This manual was written specifically as an introduction for the quantitative social scientist. To my opinion, R-Project is a magnificent statistical program, ready to be accepted and implemented in the social sciences. The flexibility of this program and the way data are handled gives the user a sense of closeness to and control over the data. I think this inspires users to analyze their data more creatively and sometimes in a more advanced way. At present, this manual has a strong focus on multilevel regression techniques. Reason for this is that in R-Project it is very easy to estimate these types of models, even the more complex variants. The more basic and fundamental aspects of R-Project are introduced as well. All this is done with the needs of the quantitative social scientist in mind.

Of course, this manual it provided without any warranty. Please realize that I wrote it almost four years ago.

I’d love to hear any feedback for (future) improvements!

Download:

Applied R for the quantitative social scientist

Introduction

Producing graphics can be a way to get familiar with your data or to strongly present your results. Fortunately, this can be done both easy as well as in a very powerful way in R-Project. R-Project comes with some standard graphical functions and a package for Trellis-graphics. Here, we will see some of the basics of the standard graphics functionality of R-Project.

R-Project creates graphics and presents them in a ‘graphics device’. This can be a window on the screen, but just as easily a file in a specified format (such as .bmp or .pdf). There are two types of functions that create graphics in R-Project. One of those sets up such a graphics device by calculating and drawing the axes, plot title, margins and so on. Then the data is plotted into the device. The other type of graphics function cannot create a graphics device and only adds data to a plot. This paragraph shows only the first type of plotting-functions.

Basic Plotting

The most basic plot-function in R-Project is called ‘plot()’. It is a function that sets up the graphics-device and is able to create some different types of graphic representations of your data.

For instance, let’s say we want to visualize a set of five values: 3,4,5,6 and 5. In the syntax below, these values are first assigned to the variable ‘y’. Then, we call the plot()-function and tell it to plot the data assigned to y.

y <- c(3,4,5,6,5)
plot (y)
plot (y, type=”l”, main=”Example line-plot”, xlab=”Predictor Value”, ylab=”Outcome Value”)

Although the syntax of the first plot-command is very simple, R-Project actually performs quite a bit of work for us. For example: a new window opens, the plotting area is set alongside margins, minimal and maximum values for the axes are calculated based on the data and drawn succeedingly, basic labels are added to the aces and finally: the data is represented. Obviously this plot is not ready for publication, but fortunately all the ‘choices’ R-Project made for us are only the defaults, so we can easily specify exactly what we want.

The next plot already looks a bit better. This is because some extra specifications are added to the second plot-command in the syntax above. By specifying “type=”l” we tell the plot-function that we want the data-points to be connected using a line. The main=” ” specification creates a header for the plot, while the xlab=” ” and ylab=” ” specify the labels for the x-axis and y-axis respectively.

x <- c(1,3,4,7,9)
plot (x,y, type=”b”, main=”Example plot”, xlab=”Predictor Value”, ylab=”Outcome Value”)

What if the values that we want to represent are related to predictor values (values on the x-axis) that are not evenly spread, such as in the graphics above? In that case, we have to specify the values on the x-axis to the plot()-function. The syntax above shows how this is done. First, we assign some values to the variable we call x. Then, we replicate the plot()-syntax from above and add the x-variable to it, before the y-variable. Additionally the type=”l” from above is changed into type=”b” (b stands for both), which results in plotting both a line as well as points. The plot this results in, is shown below.

Other types of plots

Statistics does not exist solely out of line- and points-graphics. The syntax below shows how the represent the data stored in the y-variable can be represented using a barplot, pie-chart, histogram and a boxplot. These types of graphics are only shown, not described exhaustingly. All of these functions have many parameters that can be used to create exactly what you want.

barplot(y, main=”Barplot”, names.arg=c(“a”,”b”,”c”,”d”,”e”))
pie(y, main=”Pie-chart”, labels=c(“a”,”b”,”c”,”d”,”e”))
hist(y, main=”Histogram”)
boxplot(y, main=”Boxplot”)

The syntax above results almost exactly in the graph shown above. The only difference is, that normally R-Project would create four separate graphs when the syntax above is provided. For an explanation of how to place more graphs on one graphics device, see elsewhere in this manual.

All the graphics functions shown here have the main=” ” argument specified. The barplot() function has the additional names.arg – argument specified, which here provides five letters (“a” to “e”) as labels for the bars. On the pie-chart this is done as well, but with the label-argument.

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• Discuss this article and pose additional questions in the R-Sessions Forum
• Find the original article embedded in the manual.

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R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
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R-Project is an open-source software package that can be obtained freely from the internet. It is available for a large variety of computer operating systems, such as Linux, MacOSX and Windows. Serving the majority, the installation process will be described for a computer running on windows XP.

Downloading R-Project

The website of R-Project can be found on http://www.r-project.org. The left sidebar contains a header ‘Download’. Below, a link to ‘CRAN’ is provided. CRAN stands for the ‘Comprehensive R Archive Network’ and is a network of several web-servers from which both the software as well as additional packages can be downloaded. When clicked on CRAN, a list of providers of the software is shown. Choose one near the location you’re at. Then, a page is shown with several files that can be downloaded. What we want for now is a ‘precompiled’ piece of software, that is ready for installation.

Click on the Windows (95 and later) link that is shown near the top of the page. After some words of warning, two options are offered: downloading the base distribution which contains the R-software and the contrib distribution which contains many additional packages. We go for the ‘base’ distribution for now. There, again a few options are offered. Below the ‘In this directory’ heading, some files are shown that are ready for download. Most of them are introductory text files, that assist on the downloading. Now, we want to download the actual installation program that has the name R-2.5.1-win32.exe ((please note: this filename contains the version-number of the software which at the time of writing is 2.5.1. Future versions will obviously have an other filename, although resemblance in naming is to be expected)) . When clicked on, the file will be downloaded to a location on your computer that can be specified.

Installing R-Project

Double-click on the downloaded installation file. A welcome-screen appears.

Click on ‘next’ to see (or possibly even read) the license of the software. For those familiar with open-source software the license of R-Project is the ‘GNU General Public License’ which allows the user to alter the program at will and to use it freely. It is not allowed to sell R-Project, but it may be used to make money. Click on ‘Next’ to accept the license and to select a location for installation. It is probably best to accept the standard settings.

Then, four types of installation are offered:

• User installation
• Minimal installation
• Full installation
• Personalized installation

The options offered create different selections of mostly help- and support files. Again, for general use it is best to accept the standard installation option. Click on ‘Next’ to proceed. It is then asked whether or not you want to change starting-up options. Select ‘No’ to use standard options.

The setup program then prompts for a name and location for the R-program in the Windows start-menu. Choose the nice location you want or accept the default. The next screen offers some final options. The standard is that an icon is created on the windows desktop, that the version number of the R-software is stored in the windows-register (comes in handy when updating) and that data-files with the .RData extension are to be associated with R-Project. It is best to accept these options. When clicked on ‘Next’, the software will be installed. When the installing is done, click on ‘Finish’ to end the installing-program.

R-project is now installed! An icon is placed on your desktop if the standard-options are used during the installation. Double-click on it to start R. It should start swiftly and is now ready for use.

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R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
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There are many good reasons to start using R. Obviously, there are some reasons not to use R, as well. Some of these reasons are shortly described here. In the end, it is just some kind of personal preference that leads a researcher to use one statistical package, or another. Here are some arguments as a base for your own evaluation.

Why use R?

Powerful & Flexible

Probably the best reason to use R is its power. It is not so much a statistical software, but more a statistical programming language. This results in the availability of powerful methods of analyses, but in strong capabilities of managing, manipulating and storing your data. Due to its data-structure, R gains a tremendous flexibility. Everything can be stored inside an object, from data, via functions to the output of functions. This allows the user to easily compare different sets of data, or the results of different analyses just as easy. Because the results of an analysis can be stored in objects, parts of these results can be extracted as well and used in new functions / analyses.
Besides the many already available functions, it is possible to write your own. This results in flexibility that can be used to create functions that are not available in other packages. In general: if you can think of it, you can make it. Thereby, R becomes a very attractive choice for methodological advanced studies.

Excels in Graphics

The R-project comes with two packages for creating graphics. Both are very powerful, although the lattice package seems to supersede the basic graphics package. Using either of these packages, it is very easy, as well a fast, to create basic graphics. The graphics system is set up in a way that, again, allows for great flexibility. Many parameters can be set using syntax, ranging from colors, line-styles, and plotting-characters to fundamental things such as the coordinate-system. Once a plot is made, many items can be added to it later, such as data-points from other data-sets, or plotting a regression-line over a scatterplot of the data.
The lattice-package allows the graphic to be stored in an object, which can later be used to plot the graphic, to alter the graphic or even to let the graphic to be analyzed by (statistical) functions.
A great many graphical devices are available that can output the graphics to many file-formats, besides to the screen of course. All graphics are vector-based, insuring great quality even when the graphics are scaled. Graphic devices for bitmap-graphics are available as well.

Open Source & Free

R software is open source, meaning that everybody can have access to the source-code of the software. In this way, everybody can make their own changes if he wants to. Also, it is possible to check the way a feature is implemented. In this way, it is easy to find bugs or errors that can be changed immediately for your own version, or generally in the next official version. Of course, not everyone has the programming knowledge to do so, but many users of R do. Generally, open-source software is characterized by a much lower degree of bugs and errors than closed-software.
Did I already mention that it is free? In line with the open-source philosophy (but not necessarily so!), the R-software is available freely. In this it gains advantage to many other statistical packages, that can be very expensive. When used on a large scale, such as on universities, the money gained by using R instead of other packages, can be enormous.

Large supporting user base

R is supported by a very large group of active users, from a great many disciplines. The R-Core development group presently exists of seventeen members. These people have write-access to the core of the R program (for the version that is distributed centrally. Everybody has write-access to the core of their own version of the software). They are supported by many that give suggestions or work in collaboration with the R-code team.
Besides a good, strong, core, statistical software needs a great many functions to function properly. Fortunately, a great many R users make their own functions available to the R community, free to download. This results in the availability of packages containing functions for methods that are frequently used in a diversity of disciplines.
Next to providing a great many functions, the R community is has several mailing-lists available. One of these is dedicated to helping each other. Many very experienced users, as well as some members of the R-core development team, participate actively on this mailing list. Most of the times, you’ll have some guidance, or even a full solution to your problem, within hours.

Why not to use R?

Slow

In general, due to the very open structure of R, it tends to be slower than other packages. This is because the functions that you write yourself in R are not pre-compiled into ‘computer-language’, when they are run. In many other statistical packages, the functions are all pre-compiled, but this has the drawback of losing flexibility. On the other hand, when using the powerful available functions and using these in smart programming, speed can be gained. For instance, in many cases ‘looping’ can be avoided in R by using other functions that are not available in other packages. When this is the case, R will probably win the speed-contest. In other cases, it will probably lose.
One way of avoiding the speed-drawback when programming complex functions, is to implement C or Fortran programs. R can have access to programs in both languages, that are both much faster than un-compiled syntax. By using this method, you can place the work-horse functions in a fast language and have these return the output to R, which then can further analyze these.

Chokes on large data-sets

A somewhat larger draw-back of R is that it chokes on large data-sets. All data is stored in active memory, and all calculations are ‘performed’ there as well. This leads to problems when active memory is limited. Although modern computers can easily have 4 Gb (or even more) of RAM, using large data sets and complex functions, you can easily run into problems. Until a disk-paging element is implemented in R, this problem does not seem to be fully solved easily.
Some attempts have been made though, that can be very useful in some specific cases. One package for instance allows the user to store the data in a MySQL database. The package then extracts parts of the data from the database several times to be able to analyze these parts of the data succeedingly. Finally, the partial results are combined as if the analysis was performed on just the whole set of data at once. This method doesn’t work for all functions, though. Only a selection of functions that can handle this methodology is available at present.

