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)

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.

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

{stargazer} package for beautiful LaTeX tables from R statistical models output

stargazer is a new R package that creates LaTeX code for well-formatted regression tables, with multiple models side-by-side, as well as for summary statistics tables. It can also output the content of data frames directly into LaTeX. Compared to available alternatives, stargazer excels in three regards:  its ease of use, the large number of models it supports, and its beautiful aesthetics.

Ease of use

stargazer was designed with the user’s comfort in mind. The learning curve is very mild and all arguments are very intuitive, so that even a beginning user of R or LaTeX can quickly become familiar with the package’s many capabilities. The package is intelligent, and tries to minimize the amount of effort the user has to put into adjusting argument values. If stargazer is given a set of regression model objects, for instance, the package will create a side-by-side regression table. By contrast, if the user feeds it a data frame, stargazer will know that the user is most likely looking for a summary statistics table or – if the summary argument is set to false – wants to output the content of the data frame.

A quick reproducible example shows just how easy stargazer is to use. You can install stargazer from CRAN in the usual way:

install.packages("stargazer")
library(stargazer)

Continue reading {stargazer} package for beautiful LaTeX tables from R statistical models output

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

Comparing Shiny with gWidgetsWWW2.rapache

(A guest post by John Verzani)

A few days back the RStudio blog announced Shiny, a new product for easily creating interactive web applications (http://www.rstudio.com/shiny/). I wanted to compare this new framework to one I’ve worked on, gWidgetsWWW2.rapache – a version of the gWidgets API for use with Jeffrey Horner’s rapache module for the Apache web server (available at GitHub). The gWidgets API has a similar aim to make it easy for R users to create interactive applications.

I don’t want to worry here about deployment of apps, just the writing side. The shiny package uses websockets to transfer data back and forth from browser to server. Though this may cause issues with wider deployment, the industrious RStudio folks have a hosting program in beta for internet-wide deployment. For local deployment, no problems as far as I know – as long as you avoid older versions of internet explorer.

Now, Shiny seems well suited for applications where the user can parameterize a resulting graphic, so that was the point of comparison. Peter Dalgaard’s tcltk package ships with a classic demo tkdensity.R. I use that for inspiration below. That GUI allows the user a few selections to modify a density plot of a random sample.
Continue reading Comparing Shiny with gWidgetsWWW2.rapache

Speed up your R code using a just-in-time (JIT) compiler

This post is about speeding up your R code using the JIT (just in time) compilation capabilities offered by the new (well, now a year old) {compiler} package. Specifically, dealing with the practical difference between enableJIT and the cmpfun functions.

If you do not want to read much, you can just skip to the example part.

As always, I welcome any comments to this post, and hope to update it when future JIT solutions will come along.

Continue reading Speed up your R code using a just-in-time (JIT) compiler

Do more with dates and times in R with lubridate 1.1.0

This is a guest post by Garrett Grolemund (mentored by Hadley Wickham)

Lubridate is an R package that makes it easier to work with dates and times. The newest release of lubridate (v 1.1.0) comes with even more tools and some significant changes over past versions. Below is a concise tour of some of the things lubridate can do for you. At the end of this post, I list some of the differences between lubridate (v 0.2.4) and lubridate (v 1.1.0). If you are an old hand at lubridate, please read this section to avoid surprises!

Lubridate was created by Garrett Grolemund and Hadley Wickham.

Parsing dates and times

Getting R to agree that your data contains the dates and times you think it does can be a bit tricky. Lubridate simplifies that. Identify the order in which the year, month, and day appears in your dates. Now arrange “y”, “m”, and “d” in the same order. This is the name of the function in lubridate that will parse your dates. For example,

library(lubridate)
ymd("20110604"); mdy("06-04-2011"); dmy("04/06/2011")
## "2011-06-04 UTC"
## "2011-06-04 UTC"
## "2011-06-04 UTC"

Parsing functions automatically handle a wide variety of formats and separators, which simplifies the parsing process.

