Update (2019-08-17): to see a good solution for this problem, please go to this link. The solution in the post is old and while it still works, it is better to use the newer methods from the link.
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.
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 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: