# 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 with:

``````install.packages("freqtables")
``````

And the development version from GitHub with:

``````# install.packages("devtools")
``````

## Example

Because `freqtables` is intended to be used in a `dplyr` pipeline, loading `dplyr` into your current R session is recommended.

``````library(dplyr)
library(freqtables)
``````

The examples below will use R’s built-in `mtcars` data set.

``````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:

``````mtcars %>%
freq_table(am)
#>   var cat  n n_total percent       se   t_crit      lcl      ucl
#> 1  am   0 19      32  59.375 8.820997 2.039513 40.94225 75.49765
#> 2  am   1 13      32  40.625 8.820997 2.039513 24.50235 59.05775
``````

Here is an example of using `freq_table()` to create a two-way frequency table with all function arguments left at their default values:

``````mtcars %>%
freq_table(am, cyl)
#> # A tibble: 6 x 17
#>   row_var row_cat col_var col_cat     n n_row n_total percent_total se_total
#>   <chr>   <chr>   <chr>   <chr>   <int> <int>   <int>         <dbl>    <dbl>
#> 1 am      0       cyl     4           3    19      32          9.38     5.24
#> 2 am      0       cyl     6           4    19      32         12.5      5.94
#> 3 am      0       cyl     8          12    19      32         37.5      8.70
#> 4 am      1       cyl     4           8    13      32         25        7.78
#> 5 am      1       cyl     6           3    13      32          9.38     5.24
#> 6 am      1       cyl     8           2    13      32          6.25     4.35
#> # … with 8 more variables: t_crit_total <dbl>, lcl_total <dbl>,
#> #   ucl_total <dbl>, percent_row <dbl>, se_row <dbl>, t_crit_row <dbl>,
#> #   lcl_row <dbl>, ucl_row <dbl>
``````

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:

``````mtcars %>%
freq_table(am) %>%
freq_test() %>%
select(var:percent, p_chi2_pearson)
#>   var cat  n n_total percent p_chi2_pearson
#> 1  am   0 19      32  59.375      0.2888444
#> 2  am   1 13      32  40.625      0.2888444
``````

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:

``````mtcars %>%
freq_table(am, vs) %>%
freq_test() %>%
select(row_var:n, percent_row, p_chi2_pearson)
#> # A tibble: 4 x 7
#>   row_var row_cat col_var col_cat     n percent_row p_chi2_pearson
#>   <chr>   <chr>   <chr>   <chr>   <int>       <dbl>          <dbl>
#> 1 am      0       vs      0          12        63.2          0.341
#> 2 am      0       vs      1           7        36.8          0.341
#> 3 am      1       vs      0           6        46.2          0.341
#> 4 am      1       vs      1           7        53.8          0.341
``````

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).”

``````mtcars %>%
freq_table(am) %>%
freq_format(
recipe = "percent (lcl - ucl)",
name = "percent_95",
digits = 2
) %>%
select(var, cat, percent_95)
#>   var cat            percent_95
#> 1  am   0 59.38 (40.94 - 75.50)
#> 2  am   1 40.62 (24.50 - 59.06)
``````

You can learn more about the `freq_format()` function by reading the function documentation.

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freqtables documentation built on July 20, 2020, 9:06 a.m.