Tag Archives: RStudio

R 3.0.2 and RStudio 0.9.8 are released!

R 3.0.2 (codename “Frisbee Sailing”) was released yesterday. The full list of new features and bug fixes is provided below.

Also, RStudio v0.98 (in a “secret” preview) was announced two days ago with MANY new features, including:

Upgrading to R 3.0.2

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 3.0.2. This issue is already resolved in the installr version on github, and will be released into CRAN in about a month from now..

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()

p.s: you can also use the installr package to quickly install the new RStudio by using:

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

Continue reading R 3.0.2 and RStudio 0.9.8 are released!

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:

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

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