If your date includes time information, add h, m, and/or s to the name of the function. ymd_hms() is probably the most common date time format. To read the dates in with a certain time zone, supply the official name of that time zone in the tz argument.

arrive < - ymd_hms("2011-06-04 12:00:00", tz = "Pacific/Auckland")
## "2011-06-04 12:00:00 NZST"
leave <- ymd_hms("2011-08-10 14:00:00", tz = "Pacific/Auckland")
## "2011-08-10 14:00:00 NZST"

Setting and Extracting information

Extract information from date times with the functions second(), minute(), hour(), day(), wday(), yday(), week(), month(), year(), and tz(). You can also use each of these to set (i.e, change) the given information. Notice that this will alter the date time. wday() and month() have an optional label argument, which replaces their numeric output with the name of the weekday or month.

second(arrive)
## 0
second(arrive) < - 25
arrive
## "2011-06-04 12:00:25 NZST"
second(arrive) <- 0
wday(arrive)
## 7
wday(arrive, label = TRUE)
## Sat

Time Zones

There are two very useful things to do with dates and time zones. First, display the same moment in a different time zone. Second, create a new moment by combining a given clock time with a new time zone. These are accomplished by with_tz() and force_tz().

For example, I spent last summer researching in Auckland, New Zealand. I arranged to meet with my advisor, Hadley, over skype at 9:00 in the morning Auckland time. What time was that for Hadley who was back in Houston, TX?

meeting < - ymd_hms("2011-07-01 09:00:00", tz = "Pacific/Auckland")
## "2011-07-01 09:00:00 NZST"
with_tz(meeting, "America/Chicago")
## "2011-06-30 16:00:00 CDT"

So the meetings occurred at 4:00 Hadley’s time (and the day before no less). Of course, this was the same actual moment of time as 9:00 in New Zealand. It just appears to be a different day due to the curvature of the Earth.

What if Hadley made a mistake and signed on at 9:00 his time? What time would it then be my time?

mistake < - force_tz(meeting, "America/Chicago")
## "2011-07-01 09:00:00 CDT"
with_tz(mistake, "Pacific/Auckland")
## "2011-07-02 02:00:00 NZST"

His call would arrive at 2:00 am my time! Luckily he never did that.

Continue reading Do more with dates and times in R with lubridate 1.1.0

Printing nested tables in R – bridging between the {reshape} and {tables} packages

This post shows how to print a prettier nested pivot table, created using the {reshape} package (similar to what you would get with Microsoft Excel), so you could print it either in the R terminal or as a LaTeX table. This task is done by bridging between the cast_df object produced by the {reshape} package, and the tabular function introduced by the new {tables} package.

Here is an example of the type of output we wish to produce in the R terminal:

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       ozone       solar.r        wind         temp
 month mean  sd    mean    sd     mean   sd    mean  sd
 5     23.62 22.22 181.3   115.08 11.623 3.531 65.55 6.855
 6     29.44 18.21 190.2    92.88 10.267 3.769 79.10 6.599
 7     59.12 31.64 216.5    80.57  8.942 3.036 83.90 4.316
 8     59.96 39.68 171.9    76.83  8.794 3.226 83.97 6.585
 9     31.45 24.14 167.4    79.12 10.180 3.461 76.90 8.356

Or in a latex document:

Motivation: creating pretty nested tables

In a recent post we learned how to use the {reshape} package (by Hadley Wickham) in order to aggregate and reshape data (in R) using the melt and cast functions.

The cast function is wonderful but it has one problem – the format of the output. As opposed to a pivot table in (for example) MS excel, the output of a nested table created by cast is very “flat”. That is, there is only one row for the header, and only one column for the row names. So for both the R terminal, or an Sweave document, when we deal with a more complex reshaping/aggregating, the result is not something you would be proud to send to a journal.

