Category Archives: R programming

top_8_R_Packages_over_time

Top 100 R packages for 2013 (Jan-May)!

What are the top 100 (most downloaded) R packages in 2013? Thanks to the recent release of RStudio of their “0-cloud” CRAN log files (but without including downloads from the primary CRAN mirror or any of the 88 other CRAN mirrors), we can now answer this question (at least for the months of Jan till May)!

By relying on the nice code that Felix Schonbrodt recently wrote for tracking packages downloads, I have updated my installr R package with functions that enables the user to easily download and visualize the popularity of R packages over time. In this post I will share some nice plots and quick insights that can be made from this great data. The code for this analysis is given at the end of this post.

Top 8 most downloaded R packages – downloads over time

Let’s first have a look at the number of downloads per day for these 5 months, of the top 8 most downloaded packages (click the image for a larger version):

top_8_R_Packages_over_time

We can see the strong weekly seasonality of the downloads,  with Saturday and Sunday having much fewer downloads than other days. This is not surprising since we know that the countries which uses R the most have these days as rest days (see James Cheshire’s world map of R users). It is also interesting to note how some packages had exceptional peaks on some dates. For example, I wonder what happened on January 23rd 2013 that the digest package suddenly got so many downloads, or that colorspace started getting more downloads from April 15th 2013.

“Family tree” of the top 100 most downloaded R packages

We can extract from this data the top 100 most downloaded R packages. Moreover, we can create a matrix showing for each package which of our unique ids (censored IP addresses), has downloaded which package. Using this indicator matrix, we can thing of the “similarity” (or distance) between each two packages, and based on that we can create a hierarchical clustering of the packages – showing which packages “goes along” with one another.

With this analysis, you can locate package on the list which you often use, and then see which other packages are “related” to that package.  If you don’t know that package – consider having a look at it – since other R users are clearly finding the two packages to be “of use”.

Such analysis can (and should!) be extended. For example, we can imagine creating a “suggest a package” feature based on this data, utilizing the package which you use, the OS that you use, and other parameters.  But such coding is beyond the scope of this post.

Here is the “family tree” (dendrogram) of related packages:

Family_tree_of_Top_100_R_Packages

To make it easier to navigate, here is a table with links to the top 100 R packages, and their links:

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installr_installations_per_day

Answering “How many people use my R package?”

The question “How many people use my R package?” is a natural question that (I imagine) every R package developer asks himself at some point or another. After many years in the dark, a silver lining has now emerged thanks to the good people at RStudio. Just yesterday, a blog post by Hadley Wickham was written about the newly released CRAN log files of the RStudio cloud CRAN!

Already out, and the R blogosphere started buzzing with action: James Cheshire created a beautiful world map which highlights the countries based on how much people there use of R. Felix Schonbrodt wrote a great post on Tracking CRAN packages downloads. In the meantime, I’ve started crafting some basic functions for package developers to easily check how many users downloaded their package. These functions are now available on the installr package github page.

Here is the output for the number of unique ips who downloaded the installr package around the time R 3.0.0 was released (click to see a larger image):

installr_installations_per_day

And here is the code to allow you to make a similar plot for the package which interests you:

# if (!require('devtools')) install.packages('devtools'); require('devtools')
# make sure you have Rtools installed first! if not, then run:
#install_Rtools()
#install_github('installr', 'talgalili') # get the latest installr R package
# or run the code from here:
# https://github.com/talgalili/installr/blob/master/R/RStudio_CRAN_data.r
 
if(packageVersion("installr") %in% c("0.8","0.9","0.9.2")) install.packages('installr') #If you have one of the older installr versions, install the latest one....
 
require(installr)
 
# The first two functions might take a good deal of time to run (depending on the date range)
RStudio_CRAN_data_folder <- download_RStudio_CRAN_data(START = '2013-04-02', END = '2013-04-05') # around the time R 3.0.0 was released
my_RStudio_CRAN_data <- read_RStudio_CRAN_data(RStudio_CRAN_data_folder)
 
 # barplots: (more functions can easily be added in the future)
barplot_package_users_per_day("plyr", my_RStudio_CRAN_data)
barplot_package_users_per_day("installr", my_RStudio_CRAN_data)

If you (the reader) are interested in helping me extend (/improve) these functions, please do so – I’d be happy to accept pull requests (or comments/e-mails).

R 3.0.1 is released

R 3.0.1 (codename “Good Sport”) was released last week. As mentioned earlier by David, this version improves serialization performance with big objects, improves reliability for parallel programming and fixes a few minor bugs.

