronesia (Federated States of) | 2008 | 104498 |
| Micronesia (Federated States of) | 2009 | 103983 |
| Micronesia (Federated States of) | 2010 | 103619 |
| Micronesia (Federated States of) | 2011 | 103424 |
| Micronesia (Federated States of) | 2012 | 103395 |
| Micronesia (Federated States of) | 2013 | 103549 |
| Monaco | 1995 | 30700 |
| Monaco | 1996 | 30971 |
| Monaco | 1997 | 31242 |
| Monaco | 1998 | 31517 |
| Monaco | 1999 | 31795 |
| Monaco | 2000 | 32081 |
| Monaco | 2001 | 32366 |
| Monaco | 2002 | 32653 |
| Monaco | 2003 | 32968 |
| Monaco | 2004 | 33346 |
| Monaco | 2005 | 33808 |
| Monaco | 2006 | 34369 |
| Monaco | 2007 | 35013 |
| Monaco | 2008 | 35686 |
| Monaco | 2009 | 36314 |
| Monaco | 2010 | 36845 |
| Monaco | 2011 | 37261 |
| Monaco | 2012 | 37579 |
| Monaco | 2013 | 37831 |
| Mongolia | 1995 | 2298063 |
| Mongolia | 1996 | 2316598 |
| Mongolia | 1997 | 2335723 |
| Mongolia | 1998 | 2355618 |
| Mongolia | 1999 | 2376197 |
| Mongolia | 2000 | 2397473 |
| Mongolia | 2001 | 2419669 |
| Mongolia | 2002 | 2443231 |
| Mongolia | 2003 | 2468595 |
| Mongolia | 2004 | 2496248 |
| Mongolia | 2005 | 2526502 |
| Mongolia | 2006 | 2559496 |
| Mongolia | 2007 | 2595068 |
| Mongolia | 2008 | 2632834 |
| Mongolia | 2009 | 2672223 |
| Mongolia | 2010 | 2712738 |
| Mongolia | 2011 | 2754209 |
| Mongolia | 2012 | 2796484 |
| Mongolia | 2013 | 2839073 |
| Montenegro | 2005 | 615820 |
| Montenegro | 2006 | 616854 |
| Montenegro | 2007 | 617800 |
| Montenegro | 2008 | 618649 |
| Montenegro | 2009 | 619408 |
| Montenegro | 2010 | 620078 |
| Montenegro | 2011 | 620644 |
| Montenegro | 2012 | 621081 |
| Montenegro | 2013 | 621383 |
| Montserrat | 1995 | 10232 |
| Montserrat | 1996 | 9375 |
| Montserrat | 1997 | 8217 |
| Montserrat | 1998 | 6936 |
| Montserrat | 1999 | 5788 |
| Montserrat | 2000 | 4956 |
| Montserrat | 2001 | 4505 |
| Montserrat | 2002 | 4377 |
| Montserrat | 2003 | 4474 |
| Montserrat | 2004 | 4645 |
| Montserrat | 2005 | 4777 |
| Montserrat | 2006 | 4846 |
| Montserrat | 2007 | 4884 |
| Montserrat | 2008 | 4901 |
| Montserrat | 2009 | 4918 |
| Montserrat | 2010 | 4953 |
| Montserrat | 2011 | 5000 |
| Montserrat | 2012 | 5046 |
| Montserrat | 2013 | 5091 |
| Morocco | 1995 | 26833093 |
| Morocco | 1996 | 27237150 |
| Morocco | 1997 | 27632321 |
| Morocco | 1998 | 28013585 |
| Morocco | 1999 | 28374203 |
| Morocco | 2000 | 28710123 |
| Morocco | 2001 | 29021156 |
| Morocco | 2002 | 29311443 |
| Morocco | 2003 | 29586937 |
| Morocco | 2004 | 29855820 |
| Morocco | 2005 | 30125445 |
| Morocco | 2006 | 30395097 |
| Morocco | 2007 | 30667086 |
| Morocco | 2008 | 30955151 |
| Morocco | 2009 | 31276564 |
| Morocco | 2010 | 31642360 |
| Morocco | 2011 | 32059424 |
| Morocco | 2012 | 32521143 |
| Morocco | 2013 | 33008150 |
| Mozambique | 1995 | 15981571 |
| Mozambique | 1996 | 16463426 |
| Mozambique | 1997 | 16914628 |
| Mozambique | 1998 | 17350739 |
| Mozambique | 1999 | 17798102 |
| Mozambique | 2000 | 18275618 |
| Mozambique | 2001 | 18785719 |
| Mozambique | 2002 | 19319894 |
| Mozambique | 2003 | 19873460 |
| Mozambique | 2004 | 20438827 |
| Mozambique | 2005 | 21010376 |
| Mozambique | 2006 | 21587317 |
| Mozambique | 2007 | 22171404 |
| Mozambique | 2008 | 22762525 |
| Mozambique | 2009 | 23361025 |
| Mozambique | 2010 | 23967265 |
| Mozambique | 2011 | 24581367 |
| Mozambique | 2012 | 25203395 |
| Mozambique | 2013 | 25833752 |
| Myanmar | 1995 | 45329862 |
| Myanmar | 1996 | 45991828 |
| Myanmar | 1997 | 46664455 |
| Myanmar | 1998 | 47321204 |
| Myanmar | 1999 | 47925630 |
| Myanmar | 2000 | 48453000 |
| Myanmar | 2001 | 48894203 |
| Myanmar | 2002 | 49261313 |
| Myanmar | 2003 | 49577152 |
| Myanmar | 2004 | 49875169 |
| Myanmar | 2005 | 50181020 |
| Myanmar | 2006 | 50500070 |
| Myanmar | 2007 | 50828959 |
| Myanmar | 2008 | 51174018 |
| Myanmar | 2009 | 51540490 |
| Myanmar | 2010 | 51931231 |
| Myanmar | 2011 | 52350763 |
| Myanmar | 2012 | 52797319 |
| Myanmar | 2013 | 53259018 |
| Namibia | 1995 | 1654214 |
| Namibia | 1996 | 1705309 |
| Namibia | 1997 | 1757969 |
| Namibia | 1998 | 1809719 |
| Namibia | 1999 | 1857149 |
| Namibia | 2000 | 1897953 |
| Namibia | 2001 | 1931282 |
| Namibia | 2002 | 1958303 |
| Namibia | 2003 | 1981237 |
| Namibia | 2004 | 2003320 |
| Namibia | 2005 | 2027026 |
| Namibia | 2006 | 2052931 |
| Namibia | 2007 | 2080700 |
| Namibia | 2008 | 2110791 |
| Namibia | 2009 | 2143498 |
| Namibia | 2010 | 2178967 |
| Namibia | 2011 | 2217618 |
| Namibia | 2012 | 2259393 |
| Namibia | 2013 | 2303315 |
| Nauru | 1995 | 9970 |
| Nauru | 1996 | 10033 |
| Nauru | 1997 | 10056 |
| Nauru | 1998 | 10054 |
| Nauru | 1999 | 10044 |
| Nauru | 2000 | 10042 |
| Nauru | 2001 | 10053 |
| Nauru | 2002 | 10072 |
| Nauru | 2003 | 10096 |
| Nauru | 2004 | 10112 |
| Nauru | 2005 | 10115 |
| Nauru | 2006 | 10104 |
| Nauru | 2007 | 10083 |
| Nauru | 2008 | 10058 |
| Nauru | 2009 | 10037 |
| Nauru | 2010 | 10025 |
| Nauru | 2011 | 10024 |
| Nauru | 2012 | 10032 |
| Nauru | 2013 | 10051 |
| Nepal | 1995 | 20587157 |
| Nepal | 1996 | 21115271 |
| Nepal | 1997 | 21647305 |
| Nepal | 1998 | 22175431 |
| Nepal | 1999 | 22690158 |
| Nepal | 2000 | 23184177 |
| Nepal | 2001 | 23655119 |
| Nepal | 2002 | 24102862 |
| Nepal | 2003 | 24525527 |
| Nepal | 2004 | 24921910 |
| Nepal | 2005 | 25292058 |
| Nepal | 2006 | 25634043 |
| Nepal | 2007 | 25950022 |
| Nepal | 2008 | 26249412 |
| Nepal | 2009 | 26544943 |
| Nepal | 2010 | 26846016 |
| Nepal | 2011 | 27156367 |
| Nepal | 2012 | 27474377 |
| Nepal | 2013 | 27797457 |
| Netherlands Antilles | 1995 | 194332 |
| Netherlands Antilles | 1996 | 192128 |
| Netherlands Antilles | 1997 | 188609 |
| Netherlands Antilles | 1998 | 184455 |
| Netherlands Antilles | 1999 | 180623 |
| Netherlands Antilles | 2000 | 177838 |
| Netherlands Antilles | 2001 | 176280 |
| Netherlands Antilles | 2002 | 175823 |
| Netherlands Antilles | 2003 | 176517 |
| Netherlands Antilles | 2004 | 178328 |
| Netherlands Antilles | 2005 | 181195 |
| Netherlands Antilles | 2006 | 185182 |
| Netherlands Antilles | 2007 | 190229 |
| Netherlands Antilles | 2008 | 195970 |
| Netherlands Antilles | 2009 | 201921 |
| Netherlands | 1995 | 15420000 |
| Netherlands | 1996 | 15514794 |
| Netherlands | 1997 | 15603344 |
| Netherlands | 1998 | 15688091 |
| Netherlands | 1999 | 15772758 |
| Netherlands | 2000 | 15859863 |
| Netherlands | 2001 | 15950214 |
| Netherlands | 2002 | 16042368 |
| Netherlands | 2003 | 16133907 |
| Netherlands | 2004 | 16221364 |
| Netherlands | 2005 | 16302242 |
| Netherlands | 2006 | 16375813 |
| Netherlands | 2007 | 16442848 |
| Netherlands | 2008 | 16504193 |
| Netherlands | 2009 | 16561282 |
| Netherlands | 2010 | 16615243 |
| Netherlands | 2011 | 16666206 |
| Netherlands | 2012 | 16714018 |
| Netherlands | 2013 | 16759229 |
| New Caledonia | 1995 | 189220 |
| New Caledonia | 1996 | 193493 |
| New Caledonia | 1997 | 197726 |
| New Caledonia | 1998 | 201899 |
| New Caledonia | 1999 | 205997 |
| New Caledonia | 2000 | 210010 |
| New Caledonia | 2001 | 213919 |
| New Caledonia | 2002 | 217720 |
| New Caledonia | 2003 | 221432 |
| New Caledonia | 2004 | 225088 |
| New Caledonia | 2005 | 228714 |
| New Caledonia | 2006 | 232317 |
| New Caledonia | 2007 | 235889 |
| New Caledonia | 2008 | 239429 |
| New Caledonia | 2009 | 242926 |
| New Caledonia | 2010 | 246379 |
| New Caledonia | 2011 | 249786 |
| New Caledonia | 2012 | 253155 |
| New Caledonia | 2013 | 256496 |
| New Zealand | 1995 | 3675060 |
| New Zealand | 1996 | 3717342 |
| New Zealand | 1997 | 3752131 |
| New Zealand | 1998 | 3783470 |
| New Zealand | 1999 | 3817367 |
| New Zealand | 2000 | 3858032 |
| New Zealand | 2001 | 3906817 |
| New Zealand | 2002 | 3961937 |
| New Zealand | 2003 | 4020706 |
| New Zealand | 2004 | 4079075 |
| New Zealand | 2005 | 4134117 |
| New Zealand | 2006 | 4185020 |
| New Zealand | 2007 | 4232734 |
| New Zealand | 2008 | 4278186 |