The opportunity: the {tables} package

The good news is that Duncan Murdoch have recently released a new package to CRAN called {tables}. The {tables} package can compute and display complex tables of summary statistics and turn them into nice looking tables in Sweave (LaTeX) documents. For using the full power of this package, you are invited to read through its detailed (and well written) 23 pages Vignette. However, some of us might have preferred to keep using the syntax of the {reshape} package, while also benefiting from the great formatting that is offered by the new {tables} package. For this purpose, I devised a function that bridges between cast_df (from {reshape}) and the tabular function (from {tables}).

The bridge: between the {tables} and the {reshape} packages

The code for the function is available on my github (link: tabular.cast_df.r on github) and it seems to works fine as far as I can see (though I wouldn’t run it on larger data files since it relies on melting a cast_df object.)

Here is an example for how to load and use the function:

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######################
# Loading the functions
######################
# Making sure we can source code from github
source("http://www.r-statistics.com/wp-content/uploads/2012/01/source_https.r.txt")
 
# Reading in the function for using tabular on a cast_df object:
source_https("https://raw.github.com/talgalili/R-code-snippets/master/tabular.cast_df.r")
 
 
 
######################
# example:
######################
 
############
# Loading and preparing some data
require(reshape)
names(airquality) <- tolower(names(airquality))
airquality2 <- airquality
airquality2$temp2 <- ifelse(airquality2$temp > median(airquality2$temp), "hot", "cold")
aqm <- melt(airquality2, id=c("month", "day","temp2"), na.rm=TRUE)
colnames(aqm)[4] <- "variable2"	# because otherwise the function is having problem when relying on the melt function of the cast object
head(aqm,3)
#  month day temp2 variable2 value
#1     5   1  cold     ozone    41
#2     5   2  cold     ozone    36
#3     5   3  cold     ozone    12
 
############
# Running the example:
tabular.cast_df(cast(aqm, month ~ variable2, c(mean,sd)))
tabular(cast(aqm, month ~ variable2, c(mean,sd))) # notice how we turned tabular to be an S3 method that can deal with a cast_df object
Hmisc::latex(tabular(cast(aqm, month ~ variable2, c(mean,sd)))) # this is what we would have used for an Sweave document

And here are the results in the terminal:

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>
> tabular.cast_df(cast(aqm, month ~ variable2, c(mean,sd)))
 
       ozone       solar.r        wind         temp
 month mean  sd    mean    sd     mean   sd    mean  sd
 5     23.62 22.22 181.3   115.08 11.623 3.531 65.55 6.855
 6     29.44 18.21 190.2    92.88 10.267 3.769 79.10 6.599
 7     59.12 31.64 216.5    80.57  8.942 3.036 83.90 4.316
 8     59.96 39.68 171.9    76.83  8.794 3.226 83.97 6.585
 9     31.45 24.14 167.4    79.12 10.180 3.461 76.90 8.356
> tabular(cast(aqm, month ~ variable2, c(mean,sd))) # notice how we turned tabular to be an S3 method that can deal with a cast_df object
 
       ozone       solar.r        wind         temp
 month mean  sd    mean    sd     mean   sd    mean  sd
 5     23.62 22.22 181.3   115.08 11.623 3.531 65.55 6.855
 6     29.44 18.21 190.2    92.88 10.267 3.769 79.10 6.599
 7     59.12 31.64 216.5    80.57  8.942 3.036 83.90 4.316
 8     59.96 39.68 171.9    76.83  8.794 3.226 83.97 6.585
 9     31.45 24.14 167.4    79.12 10.180 3.461 76.90 8.356

And in an Sweave document:

Here is an example for the Rnw file that produces the above table:
cast_df to tabular.Rnw

I will finish with saying that the tabular function offers more flexibility then the one offered by the function I provided. If you find any bugs or have suggestions of improvement, you are invited to leave a comment here or inside the code on github.

(Link-tip goes to Tony Breyal for putting together a solution for sourcing r code from github.)

Interactive Graphics with the iplots Package (from “R in Action”)

The followings introductory post is intended for new users of R.  It deals with interactive visualization using R through the iplots package.

