README.md

freqtables

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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")
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.

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.