Upgrading to R 3.0.1

You can download the latest version from here. Or, if you are using windows, you can upgrade to the latest version using the installr package (also available on CRAN and github). Simply run the following code:

# installing/loading the package:
if(!require(installr)) { 
install.packages("installr"); require(installr)} #load / install+load installr
 
updateR(to_checkMD5sums = FALSE) # the use of to_checkMD5sums is because of a slight bug in the MD5 file on R 3.0.1. Soon this should get resolved and you could go back to using updateR(), install.R() or the menu upgrade system.

I try to keep the installr package updated and useful. If you have any suggestions or remarks on the package, you’re invited to leave a comment below.

If you use the global library system (as I do), you can run the following in the new version of R:

source("http://www.r-statistics.com/wp-content/uploads/2010/04/upgrading-R-on-windows.r.txt")
New.R.RunMe()

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installr_menubar_updateR

Updating R (on Windows) through a menu-bar: installr 0.9 released on CRAN

In preparation for the upcoming release of R 3.0.0, a new release 0.9 of installr is now on CRAN.

The package can be installed and loaded using:

# installing/loading the package:
if(!require(installr)) { 
install.packages("installr"); require(installr)} #load / install+load installr

The new version includes various bug fixes (as can be seen in the NEWS file) and new functions and features. The most user visible feature is that from now on, whenever loading installr in the Rgui, it will add a new menu-bar for updating your R version (the menu is removed when the package is detached).

installr_menubar_updateR

When choosing to update R, a new GUI based system will guide you step by step through the updating process. It will first check if a newer version of R is available, if so, it will offer to show the latest NEWS of that release, download and install the new version, and copy/move your packages from the previous library folder, to the one in the new installation. If you have a global library folder, you can simply stop the updating once your new R is installed, and continue as you would otherwise (in the future, I intend to update the package to also allow it to deal with people using a global library folder).

installr_updateR_noupdate_needed

(for using {installr} to update R through R terminal, see my previous post: Updating R from R (on Windows) – using the {installr} package)

Another new feature is the “installr()” function (which can also be run through the menubar), running it will open a window with a list of software you can download and install using the installr package (From Rtools and RStudio to pandoc and MikTeX).

installr_installr_function

I hope you’ll enjoy this new release, and as always – please let me know in the comments (or via e-mail) if you come across any bugs or have suggestions for new features.

Writing a MS-Word document using R (with as little overhead as possible)

The problem: producing a Word (.docx) file of a statistical report created in R, with as little overhead as possible.
The solution: combining R+knitr+rmarkdown+pander+pandoc (it is easier than it is spelled).

If you get what this post is about, just jump to the “Solution: the workflow” section.

rmd_to_docx

Preface: why is this a problem (/still)

Before turning to the solution, let’s address two preliminary questions:

Q: Why is it important to be able to create report in Word from R?

A: Because many researchers we may work with are used to working with Word for editing their text, tracking changes and merging edits between different authors, and copy-pasting text/tables/images from various sources.
This means that a report produced as a PDF file is less useful for collaborating with less-tech-savvy researchers (copying text or tables from PDF is not fun). Even exchanging HTML files may appear somewhat awkward to fellow researchers.
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Updating R from R (on Windows) – using the {installr} package

Upgrading R on Windows is not easy. While the R FAQ offer guidelines, some users may prefer to simply run a command in order to upgrade their R to the latest version. That is what the new {installr} package is all about.

The {installr} package offers a set of R functions for the installation and updating of software (currently, only on Windows OS), with a special focus on R itself. To update R, you can simply run the following code:

# installing/loading the package:
if(!require(installr)) { 
install.packages("installr"); require(installr)} #load / install+load installr
 
# using the package:
updateR() # this will start the updating process of your R installation.  It will check for newer versions, and if one is available, will guide you through the decisions you'd need to make.

Running this function will perform the following steps:

  • Check what is the latest R version. If the current installed R version is up-to-date, the function ends (and returns FALSE)

  • If a newer version of R is available, you will be asked if to review the NEWS of the latest R version – in order to decide if to install the
    newest R or not.

  • If you wish it – the function will download and install the latest R version. (you will need to press the "next" buttons on your own)

  • Once the installation is done, you should press "any-key", and the function will proceed with copying all of your packages from your old (well, current) R installation, into your newer R installation.

  • You can then erase all of the packages in your old R installation.

  • After your packages are moved (and the old ones possibly erased), you will get the option to update all of your packages in the new version of R.

