A Crash Course in R

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which I think is really useful for beginners is the one on data wrangling (although some of the functions on that are out of date).

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 tidyverse packages. All of these are installed when you run install.packages("tidyverse"), but only the core packages are loaded when you run library(tidyverse) (plus a couple of back-end ones that you don’t really see). 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 tidyverse packages that are beyond the core ones. Extra tidyverse packages that I would often use include scales for formatting axes, lubridate for handling dates and times, and readxl for reading and writing Excel files. Some packages I like which aren’t from tidyverse (and require separate installation) include sf for maps, tictoc for timing how long it takes code to run, beepr for making ‘ding’ noises after code has finished running, and knitr for making html documents like this one.

Click for a summary on types of packages

A diagram which summarises the different types of package is shown below. The five main types (A, B, C, D and E) are described below. As a beginner you will probably only encounter types A and B.

Diagram summarising types of packages

1. Base R: These packages are already installed with R and always loaded by default, so you don’t need to use install.packages or library. Examples include base which has lots of fundamental functions and stats which has functions for statistical analysis.
2. Core Tidyverse: These are the main tidyverse packages. They are all installed using install.packages("tidyverse") and loaded using library(tidyverse). Examples include dplyr for data manipulation and stringr for manipulating strings.
3. Other Tidyverse: These are part of tidyverse but not among the core packages. They are also all installed using install.packages("tidyverse") but they are not loaded with library(tidyverse), you have to use a separate library line to load these. Examples include lubridate for managing dates and times and haven for reading more unusual file types. You might use these kinds of packages as an intermediate user but not so much as a beginner.
4. Other packages on CRAN: There are over 18,000 packages on CRAN made by developers and organisations all over the world. These can be downloaded using install.packages and loaded using library. Examples include csodata for downloading CSO data, and caret for machine learning. As a beginner you will not encounter many of these.
5. Other packages not on CRAN: Many people make packages which for one reason or another are not on CRAN. For example a company may make a package for internal use only, or an individual might store several functions that they made themselves as a package on their own device (or maybe on GitHub) so that they can be easily added to their projects. These would be installed using a special configuration of install.packages or some other function, but once installed they can be loaded using library.

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. How you actually go about running code that you have written here is covered further on under ‘How do I run code?’ 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 would rarely have occasion to use 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 used mtcars to make a new duplicate dataset called my_data which 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 <- 5

After 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] 7

Let’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 9

Click to see 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  TRUE

I 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] 6

Similarly we can access the i^th row and the j^th 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        27

To 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

You can select all the columns of a particular type (numerical, character, etc.) using the selection helper function where. It takes as its argument the name of another function which performs a logical check on a variable, examples include is.numeric, is.character, is.logical, is.factor. Let’s select the numerical variables.

cancer %>%
  select(where(is.numeric))

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

Note that you can select the opposite (non-numeric columns, for example), by putting a minus or the ‘NOT’ logical operator ! in front of the where function.

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 of some mutate process. 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 functions save 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.

Pivoting

Pivoting dataframes can be tricky but I need to do it a lot with data imported from the CSO’s open data portal so I’ll give a guide here.

To keep things simple we will only consider pivots involving one column for names and one for values when pivoting wider, and one resultant column for names and one resultant column for values when pivoting longer.

pivot_wider

Let’s take our cancer dataset again, forget about ncases (we’ll drop that) and pivot the table so that we have four new columns for each of the tobgp values, and the values within those columns will be ncontrols for the appropriate combination of tobgp, agegp and alcgp. For this we use pivot_wider. The two important arguments to this are names_from which is equal to the name of the variable that will provide names for our new columns, and values_from which is the values that will sit in those new columns. We’ll pop the output into a new dataframe called wide_cancer_dataset

agegp	alcgp	0-9g/day	10-19	20-29	30+
25-34	0-39g/day	40	10	6	5
25-34	40-79	27	7	4	7
25-34	80-119	2	1	NA	2
25-34	120+	1	0	1	2
35-44	0-39g/day	60	13	7	8
35-44	40-79	35	20	13	8
35-44	80-119	11	6	2	1
35-44	120+	1	3	2	NA
45-54	0-39g/day	45	18	10	4
45-54	40-79	32	17	10	2
45-54	80-119	13	8	4	2
45-54	120+	0	1	1	0
55-64	0-39g/day	47	19	9	2
55-64	40-79	31	15	13	3
55-64	80-119	9	7	3	0
55-64	120+	5	1	1	1
65-74	0-39g/day	43	10	5	2
65-74	40-79	17	7	4	NA
65-74	80-119	7	8	1	0
65-74	120+	1	1	0	0
75+	0-39g/day	17	4	NA	2
75+	40-79	3	2	3	0
75+	80-119	0	0	NA	NA
75+	120+	0	0	NA	NA

wide_cancer_dataset <- cancer %>% 
  select(-ncases) %>% 
  pivot_wider(names_from = tobgp, values_from = ncontrols)

You might notice some NA values among the new dataset. This is because wide_cancer_dataset include some combinations that weren’t in the original cancer dataset, e.g. agegp = "25-34", alcgp = "80-119" and tobgp = "20-29" didn’t appear in the cancer dataset. If you wanted to set those NA values to zero at the time of pivoting, you could use the extra argument values_fill = 0.

