Create a summary table for categorical variables and their relationship with a continuous outcome variable

This takes a set of categorical variables and a continuous dependent variable and produces a table that shows the distribution of data across the levels of the categorical variables, as well as the mean of the dependent variable for each level and the significance of pairwise comparisons between these means.

report_cat_vars(
  data,
  dv,
  ...,
  var_names = NULL,
  level_names = NULL,
  p_adjust = p.adjust.methods,
  alpha_level = 0.05,
  filename = NULL,
  notes = list(),
  dv_name = NULL,
  bold_vars = TRUE,
  apa_style = TRUE,
  css_tags = list(),
  na.rm = TRUE,
  exclude_na = FALSE
)

Arguments

data: Dataframe containing the variables specified
dv: The continuous dependent variable to be presented alongside the levels of categorical variables. Set to NULL to just show the distribution of the categorical variables.
...: Categorical variables to be included
var_names: Tibble of old and new variable names, if variables are to be renamed for display. See get_rename_tribbles() for required format
level_names: Tibble of old and new level names, if levels are to be renamed for display. See get_rename_tribbles() for required format
p_adjust: One of p_adjust.methods, defaults to Holm
alpha_level: The level of significance for the pairwise comparisons (after p.adjust). Defaults to .05
filename: If provided, the table will be saved as an HTML file with this name.
notes: List of notes to append to bottom of table.
dv_name: Optional. A different name to use for the dependent variable in the automatic table footnote explaining the M(SD) column. Defaults to dv.
bold_vars: Should rows with variable names be bold. Defaults to TRUE
apa_style: Logical, should APA-style formatting be applied
css_tags: List of css tags to be added, each named with the class that the tag should be added to.
na.rm: Should missing values be dropped in the dv when summary statistics are calculated?
exclude_na: Should cases that are NA on a categorical variable be dropped from that section?

Value

A list including a tibble of descriptive statistics (descr), the gt-table (tab) and the HTML code (html_code) with css_tags added

Examples


report_cat_vars(iris, Sepal.Length, Species)
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#> 
#> 
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#> 
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#> 
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#> </style>
#>   <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
#>   <thead>
#>     
#>     <tr class="gt_col_headings">
#>       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id=""></th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="N">N</th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Share">Share</th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="&lt;em&gt;M (SD)&lt;/em&gt;"><em>M (SD)</em></th>
#>     </tr>
#>   </thead>
#>   <tbody class="gt_table_body">
#>     <tr class="gt_group_heading_row">
#>       <th colspan="4" class="gt_group_heading" style="font-weight: bold;" scope="colgroup" id="Species">Species</th>
#>     </tr>
#>     <tr class="gt_row_group_first"><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub"><div class='gt_from_md'><p>setosa</p>
#> </div></th>
#> <td headers="Species stub_1_1 N" class="gt_row gt_right"><div class='gt_from_md'><p>50</p>
#> </div></td>
#> <td headers="Species stub_1_1 Share" class="gt_row gt_right">33.3%</td>
#> <td headers="Species stub_1_1 *M (SD)*" class="gt_row gt_left"><div class='gt_from_md'><p>5.01 (0.35) <sup>a</sup></p>
#> </div></td></tr>
#>     <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub"><div class='gt_from_md'><p>versicolor</p>
#> </div></th>
#> <td headers="Species stub_1_2 N" class="gt_row gt_right"><div class='gt_from_md'><p>50</p>
#> </div></td>
#> <td headers="Species stub_1_2 Share" class="gt_row gt_right">33.3%</td>
#> <td headers="Species stub_1_2 *M (SD)*" class="gt_row gt_left"><div class='gt_from_md'><p>5.94 (0.52) <sup>b</sup></p>
#> </div></td></tr>
#>     <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub"><div class='gt_from_md'><p>virginica</p>
#> </div></th>
#> <td headers="Species stub_1_3 N" class="gt_row gt_right"><div class='gt_from_md'><p>50</p>
#> </div></td>
#> <td headers="Species stub_1_3 Share" class="gt_row gt_right">33.3%</td>
#> <td headers="Species stub_1_3 *M (SD)*" class="gt_row gt_left"><div class='gt_from_md'><p>6.59 (0.64) <sup>c</sup></p>
#> </div></td></tr>
#>   </tbody>
#>   <tfoot class="gt_sourcenotes">
#>     <tr>
#>       <td class="gt_sourcenote" colspan="4"><em>M</em> and <em>SD</em> are used to represent mean and standard deviation for Sepal.Length for that group, respectively.<br></td>
#>     </tr>
#>     <tr>
#>       <td class="gt_sourcenote" colspan="4">Within each variable, the means of groups with different superscripts differ with <em>p</em> &lt; .05 <br> (<em>p</em>-values were adjusted using the Holm-method.)</td>
#>     </tr>
#>   </tfoot>
#>   
#> </table>
#> </div>
#> </body>
#> </html>
#> 