  • Lastely – you can open the new Rgui and close the current session of your old R. (This is a bit buggy in version 0.8, but has been fixed in version 0.8.1)

If you know you wish to upgrade R, and you want the packages moved (not copied, MOVED), you can simply run:

# installing/loading the package:
if(!require(installr)) { install.packages("installr"); require(installr)} #load / install+load installr
 
updateR(F, T, T, F, T, F, T) # install, move, update.package, quit R.

Since the various steps are broken into individual functions, you can also pick and choose what to run using the relevant function:

# installing/loading the package:
if(!require(installr)) { install.packages("installr"); require(installr)} #load / install+load installr
 
# step by step functions:
check.for.updates.R() # tells you if there is a new version of R or not.
install.R() # download and run the latest R installer
copy.packages.between.libraries() # copy your packages to the newest R installation from the one version before it (if ask=T, it will ask you between which two versions to perform the copying)

If you like using the global library system, you can run the following in the old R:

# installing/loading the package:
if(!require(installr)) { install.packages("installr"); require(installr)} #load / install+load installr
 
updateR(F, T, F, F, F, F, T) # only install R (if there is a newer version), and quits it.

And then run the following in the new version of R:

source("http://www.r-statistics.com/wp-content/uploads/2010/04/upgrading-R-on-windows.r.txt")
New.R.RunMe()

The {installr} package also offers functions for installing various other software on Windows. These functions include: install.pandoc (which was mentioned on this blog recently), install.git, install.Rtools, install.MikTeX, install.RStudio, and a general install.URL and install.packages.zip functions. You can see these further explained in the package’s Reference manual.

Feature requests, bug reports – and your help in improving the package

You can see the latest version of installr on github, where you can also submit bug reports (you may also just leave a comment in this post). Since this is my first R package, I might have (e.g: probably have) missed something here or there. So any comment on how to improve my code/documentation/R-fu, will be most welcomed (here or on github).

If this type of coding is fun/easy for you, you can help me improve this package on github. Cool new features I think may be added (by me or others) are:

  • Add an uninstall.R function – to remove the old R version.
  • Add more support for upgrading R for people who uses a global library for their packages.
  • Add support for Linux and Mac! This one I am less likely to do on my own – and would love to see someone else extend my code to other operation systems.
  • GUI – add a menu based option for running updateR. Something like help->”check for updates” would be great. (p.s: this idea came from Yihui Xie)
  • add even more install.software functions. If you have functions for which you’d like to be able to easily install them – just let me know and it could be included in future releases.

Thanks

Final note, I would like to thank the many people who have developed WONDERFUL tools for making R package development possible (and even somewhat fast), on Windows. These include Prof. Brian Ripley and Duncan Murdoch for Rtools, also Uwe Ligges for his work on CRAN, Hadley Wickham for devtools (in general, and for its documentation), Yihui Xie for roxygen2, JJ and others in the RStudio team for RStudio, the people behind git and github, and more. There are probably more things I can thank these people for, and many more people I should thank, but I can’t figure who you are probably (feel free to e-mail me, I appreciate you work even if it is not clear to me your are behind it).

Installing Pandoc from R (on Windows) – using the {installr} package

The R blogger Rolf Fredheim has recently wrote a great piece called “Reproducible research with R, Knitr, Pandoc and Word“, where he advocates for Pandoc as an essential part of reproducible research workflow in R, in helping to turn documents which are knitted in R into high quality Word for exchanging with our colleagues. It is a great post, with many useful bits of code, and I wanted to supplement it with one missing function: “install.pandoc“.

Update: the install.pandoc function is now part of the {installr} package.

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Generation of E-Learning Exams in R for Moodle, OLAT, etc.

(Guest post by Achim Zeileis)
Development of the R package exams for automatic generation of (statistical) exams in R started in 2006 and version 1 was published in JSS by Grün and Zeileis (2009). It was based on standalone Sweave exercises, that can be combined into exams, and then rendered into different kinds of PDF output (exams, solutions, self-study materials, etc.). Now, a major revision of the package has been released that extends the capabilities and adds support for learning management systems. It is still based on the same type of
Sweave files for each exercise but can also render them into output formats like HTML (with various options for displaying mathematical content) and XML specifications for online exams in learning management systems such as Moodle or OLAT. Supplementary files such as graphics or data are
handled automatically. Here, I give a brief overview of the new capabilities. A detailed discussion is in the working paper by Zeileis, Umlauf, and Leisch (2012) that is also contained in the package as a vignette.
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