R 3.2.3 is released (with improvements for Windows users, and general bug fixes)

R 3.2.3 (codename “Wooden Christmas Tree”) was released several days ago. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Major changes in R 3.2.3

As highlighted by David Smith, this release makes a few small improvements and bug fixes to R, including:

  • Improved support for users of the Windows OS in time zones, OS version identification, FTP connections, and printing (in the GUI).
  • Performance improvements and more support for long vectors in some functions including which.max
  • Improved accuracy for the Chi-Square distribution functions in some extreme cases

Upgrading to R 3.2.3 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code in Rgui:

install.packages("installr") # install 
setInternet2(TRUE)
installr::updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the installr package.

I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page.

NEW FEATURES

  • Some recently-added Windows time zone names have been added to the conversion table used to convert these to Olson names. (Including those relating to changes for Russia in Oct 2014, as in PR#16503.)
  • (Windows) Compatibility information has been added to the manifests for ‘Rgui.exe’, ‘Rterm.exe’ and ‘Rscript.exe’. This should allow win.version() andSys.info() to report the actual Windows version up to Windows 10.
  • Windows "wininet" FTP first tries EPSV / PASV mode rather than only using active mode (reported by Dan Tenenbaum).
  • which.min(x) and which.max(x) may be much faster for logical and integer x and now also work for long vectors.
  • The ‘emulation’ part of tools::texi2dvi() has been somewhat enhanced, including supporting quiet = TRUE. It can be selected by texi2dvi = "emulation".(Windows) MiKTeX removed its texi2dvi.exe command in Sept 2015: tools::texi2dvi() tries texify.exe if it is not found.
  • (Windows only) Shortcuts for printing and saving have been added to menus in Rgui.exe. (Request of PR#16572.)
  • loess(..., iterTrace=TRUE) now provides diagnostics for robustness iterations, and the print() method for summary(<loess>) shows slightly more.
  • The included version of PCRE has been updated to 8.38, a bug-fix release.
  • View() now displays nested data frames in a more friendly way. (Request with patch in PR#15915.)

BUG FIXES

  • regexpr(pat, x, perl = TRUE) with Python-style named capture did not work correctly when x contained NA strings. (PR#16484)
  • The description of dataset ToothGrowth has been improved/corrected. (PR#15953)
  • model.tables(type = "means") and hence TukeyHSD() now support "aov" fits without an intercept term. (PR#16437)
  • close() now reports the status of a pipe() connection opened with an explicit open argument. (PR#16481)
  • Coercing a list without names to a data frame is faster if the elements are very long. (PR#16467)
  • (Unix-only) Under some rare circumstances piping the output from Rscript or R -f could result in attempting to close the input file twice, possibly crashing the process. (PR#16500)
  • (Windows) Sys.info() was out of step with win.version() and did not report Windows 8.
  • topenv(baseenv()) returns baseenv() again as in R 3.1.0 and earlier. This also fixes compilerJIT(3) when used in ‘.Rprofile’.
  • detach()ing the methods package keeps .isMethodsDispatchOn() true, as long as the methods namespace is not unloaded.
  • Removed some spurious warnings from configure about the preprocessor not finding header files. (PR#15989)
  • rchisq(*, df=0, ncp=0) now returns 0 instead of NaN, and dchisq(*, df=0, ncp=*) also no longer returns NaN in limit cases (where the limit is unique). (PR#16521)
  • pchisq(*, df=0, ncp > 0, log.p=TRUE) no longer underflows (for ncp > ~60).
  • nchar(x, "w") returned -1 for characters it did not know about (e.g. zero-width spaces): it now assumes 1. It now knows about most zero-width characters and a few more double-width characters.
  • Help for which.min() is now more precise about behavior with logical arguments. (PR#16532)
  • The print width of character strings marked as "latin1" or "bytes" was in some cases computed incorrectly.
  • abbreviate() did not give names to the return value if minlength was zero, unlike when it was positive.
  • (Windows only) dir.create() did not always warn when it failed to create a directory. (PR#16537)
  • When operating in a non-UTF-8 multibyte locale (e.g. an East Asian locale on Windows), grep() and related functions did not handle UTF-8 strings properly. (PR#16264)
  • read.dcf() sometimes misread lines longer than 8191 characters. (Reported by Hervé Pagès with a patch.)
  • within(df, ..) no longer drops columns whose name start with a ".".
  • The built-in HTTP server converted entire Content-Type to lowercase including parameters which can cause issues for multi-part form boundaries (PR#16541).
  • Modifying slots of S4 objects could fail when the methods package was not attached. (PR#16545)
  • splineDesign(*, outer.ok=TRUE) (splines) is better now (PR#16549), and interpSpline() now allows sparse=TRUE for speedup with non-small sizes.
  • If the expression in the traceback was too long, traceback() did not report the source line number. (Patch by Kirill Müller.)
  • The browser did not truncate the display of the function when exiting with options("deparse.max.lines") set. (PR#16581)
  • When bs(*, Boundary.knots=) had boundary knots inside the data range, extrapolation was somewhat off. (Patch by Trevor Hastie.)
  • var() and hence sd() warn about factor arguments which are deprecated now. (PR#16564)
  • loess(*, weights = *) stored wrong weights and hence gave slightly wrong predictions for newdata. (PR#16587)
  • aperm(a, *) now preserves names(dim(a)).
  • poly(x, ..) now works when either raw=TRUE or coef is specified. (PR#16597)
  • data(package=*) is more careful in determining the path.
  • prettyNum(*, decimal.mark, big.mark): fixed bug introduced when fixing PR#16411.