| New Zealand | 2009 | 4322941 |
| New Zealand | 2010 | 4368136 |
| New Zealand | 2011 | 4413923 |
| New Zealand | 2012 | 4459852 |
| New Zealand | 2013 | 4505761 |
| Nicaragua | 1995 | 4659458 |
| Nicaragua | 1996 | 4756631 |
| Nicaragua | 1997 | 4849272 |
| Nicaragua | 1998 | 4937320 |
| Nicaragua | 1999 | 5021079 |
| Nicaragua | 2000 | 5100920 |
| Nicaragua | 2001 | 5176685 |
| Nicaragua | 2002 | 5248577 |
| Nicaragua | 2003 | 5317878 |
| Nicaragua | 2004 | 5386299 |
| Nicaragua | 2005 | 5455219 |
| Nicaragua | 2006 | 5524927 |
| Nicaragua | 2007 | 5595533 |
| Nicaragua | 2008 | 5667983 |
| Nicaragua | 2009 | 5743329 |
| Nicaragua | 2010 | 5822209 |
| Nicaragua | 2011 | 5905146 |
| Nicaragua | 2012 | 5991733 |
| Nicaragua | 2013 | 6080478 |
| Niger | 1995 | 9167078 |
| Niger | 1996 | 9499605 |
| Niger | 1997 | 9849621 |
| Niger | 1998 | 10215806 |
| Niger | 1999 | 10596258 |
| Niger | 2000 | 10989815 |
| Niger | 2001 | 11396434 |
| Niger | 2002 | 11817297 |
| Niger | 2003 | 12254040 |
| Niger | 2004 | 12708897 |
| Niger | 2005 | 13183798 |
| Niger | 2006 | 13679705 |
| Niger | 2007 | 14197289 |
| Niger | 2008 | 14737895 |
| Niger | 2009 | 15302948 |
| Niger | 2010 | 15893746 |
| Niger | 2011 | 16511462 |
| Niger | 2012 | 17157042 |
| Niger | 2013 | 17831270 |
| Nigeria | 1995 | 108424827 |
| Nigeria | 1996 | 111166210 |
| Nigeria | 1997 | 113979481 |
| Nigeria | 1998 | 116867371 |
| Nigeria | 1999 | 119831888 |
| Nigeria | 2000 | 122876727 |
| Nigeria | 2001 | 126004992 |
| Nigeria | 2002 | 129224641 |
| Nigeria | 2003 | 132550146 |
| Nigeria | 2004 | 135999250 |
| Nigeria | 2005 | 139585891 |
| Nigeria | 2006 | 143314909 |
| Nigeria | 2007 | 147187353 |
| Nigeria | 2008 | 151208080 |
| Nigeria | 2009 | 155381020 |
| Nigeria | 2010 | 159707780 |
| Nigeria | 2011 | 164192925 |
| Nigeria | 2012 | 168833776 |
| Nigeria | 2013 | 173615345 |
| Niue | 1995 | 2167 |
| Niue | 1996 | 2122 |
| Niue | 1997 | 2069 |
| Niue | 1998 | 2010 |
| Niue | 1999 | 1952 |
| Niue | 2000 | 1900 |
| Niue | 2001 | 1852 |
| Niue | 2002 | 1809 |
| Niue | 2003 | 1768 |
| Niue | 2004 | 1728 |
| Niue | 2005 | 1686 |
| Niue | 2006 | 1643 |
| Niue | 2007 | 1600 |
| Niue | 2008 | 1555 |
| Niue | 2009 | 1511 |
| Niue | 2010 | 1468 |
| Niue | 2011 | 1426 |
| Niue | 2012 | 1384 |
| Niue | 2013 | 1344 |
| Northern Mariana Islands | 1995 | 57518 |
| Northern Mariana Islands | 1996 | 60248 |
| Northern Mariana Islands | 1997 | 62939 |
| Northern Mariana Islands | 1998 | 65374 |
| Northern Mariana Islands | 1999 | 67271 |
| Northern Mariana Islands | 2000 | 68434 |
| Northern Mariana Islands | 2001 | 68817 |
| Northern Mariana Islands | 2002 | 68499 |
| Northern Mariana Islands | 2003 | 67562 |
| Northern Mariana Islands | 2004 | 66143 |
| Northern Mariana Islands | 2005 | 64372 |
| Northern Mariana Islands | 2006 | 62235 |
| Northern Mariana Islands | 2007 | 59792 |
| Northern Mariana Islands | 2008 | 57345 |
| Northern Mariana Islands | 2009 | 55278 |
| Northern Mariana Islands | 2010 | 53860 |
| Northern Mariana Islands | 2011 | 53230 |
| Northern Mariana Islands | 2012 | 53305 |
| Northern Mariana Islands | 2013 | 53855 |
| Norway | 1995 | 4359788 |
| Norway | 1996 | 4385951 |
| Norway | 1997 | 4412958 |
| Norway | 1998 | 4440109 |
| Norway | 1999 | 4466468 |
| Norway | 2000 | 4491572 |
| Norway | 2001 | 4514723 |
| Norway | 2002 | 4536647 |
| Norway | 2003 | 4560013 |
| Norway | 2004 | 4588444 |
| Norway | 2005 | 4624388 |
| Norway | 2006 | 4668989 |
| Norway | 2007 | 4721080 |
| Norway | 2008 | 4777943 |
| Norway | 2009 | 4835629 |
| Norway | 2010 | 4891251 |
| Norway | 2011 | 4943754 |
| Norway | 2012 | 4993875 |
| Norway | 2013 | 5042671 |
| Oman | 1995 | 2154600 |
| Oman | 1996 | 2175998 |
| Oman | 1997 | 2177723 |
| Oman | 1998 | 2171135 |
| Oman | 1999 | 2172287 |
| Oman | 2000 | 2192535 |
| Oman | 2001 | 2239025 |
| Oman | 2002 | 2308409 |
| Oman | 2003 | 2389121 |
| Oman | 2004 | 2464001 |
| Oman | 2005 | 2522325 |
| Oman | 2006 | 2554905 |
| Oman | 2007 | 2569739 |
| Oman | 2008 | 2593523 |
| Oman | 2009 | 2663224 |
| Oman | 2010 | 2802768 |
| Oman | 2011 | 3024774 |
| Oman | 2012 | 3314001 |
| Oman | 2013 | 3632444 |
| Pakistan | 1995 | 126689577 |
| Pakistan | 1996 | 130083700 |
| Pakistan | 1997 | 133597492 |
| Pakistan | 1998 | 137139290 |
| Pakistan | 1999 | 140580398 |
| Pakistan | 2000 | 143832014 |
| Pakistan | 2001 | 146857081 |
| Pakistan | 2002 | 149693684 |
| Pakistan | 2003 | 152419974 |
| Pakistan | 2004 | 155151394 |
| Pakistan | 2005 | 157971415 |
| Pakistan | 2006 | 160905794 |
| Pakistan | 2007 | 163928329 |
| Pakistan | 2008 | 167008083 |
| Pakistan | 2009 | 170093999 |
| Pakistan | 2010 | 173149306 |
| Pakistan | 2011 | 176166353 |
| Pakistan | 2012 | 179160111 |
| Pakistan | 2013 | 182142594 |
| Palau | 1995 | 17255 |
| Palau | 1996 | 17695 |
| Palau | 1997 | 18123 |
| Palau | 1998 | 18524 |
| Palau | 1999 | 18878 |
| Palau | 2000 | 19174 |
| Palau | 2001 | 19404 |
| Palau | 2002 | 19575 |
| Palau | 2003 | 19700 |
| Palau | 2004 | 19805 |
| Palau | 2005 | 19907 |
| Palau | 2006 | 20012 |
| Palau | 2007 | 20118 |
| Palau | 2008 | 20228 |
| Palau | 2009 | 20344 |
| Palau | 2010 | 20470 |
| Palau | 2011 | 20606 |
| Palau | 2012 | 20754 |
| Palau | 2013 | 20918 |
| Panama | 1995 | 2757004 |
| Panama | 1996 | 2814525 |
| Panama | 1997 | 2873288 |
| Panama | 1998 | 2933100 |
| Panama | 1999 | 2993685 |
| Panama | 2000 | 3054812 |
| Panama | 2001 | 3116409 |
| Panama | 2002 | 3178450 |
| Panama | 2003 | 3240805 |
| Panama | 2004 | 3303335 |
| Panama | 2005 | 3365929 |
| Panama | 2006 | 3428509 |
| Panama | 2007 | 3491034 |
| Panama | 2008 | 3553480 |
| Panama | 2009 | 3615846 |
| Panama | 2010 | 3678128 |
| Panama | 2011 | 3740282 |
| Panama | 2012 | 3802281 |
| Panama | 2013 | 3864170 |
| Papua New Guinea | 1995 | 4715929 |
| Papua New Guinea | 1996 | 4841020 |
| Papua New Guinea | 1997 | 4970823 |
| Papua New Guinea | 1998 | 5104516 |
| Papua New Guinea | 1999 | 5240941 |
| Papua New Guinea | 2000 | 5379226 |
| Papua New Guinea | 2001 | 5518971 |
| Papua New Guinea | 2002 | 5660267 |
| Papua New Guinea | 2003 | 5803302 |
| Papua New Guinea | 2004 | 5948461 |
| Papua New Guinea | 2005 | 6095959 |
| Papua New Guinea | 2006 | 6245797 |
| Papua New Guinea | 2007 | 6397623 |
| Papua New Guinea | 2008 | 6550877 |
| Papua New Guinea | 2009 | 6704829 |
| Papua New Guinea | 2010 | 6858945 |
| Papua New Guinea | 2011 | 7012977 |
| Papua New Guinea | 2012 | 7167010 |
| Papua New Guinea | 2013 | 7321262 |
| Paraguay | 1995 | 4801834 |
| Paraguay | 1996 | 4911701 |
| Paraguay | 1997 | 5021271 |
| Paraguay | 1998 | 5130723 |
| Paraguay | 1999 | 5240321 |
| Paraguay | 2000 | 5350253 |
| Paraguay | 2001 | 5460621 |
| Paraguay | 2002 | 5571371 |
| Paraguay | 2003 | 5682350 |
| Paraguay | 2004 | 5793330 |
| Paraguay | 2005 | 5904170 |
| Paraguay | 2006 | 6014781 |
| Paraguay | 2007 | 6125285 |
| Paraguay | 2008 | 6236005 |
| Paraguay | 2009 | 6347383 |
| Paraguay | 2010 | 6459721 |
| Paraguay | 2011 | 6573097 |
| Paraguay | 2012 | 6687361 |
| Paraguay | 2013 | 6802295 |
| Peru | 1995 | 23939261 |
| Peru | 1996 | 24365985 |
| Peru | 1997 | 24789855 |
| Peru | 1998 | 25206817 |
| Peru | 1999 | 25611482 |
| Peru | 2000 | 26000080 |
| Peru | 2001 | 26372358 |
| Peru | 2002 | 26729909 |
| Peru | 2003 | 27073334 |
| Peru | 2004 | 27403845 |
| Peru | 2005 | 27723281 |
| Peru | 2006 | 28030688 |
| Peru | 2007 | 28328410 |
| Peru | 2008 | 28625628 |
| Peru | 2009 | 28934303 |
| Peru | 2010 | 29262830 |
| Peru | 2011 | 29614887 |
| Peru | 2012 | 29987800 |
| Peru | 2013 | 30375603 |
| Philippines | 1995 | 69606539 |
| Philippines | 1996 | 71184718 |
| Philippines | 1997 | 72780928 |
| Philippines | 1998 | 74393147 |
| Philippines | 1999 | 76018006 |
| Philippines | 2000 | 77651848 |
| Philippines | 2001 | 79297756 |
| Philippines | 2002 | 80953652 |
| Philippines | 2003 | 82604681 |