  
      N
      Share
      M (SD)
    
Species
    
setosa

50

33.3%
5.01 (0.35) ^a
versicolor

50

33.3%
5.94 (0.52) ^b
virginica

50

33.3%
6.59 (0.64) ^c
M and SD are used to represent mean and standard deviation for Sepal.Length for that group, respectively.

    
Within each variable, the means of groups with different superscripts differ with p < .05 
 (p-values were adjusted using the Holm-method.)
    



# You can rename variables and levels reasonably easily
# Use get_rename_tribbles() to get the basis of these tibbles

var_renames <- tibble::tribble(
 ~old,     ~new,     
 "gndr",   "Gender",  
 "cntry",  "Country"
 )

level_renames <- tibble::tribble(
 ~var,     ~level_old, ~level_new, 
 "gndr",   "1",        "male",       
 "gndr",   "2",        "female",       
 "cntry",  "DE",       "Germany",      
 "cntry",  "FR",       "France",      
 "cntry",  "GB",       "UK"
 )
 
report_cat_vars(ess_health, health, gndr, cntry, var_names = var_renames, 
             level_names = level_renames)
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#>       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="&lt;em&gt;M (SD)&lt;/em&gt;"><em>M (SD)</em></th>
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#> <td headers="Country stub_1_3 N" class="gt_row gt_right"><div class='gt_from_md'><p>3045</p>
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#>       <td class="gt_sourcenote" colspan="4"><em>M</em> and <em>SD</em> are used to represent mean and standard deviation for health for that group, respectively.<br></td>
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#>       <td class="gt_sourcenote" colspan="4">Within each variable, the means of groups with different superscripts differ with <em>p</em> &lt; .05 <br> (<em>p</em>-values were adjusted using the Holm-method.)</td>
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#> 




  
      N
      Share
      M (SD)
    
Gender
    
male

3482

48.2%
2.23 (0.90) ^a
female

3744

51.8%
2.30 (0.93) ^b
Country
    
Germany

3045

42.1%
2.34 (0.88) ^a
France

1917

26.5%
2.29 (0.89) ^a
UK

2264

31.3%
2.14 (0.97) ^b
M and SD are used to represent mean and standard deviation for health for that group, respectively.

    
Within each variable, the means of groups with different superscripts differ with p < .05 
 (p-values were adjusted using the Holm-method.)

	N	Share	M (SD)
Species
setosa	50	33.3%	5.01 (0.35) ^a
versicolor	50	33.3%	5.94 (0.52) ^b
virginica	50	33.3%	6.59 (0.64) ^c
M and SD are used to represent mean and standard deviation for Sepal.Length for that group, respectively.
Within each variable, the means of groups with different superscripts differ with p < .05 (p-values were adjusted using the Holm-method.)

	N	Share	M (SD)
Gender
male	3482	48.2%	2.23 (0.90) ^a
female	3744	51.8%	2.30 (0.93) ^b
Country
Germany	3045	42.1%	2.34 (0.88) ^a
France	1917	26.5%	2.29 (0.89) ^a
UK	2264	31.3%	2.14 (0.97) ^b
M and SD are used to represent mean and standard deviation for health for that group, respectively.
Within each variable, the means of groups with different superscripts differ with p < .05 (p-values were adjusted using the Holm-method.)