INSTALLATION and INCLUDED SOFTWARE

  • The included configuration code for libintl has been updated to that from gettext version 0.19.5.1 — this should only affect how an external library is detected (and the only known instance is under OpenBSD). (Wish of PR#16464.)
  • configure has a new argument –disable-java to disable the checks for Java.
  • The configure default for MAIN_LDFLAGS has been changed for the FreeBSD, NetBSD and Hurd OSes to one more likely to work with compilers other than gcc(FreeBSD 10 defaults to clang).
  • configure now supports the OpenMP flags -fopenmp=libomp (clang) and -qopenmp (Intel C).
  • Various macros can be set to override the default behaviour of configure when detecting OpenMP: see file ‘config.site’.
  • Source installation on Windows has been modified to allow for MiKTeX installations without texi2dvi.exe. See file ‘MkRules.dist’.

“Why do people contribute to the R?” – concolusions from a new PNAS article

tl;dr: People contribute to R for various reasons, which evolves with time. The main reasons appear to be: “fun coding”, personal commitment to the community, interaction with like-minded and/or important people  – leading to higher self-esteem, future job opportunities, a chance to express oneself and enjoyable social inclusion.

From the abstract

One of the cornerstones of the R system for statistical computing is the multitude of packages contributed by numerous package authors. This amount of packages makes an extremely broad range of statistical techniques and other quantitative methods freely available. Thus far, no empirical study has investigated psychological factors that drive authors to participate in the R project. This article presents a study of R package authors, collecting data on different types of participation (number of packages, participation in mailing lists, participation in conferences), three psychological scales (types of motivation, psychological values, and work design characteristics), and various socio-demographic factors. The data are analyzed using item response models and subsequent generalized linear models, showing that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Other factors are found to have less impact or influence only specific aspects of participation.

Summary of results

R developers, statisticians, and psychologists from Harvard University, University of Vienna, WU Vienna University of Economics, and University of Innsbruck empirically studied psychosocial drivers of participation of R package authors. Through an online survey they collected data from 1,448 package authors. The questionnaire included psychometric scales (types of motivation, psychological values, work design), sociodemografic variables related to the work on R, and three participation measures (number of packages, participation in mailing lists, participation in conferences).

cranpnas-dia

The data were analyzed using item response models and subsequently generalized linear models (logistic regressions, negative-binomial regression) with SIMEX corrected parameters.