| Philippines | 2004 | 84231329 |
| Philippines | 2005 | 85821214 |
| Philippines | 2006 | 87366573 |
| Philippines | 2007 | 88875548 |
| Philippines | 2008 | 90371287 |
| Philippines | 2009 | 91886400 |
| Philippines | 2010 | 93444322 |
| Philippines | 2011 | 95053437 |
| Philippines | 2012 | 96706764 |
| Philippines | 2013 | 98393574 |
| Poland | 1995 | 38479899 |
| Poland | 1996 | 38480234 |
| Poland | 1997 | 38461279 |
| Poland | 1998 | 38428791 |
| Poland | 1999 | 38390217 |
| Poland | 2000 | 38351437 |
| Poland | 2001 | 38314744 |
| Poland | 2002 | 38280547 |
| Poland | 2003 | 38250426 |
| Poland | 2004 | 38225455 |
| Poland | 2005 | 38206337 |
| Poland | 2006 | 38194163 |
| Poland | 2007 | 38189136 |
| Poland | 2008 | 38189735 |
| Poland | 2009 | 38193591 |
| Poland | 2010 | 38198754 |
| Poland | 2011 | 38204598 |
| Poland | 2012 | 38210924 |
| Poland | 2013 | 38216635 |
| Portugal | 1995 | 10097055 |
| Portugal | 1996 | 10140627 |
| Portugal | 1997 | 10182158 |
| Portugal | 1998 | 10222540 |
| Portugal | 1999 | 10263532 |
| Portugal | 2000 | 10306192 |
| Portugal | 2001 | 10350660 |
| Portugal | 2002 | 10395630 |
| Portugal | 2003 | 10439031 |
| Portugal | 2004 | 10478122 |
| Portugal | 2005 | 10510967 |
| Portugal | 2006 | 10536701 |
| Portugal | 2007 | 10555902 |
| Portugal | 2008 | 10569881 |
| Portugal | 2009 | 10580673 |
| Portugal | 2010 | 10589792 |
| Portugal | 2011 | 10597629 |
| Portugal | 2012 | 10603804 |
| Portugal | 2013 | 10608156 |
| Puerto Rico | 1995 | 3689649 |
| Puerto Rico | 1996 | 3719446 |
| Puerto Rico | 1997 | 3746634 |
| Puerto Rico | 1998 | 3769718 |
| Puerto Rico | 1999 | 3786884 |
| Puerto Rico | 2000 | 3796981 |
| Puerto Rico | 2001 | 3799358 |
| Puerto Rico | 2002 | 3794728 |
| Puerto Rico | 2003 | 3785058 |
| Puerto Rico | 2004 | 3773147 |
| Puerto Rico | 2005 | 3761143 |
| Puerto Rico | 2006 | 3749763 |
| Puerto Rico | 2007 | 3738829 |
| Puerto Rico | 2008 | 3728531 |
| Puerto Rico | 2009 | 3718812 |
| Puerto Rico | 2010 | 3709671 |
| Puerto Rico | 2011 | 3701373 |
| Puerto Rico | 2012 | 3694237 |
| Puerto Rico | 2013 | 3688318 |
| Qatar | 1995 | 501154 |
| Qatar | 1996 | 512476 |
| Qatar | 1997 | 529491 |
| Qatar | 1998 | 550367 |
| Qatar | 1999 | 572155 |
| Qatar | 2000 | 593693 |
| Qatar | 2001 | 611808 |
| Qatar | 2002 | 629745 |
| Qatar | 2003 | 660238 |
| Qatar | 2004 | 720383 |
| Qatar | 2005 | 821159 |
| Qatar | 2006 | 967602 |
| Qatar | 2007 | 1152459 |
| Qatar | 2008 | 1359114 |
| Qatar | 2009 | 1564082 |
| Qatar | 2010 | 1749713 |
| Qatar | 2011 | 1910902 |
| Qatar | 2012 | 2050514 |
| Qatar | 2013 | 2168673 |
| Republic of Korea | 1995 | 44652994 |
| Republic of Korea | 1996 | 44940974 |
| Republic of Korea | 1997 | 45220543 |
| Republic of Korea | 1998 | 45489131 |
| Republic of Korea | 1999 | 45742103 |
| Republic of Korea | 2000 | 45977210 |
| Republic of Korea | 2001 | 46192932 |
| Republic of Korea | 2002 | 46393993 |
| Republic of Korea | 2003 | 46591762 |
| Republic of Korea | 2004 | 46801310 |
| Republic of Korea | 2005 | 47033082 |
| Republic of Korea | 2006 | 47291491 |
| Republic of Korea | 2007 | 47572585 |
| Republic of Korea | 2008 | 47867970 |
| Republic of Korea | 2009 | 48164969 |
| Republic of Korea | 2010 | 48453931 |
| Republic of Korea | 2011 | 48732640 |
| Republic of Korea | 2012 | 49002683 |
| Republic of Korea | 2013 | 49262698 |
| Republic of Moldova | 1995 | 4339081 |
| Republic of Moldova | 1996 | 4308050 |
| Republic of Moldova | 1997 | 4268774 |
| Republic of Moldova | 1998 | 4221726 |
| Republic of Moldova | 1999 | 4167552 |
| Republic of Moldova | 2000 | 4107184 |
| Republic of Moldova | 2001 | 4040579 |
| Republic of Moldova | 2002 | 3969040 |
| Republic of Moldova | 2003 | 3896494 |
| Republic of Moldova | 2004 | 3827963 |
| Republic of Moldova | 2005 | 3767077 |
| Republic of Moldova | 2006 | 3715541 |
| Republic of Moldova | 2007 | 3672638 |
| Republic of Moldova | 2008 | 3636510 |
| Republic of Moldova | 2009 | 3604078 |
| Republic of Moldova | 2010 | 3573024 |
| Republic of Moldova | 2011 | 3542928 |
| Republic of Moldova | 2012 | 3514381 |
| Republic of Moldova | 2013 | 3487204 |
| Romania | 1995 | 22963512 |
| Romania | 1996 | 22841943 |
| Romania | 1997 | 22717415 |
| Romania | 1998 | 22596183 |
| Romania | 1999 | 22484940 |
| Romania | 2000 | 22388354 |
| Romania | 2001 | 22310092 |
| Romania | 2002 | 22249564 |
| Romania | 2003 | 22201592 |
| Romania | 2004 | 22158163 |
| Romania | 2005 | 22113264 |
| Romania | 2006 | 22065409 |
| Romania | 2007 | 22015937 |
| Romania | 2008 | 21964962 |
| Romania | 2009 | 21913311 |
| Romania | 2010 | 21861476 |
| Romania | 2011 | 21808931 |
| Romania | 2012 | 21754741 |
| Romania | 2013 | 21698585 |
| Russian Federation | 1995 | 148602147 |
| Russian Federation | 1996 | 148375246 |
| Russian Federation | 1997 | 148079919 |
| Russian Federation | 1998 | 147715550 |
| Russian Federation | 1999 | 147275875 |
| Russian Federation | 2000 | 146762881 |
| Russian Federation | 2001 | 146170247 |
| Russian Federation | 2002 | 145520813 |
| Russian Federation | 2003 | 144879644 |
| Russian Federation | 2004 | 144331352 |
| Russian Federation | 2005 | 143932966 |
| Russian Federation | 2006 | 143715023 |
| Russian Federation | 2007 | 143652377 |
| Russian Federation | 2008 | 143677033 |
| Russian Federation | 2009 | 143689741 |
| Russian Federation | 2010 | 143617913 |
| Russian Federation | 2011 | 143438152 |
| Russian Federation | 2012 | 143169653 |
| Russian Federation | 2013 | 142833689 |
| Rwanda | 1995 | 5663838 |
| Rwanda | 1996 | 5929575 |
| Rwanda | 1997 | 6470628 |
| Rwanda | 1998 | 7169658 |
| Rwanda | 1999 | 7853015 |
| Rwanda | 2000 | 8395577 |
| Rwanda | 2001 | 8760003 |
| Rwanda | 2002 | 8987523 |
| Rwanda | 2003 | 9126167 |
| Rwanda | 2004 | 9254379 |
| Rwanda | 2005 | 9429457 |
| Rwanda | 2006 | 9660946 |
| Rwanda | 2007 | 9928143 |
| Rwanda | 2008 | 10222961 |
| Rwanda | 2009 | 10529668 |
| Rwanda | 2010 | 10836732 |
| Rwanda | 2011 | 11144315 |
| Rwanda | 2012 | 11457801 |
| Rwanda | 2013 | 11776522 |
| Saint Kitts and Nevis | 1995 | 42888 |
| Saint Kitts and Nevis | 1996 | 43391 |
| Saint Kitts and Nevis | 1997 | 43885 |
| Saint Kitts and Nevis | 1998 | 44391 |
| Saint Kitts and Nevis | 1999 | 44938 |
| Saint Kitts and Nevis | 2000 | 45544 |
| Saint Kitts and Nevis | 2001 | 46214 |
| Saint Kitts and Nevis | 2002 | 46934 |
| Saint Kitts and Nevis | 2003 | 47679 |
| Saint Kitts and Nevis | 2004 | 48421 |
| Saint Kitts and Nevis | 2005 | 49139 |
| Saint Kitts and Nevis | 2006 | 49823 |
| Saint Kitts and Nevis | 2007 | 50478 |
| Saint Kitts and Nevis | 2008 | 51110 |
| Saint Kitts and Nevis | 2009 | 51731 |
| Saint Kitts and Nevis | 2010 | 52352 |
| Saint Kitts and Nevis | 2011 | 52971 |
| Saint Kitts and Nevis | 2012 | 53584 |
| Saint Kitts and Nevis | 2013 | 54191 |
| Saint Lucia | 1995 | 147040 |
| Saint Lucia | 1996 | 148962 |
| Saint Lucia | 1997 | 150994 |
| Saint Lucia | 1998 | 153066 |
| Saint Lucia | 1999 | 155073 |
| Saint Lucia | 2000 | 156949 |
| Saint Lucia | 2001 | 158650 |
| Saint Lucia | 2002 | 160217 |
| Saint Lucia | 2003 | 161766 |
| Saint Lucia | 2004 | 163460 |
| Saint Lucia | 2005 | 165407 |
| Saint Lucia | 2006 | 167658 |
| Saint Lucia | 2007 | 170149 |
| Saint Lucia | 2008 | 172734 |
| Saint Lucia | 2009 | 175200 |
| Saint Lucia | 2010 | 177397 |
| Saint Lucia | 2011 | 179271 |
| Saint Lucia | 2012 | 180870 |
| Saint Lucia | 2013 | 182273 |
| Saint Vincent and the Grenadines | 1995 | 108122 |
| Saint Vincent and the Grenadines | 1996 | 108078 |
| Saint Vincent and the Grenadines | 1997 | 108001 |
| Saint Vincent and the Grenadines | 1998 | 107923 |
| Saint Vincent and the Grenadines | 1999 | 107879 |
| Saint Vincent and the Grenadines | 2000 | 107897 |
| Saint Vincent and the Grenadines | 2001 | 107989 |
| Saint Vincent and the Grenadines | 2002 | 108150 |
| Saint Vincent and the Grenadines | 2003 | 108353 |