This is a guest article by Dr. Robert I. Kabacoff, the founder of (one of) the first online R tutorials websites: Quick-R. Kabacoff has recently published the book ”R in Action“, providing a detailed walk-through for the R language based on various examples for illustrating R’s features (data manipulation, statistical methods, graphics, and so on…). In previous guest posts by Kabacoff we introduced data.frame objects in R and dealt with the Aggregation and Restructuring of data (using base R functions and the reshape package).

For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout.

Let us now talk about Interactive Graphics with the iplots Package:

Interactive Graphics with the iplots Package

The base installation of R provides limited interactivity with graphs. You can modify graphs by issuing additional program statements, but there’s little that you can do to modify them or gather new information from them using the mouse. However, there are contributed packages that greatly enhance your ability to interact with the graphs you create—playwith, latticist, iplots, and rggobi. In this article, we’ll focus on functions provided by the iplots package. Be sure to install it before first use.

While playwith and latticist allow you to interact with a single graph, the iplots package takes interaction in a different direction. This package provides interactive mosaic plots, bar plots, box plots, parallel plots, scatter plots, and histograms that can be linked together and color brushed. This means that you can select and identify observations using the mouse, and highlighting observations in one graph will automatically highlight the same observations in all other open graphs. You can also use the mouse to obtain information about graphic objects such as points, bars, lines, and box plots.

The iplots package is implemented through Java and the primary functions are listed in table 1.

Table 1 iplot functions

Function

Description

ibar() Interactive bar chart
ibox() Interactive box plot
ihist() Interactive histogram
imap() Interactive map
imosaic() Interactive mosaic plot
ipcp() Interactive parallel coordinates plot
iplot() Interactive scatter plot

To understand how iplots works, execute the code provided in listing 1.

Listing 1 iplots demonstration

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library(iplots)
attach(mtcars)
cylinders <- factor(cyl)
gears <- factor(gear)
transmission <- factor(am)
ihist(mpg)
ibar(gears)
iplot(mpg, wt)
ibox(mtcars[c("mpg", "wt", "qsec", "disp", "hp")])
ipcp(mtcars[c("mpg", "wt", "qsec", "disp", "hp")])
imosaic(transmission, cylinders)
detach(mtcars)

Six windows containing graphs will open. Rearrange them on the desktop so that each is visible (each can be resized if necessary). A portion of the display is provided in figure 1.

Figure 1 An iplots demonstration created by listing 1. Only four of the six windows are displayed to save room. In these graphs, the user has clicked on the three-gear bar in the bar chart window.

Now try the following:

  • Click on the three-gear bar in the Barchart (gears) window. The bar will turn red. In addition, all cars with three-gear engines will be highlighted in the other graph windows.
  • Mouse down and drag to select a rectangular region of points in the Scatter plot (wt vs mpg) window. These points will be highlighted and the corresponding observations in every other graph window will also turn red.
  • Hold down the Ctrl key and move the mouse pointer over a point, bar, box plot, or line in one of the graphs. Details about that object will appear in a pop-up window.
  • Right-click on any object and note the options that are offered in the context menu. For example, you can right-click on the Boxplot (mpg) window and change the graph to a parallel coordinates plot (PCP).
  • You can drag to select more than one object (point, bar, and so on) or use Shift-click to select noncontiguous objects. Try selecting both the three- and five-gear bars in the Barchart (gears) window.

The functions in the iplots package allow you to explore the variable distributions and relationships among variables in subgroups of observations that you select interactively. This can provide insights that would be difficult and time-consuming to obtain in other ways. For more information on the iplots package, visit the project website at http://rosuda.org/iplots/.

Summary

In this article, we explored one of the several packages for dynamically interacting with graphs, iplots. This package allows you to interact directly with data in graphs, leading to a greater intimacy with your data and expanded opportunities for developing insights.


This article first appeared as chapter 16.4.4 from the “R in action book, and is published with permission from Manning publishing house.  Other books in this serious which you might be interested in are (see the beginning of this post for a discount code):