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https://github.com/brad-cannell/freqtables
Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.
https://github.com/brad-cannell/freqtables
categorical-data data-analysis descriptive-statistics epidemiology r
Last synced: about 1 month ago
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Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.
- Host: GitHub
- URL: https://github.com/brad-cannell/freqtables
- Owner: brad-cannell
- License: other
- Created: 2019-12-12T21:39:59.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-02-17T22:08:52.000Z (7 months ago)
- Last Synced: 2024-07-11T09:02:19.017Z (2 months ago)
- Topics: categorical-data, data-analysis, descriptive-statistics, epidemiology, r
- Language: R
- Size: 3.55 MB
- Stars: 12
- Watchers: 3
- Forks: 1
- Open Issues: 23
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - brad-cannell/freqtables - Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a tidyverse pipeline, and consideration h (R)
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# freqtables 
[](https://cran.r-project.org/package=freqtables)
[](https://www.r-pkg.org/pkg/freqtables)
The goal of `freqtables` is to quickly make tables of descriptive statistics for categorical variables (i.e., counts, percentages, confidence intervals). This package is designed to work in a `tidyverse` pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.
## Installation
You can install the released version of `freqtables` from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("freqtables")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("brad-cannell/freqtables")
```
## Example
Because `freqtables` is intended to be used in a `dplyr` pipeline, loading `dplyr` into your current R session is recommended.
```{r message=FALSE}
library(dplyr)
library(freqtables)
```
The examples below will use R's built-in `mtcars` data set.
```{r}
data("mtcars")
```
### freq_table()
The `freq_table()` function produces one-way and two-way frequency tables for categorical variables. In addition to frequencies, the `freq_table()` function displays percentages, and the standard errors and confidence intervals of the percentages. For two-way tables only, `freq_table()` also displays row (subgroup) percentages, standard errors, and confidence intervals.
For one-way tables, the default 95 percent confidence intervals displayed are logit transformed confidence intervals equivalent to those used by Stata. Additionally, `freq_table()` will return Wald ("linear") confidence intervals if the argument to ci_type = "wald".
For two-way tables, `freq_table()` returns logit transformed confidence intervals equivalent to those used by Stata.
Here is an example of using `freq_table()` to create a one-way frequency table with all function arguments left at their default values:
```{r}
mtcars %>%
freq_table(am)
```
Here is an example of using `freq_table()` to create a two-way frequency table with all function arguments left at their default values:
```{r}
mtcars %>%
freq_table(am, cyl)
```
You can learn more about the `freq_table()` function and ways to adjust default behaviors in vignette("descriptive_analysis").
### freq_test()
The `freq_test()` function is an S3 generic. It currently has methods for conducting hypothesis tests on one-way and two-way frequency tables. Further, it is made to work in a dplyr pipeline with the `freq_table()` function.
For the `freq_table_two_way` class, the methods used are Pearson's chi-square test of independence Fisher's exact test. When cell counts are <= 5, Fisher's Exact Test is considered more reliable.
Here is an example of using `freq_test()` to test the equality of proportions on a one-way frequency table with all function arguments left at their default values:
```{r}
mtcars %>%
freq_table(am) %>%
freq_test() %>%
select(var:percent, p_chi2_pearson)
```
Here is an example of using `freq_test()` to conduct a chi-square test of independence on a two-way frequency table with all function arguments left at their default values:
```{r}
mtcars %>%
freq_table(am, vs) %>%
freq_test() %>%
select(row_var:n, percent_row, p_chi2_pearson)
```
You can learn more about the `freq_table()` function and ways to adjust default behaviors in vignette("using_freq_test").
### freq_format()
The freq_format function is intended to make it quick and easy to format the output of the freq_table function for tables that may be used for publication. For example, a proportion and 95% confidence interval could be formatted as "24.00 (21.00 - 27.00)."
```{r}
mtcars %>%
freq_table(am) %>%
freq_format(
recipe = "percent (lcl - ucl)",
name = "percent_95",
digits = 2
) %>%
select(var, cat, percent_95)
```
You can learn more about the `freq_format()` function by reading the function documentation.