| Saint Vincent and the Grenadines | 2004 | 108562 |
| Saint Vincent and the Grenadines | 2005 | 108749 |
| Saint Vincent and the Grenadines | 2006 | 108908 |
| Saint Vincent and the Grenadines | 2007 | 109045 |
| Saint Vincent and the Grenadines | 2008 | 109158 |
| Saint Vincent and the Grenadines | 2009 | 109249 |
| Saint Vincent and the Grenadines | 2010 | 109316 |
| Saint Vincent and the Grenadines | 2011 | 109357 |
| Saint Vincent and the Grenadines | 2012 | 109373 |
| Saint Vincent and the Grenadines | 2013 | 109373 |
| Samoa | 1995 | 170158 |
| Samoa | 1996 | 171276 |
| Samoa | 1997 | 172191 |
| Samoa | 1998 | 172979 |
| Samoa | 1999 | 173758 |
| Samoa | 2000 | 174614 |
| Samoa | 2001 | 175567 |
| Samoa | 2002 | 176592 |
| Samoa | 2003 | 177677 |
| Samoa | 2004 | 178794 |
| Samoa | 2005 | 179928 |
| Samoa | 2006 | 181073 |
| Samoa | 2007 | 182240 |
| Samoa | 2008 | 183444 |
| Samoa | 2009 | 184704 |
| Samoa | 2010 | 186029 |
| Samoa | 2011 | 187429 |
| Samoa | 2012 | 188889 |
| Samoa | 2013 | 190372 |
| San Marino | 1995 | 25680 |
| San Marino | 1996 | 25914 |
| San Marino | 1997 | 26114 |
| San Marino | 1998 | 26321 |
| San Marino | 1999 | 26594 |
| San Marino | 2000 | 26969 |
| San Marino | 2001 | 27467 |
| San Marino | 2002 | 28064 |
| San Marino | 2003 | 28700 |
| San Marino | 2004 | 29290 |
| San Marino | 2005 | 29775 |
| San Marino | 2006 | 30130 |
| San Marino | 2007 | 30377 |
| San Marino | 2008 | 30549 |
| San Marino | 2009 | 30698 |
| San Marino | 2010 | 30861 |
| San Marino | 2011 | 31048 |
| San Marino | 2012 | 31247 |
| San Marino | 2013 | 31448 |
| Sao Tome and Principe | 1995 | 130378 |
| Sao Tome and Principe | 1996 | 132323 |
| Sao Tome and Principe | 1997 | 134029 |
| Sao Tome and Principe | 1998 | 135650 |
| Sao Tome and Principe | 1999 | 137396 |
| Sao Tome and Principe | 2000 | 139428 |
| Sao Tome and Principe | 2001 | 141783 |
| Sao Tome and Principe | 2002 | 144447 |
| Sao Tome and Principe | 2003 | 147455 |
| Sao Tome and Principe | 2004 | 150842 |
| Sao Tome and Principe | 2005 | 154615 |
| Sao Tome and Principe | 2006 | 158806 |
| Sao Tome and Principe | 2007 | 163390 |
| Sao Tome and Principe | 2008 | 168253 |
| Sao Tome and Principe | 2009 | 173240 |
| Sao Tome and Principe | 2010 | 178228 |
| Sao Tome and Principe | 2011 | 183177 |
| Sao Tome and Principe | 2012 | 188098 |
| Sao Tome and Principe | 2013 | 192993 |
| Saudi Arabia | 1995 | 18567343 |
| Saudi Arabia | 1996 | 18848350 |
| Saudi Arabia | 1997 | 19060850 |
| Saudi Arabia | 1998 | 19282965 |
| Saudi Arabia | 1999 | 19620692 |
| Saudi Arabia | 2000 | 20144584 |
| Saudi Arabia | 2001 | 20891594 |
| Saudi Arabia | 2002 | 21825217 |
| Saudi Arabia | 2003 | 22852333 |
| Saudi Arabia | 2004 | 23839231 |
| Saudi Arabia | 2005 | 24690067 |
| Saudi Arabia | 2006 | 25371936 |
| Saudi Arabia | 2007 | 25915624 |
| Saudi Arabia | 2008 | 26366358 |
| Saudi Arabia | 2009 | 26796375 |
| Saudi Arabia | 2010 | 27258387 |
| Saudi Arabia | 2011 | 27761728 |
| Saudi Arabia | 2012 | 28287855 |
| Saudi Arabia | 2013 | 28828870 |
| Senegal | 1995 | 8711528 |
| Senegal | 1996 | 8940298 |
| Senegal | 1997 | 9164050 |
| Senegal | 1998 | 9387783 |
| Senegal | 1999 | 9618564 |
| Senegal | 2000 | 9861679 |
| Senegal | 2001 | 10119118 |
| Senegal | 2002 | 10390050 |
| Senegal | 2003 | 10673535 |
| Senegal | 2004 | 10967568 |
| Senegal | 2005 | 11270826 |
| Senegal | 2006 | 11582925 |
| Senegal | 2007 | 11904974 |
| Senegal | 2008 | 12238791 |
| Senegal | 2009 | 12586827 |
| Senegal | 2010 | 12950564 |
| Senegal | 2011 | 13330737 |
| Senegal | 2012 | 13726021 |
| Senegal | 2013 | 14133280 |
| Serbia & Montenegro | 1995 | 10989446 |
| Serbia & Montenegro | 1996 | 11028631 |
| Serbia & Montenegro | 1997 | 11025594 |
| Serbia & Montenegro | 1998 | 10990409 |
| Serbia & Montenegro | 1999 | 10939047 |
| Serbia & Montenegro | 2000 | 10883641 |
| Serbia & Montenegro | 2001 | 10826817 |
| Serbia & Montenegro | 2002 | 10766194 |
| Serbia & Montenegro | 2003 | 10703165 |
| Serbia & Montenegro | 2004 | 10638276 |
| Serbia | 2005 | 9956359 |
| Serbia | 2006 | 9889237 |
| Serbia | 2007 | 9823743 |
| Serbia | 2008 | 9760977 |
| Serbia | 2009 | 9701912 |
| Serbia | 2010 | 9647109 |
| Serbia | 2011 | 9597413 |
| Serbia | 2012 | 9552553 |
| Serbia | 2013 | 9510506 |
| Seychelles | 1995 | 75433 |
| Seychelles | 1996 | 76391 |
| Seychelles | 1997 | 77173 |
| Seychelles | 1998 | 77891 |
| Seychelles | 1999 | 78720 |
| Seychelles | 2000 | 79774 |
| Seychelles | 2001 | 81093 |
| Seychelles | 2002 | 82611 |
| Seychelles | 2003 | 84218 |
| Seychelles | 2004 | 85751 |
| Seychelles | 2005 | 87094 |
| Seychelles | 2006 | 88210 |
| Seychelles | 2007 | 89134 |
| Seychelles | 2008 | 89902 |
| Seychelles | 2009 | 90577 |
| Seychelles | 2010 | 91208 |
| Seychelles | 2011 | 91799 |
| Seychelles | 2012 | 92339 |
| Seychelles | 2013 | 92838 |
| Sierra Leone | 1995 | 3927105 |
| Sierra Leone | 1996 | 3919708 |
| Sierra Leone | 1997 | 3928313 |
| Sierra Leone | 1998 | 3961869 |
| Sierra Leone | 1999 | 4030443 |
| Sierra Leone | 2000 | 4139757 |
| Sierra Leone | 2001 | 4295667 |
| Sierra Leone | 2002 | 4493047 |
| Sierra Leone | 2003 | 4712763 |
| Sierra Leone | 2004 | 4928175 |
| Sierra Leone | 2005 | 5119895 |
| Sierra Leone | 2006 | 5280909 |
| Sierra Leone | 2007 | 5416015 |
| Sierra Leone | 2008 | 5532139 |
| Sierra Leone | 2009 | 5641182 |
| Sierra Leone | 2010 | 5751976 |
| Sierra Leone | 2011 | 5865491 |
| Sierra Leone | 2012 | 5978727 |
| Sierra Leone | 2013 | 6092075 |
| Singapore | 1995 | 3482640 |
| Singapore | 1996 | 3570085 |
| Singapore | 1997 | 3653156 |
| Singapore | 1998 | 3735537 |
| Singapore | 1999 | 3822625 |
| Singapore | 2000 | 3918189 |
| Singapore | 2001 | 4023284 |
| Singapore | 2002 | 4136239 |
| Singapore | 2003 | 4254766 |
| Singapore | 2004 | 4375413 |
| Singapore | 2005 | 4495537 |
| Singapore | 2006 | 4614310 |
| Singapore | 2007 | 4732065 |
| Singapore | 2008 | 4848676 |
| Singapore | 2009 | 4964312 |
| Singapore | 2010 | 5078969 |
| Singapore | 2011 | 5192183 |
| Singapore | 2012 | 5303264 |
| Singapore | 2013 | 5411737 |
| Sint Maarten (Dutch part) | 2010 | 42519 |
| Sint Maarten (Dutch part) | 2011 | 43451 |
| Sint Maarten (Dutch part) | 2012 | 44355 |
| Sint Maarten (Dutch part) | 2013 | 45233 |
| Slovakia | 1995 | 5362950 |
| Slovakia | 1996 | 5372351 |
| Slovakia | 1997 | 5379101 |
| Slovakia | 1998 | 5383590 |
| Slovakia | 1999 | 5386384 |
| Slovakia | 2000 | 5388010 |
| Slovakia | 2001 | 5388448 |
| Slovakia | 2002 | 5387913 |
| Slovakia | 2003 | 5387441 |
| Slovakia | 2004 | 5388338 |
| Slovakia | 2005 | 5391489 |
| Slovakia | 2006 | 5397396 |
| Slovakia | 2007 | 5405718 |
| Slovakia | 2008 | 5415434 |
| Slovakia | 2009 | 5425044 |
| Slovakia | 2010 | 5433437 |
| Slovakia | 2011 | 5440253 |
| Slovakia | 2012 | 5445757 |
| Slovakia | 2013 | 5450223 |
| Slovenia | 1995 | 1991848 |
| Slovenia | 1996 | 1989709 |
| Slovenia | 1997 | 1988790 |
| Slovenia | 1998 | 1988806 |
| Slovenia | 1999 | 1989178 |
| Slovenia | 2000 | 1989545 |
| Slovenia | 2001 | 1989732 |
| Slovenia | 2002 | 1990090 |
| Slovenia | 2003 | 1991368 |
| Slovenia | 2004 | 1994578 |
| Slovenia | 2005 | 2000349 |
| Slovenia | 2006 | 2009036 |
| Slovenia | 2007 | 2020156 |
| Slovenia | 2008 | 2032470 |
| Slovenia | 2009 | 2044248 |
| Slovenia | 2010 | 2054232 |
| Slovenia | 2011 | 2061952 |
| Slovenia | 2012 | 2067717 |
| Slovenia | 2013 | 2071997 |
| Solomon Islands | 1995 | 359236 |
| Solomon Islands | 1996 | 369439 |
| Solomon Islands | 1997 | 379861 |
| Solomon Islands | 1998 | 390493 |
| Solomon Islands | 1999 | 401323 |
| Solomon Islands | 2000 | 412336 |
| Solomon Islands | 2001 | 423529 |
| Solomon Islands | 2002 | 434880 |
| Solomon Islands | 2003 | 446335 |
| Solomon Islands | 2004 | 457827 |
| Solomon Islands | 2005 | 469306 |
| Solomon Islands | 2006 | 480745 |
| Solomon Islands | 2007 | 492148 |
| Solomon Islands | 2008 | 503541 |