The analysis reveals that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Hybrid motivation acknowledges that motivation is a complex continuum of intrinsic, extrinsic, and internalized extrinsic motives.
Motives evolve over time, as task characteristics shift from need-driven problem solving to mundane maintenance tasks within the R community.
For instance, motivation can evolve from pure “fun coding” towards a personal commitment with associated higher responsibilities within the community. The community itself provides a social work environment with high degrees of interaction, two facets of which are strong motivators. First, interaction with persons perceived as important increases one’s own reputation (self-esteem, future job opportunities, etc.) Second, interaction with alike minded persons (i.e., interested in solving statistical problems) creates opportunities to express oneself and enjoy social inclusion.

The findings do not substantiate the commonly held perception that people develop packages out of purely altruistic motives. It is also notable that in most cases package development is undertaken as part of an individual’s research, which is paid by an (academic) institution, rather than uncompensated developments that cut into leisure time.

Full paper (behind PNAS’s paywall for now) is available here:

Mair, P., Hofmann, E., Gruber, K., Hatzinger, R., Zeileis, A., and Hornik, K. (2015). Motivation, values, and work design as drivers of participation in the R
open source project for statistical computing. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14788-14792

 

R 3.2.2 is released

R 3.2.2 (codename “Fire Safety”) was released last weekend. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

SOME OF THE CHANGES

I personally found two things particularly interesting in this release:

  1. setInternet2(TRUE) is now the default for windows (which will save people from getting “Error in file(con, “r”)” when using the installr package)
  2. The dendrogram method of labels() is much more efficient for large dendrograms since it now uses rapply(). This is expected to speedup various functions in the dendextend R package (a package for visualizing, adjusting, and comparing dendrograms, which heavily relies on labels.dendrogram).

Also, David Smith (from Revolution/Microsoft) highlighted in his post several of the updates in R 3.2.2 he found interesting – mentioning how the new default for accessing the web with R will rely on the HTTPS protocol, and of improving the accuracy in the extreme tails of the t and hypergeometric distributions.

Upgrading to R 3.2.2 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code in Rgui:

install.packages("installr") # install 
setInternet2(TRUE)
installr::updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the installr package.

I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page.

CHANGES IN R 3.2.2:

SIGNIFICANT USER-VISIBLE CHANGES

  • It is now easier to use secure downloads from https:// URLs on builds which support them: no longer do non-default options need to be selected to do so. In particular, packages can be installed from repositories which offer https:// URLs, and those listed by setRepositories()now do so (for some of their mirrors).Support for https:// URLs is available on Windows, and on other platforms if support forlibcurl was compiled in and if that supports the https protocol (system installations can be expected to do). So https:// support can be expected except on rather old OSes (an example being OS X ‘Snow Leopard’, where a non-system version of libcurl can be used).(Windows only) The default method for accessing URLs via download.file() and url() has been changed to be "wininet" using Windows API calls. This changes the way proxies need to be set and security settings made: there have been some reports of sites being inaccessible under the new default method (but the previous methods remain available).

NEW FEATURES

Continue reading “R 3.2.2 is released”

Slides from my JSM 2015 talk on dendextend

If you happen to be at the JSM 2015 conference this week, then this Monday, at 2pm, I will give a talk on the dendextend R package  (in the session “Advances in Graphical Frameworks and Methods Part 1“) – feel free to drop by and say hi.

Here are my slides for the intended talk:

 

p.s.: Yes – this presentation is very similar, although not identical, to the one I gave at useR2015. For example, I mention the new bioinformatics paper on dendextend.

dendextend version 1.0.1 + useR!2015 presentation

When using the dendextend package in your work, please cite it using:

Tal Galili (2015). dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics. doi:10.1093/bioinformatics/btv428

My R package dendextend (version 1.0.1) is now on CRAN!

