You can install and load tab from GitHub via the following code:
The main purpose of tab is to create neatly formatted summary tables for papers and presentations. The following functions are included:
glm_vprints a GLM summary table to the RStudio Viewer
tabglmsummarizes generalized linear models (GLM’s) fit via
tabgeesummarizes generalized estimating equation models (GEE’s) fit via
tabcoxphsummarizes Cox Proportional Hazards models fit via
tabmulticompares variables across two or more groups, e.g. to create a “Table 1”
tabmulti.svydoes the same thing as
tabmultibut for complex survey data
To summarize a fitted generalized linear model, simply call
glm_v as you would
glm. The result will be a formatted summary table printed to the RStudio Viewer. Here’s an example for logistic regression:
From here, you can “snip” the summary table and save it as a figure (as I did for this README) or copy directly from the Viewer and paste outside of R.
For more flexibility, see
tabglm. That function lets you control things like what columns to present, how categorical predictors are presented, and so on.
You can use
tabmulti to summarize variables across two or more groups, using a formula interface. Here’s an example:
The functions all return
kable objects, so they should work perfectly well in R Markdown and knitr documents.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.
———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Chapman; Hall/CRC.
———. 2021. Knitr: A General-Purpose Package for Dynamic Report Generation in R.