| Solomon Islands | 2009 | 514964 |
| Solomon Islands | 2010 | 526447 |
| Solomon Islands | 2011 | 537997 |
| Solomon Islands | 2012 | 549598 |
| Solomon Islands | 2013 | 561231 |
| Somalia | 1995 | 6346440 |
| Somalia | 1996 | 6481035 |
| Somalia | 1997 | 6673254 |
| Somalia | 1998 | 6904978 |
| Somalia | 1999 | 7149044 |
| Somalia | 2000 | 7385416 |
| Somalia | 2001 | 7609265 |
| Somalia | 2002 | 7825924 |
| Somalia | 2003 | 8037706 |
| Somalia | 2004 | 8249965 |
| Somalia | 2005 | 8466938 |
| Somalia | 2006 | 8687671 |
| Somalia | 2007 | 8910851 |
| Somalia | 2008 | 9140259 |
| Somalia | 2009 | 9380854 |
| Somalia | 2010 | 9636173 |
| Somalia | 2011 | 9907903 |
| Somalia | 2012 | 10195134 |
| Somalia | 2013 | 10495583 |
| South Africa | 1995 | 41426810 |
| South Africa | 1996 | 42204043 |
| South Africa | 1997 | 42906581 |
| South Africa | 1998 | 43558285 |
| South Africa | 1999 | 44195890 |
| South Africa | 2000 | 44846286 |
| South Africa | 2001 | 45513217 |
| South Africa | 2002 | 46187794 |
| South Africa | 2003 | 46869028 |
| South Africa | 2004 | 47553025 |
| South Africa | 2005 | 48235291 |
| South Africa | 2006 | 48919359 |
| South Africa | 2007 | 49602778 |
| South Africa | 2008 | 50267488 |
| South Africa | 2009 | 50889543 |
| South Africa | 2010 | 51452352 |
| South Africa | 2011 | 51949041 |
| South Africa | 2012 | 52385920 |
| South Africa | 2013 | 52776130 |
| South Sudan | 2011 | 10381110 |
| South Sudan | 2012 | 10837527 |
| South Sudan | 2013 | 11296173 |
| Spain | 1995 | 39420568 |
| Spain | 1996 | 39513721 |
| Spain | 1997 | 39603778 |
| Spain | 1998 | 39729942 |
| Spain | 1999 | 39944692 |
| Spain | 2000 | 40282828 |
| Spain | 2001 | 40756675 |
| Spain | 2002 | 41346738 |
| Spain | 2003 | 42015864 |
| Spain | 2004 | 42710301 |
| Spain | 2005 | 43387477 |
| Spain | 2006 | 44038456 |
| Spain | 2007 | 44664067 |
| Spain | 2008 | 45243381 |
| Spain | 2009 | 45754265 |
| Spain | 2010 | 46182038 |
| Spain | 2011 | 46514117 |
| Spain | 2012 | 46754541 |
| Spain | 2013 | 46926963 |
| Sri Lanka | 1995 | 18241711 |
| Sri Lanka | 1996 | 18367795 |
| Sri Lanka | 1997 | 18473862 |
| Sri Lanka | 1998 | 18575763 |
| Sri Lanka | 1999 | 18695041 |
| Sri Lanka | 2000 | 18846401 |
| Sri Lanka | 2001 | 19037119 |
| Sri Lanka | 2002 | 19260853 |
| Sri Lanka | 2003 | 19501969 |
| Sri Lanka | 2004 | 19737578 |
| Sri Lanka | 2005 | 19951291 |
| Sri Lanka | 2006 | 20137560 |
| Sri Lanka | 2007 | 20301912 |
| Sri Lanka | 2008 | 20452387 |
| Sri Lanka | 2009 | 20601663 |
| Sri Lanka | 2010 | 20758779 |
| Sri Lanka | 2011 | 20925532 |
| Sri Lanka | 2012 | 21098099 |
| Sri Lanka | 2013 | 21273228 |
| Sudan | 1995 | 29963988 |
| Sudan | 1996 | 30824141 |
| Sudan | 1997 | 31682982 |
| Sudan | 1998 | 32551853 |
| Sudan | 1999 | 33447666 |
| Sudan | 2000 | 34382782 |
| Sudan | 2001 | 35359265 |
| Sudan | 2002 | 36373247 |
| Sudan | 2003 | 37423884 |
| Sudan | 2004 | 38508752 |
| Sudan | 2005 | 39625222 |
| Sudan | 2006 | 40774428 |
| Sudan | 2007 | 41954986 |
| Sudan | 2008 | 43158451 |
| Sudan | 2009 | 44373749 |
| Sudan | 2010 | 45592931 |
| Sudan | 2011 | 36430923 |
| Sudan | 2012 | 37195349 |
| Sudan | 2013 | 37964306 |
| Suriname | 1995 | 435776 |
| Suriname | 1996 | 441734 |
| Suriname | 1997 | 447771 |
| Suriname | 1998 | 453920 |
| Suriname | 1999 | 460215 |
| Suriname | 2000 | 466668 |
| Suriname | 2001 | 473312 |
| Suriname | 2002 | 480099 |
| Suriname | 2003 | 486867 |
| Suriname | 2004 | 493394 |
| Suriname | 2005 | 499523 |
| Suriname | 2006 | 505186 |
| Suriname | 2007 | 510433 |
| Suriname | 2008 | 515372 |
| Suriname | 2009 | 520173 |
| Suriname | 2010 | 524960 |
| Suriname | 2011 | 529761 |
| Suriname | 2012 | 534541 |
| Suriname | 2013 | 539276 |
| Swaziland | 1995 | 963428 |
| Swaziland | 1996 | 984506 |
| Swaziland | 1997 | 1006760 |
| Swaziland | 1998 | 1028694 |
| Swaziland | 1999 | 1048151 |
| Swaziland | 2000 | 1063715 |
| Swaziland | 2001 | 1074761 |
| Swaziland | 2002 | 1082183 |
| Swaziland | 2003 | 1087929 |
| Swaziland | 2004 | 1094758 |
| Swaziland | 2005 | 1104642 |
| Swaziland | 2006 | 1118253 |
| Swaziland | 2007 | 1134977 |
| Swaziland | 2008 | 1153929 |
| Swaziland | 2009 | 1173678 |
| Swaziland | 2010 | 1193148 |
| Swaziland | 2011 | 1212159 |
| Swaziland | 2012 | 1230985 |
| Swaziland | 2013 | 1249514 |
| Sweden | 1995 | 8826720 |
| Sweden | 1996 | 8849420 |
| Sweden | 1997 | 8859106 |
| Sweden | 1998 | 8861204 |
| Sweden | 1999 | 8863595 |
| Sweden | 2000 | 8872284 |
| Sweden | 2001 | 8888444 |
| Sweden | 2002 | 8911156 |
| Sweden | 2003 | 8941754 |
| Sweden | 2004 | 8981282 |
| Sweden | 2005 | 9030163 |
| Sweden | 2006 | 9089677 |
| Sweden | 2007 | 9159155 |
| Sweden | 2008 | 9234561 |
| Sweden | 2009 | 9310295 |
| Sweden | 2010 | 9382297 |
| Sweden | 2011 | 9448965 |
| Sweden | 2012 | 9511313 |
| Sweden | 2013 | 9571105 |
| Switzerland | 1995 | 7017042 |
| Switzerland | 1996 | 7059633 |
| Switzerland | 1997 | 7090176 |
| Switzerland | 1998 | 7113505 |
| Switzerland | 1999 | 7136813 |
| Switzerland | 2000 | 7165581 |
| Switzerland | 2001 | 7200783 |
| Switzerland | 2002 | 7241511 |
| Switzerland | 2003 | 7289079 |
| Switzerland | 2004 | 7344597 |
| Switzerland | 2005 | 7408608 |
| Switzerland | 2006 | 7482167 |
| Switzerland | 2007 | 7564712 |
| Switzerland | 2008 | 7653062 |
| Switzerland | 2009 | 7742768 |
| Switzerland | 2010 | 7830534 |
| Switzerland | 2011 | 7915200 |
| Switzerland | 2012 | 7997399 |
| Switzerland | 2013 | 8077833 |
| Syrian Arab Republic | 1995 | 14338240 |
| Syrian Arab Republic | 1996 | 14746306 |
| Syrian Arab Republic | 1997 | 15168523 |
| Syrian Arab Republic | 1998 | 15591261 |
| Syrian Arab Republic | 1999 | 15995760 |
| Syrian Arab Republic | 2000 | 16371208 |
| Syrian Arab Republic | 2001 | 16700984 |
| Syrian Arab Republic | 2002 | 16994676 |
| Syrian Arab Republic | 2003 | 17298476 |
| Syrian Arab Republic | 2004 | 17676012 |
| Syrian Arab Republic | 2005 | 18167367 |
| Syrian Arab Republic | 2006 | 18804914 |
| Syrian Arab Republic | 2007 | 19561477 |
| Syrian Arab Republic | 2008 | 20346056 |
| Syrian Arab Republic | 2009 | 21031546 |
| Syrian Arab Republic | 2010 | 21532647 |
| Syrian Arab Republic | 2011 | 21804363 |
| Syrian Arab Republic | 2012 | 21889682 |
| Syrian Arab Republic | 2013 | 21898061 |
| Tajikistan | 1995 | 5784330 |
| Tajikistan | 1996 | 5862347 |
| Tajikistan | 1997 | 5937177 |
| Tajikistan | 1998 | 6012933 |
| Tajikistan | 1999 | 6094661 |
| Tajikistan | 2000 | 6186152 |
| Tajikistan | 2001 | 6289340 |
| Tajikistan | 2002 | 6404118 |
| Tajikistan | 2003 | 6529609 |
| Tajikistan | 2004 | 6663929 |
| Tajikistan | 2005 | 6805655 |
| Tajikistan | 2006 | 6954522 |
| Tajikistan | 2007 | 7111025 |
| Tajikistan | 2008 | 7275252 |
| Tajikistan | 2009 | 7447396 |
| Tajikistan | 2010 | 7627326 |
| Tajikistan | 2011 | 7814850 |
| Tajikistan | 2012 | 8008990 |
| Tajikistan | 2013 | 8207834 |
| Thailand | 1995 | 58983954 |
| Thailand | 1996 | 59562136 |
| Thailand | 1997 | 60206941 |
| Thailand | 1998 | 60903042 |
| Thailand | 1999 | 61623143 |
| Thailand | 2000 | 62343379 |
| Thailand | 2001 | 63069070 |
| Thailand | 2002 | 63797841 |
| Thailand | 2003 | 64488338 |
| Thailand | 2004 | 65087400 |
| Thailand | 2005 | 65559487 |
| Thailand | 2006 | 65883961 |
| Thailand | 2007 | 66076927 |
| Thailand | 2008 | 66185340 |
| Thailand | 2009 | 66277335 |
| Thailand | 2010 | 66402316 |
| Thailand | 2011 | 66576332 |
| Thailand | 2012 | 66785001 |
| Thailand | 2013 | 67010502 |
| The Former Yugoslav Republic of Macedonia | 1995 | 1967013 |
| The Former Yugoslav Republic of Macedonia | 1996 | 1975207 |
| The Former Yugoslav Republic of Macedonia | 1997 | 1991687 |
| The Former Yugoslav Republic of Macedonia | 1998 | 2013117 |
| The Former Yugoslav Republic of Macedonia | 1999 | 2034487 |
| The Former Yugoslav Republic of Macedonia | 2000 | 2052129 |
