Author: Dane Van Domelen vandomed@gmail.com
Date: 2021-08-01

# Summary Tables with ‘tab’

#### Dane Van Domelen
vandomed@gmail.com

#### 2021-08-01

## Installation

You can install and load tab from GitHub via the following code:

    devtools::install_github("vandomed/tab")
    library("tab")

## Functions

The main purpose of tab is to create neatly formatted summary tables for papers and presentations. The following functions are included:

- `glm_v` prints a GLM summary table to the RStudio Viewer
- `tabglm` summarizes generalized linear models (GLM’s) fit via `glm` or `survey::svyglm`
- `tabgee` summarizes generalized estimating equation models (GEE’s) fit via `gee::gee`
- `tabcoxph` summarizes Cox Proportional Hazards models fit via `survival::coxph` or `survey::svycoxph`
- `tabmulti` compares variables across two or more groups, e.g. to create a “Table 1”
- `tabmulti.svy` does the same thing as `tabmulti` but for complex survey data

## Regression summaries with just 2 extra keystrokes

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:

    glm_v(
      death_1yr ~ poly(Age, 2, raw = TRUE) + Sex * BMI,
      data = tabdata,
      family = binomial
    )

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.

## Summary tables for continuous and categorical variables

You can use `tabmulti` to summarize variables across two or more groups, using a formula interface. Here’s an example:

    tabmulti(Age + Sex + Race + BMI ~ Group, data = tabdata)

## Compatibility with Markdown/Knitr

The functions all return `kable` objects, so they should work perfectly well in R Markdown and knitr documents.

## References

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.