The dendextend package Offers a set of functions for extending dendrogram objects in R, letting you visualize and compare trees of hierarchical clusterings. With it you can (1) Adjust a tree’s graphical parameters – the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different dendrograms to one another.

The previous release of dendextend (0.18.3) was half a year ago, and this version includes many new features and functions.

To help you discover how dendextend can solve your dendrogram/hierarchical-clustering issues, you may consult one of the following vignettes:

Here is an example figure from the first vignette (analyzing the Iris dataset)

iris_heatmap_dend

 

This week, at useR!2015, I will give a talk on the package. This will offer a quick example, and a step-by-step example of some of the most basic/useful functions of the package. Here are the slides:

 

Lastly, I would like to mention the new d3heatmap package for interactive heat maps. This package is by Joe Cheng from Rstudio, and integrates well with dendrograms in general and dendextend in particular (thanks to some lovely github-commit-discussion between Joe and I). You are invited to see lively examples of the package in the post at the RStudio blog. Here is just one quick example:

d3heatmap(nba_players, colors = “Blues”, scale = “col”, dendrogram = “row”, k_row = 3)

d3heatmap

R 3.2.1 is released

R 3.2.1 (codename “World-Famous Astronaut”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.2.1 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code in Rgui:

install.packages("installr") # install 
installr::updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). There is also a step by step tutorial (with screenshots) on how to upgrade R on Windows, using the installr package.

I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to open an issue in the github page.

CHANGES IN R 3.2.1:

 

NEW FEATURES

  • utf8ToInt() now checks that its input is valid UTF-8 and returns NA if it is not.
  • install.packages() now allows type = "both" with repos = NULL if it can infer the type of file.
  • nchar(x, *) and nzchar(x) gain a new argument keepNA which governs how the result for NAs in x is determined. For the R 3.2.x series, the default remains FALSE which is fully back compatible. From R 3.3.0, the default will change to keepNA = NA and you are advised to consider this for code portability.
  • news() more flexibly extracts dates from package ‘NEWS.Rd’ files.
  • lengths(x) now also works (trivially) for atomic x and hence can be used more generally as an efficient replacement of sapply(x, length) and similar.
  • The included version of PCRE has been updated to 8.37, a bug-fix release.
  • diag() no longer duplicates a matrix when extracting its diagonal.
  • as.character.srcref() gains an argument to allow characters corresponding to a range of source references to be extracted.

BUG FIXES

Continue reading “R 3.2.1 is released”

A step by step (screenshots) tutorial for upgrading R on Windows

tl;dr

If you are running R on Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) # install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.). just press “next”, “OK”, and “Yes” on everything…

A GUI interface to updating R on Windows

Starting from installr version 0.15.0, the upgradingprocess can be done with a click-on-menus GUI interface. Here is how to use it.

Continue reading “A step by step (screenshots) tutorial for upgrading R on Windows”

R 3.2.0 is released (+ using the installr package to upgrade in Windows OS)

R 3.2.0 (codename “Full of Ingredients”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.2.0 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to leave a comment below.

CHANGES IN R 3.2.0:

As always, David smith mentioned in his post some of the main changes, writing how many of the changes in this release have happened behind the scenes to improve R’s engine for performance and reliability. These include:

  • A number of fixes proposed by Radford Neal, bringing some of the performance improvements of pqR to R while maintaining backwards compatibility.
  • more progress in handling big in-memory data objects (for example, you can now cbind/rbind matrices with more than 2 billion elements).
  • some significant updates to R’s byte compiler with new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.
  • the package-checking system now does a more thorough job of making sure contributed packages comply with CRAN policies.