You can also add a prefix to your new columns using the additional argument names_prefix, which takes a string, e.g. names_prefix = "tobgp_".

pivot_longer

Let’s take wide_cancer_dataset and convert it back into a longer format. We’ll use the function pivot_longer. It has three key arguments: - cols: this specifies the columns to be pivoted into rows - names_to: the name for the new column which contains the names of the columns that you are pivoting - values_to: the name for the new column which contains the values from the columns that you are pivoting.

wide_cancer_dataset %>% 
  pivot_longer(cols = c("0-9g/day", "10-19",  "20-29","30+"), 
               names_to = "tobgp", 
               values_to = "ncontrols")

agegp	alcgp	tobgp	ncontrols
25-34	0-39g/day	0-9g/day	40
25-34	0-39g/day	10-19	10
25-34	0-39g/day	20-29	6
25-34	0-39g/day	30+	5
25-34	40-79	0-9g/day	27
25-34	40-79	10-19	7
25-34	40-79	20-29	4
25-34	40-79	30+	7
25-34	80-119	0-9g/day	2
25-34	80-119	10-19	1
25-34	80-119	20-29	NA
25-34	80-119	30+	2
25-34	120+	0-9g/day	1
25-34	120+	10-19	0
25-34	120+	20-29	1
25-34	120+	30+	2
35-44	0-39g/day	0-9g/day	60
35-44	0-39g/day	10-19	13
35-44	0-39g/day	20-29	7
35-44	0-39g/day	30+	8
35-44	40-79	0-9g/day	35
35-44	40-79	10-19	20
35-44	40-79	20-29	13
35-44	40-79	30+	8
35-44	80-119	0-9g/day	11
35-44	80-119	10-19	6
35-44	80-119	20-29	2
35-44	80-119	30+	1
35-44	120+	0-9g/day	1
35-44	120+	10-19	3
35-44	120+	20-29	2
35-44	120+	30+	NA
45-54	0-39g/day	0-9g/day	45
45-54	0-39g/day	10-19	18
45-54	0-39g/day	20-29	10
45-54	0-39g/day	30+	4
45-54	40-79	0-9g/day	32
45-54	40-79	10-19	17
45-54	40-79	20-29	10
45-54	40-79	30+	2
45-54	80-119	0-9g/day	13
45-54	80-119	10-19	8
45-54	80-119	20-29	4
45-54	80-119	30+	2
45-54	120+	0-9g/day	0
45-54	120+	10-19	1
45-54	120+	20-29	1
45-54	120+	30+	0
55-64	0-39g/day	0-9g/day	47
55-64	0-39g/day	10-19	19
55-64	0-39g/day	20-29	9
55-64	0-39g/day	30+	2
55-64	40-79	0-9g/day	31
55-64	40-79	10-19	15
55-64	40-79	20-29	13
55-64	40-79	30+	3
55-64	80-119	0-9g/day	9
55-64	80-119	10-19	7
55-64	80-119	20-29	3
55-64	80-119	30+	0
55-64	120+	0-9g/day	5
55-64	120+	10-19	1
55-64	120+	20-29	1
55-64	120+	30+	1
65-74	0-39g/day	0-9g/day	43
65-74	0-39g/day	10-19	10
65-74	0-39g/day	20-29	5
65-74	0-39g/day	30+	2
65-74	40-79	0-9g/day	17
65-74	40-79	10-19	7
65-74	40-79	20-29	4
65-74	40-79	30+	NA
65-74	80-119	0-9g/day	7
65-74	80-119	10-19	8
65-74	80-119	20-29	1
65-74	80-119	30+	0
65-74	120+	0-9g/day	1
65-74	120+	10-19	1
65-74	120+	20-29	0
65-74	120+	30+	0
75+	0-39g/day	0-9g/day	17
75+	0-39g/day	10-19	4
75+	0-39g/day	20-29	NA
75+	0-39g/day	30+	2
75+	40-79	0-9g/day	3
75+	40-79	10-19	2
75+	40-79	20-29	3
75+	40-79	30+	0
75+	80-119	0-9g/day	0
75+	80-119	10-19	0
75+	80-119	20-29	NA
75+	80-119	30+	NA
75+	120+	0-9g/day	0
75+	120+	10-19	0
75+	120+	20-29	NA
75+	120+	30+	NA

That’s all fine, but specifying the four column names is a bit fiddly. Instead we can specify the columns which will not be pivoted, which are agegp and alcgp. To do that we pop in these column names with the argument cols = but put a minus in front of the vector containing these names. The output isn’t shown here but the result is the same as the above.

wide_cancer_dataset %>% 
  pivot_longer(cols = -c("alcgp", "agegp"), 
               names_to = "tobgp", 
               values_to = "ncontrols")

You should know that the selection helper functions (like contains, starts_with and ends_with) can be used when specifying the list of columns which are to be pivoted. Suppose in making wide_cancer_dataset that we had used names_prefix = "tobgp_" so that each of our tobacco columns started with the string "tobgp_". Then we could have pivoted longer by specifying our column selection as cols = starts_with("tobgp_").

We could also have used the where selection helper together with is.numeric to pivot only the numerical type columns. The code for this is shown below but the results are not since they are the same as before.

wide_cancer_dataset %>% 
  pivot_longer(cols = where(is.numeric), 
               names_to = "tobgp", 
               values_to = "ncontrols")

One last thing is that the NA values from wide_cancer_dataset have appeared in the output. If you didn’t want that to be the case you could use the additional argument values_drop_na, setting it equal to TRUE.

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.

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(data = 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(data = 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()