No point-and-click

I don’t really know it this is a true drawback on the R software, but it doesn’t come with a point-click-analyse interface. All commands need to be given as syntax. This results in a somewhat steeper learning-curve compared to some other statistical programs, which can be discouraging for starting users. But, to my opinion this pays of on the long term. Syntax seems to be a lot faster, and more precise, when working on complex analyses.
Mentioning this as a draw-back of R is not entirely fair, since John Fox wrote a package R Commander, which provides in a point-and-click interface. It is freely available, as all packages, and can be used a an introduction to the capabilities of R.

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– – — — —– ——–
R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
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R is a software package that is used for statistical analyses. It has a syntax-driven interface which allows for a high level of control, many add-on packages, an active community supporting the program and it’s users and an open structure. All in all, it aims to be statistical software that goes beyond pre-set analyses. Oh, and it is free too.

The R software is developed by the R Core Development Team, presently having seventeen members. New versions of the R software are coming out regularly, so apparently progress is made. The source code of the R software is open-source. This means that everybody is allowed to read and change the program code. The consequence of this is that many people have written extensions to R which are able to nest itself in the fundaments of the software. For instance, it can interact with programs such as (WIN)BUGS or have extensions based on C or Fortran code.

A typical R session can be characterized by its flexibility. The software is set up in such a way, that functions or command can interact and thereby be combined to new ones. Obviously many statistical methods are already available, but if a command just doesn’t do exactly what you want it to do, it can easily be altered. Or, you build your analyses from the ground up using the most basic of functions. If you can think of it, you can create it.

So basically, you can invent your own set of wheels. Of course, many wheels have been invented yet, so it is not necessary to do it again yourself. Snippets of R-syntax are readily available on the internet. They can even be combined into ‘packages’, which can easily be downloaded from within the R-software itself or the R-website. Many of these packages are actively maintained and constantly improved. So don’t worry about being confronted with outdated software.

Another distinguishing aspect of R is its data-structure. All data are assigned to objects. And since R can handle more than just one object, several (read: virtually unlimited) sets of data can be used simultaneously. Functions are stored in objects too and finally the output of functions are stored in an object as well. This opens up many possibilities. For instance, when we want to compare several statistical models, we can store these models in different objects, that can be compared using the right functions (ANOVA, for instance). In a later stage, these objects can be used to extract data, for instance to graph the results.

So, in order to answer the question what R actually is, it can be stated that R is a very open-structured and flexible software package, readily available and very suitable for statistical analyses.

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– – — — —– ——–
R-Sessions is a collection of manual chapters for R-Project, which are maintained on Curving Normality. All posts are linked to the chapters from the R-Project manual on this site. The manual is free to use, for it is paid by the advertisements, but please refer to it in your work inspired by it. Feedback and topic requests are highly appreciated.
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Curving Normality generally consists of two parts: my personal blog and a manual for R-Project I wrote last year. I want to continue working on this manual, and to increase the exposure it gets I decided to regularly post chapters of it on my blog. I will do so under the name R-Sessions

This manual is already set up in a way that allows the reader to try all the examples by copy-pasting the syntax. I want to extend this feature and add chapters on a specific statistical problem that is dealt with, from a description of the problem and initial exploration of the data to the final solution to the problem. However, I’m not at that point yet. In the near future expect several updates each week in which I present the existing manual.

The reason that I’m writing such a manual for R-Project is that to my opinion, R-Project is a magnificent statistical program, even though it has some severe limitations. It is ready to be accepted and implemented in the social sciences. The flexibility of this program and the way data are handled gives the user a feeling of closeness to the data. I think this inspires users to analyze their data more creatively and sometimes in a more advanced way. At present, this manual has a strong focus on multilevel regression techniques. Reason for this is that in R-Project it is very easy to estimate these types of models, even the more complex variants. The more basic and fundamental aspects of R-Project are introduced as well. All this is done with the needs of the quantitative social scientist in mind.