And here is also the full list of new features, bug fixes, etc:

Continue reading “R 3.2.0 is released (+ using the installr package to upgrade in Windows OS)”

Israel’s 2015 election polls’ analysis with Shiny + ggplot2

(This is a guest post by my friend Yoni Sidi, a PhD candidate in statistics at the Hebrew University)

Background

The Israeli elections are coming up this Tuesday, 17/3/2015 (i.e.: tomorrow!). They are a bit more complicated than your average US presidential race. The elections in Israel are based on nationwide proportional representation. The electoral threshold is 3.25% and the number of seats (or mandates) out of a total of 120 is proportional to the number of votes it recieves, so the threshold roughly translates to at least four mandates. The Israeli system is a multi-party system and is based on coalition governments. Multi-party is putting it mildly, there are 11 that have a chance (and are expected) to pass the mandate threshold.
There are two major parties, Hamachane Hazioni (Left Wing) and the Likud (Right Wing), that are hoping to garner between 16%-25% of the votes, 20-30 mandates. The main winners though are the medium size parties that recomend to the President who they think has the best chance to construct the next government, so yes there is a good possibility that the general elections winner will not be one constructing the coalition. Making the actual winners the parties that create the biggest coalition which exceeds 60 mandates.
An abundance of polling has been continually published during the run up and the variaety of pollsters and publishers is hard to keep track of as a casual voter trying to gauge the temperature of the political landscape. I came across a great realtime database by Project 61 on google docs of past and present polling result information and decided that it was a great opportunity to learn the Shiny library of RStudio and create an app that I can check current and past results. So after I figured out how to connect google docs to R, I created a self updating app that became a nice place to keep track of polling every day, check trends and distributions using interactive ggplot2 graphs and simulate coalition outcomes.
Please note that as of Friday (March 13th), until election day (March 17th), it is forbidden to perform new polls in Israel, hence the data presented here cannot allow for an up-to-date inference about the expected results of the election. This post is for educational purposes.

Running the election polls Shiny app on your computer

The github repo is available here.

#changing locale to run on Windows
if (Sys.info()[1] == "Windows") Sys.setlocale("LC_ALL","Hebrew_Israel.1255") 
 
#check to see if libraries need to be installed
libs <- c("shiny","shinyAce","httr","XML","stringr","ggplot2","scales","plyr","reshape2","dplyr")
x <- sapply(libs,function(x)if(!require(x,character.only = T)) install.packages(x))
rm(x,libs)
 
#run App
shiny::runGitHub("Elections","yonicd",subdir="shiny")
 
#reset to original locale on Windows
if (Sys.info()[1] == "Windows") Sys.setlocale("LC_ALL")

 

Usage Instructions:

  1. Current Polling
  2. Election Analyis
  3. Mandate Simulator and Coalition Whiteboard
  4. Polling Database

Current Polling

  • The latest polling day results published in the media and the prediction made using the Project 61 weighting schemes. The parties are stacked into blocks to see which block has best chance to create a coalition.

LastDayPlot

The Project 61 prediction is based past pollster error deriving weights from the 2003,2006,2009 and 2013 elections, dependant on days to elections and parties. In their site there is an extensive analysis on pollster bias towards certain parties and party blocks.

Election Analysis

  • An interactive polling analysis layout where the user can filter elections, parties, publishers and pollster, dates and create different types of plots using any variable as the x and y axis.
  • The default layer is the 60 day trend (estimated with loess smoother) of mandates published by each pollster by party

pad_screen_grab

The user can choose to include in the plots Elections (2003,2006,2009,2013,2015) and the subsequent filters are populated with the relevant parties, pollsters and publishers relevant to the chosen elections. Next there is a slider to choose the days before the election you want to view in the plot. This was used instead of a calendar to make a uniform timeline when comparing across elections.

