# tableone
**Repository Path**: dhxia007/tableone
## Basic Information
- **Project Name**: tableone
- **Description**: No description available
- **Primary Language**: R
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-23
- **Last Updated**: 2024-07-23
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
tableone
========
[](https://github.com/kaz-yos/tableone/actions)
[](https://www.r-pkg.org/pkg/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
==================
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.
## 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 = ","))
## Stratified by trt
## 1 2 p test SMD
## n 158 154
## time (mean (SD)) 2,015.62 (1,094.12) 1,996.86 (1,155.93) 0.883 0.017
## status (%) 0.884 exact 0.054
## 0 83 (52.5) 85 (55.2)
## 1 10 ( 6.3) 9 ( 5.8)
## 2 65 (41.1) 60 (39.0)
## trt (mean (SD)) 1.00 (0.00) 2.00 (0.00) <0.001 Inf
## age (mean (SD)) 51.42 (11.01) 48.58 (9.96) 0.018 0.270
## sex = f (%) 137 (86.7) 139 (90.3) 0.421 0.111
## ascites (mean (SD)) 0.09 (0.29) 0.06 (0.25) 0.434 0.089
## hepato (mean (SD)) 0.46 (0.50) 0.56 (0.50) 0.069 0.206
## spiders (mean (SD)) 0.28 (0.45) 0.29 (0.46) 0.886 0.016
## edema (%) 0.877 0.058
## 0 132 (83.5) 131 (85.1)
## 0.5 16 (10.1) 13 ( 8.4)
## 1 10 ( 6.3) 10 ( 6.5)
## bili (median [IQR]) 1.40 [0.80, 3.20] 1.30 [0.72, 3.60] 0.842 nonnorm 0.171
## chol (median [IQR]) 315.50 [247.75, 417.00] 303.50 [254.25, 377.00] 0.544 nonnorm 0.038
## albumin (mean (SD)) 3.52 (0.44) 3.52 (0.40) 0.874 0.018
## copper (median [IQR]) 73.00 [40.00, 121.00] 73.00 [43.00, 139.00] 0.717 nonnorm <0.001
## alk.phos (median [IQR]) 1,214.50 [840.75, 2,028.00] 1,283.00 [922.50, 1,949.75] 0.812 nonnorm 0.037
## ast (mean (SD)) 120.21 (54.52) 124.97 (58.93) 0.460 0.084
## trig (median [IQR]) 106.00 [84.50, 146.00] 113.00 [84.50, 155.00] 0.370 nonnorm 0.017
## platelet (mean (SD)) 258.75 (100.32) 265.20 (90.73) 0.555 0.067
## protime (mean (SD)) 10.65 (0.85) 10.80 (1.14) 0.197 0.146
## stage (%) 0.205 exact 0.246
## 1 12 ( 7.6) 4 ( 2.6)
## 2 35 (22.2) 32 (20.8)
## 3 56 (35.4) 64 (41.6)
## 4 55 (34.8) 54 (35.1)
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.
## 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.
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.
- Gmisc: Descriptive Statistics, Transition Plots, and More.
- Hmisc (summary.formula): Advanced table making and many more.
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- compareGroups: Descriptive Analysis by Groups.
- expss: Tables with Labels and Some Useful Functions from
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- finalfit: Quickly Create Elegant Regression Results Tables and Plots
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- framework for easily create tables for reporting: framework for
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- furniture: Furniture for Quantitative Scientists.
- gtsummary: Presentation-Ready Data Summary and Analytic Result
Tables.
- htmlTable: An R package for generating advanced tables.
- kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax.
- pander: An R Pandoc Writer.
- pixiedust: Format models for console and to markdown, HTML, and
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- qwraps2: quickly placing data summaries and formatted regression
results into .Rnw or .Rmd files.
- stargazer: Well-Formatted Regression and Summary Statistics Tables.
- tab: Functions for Creating Summary Tables for Statistical Reports.
- table1: Tables of Descriptive Statistics in HTML.
- table1xls: Exports Reproducible Summary Tables to Multi-Tab
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- xtable: Export Tables to LaTeX or HTML.
- (Python) tableone: Create “Table 1” for research papers in Python.