tableone

R package to create "Table 1", description of baseline characteristics with or without propensity score weighting

https://github.com/kaz-yos/tableone

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R package to create "Table 1", description of baseline characteristics with or without propensity score weighting

README.Rmd

---
output: github_document
---

```{r, message = FALSE, tidy = FALSE, echo = F}
## knitr configuration: https://yihui.name/knitr/options#chunk_options
library(knitr)
showMessage <- FALSE
showWarning <- TRUE
set_alias(w = "fig.width", h = "fig.height", res = "results")
opts_chunk$set(comment = "##", error= TRUE, warning = showWarning, message = showMessage,
               tidy = FALSE, cache = FALSE, echo = TRUE,
               fig.width = 7, fig.height = 7,
               fig.path = "man/figures")
## for rgl
## knit_hooks$set(rgl = hook_rgl, webgl = hook_webgl)
## for animation
opts_knit$set(animation.fun = hook_ffmpeg_html)
## R configuration
options(width = 116, scipen = 5)
```

# tableone

[![R-CMD-check](https://github.com/kaz-yos/tableone/workflows/R-CMD-check/badge.svg)](https://github.com/kaz-yos/tableone/actions)
[![](https://www.r-pkg.org/badges/version/tableone)](https://www.r-pkg.org/pkg/tableone)
[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/tableone)](https://www.r-pkg.org/pkg/tableone)

**An R package to create "Table 1", description of baseline characteristics**

Creates "Table 1", i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the survey package.

tableone was inspired by descriptive statistics functions in Deducer , a Java-based GUI package by Ian Fellows. This package does not require GUI or Java, and intended for command-line users.


# tableone in action

![screencast](man/figures/tableone.gif "screencast")

The code being executed can be found in the introduction vignette.


# tableone code example

In this table, continuous and categorical variables can be placed in any order. The p-valeus are from exact tests for pre-specified variables. For nonnormal variables, it shows median and IQR instead of mean and SD, and p-values are from nonparametric tests. Numerically coded categorical variables can be transformed on the fly with factorVars. SMD stands for standardized mean differences. For weighted data, first created a svydesign object, and use the svyCreateTableOne() function. Most other options remain the same.

```{r}
## Load package
library(tableone)
## Load data
data(pbc, package = "survival")
# drop ID from variable list
vars <- names(pbc)[-1]
## Create Table 1 stratified by trt (can add more stratifying variables)
tableOne <- CreateTableOne(vars = vars, strata = c("trt"), data = pbc,
                            factorVars = c("status","edema","stage"))
## Specifying nonnormal variables will show the variables appropriately,
## and show nonparametric test p-values. Specify variables in the exact
## argument to obtain the exact test p-values.
print(tableOne, nonnormal = c("bili","chol","copper","alk.phos","trig"),
      exact = c("status","stage"), smd = TRUE,
      formatOptions = list(big.mark = ","))
```


# Installation

This version of tableone package for R is developmetal, and may not be available from the CRAN. You can install it using one of the following way.

**Direct installation from github**

You first need to install the devtools package to do the following. You can choose from the latest stable version and the latest development version.

```{r, eval = FALSE}
## Install devtools (if you do not have it already)
install.packages("devtools")
## Install directly from github (develop branch)
devtools::install_github(repo = "kaz-yos/tableone", ref = "develop")
```

Using devtools may requires some preparation, please see the following link for information.

https://www.rstudio.com/projects/devtools/


# Contributors

I would like to thank all the contributors!

- Alexander Bartel [ndevln](https://github.com/ndevln)
- Jonathan J Chipman [chipmanj](https://github.com/chipmanj)
- Justin Bohn [jmb01](https://github.com/jmb01)
- Lucy D'Agostino McGowan [LucyMcGowan](https://github.com/LucyMcGowan)
- Malcolm Barrett [malcolmbarrett](https://github.com/malcolmbarrett)
- Rune Haubo B Christensen [runehaubo](https://github.com/runehaubo)
- [gbouzill](https://github.com/gbouzill)


# Similar or complementary projects

There are multiple similar or complementary projects of interest.

- DescTools: Tools for Descriptive Statistics. https://cran.r-project.org/web/packages/DescTools/index.html
- Gmisc: Descriptive Statistics, Transition Plots, and More. https://cran.r-project.org/web/packages/Gmisc/
- Hmisc (summary.formula): Advanced table making and many more. https://github.com/harrelfe/Hmisc/
- arsenal: An Arsenal of 'R' Functions for Large-Scale Statistical Summaries. https://github.com/eheinzen/arsenal
- atable: Create Tables for Reporting Clinical Trials. https://github.com/arminstroebel/atable
- compareGroups: Descriptive Analysis by Groups. http://www.comparegroups.eu
- expss: Tables with Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics. https://github.com/gdemin/expss
- finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling. https://finalfit.org/index.html
- framework for easily create tables for reporting: framework for easily create tables for reporting. https://davidgohel.github.io/flextable/
- furniture: Furniture for Quantitative Scientists. https://cran.r-project.org/web/packages/furniture/
- gtsummary: Presentation-Ready Data Summary and Analytic Result Tables.  https://CRAN.R-project.org/package=gtsummary
- htmlTable: An R package for generating advanced tables. https://github.com/gforge/htmlTable
- kableExtra: Construct Complex Table with 'kable' and Pipe Syntax. https://github.com/haozhu233/kableExtra
- pander: An R Pandoc Writer. https://rapporter.github.io/pander/
- pixiedust: Format models for console and to markdown, HTML, and LaTeX. https://github.com/nutterb/pixiedust
- qwraps2: quickly placing data summaries and formatted regression results into .Rnw or .Rmd files. https://github.com/dewittpe/qwraps2/
- stargazer: Well-Formatted Regression and Summary Statistics Tables. https://cran.r-project.org/web/packages/stargazer/index.html
- tab: Functions for Creating Summary Tables for Statistical Reports. https://cran.r-project.org/package=tab
- table1: Tables of Descriptive Statistics in HTML. https://github.com/benjaminrich/table1
- table1xls: Exports Reproducible Summary Tables to Multi-Tab Spreadsheet Files. https://cran.r-project.org/web/packages/table1xls/index.html
- xtable: Export Tables to LaTeX or HTML. https://cran.r-project.org/web/packages/xtable/index.html
- (Python) tableone: Create "Table 1" for research papers in Python. https://github.com/tompollard/tableone

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