| The Former Yugoslav Republic of Macedonia | 2001 | 2065098 |
| The Former Yugoslav Republic of Macedonia | 2002 | 2074441 |
| The Former Yugoslav Republic of Macedonia | 2003 | 2080866 |
| The Former Yugoslav Republic of Macedonia | 2004 | 2085728 |
| The Former Yugoslav Republic of Macedonia | 2005 | 2090044 |
| The Former Yugoslav Republic of Macedonia | 2006 | 2093801 |
| The Former Yugoslav Republic of Macedonia | 2007 | 2096620 |
| The Former Yugoslav Republic of Macedonia | 2008 | 2098769 |
| The Former Yugoslav Republic of Macedonia | 2009 | 2100558 |
| The Former Yugoslav Republic of Macedonia | 2010 | 2102216 |
| The Former Yugoslav Republic of Macedonia | 2011 | 2103890 |
| The Former Yugoslav Republic of Macedonia | 2012 | 2105575 |
| The Former Yugoslav Republic of Macedonia | 2013 | 2107158 |
| Timor-Leste | 2002 | 899367 |
| Timor-Leste | 2003 | 933369 |
| Timor-Leste | 2004 | 966987 |
| Timor-Leste | 2005 | 995619 |
| Timor-Leste | 2006 | 1018013 |
| Timor-Leste | 2007 | 1035706 |
| Timor-Leste | 2008 | 1050244 |
| Timor-Leste | 2009 | 1064191 |
| Timor-Leste | 2010 | 1079450 |
| Timor-Leste | 2011 | 1096293 |
| Timor-Leste | 2012 | 1114106 |
| Timor-Leste | 2013 | 1132879 |
| Togo | 1995 | 4284497 |
| Togo | 1996 | 4392177 |
| Togo | 1997 | 4504291 |
| Togo | 1998 | 4620710 |
| Togo | 1999 | 4740974 |
| Togo | 2000 | 4864753 |
| Togo | 2001 | 4992225 |
| Togo | 2002 | 5123674 |
| Togo | 2003 | 5258956 |
| Togo | 2004 | 5397851 |
| Togo | 2005 | 5540214 |
| Togo | 2006 | 5685845 |
| Togo | 2007 | 5834806 |
| Togo | 2008 | 5987491 |
| Togo | 2009 | 6144457 |
| Togo | 2010 | 6306014 |
| Togo | 2011 | 6472304 |
| Togo | 2012 | 6642928 |
| Togo | 2013 | 6816982 |
| Tokelau | 1995 | 1520 |
| Tokelau | 1996 | 1529 |
| Tokelau | 1997 | 1548 |
| Tokelau | 1998 | 1566 |
| Tokelau | 1999 | 1571 |
| Tokelau | 2000 | 1552 |
| Tokelau | 2001 | 1504 |
| Tokelau | 2002 | 1434 |
| Tokelau | 2003 | 1351 |
| Tokelau | 2004 | 1272 |
| Tokelau | 2005 | 1210 |
| Tokelau | 2006 | 1167 |
| Tokelau | 2007 | 1141 |
| Tokelau | 2008 | 1130 |
| Tokelau | 2009 | 1129 |
| Tokelau | 2010 | 1135 |
| Tokelau | 2011 | 1148 |
| Tokelau | 2012 | 1169 |
| Tokelau | 2013 | 1195 |
| Tonga | 1995 | 95928 |
| Tonga | 1996 | 96218 |
| Tonga | 1997 | 96574 |
| Tonga | 1998 | 96991 |
| Tonga | 1999 | 97457 |
| Tonga | 2000 | 97962 |
| Tonga | 2001 | 98504 |
| Tonga | 2002 | 99083 |
| Tonga | 2003 | 99691 |
| Tonga | 2004 | 100319 |
| Tonga | 2005 | 100960 |
| Tonga | 2006 | 101617 |
| Tonga | 2007 | 102289 |
| Tonga | 2008 | 102947 |
| Tonga | 2009 | 103557 |
| Tonga | 2010 | 104098 |
| Tonga | 2011 | 104554 |
| Tonga | 2012 | 104941 |
| Tonga | 2013 | 105323 |
| Trinidad and Tobago | 1995 | 1255001 |
| Trinidad and Tobago | 1996 | 1258365 |
| Trinidad and Tobago | 1997 | 1260677 |
| Trinidad and Tobago | 1998 | 1262544 |
| Trinidad and Tobago | 1999 | 1264781 |
| Trinidad and Tobago | 2000 | 1267980 |
| Trinidad and Tobago | 2001 | 1272347 |
| Trinidad and Tobago | 2002 | 1277723 |
| Trinidad and Tobago | 2003 | 1283868 |
| Trinidad and Tobago | 2004 | 1290379 |
| Trinidad and Tobago | 2005 | 1296933 |
| Trinidad and Tobago | 2006 | 1303478 |
| Trinidad and Tobago | 2007 | 1310040 |
| Trinidad and Tobago | 2008 | 1316449 |
| Trinidad and Tobago | 2009 | 1322518 |
| Trinidad and Tobago | 2010 | 1328095 |
| Trinidad and Tobago | 2011 | 1333082 |
| Trinidad and Tobago | 2012 | 1337439 |
| Trinidad and Tobago | 2013 | 1341151 |
| Tunisia | 1995 | 8982649 |
| Tunisia | 1996 | 9116617 |
| Tunisia | 1997 | 9237249 |
| Tunisia | 1998 | 9347451 |
| Tunisia | 1999 | 9451481 |
| Tunisia | 2000 | 9552776 |
| Tunisia | 2001 | 9652120 |
| Tunisia | 2002 | 9749636 |
| Tunisia | 2003 | 9847323 |
| Tunisia | 2004 | 9947369 |
| Tunisia | 2005 | 10051352 |
| Tunisia | 2006 | 10160243 |
| Tunisia | 2007 | 10273932 |
| Tunisia | 2008 | 10391460 |
| Tunisia | 2009 | 10511204 |
| Tunisia | 2010 | 10631830 |
| Tunisia | 2011 | 10753073 |
| Tunisia | 2012 | 10874915 |
| Tunisia | 2013 | 10996515 |
| Turkey | 1995 | 58522320 |
| Turkey | 1996 | 59442502 |
| Turkey | 1997 | 60372413 |
| Turkey | 1998 | 61308204 |
| Turkey | 1999 | 62243779 |
| Turkey | 2000 | 63174483 |
| Turkey | 2001 | 64100297 |
| Turkey | 2002 | 65022300 |
| Turkey | 2003 | 65938265 |
| Turkey | 2004 | 66845635 |
| Turkey | 2005 | 67743052 |
| Turkey | 2006 | 68626337 |
| Turkey | 2007 | 69496513 |
| Turkey | 2008 | 70363511 |
| Turkey | 2009 | 71241080 |
| Turkey | 2010 | 72137546 |
| Turkey | 2011 | 73058638 |
| Turkey | 2012 | 73997128 |
| Turkey | 2013 | 74932641 |
| Turkmenistan | 1995 | 4188010 |
| Turkmenistan | 1996 | 4267690 |
| Turkmenistan | 1997 | 4335991 |
| Turkmenistan | 1998 | 4395293 |
| Turkmenistan | 1999 | 4449427 |
| Turkmenistan | 2000 | 4501419 |
| Turkmenistan | 2001 | 4551762 |
| Turkmenistan | 2002 | 4600171 |
| Turkmenistan | 2003 | 4648037 |
| Turkmenistan | 2004 | 4696876 |
| Turkmenistan | 2005 | 4747839 |
| Turkmenistan | 2006 | 4801595 |
| Turkmenistan | 2007 | 4858236 |
| Turkmenistan | 2008 | 4917543 |
| Turkmenistan | 2009 | 4978962 |
| Turkmenistan | 2010 | 5041995 |
| Turkmenistan | 2011 | 5106668 |
| Turkmenistan | 2012 | 5172931 |
| Turkmenistan | 2013 | 5240072 |
| Turks and Caicos Islands | 1995 | 15334 |
| Turks and Caicos Islands | 1996 | 15967 |
| Turks and Caicos Islands | 1997 | 16527 |
| Turks and Caicos Islands | 1998 | 17114 |
| Turks and Caicos Islands | 1999 | 17866 |
| Turks and Caicos Islands | 2000 | 18876 |
| Turks and Caicos Islands | 2001 | 20186 |
| Turks and Caicos Islands | 2002 | 21740 |
| Turks and Caicos Islands | 2003 | 23412 |
| Turks and Caicos Islands | 2004 | 25025 |
| Turks and Caicos Islands | 2005 | 26450 |
| Turks and Caicos Islands | 2006 | 27642 |
| Turks and Caicos Islands | 2007 | 28638 |
| Turks and Caicos Islands | 2008 | 29481 |
| Turks and Caicos Islands | 2009 | 30247 |
| Turks and Caicos Islands | 2010 | 30993 |
| Turks and Caicos Islands | 2011 | 31726 |
| Turks and Caicos Islands | 2012 | 32427 |
| Turks and Caicos Islands | 2013 | 33098 |
| Tuvalu | 1995 | 9227 |
| Tuvalu | 1996 | 9264 |
| Tuvalu | 1997 | 9298 |
| Tuvalu | 1998 | 9334 |
| Tuvalu | 1999 | 9374 |
| Tuvalu | 2000 | 9419 |
| Tuvalu | 2001 | 9471 |
| Tuvalu | 2002 | 9530 |
| Tuvalu | 2003 | 9590 |
| Tuvalu | 2004 | 9646 |
| Tuvalu | 2005 | 9694 |
| Tuvalu | 2006 | 9732 |
| Tuvalu | 2007 | 9764 |
| Tuvalu | 2008 | 9788 |
| Tuvalu | 2009 | 9808 |
| Tuvalu | 2010 | 9827 |
| Tuvalu | 2011 | 9844 |
| Tuvalu | 2012 | 9860 |
| Tuvalu | 2013 | 9876 |
| Uganda | 1995 | 20740726 |
| Uganda | 1996 | 21407693 |
| Uganda | 1997 | 22084527 |
| Uganda | 1998 | 22780451 |
| Uganda | 1999 | 23507800 |
| Uganda | 2000 | 24275641 |
| Uganda | 2001 | 25088033 |
| Uganda | 2002 | 25943441 |
| Uganda | 2003 | 26838428 |
| Uganda | 2004 | 27766986 |
| Uganda | 2005 | 28724869 |
| Uganda | 2006 | 29711397 |
| Uganda | 2007 | 30728747 |
| Uganda | 2008 | 31778799 |
| Uganda | 2009 | 32864328 |
| Uganda | 2010 | 33987213 |
| Uganda | 2011 | 35148064 |
| Uganda | 2012 | 36345860 |
| Uganda | 2013 | 37578876 |
| Ukraine | 1995 | 51146967 |
| Ukraine | 1996 | 50821019 |
| Ukraine | 1997 | 50421998 |
| Ukraine | 1998 | 49974187 |
| Ukraine | 1999 | 49510626 |
| Ukraine | 2000 | 49057226 |
| Ukraine | 2001 | 48620596 |
| Ukraine | 2002 | 48200504 |
| Ukraine | 2003 | 47807199 |
| Ukraine | 2004 | 47450472 |
| Ukraine | 2005 | 47135932 |
| Ukraine | 2006 | 46871328 |
| Ukraine | 2007 | 46653183 |
| Ukraine | 2008 | 46461350 |
| Ukraine | 2009 | 46267306 |
| Ukraine | 2010 | 46050220 |
| Ukraine | 2011 | 45802721 |
| Ukraine | 2012 | 45529944 |
| Ukraine | 2013 | 45238805 |
| United Arab Emirates | 1995 | 2346305 |
| United Arab Emirates | 1996 | 2470810 |
| United Arab Emirates | 1997 | 2608993 |
| United Arab Emirates | 1998 | 2753498 |
| United Arab Emirates | 1999 | 2893648 |
| United Arab Emirates | 2000 | 3026352 |
| United Arab Emirates | 2001 | 3132104 |
| United Arab Emirates | 2002 | 3223969 |
| United Arab Emirates | 2003 | 3369254 |
| United Arab Emirates | 2004 | 3658658 |
| United Arab Emirates | 2005 | 4148883 |
| United Arab Emirates | 2006 | 4875639 |
| United Arab Emirates | 2007 | 5797347 |
| United Arab Emirates | 2008 | 6798635 |
| United Arab Emirates | 2009 | 7718319 |
| United Arab Emirates | 2010 | 8441537 |
| United Arab Emirates | 2011 | 8925096 |
| United Arab Emirates | 2012 | 9205651 |
| United Arab Emirates | 2013 | 9346129 |
| United Kingdom of Great Britain and Northern Ireland | 1995 | 57997197 |
| United Kingdom of Great Britain and Northern Ireland | 1996 | 58168519 |
| United Kingdom of Great Britain and Northern Ireland | 1997 | 58346633 |
| United Kingdom of Great Britain and Northern Ireland | 1998 | 58534306 |
| United Kingdom of Great Britain and Northern Ireland | 1999 | 58735006 |
| United Kingdom of Great Britain and Northern Ireland | 2000 | 58951444 |
| United Kingdom of Great Britain and Northern Ireland | 2001 | 59183774 |
| United Kingdom of Great Britain and Northern Ireland | 2002 | 59431937 |
| United Kingdom of Great Britain and Northern Ireland | 2003 | 59697954 |
| United Kingdom of Great Britain and Northern Ireland | 2004 | 59984118 |
| United Kingdom of Great Britain and Northern Ireland | 2005 | 60291414 |
| United Kingdom of Great Britain and Northern Ireland | 2006 | 60620907 |
| United Kingdom of Great Britain and Northern Ireland | 2007 | 60970360 |
| United Kingdom of Great Britain and Northern Ireland | 2008 | 61333257 |
| United Kingdom of Great Britain and Northern Ireland | 2009 | 61700797 |
| United Kingdom of Great Britain and Northern Ireland | 2010 | 62066350 |
| United Kingdom of Great Britain and Northern Ireland | 2011 | 62426923 |
| United Kingdom of Great Britain and Northern Ireland | 2012 | 62783115 |
| United Kingdom of Great Britain and Northern Ireland | 2013 | 63136265 |
| United Republic of Tanzania | 1995 | 29944302 |
| United Republic of Tanzania | 1996 | 30780453 |
| United Republic of Tanzania | 1997 | 31586039 |
| United Republic of Tanzania | 1998 | 32378333 |
| United Republic of Tanzania | 1999 | 33183093 |
| United Republic of Tanzania | 2000 | 34020512 |
| United Republic of Tanzania | 2001 | 34895398 |
| United Republic of Tanzania | 2002 | 35806497 |
| United Republic of Tanzania | 2003 | 36760831 |
| United Republic of Tanzania | 2004 | 37765139 |
| United Republic of Tanzania | 2005 | 38824384 |
| United Republic of Tanzania | 2006 | 39942347 |
| United Republic of Tanzania | 2007 | 41119693 |
| United Republic of Tanzania | 2008 | 42353790 |
| United Republic of Tanzania | 2009 | 43639752 |
| United Republic of Tanzania | 2010 | 44973330 |
| United Republic of Tanzania | 2011 | 46354607 |
| United Republic of Tanzania | 2012 | 47783107 |
| United Republic of Tanzania | 2013 | 49253126 |
| United States of America | 1995 | 268039654 |
| United States of America | 1996 | 271231546 |
| United States of America | 1997 | 274606475 |
| United States of America | 1998 | 278053607 |
| United States of America | 1999 | 281419130 |
| United States of America | 2000 | 284594395 |
| United States of America | 2001 | 287532638 |
| United States of America | 2002 | 290270187 |
| United States of America | 2003 | 292883010 |
| United States of America | 2004 | 295487267 |
| United States of America | 2005 | 298165797 |
| United States of America | 2006 | 300942917 |
| United States of America | 2007 | 303786752 |
| United States of America | 2008 | 306657153 |
| United States of America | 2009 | 309491893 |
| United States of America | 2010 | 312247116 |
| United States of America | 2011 | 314911752 |
| United States of America | 2012 | 317505266 |
| United States of America | 2013 | 320050716 |
| Uruguay | 1995 | 3224383 |
| Uruguay | 1996 | 3247585 |
| Uruguay | 1997 | 3270549 |
| Uruguay | 1998 | 3291677 |
| Uruguay | 1999 | 3308884 |
| Uruguay | 2000 | 3320841 |
| Uruguay | 2001 | 3326762 |
| Uruguay | 2002 | 3327500 |
| Uruguay | 2003 | 3325411 |
| Uruguay | 2004 | 3323822 |
| Uruguay | 2005 | 3325155 |
| Uruguay | 2006 | 3330217 |
| Uruguay | 2007 | 3338384 |
| Uruguay | 2008 | 3348898 |
| Uruguay | 2009 | 3360431 |
| Uruguay | 2010 | 3371982 |
| Uruguay | 2011 | 3383486 |
| Uruguay | 2012 | 3395253 |
| Uruguay | 2013 | 3407062 |
| US Virgin Islands | 1995 | 107018 |
| US Virgin Islands | 1996 | 107589 |
| US Virgin Islands | 1997 | 108002 |
| US Virgin Islands | 1998 | 108271 |
| US Virgin Islands | 1999 | 108431 |
| US Virgin Islands | 2000 | 108511 |
| US Virgin Islands | 2001 | 108511 |
| US Virgin Islands | 2002 | 108422 |
| US Virgin Islands | 2003 | 108259 |
| US Virgin Islands | 2004 | 108034 |
| US Virgin Islands | 2005 | 107768 |
| US Virgin Islands | 2006 | 107458 |
| US Virgin Islands | 2007 | 107116 |
| US Virgin Islands | 2008 | 106788 |
| US Virgin Islands | 2009 | 106530 |
| US Virgin Islands | 2010 | 106382 |
| US Virgin Islands | 2011 | 106364 |
| US Virgin Islands | 2012 | 106462 |
| US Virgin Islands | 2013 | 106627 |
| Uzbekistan | 1995 | 22950898 |
| Uzbekistan | 1996 | 23381250 |
| Uzbekistan | 1997 | 23789736 |
| Uzbekistan | 1998 | 24170760 |
| Uzbekistan | 1999 | 24517962 |
| Uzbekistan | 2000 | 24828562 |
| Uzbekistan | 2001 | 25098636 |
| Uzbekistan | 2002 | 25334021 |
| Uzbekistan | 2003 | 25553928 |
| Uzbekistan | 2004 | 25784397 |
| Uzbekistan | 2005 | 26044401 |
| Uzbekistan | 2006 | 26340696 |
| Uzbekistan | 2007 | 26668950 |
| Uzbekistan | 2008 | 27023200 |
| Uzbekistan | 2009 | 27392784 |
| Uzbekistan | 2010 | 27769270 |
| Uzbekistan | 2011 | 28151746 |
| Uzbekistan | 2012 | 28541423 |
| Uzbekistan | 2013 | 28934102 |
| Vanuatu | 1995 | 168236 |
| Vanuatu | 1996 | 171802 |
| Vanuatu | 1997 | 175004 |
| Vanuatu | 1998 | 178074 |
| Vanuatu | 1999 | 181346 |
| Vanuatu | 2000 | 185058 |
| Vanuatu | 2001 | 189285 |
| Vanuatu | 2002 | 193950 |
| Vanuatu | 2003 | 198952 |
| Vanuatu | 2004 | 204135 |
| Vanuatu | 2005 | 209375 |
| Vanuatu | 2006 | 214654 |
| Vanuatu | 2007 | 220001 |
| Vanuatu | 2008 | 225398 |
| Vanuatu | 2009 | 230833 |
| Vanuatu | 2010 | 236299 |
| Vanuatu | 2011 | 241778 |
| Vanuatu | 2012 | 247262 |
| Vanuatu | 2013 | 252763 |
| Venezuela (Bolivarian Republic of) | 1995 | 22092144 |
| Venezuela (Bolivarian Republic of) | 1996 | 22556838 |
| Venezuela (Bolivarian Republic of) | 1997 | 23020184 |
| Venezuela (Bolivarian Republic of) | 1998 | 23482700 |
| Venezuela (Bolivarian Republic of) | 1999 | 23945002 |
| Venezuela (Bolivarian Republic of) | 2000 | 24407553 |
| Venezuela (Bolivarian Republic of) | 2001 | 24870441 |
| Venezuela (Bolivarian Republic of) | 2002 | 25333622 |
| Venezuela (Bolivarian Republic of) | 2003 | 25797219 |
| Venezuela (Bolivarian Republic of) | 2004 | 26261326 |
| Venezuela (Bolivarian Republic of) | 2005 | 26725897 |
| Venezuela (Bolivarian Republic of) | 2006 | 27190882 |
| Venezuela (Bolivarian Republic of) | 2007 | 27655937 |
| Venezuela (Bolivarian Republic of) | 2008 | 28120312 |
| Venezuela (Bolivarian Republic of) | 2009 | 28583040 |
| Venezuela (Bolivarian Republic of) | 2010 | 29043283 |
| Venezuela (Bolivarian Republic of) | 2011 | 29500625 |
| Venezuela (Bolivarian Republic of) | 2012 | 29954782 |
| Venezuela (Bolivarian Republic of) | 2013 | 30405207 |
| Viet Nam | 1995 | 76020043 |
| Viet Nam | 1996 | 77166873 |
| Viet Nam | 1997 | 78199254 |
| Viet Nam | 1998 | 79140734 |
| Viet Nam | 1999 | 80027521 |
| Viet Nam | 2000 | 80887879 |
| Viet Nam | 2001 | 81728768 |
| Viet Nam | 2002 | 82547682 |
| Viet Nam | 2003 | 83352595 |
| Viet Nam | 2004 | 84150651 |
| Viet Nam | 2005 | 84947852 |
| Viet Nam | 2006 | 85747625 |
| Viet Nam | 2007 | 86553201 |
| Viet Nam | 2008 | 87369203 |
| Viet Nam | 2009 | 88199997 |
| Viet Nam | 2010 | 89047397 |
| Viet Nam | 2011 | 89913956 |
| Viet Nam | 2012 | 90795769 |
| Viet Nam | 2013 | 91679733 |
