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

Update (2018-08-17): to see a good solution for this problem, please go to . The solution in the post is old and while it still works, it is better to use the newer methods from the .


The problem: producing a Word (.docx) file of a statistical report created in R, with as little overhead as possible.
The solution牵手常德棋牌官方下载: combining R+knitr+rmarkdown+pander+pandoc (it is easier than it is spelled).

牵手常德棋牌官方下载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)”

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

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”

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

牵手常德棋牌官方下载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, […]

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:

1
2
3
4
5
6
7
       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

牵手常德棋牌官方下载Or in a latex document:

Motivation: creating pretty nested tables

In a recent post we learned how to use the {reshape} package牵手常德棋牌官方下载 (by ) 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

牵手常德棋牌官方下载The good news is that Duncan Murdoch have released a new package to CRAN called . 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 . 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

牵手常德棋牌官方下载The code for the function is available on my github (link: ) 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:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
######################
# Loading the functions
######################
# Making sure we can source code from github
source("http://oi02img.icu/wp-content/uploads/2012/01/source_https.r.txt")
 
# Reading in the function for using tabular on a cast_df object:
source_https("http://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

牵手常德棋牌官方下载And here are the results in the terminal:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
>
> 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

牵手常德棋牌官方下载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 .

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