In addition the plot itself is a ggplot thus the options above the graph give the user control on nearly all the options to build a plot. The user can choose from the following variables:

Time Party Results Poll
Election Party Mandates Publisher
DaysLeft Ideology (5 Party Blocks) Mandate.Group Pollster
Date Ideology.Group (2 Party Blocks) Results
year Attribute (Party History) (Pollster) Error
month
week

To define the following plot attributes:

Plot Type Axes Grouping Plot Facets
Point X axis variable Split Y by colors using a different variable Row Facet
Bar Discrete/Continuous Column Facet
Line Rotation of X tick labels
Step Y axis variable
Boxplot
Density
  • Create Facets to display subsets of the data in different panels (two more variables to cut data) there are two type of facets to choose from
    • Wrap: Wrap 1d ribbon of panels into 2d
    • Grid: Layout panels in a grid (matrix)

An example of filtering pollsters to compare different tendencies for each party in the 2015 elections:

ElectionPlot_pollster_trend

An example of comparing distribution mandates per party in the last two months of polling

boxplot_month

An example of comparing distribution of pollster errors across elections (up to 10 days prior end of polling), by splitting the parties into five groups compared to previous election: old party,new party, combined (combination of two or more old parties), new.split (new party created from a split of a party from last election), old.split (old party that was a left from the split).

ElectionPlot_longitudinal

 

As we can see the pollster do not get a good indication of new,new.split or combined parties, which could be a problem this election since there are: 3 combined, 2 new splits.

attribute_compare

  • If you are an R user and know ggplot there is an additional editor console,below the plot, where you can create advanced plots freehand, just add to the final object from the GUI called p and the data.frame is x, eg p+geom_point(). Just notice that all aesthetics must be given they are not defined in the original ggplot() definition. It is also possible to use any library you want just add it to the top of the code, the end object must be a ggplot.

pad_screen_grab_ace

 

#new layer
p+geom_smooth(aes(x=DaysLeft,y=Mandates,fill=Party.En))+
scale_x_reverse()+scale_fill_discrete(name="Party")
  • You can also remove the original layer if you want using the function remove_geom(ggplot_object,geom_layer), eg p=p+remove_geom(p,“point”) will remove the geom_point layer in the original graph

pad_screen_grab_ace_remove_geom

p=remove_geom(p,"point") #blank ggplot with facets in place
#new layer
p+geom_smooth(aes(x=DaysLeft,y=Mandates,fill=Party.En))+
scale_x_reverse()+scale_fill_discrete(name="Party")
  • Finally the plots can be viewed in English or Hebrew, and can be downloaded to you local computer using the download button.

Mandate Simulator and Coalition Whiteboard

  • A bootstrap simulation is run on Polling results from up to 10 of the latest polls using the sampling error as the uncertainty of each mandate published. Taking into account mandate surplus agreements using the Hagenbach-Bischoff quota method and the mandate threshold limit (in this election it is 4 mandates), calculating the simulated final tally of mandates. The distributions are plotted per party and the location of the median published results in the media.
  • The user can choose how many polls to take into account, up to last 10 polls, and how big a simulation they want to run: 50,100,500,1000 random polling results per each party and poll.

sim_screen_grab

  • Once the simulator is complete you can create coalitions based on either the simulated distribution or actual published polls and see who can pass 60 mandates. Choose the coalition parties and the opposition parties from dropdown lists. (Yes the ones chosen are nonsensical on purpose…)

coal_screen_grab

Polling Database

  • All raw data used in the application can be viewed and filtered in a datatable.

R 3.1.3 is released (+ easy upgrading for Windows users with the installr package)

R 3.1.3 (codename “Smooth Sidewalk”) was released today. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.1.3 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

I try to keep the installr package updated and useful, so if you have any suggestions or remarks on the package – you are invited to leave a comment below.

CHANGES IN R 3.1.3:

As always, David smith mentioned in his post some of the main changes, writing:

As has become usual in March, this release is primarily for minor bugs and improvements in R, as the development switches focus to the next major release (R 3.2.0 is expected on April 16). Improvements include: ability to download files greater than 2GB on 32-bit builds; better handling of complex (imaginary) numbers for several functions; improved command completion in the Windows GUI; and improved performance when using S4 classes. (See the complete list here.)

And here is also the full list:

Continue reading “R 3.1.3 is released (+ easy upgrading for Windows users with the installr package)”