| Wallis and Futuna Islands | 1995 | 14143 |
| Wallis and Futuna Islands | 1996 | 14221 |
| Wallis and Futuna Islands | 1997 | 14309 |
| Wallis and Futuna Islands | 1998 | 14394 |
| Wallis and Futuna Islands | 1999 | 14460 |
| Wallis and Futuna Islands | 2000 | 14497 |
| Wallis and Futuna Islands | 2001 | 14501 |
| Wallis and Futuna Islands | 2002 | 14476 |
| Wallis and Futuna Islands | 2003 | 14422 |
| Wallis and Futuna Islands | 2004 | 14344 |
| Wallis and Futuna Islands | 2005 | 14246 |
| Wallis and Futuna Islands | 2006 | 14126 |
| Wallis and Futuna Islands | 2007 | 13988 |
| Wallis and Futuna Islands | 2008 | 13840 |
| Wallis and Futuna Islands | 2009 | 13697 |
| Wallis and Futuna Islands | 2010 | 13565 |
| Wallis and Futuna Islands | 2011 | 13451 |
| Wallis and Futuna Islands | 2012 | 13353 |
| Wallis and Futuna Islands | 2013 | 13272 |
| West Bank and Gaza Strip | 1995 | 2598393 |
| West Bank and Gaza Strip | 1996 | 2722497 |
| West Bank and Gaza Strip | 1997 | 2851993 |
| West Bank and Gaza Strip | 1998 | 2980563 |
| West Bank and Gaza Strip | 1999 | 3099951 |
| West Bank and Gaza Strip | 2000 | 3204572 |
| West Bank and Gaza Strip | 2001 | 3291620 |
| West Bank and Gaza Strip | 2002 | 3363542 |
| West Bank and Gaza Strip | 2003 | 3426549 |
| West Bank and Gaza Strip | 2004 | 3489743 |
| West Bank and Gaza Strip | 2005 | 3559856 |
| West Bank and Gaza Strip | 2006 | 3638829 |
| West Bank and Gaza Strip | 2007 | 3725076 |
| West Bank and Gaza Strip | 2008 | 3817551 |
| West Bank and Gaza Strip | 2009 | 3914035 |
| West Bank and Gaza Strip | 2010 | 4012880 |
| West Bank and Gaza Strip | 2011 | 4114199 |
| West Bank and Gaza Strip | 2012 | 4218771 |
| West Bank and Gaza Strip | 2013 | 4326295 |
| Yemen | 1995 | 15018201 |
| Yemen | 1996 | 15578640 |
| Yemen | 1997 | 16088019 |
| Yemen | 1998 | 16564235 |
| Yemen | 1999 | 17035531 |
| Yemen | 2000 | 17522537 |
| Yemen | 2001 | 18029989 |
| Yemen | 2002 | 18551068 |
| Yemen | 2003 | 19081306 |
| Yemen | 2004 | 19612696 |
| Yemen | 2005 | 20139661 |
| Yemen | 2006 | 20661714 |
| Yemen | 2007 | 21182162 |
| Yemen | 2008 | 21703571 |
| Yemen | 2009 | 22229625 |
| Yemen | 2010 | 22763008 |
| Yemen | 2011 | 23304206 |
| Yemen | 2012 | 23852409 |
| Yemen | 2013 | 24407381 |
| Zambia | 1995 | 8841338 |
| Zambia | 1996 | 9073311 |
| Zambia | 1997 | 9320089 |
| Zambia | 1998 | 9577483 |
| Zambia | 1999 | 9839179 |
| Zambia | 2000 | 10100981 |
| Zambia | 2001 | 10362137 |
| Zambia | 2002 | 10625423 |
| Zambia | 2003 | 10894519 |
| Zambia | 2004 | 11174650 |
| Zambia | 2005 | 11470022 |
| Zambia | 2006 | 11781612 |
| Zambia | 2007 | 12109620 |
| Zambia | 2008 | 12456527 |
| Zambia | 2009 | 12825031 |
| Zambia | 2010 | 13216985 |
| Zambia | 2011 | 13633796 |
| Zambia | 2012 | 14075099 |
| Zambia | 2013 | 14538640 |
| Zimbabwe | 1995 | 11639364 |
| Zimbabwe | 1996 | 11846110 |
| Zimbabwe | 1997 | 12045813 |
| Zimbabwe | 1998 | 12229500 |
| Zimbabwe | 1999 | 12384727 |
| Zimbabwe | 2000 | 12503652 |
| Zimbabwe | 2001 | 12586763 |
| Zimbabwe | 2002 | 12640922 |
| Zimbabwe | 2003 | 12673103 |
| Zimbabwe | 2004 | 12693047 |
| Zimbabwe | 2005 | 12710589 |
| Zimbabwe | 2006 | 12724308 |
| Zimbabwe | 2007 | 12740160 |
| Zimbabwe | 2008 | 12784041 |
| Zimbabwe | 2009 | 12888918 |
| Zimbabwe | 2010 | 13076978 |
| Zimbabwe | 2011 | 13358738 |
| Zimbabwe | 2012 | 13724317 |
| Zimbabwe | 2013 | 14149648 |
Some simple plots
Ok, let’s make a dataset just containing
country == "Ireland" and make a scatterplot with
year on the x and population on the y.
dataset_for_plot <- population %>%
filter(country == "Ireland")
ggplot(data = dataset_for_plot) +
geom_point(mapping = aes(x = year, y = population))
Nice. We can easily make a line plot by swapping
geom_point for geom_line in the above.
Let’s make a line plot with the countries France, Germany and Spain
each having a different line colour, plotting their populations over
time. Note that unlike x and y, we have to specify the argument
colour =.
dataset_for_plot <- population %>%
filter(country %in% c("France", "Germany", "Spain"))
ggplot(data = dataset_for_plot) +
geom_line(mapping = aes(x = year, y = population, colour = country))
Great. Let’s make a column plot using geom_col. We’ll
filter a couple of countries and take the data just from 2010.
dataset_for_plot <- population %>%
filter(country %in% c("France", "Germany", "Spain", "Italy", "Poland"), year == 2010)
ggplot(dat = dataset_for_plot) +
geom_col(mapping = aes(x = country, y = population))
We can turn this into a bar plot easily by adding
coord_flip():
ggplot(dat = dataset_for_plot) +
geom_col(mapping = aes(x = country, y = population)) +
coord_flip()
Let’s return to our line plot for France, Germany and Spain. If we
wanted lines and points it would make more sense to put the
aes mapping function within the ggplot rather
than in both the geom_line and geom_point:
dataset_for_plot <- population %>%
filter(country %in% c("France", "Germany", "Spain"))
ggplot(data = dataset_for_plot , mapping = aes(x = year, y = population, colour = country)) +
geom_line() +
geom_point()
Adjusting labels
Let’s do the same plot, but change some of the labels. We’ll change
the y-axis label to “Persons”, leave both the x-axis title and legend
title blank, and set the heading to “Population of selected countries
over time”. All of that can be done through the labs
function.
ggplot(data = dataset_for_plot , mapping = aes(x = year, y = population, colour = country)) +
geom_line() +
geom_point() +
labs(x = "",
y = "Persons",
colour = "",
title = "Population of selected countries over time")
Adjusting axes
Let’s do the same plot (we’ll forget about the labels) but make two
adjustments to the y-axis. We will use the function
scale_y_continuous which has lots of tools within it for
tailoring a continuous y-axis.
The function scale_y_continuous is one of a large family
of functions with the format scale_DIM_FORMAT where DIM is
the dimension (x, y, fill, colour, etc.) and FORMAT is the type of axis.
See the second page of the ggplot2 cheatsheet for more information.
We’ll change the limits of the y-axis so that it starts at zero. This
is done using the limits argument to
scale_y_continuous, and limits takes a vector
of length two, the first entry being the lower limit and the second
entry being the upper limit. I’ll set the lower limit to zero, but I’ll
leave the upper limit as NA, meaning that I’ll leave it up
to the data to determine how high the y-axis should go.
I’ll change the labels on the y so that they show the whole number
with comma separators rather than the scientific format which has
appeared. That is done with the labels argument to
scale_y_continuous. I’m setting that argument as being
equal to comma, which is actually a function from the
package scales. This package provide a bunch of tools that make it
easier to tailor axes and legends. The scales package is installed with
tidyverse but needs to be loaded in using library(scales).
If the y-axis needed to be formatted as a percentage, then you’d replace
comma with percent (percent is
another function from scales).
library(scales)
ggplot(data = dataset_for_plot , mapping = aes(x = year, y = population, colour = country)) +
geom_line() +
geom_point() +
scale_y_continuous(limits = c(0,NA) , label = comma )
There are tons of other ways to edit the appearance of plots through
ggplot, but these would be beyond a crash course. I’ll wrap up with one
tool for altering the appearance which is to add a theme. There are 8
themes including the default theme_grey(). There is
theme_light(), theme_dark(),
theme_bw(). My favourite is theme_minimal().
Use the help (?theme_grey) to find out more.
library(scales)
ggplot(data = dataset_for_plot , mapping = aes(x = year, y = population, colour = country)) +
geom_line() +
geom_point() +
scale_y_continuous(limits = c(0,NA) , label = comma ) +
theme_minimal()