Adding sample size

The add_n argument will add the sample size to the label for the stratification group:

heart_disease %>%
  univariate_table(
    strata = ~ Sex,
    add_n = TRUE
  )

When multiple stratification variables are added on one side of the formula, the sample size will show up on the lowest level of the hierarchy, excluding summary columns:

heart_disease %>%
  univariate_table(
    strata = ~ Sex + HeartDisease,
    add_n = TRUE
  )

A limitation is that when sample size is added in the presence of row and column strata, it is displayed for the marginal groups only:

heart_disease %>%
  univariate_table(
    strata = Sex ~ HeartDisease,
    add_n = TRUE
  )

Association metrics

Often when a descriptive analysis is stratified by one or more variables, it is also of interest to add statistics that compare each variable across the groups. The associations argument allows you to add a list containing an unlimited number of functions that can produce a scalar value to be placed in the table. First, let’s define a function:

#Function for a p-value
pval <-
  function(y, x) {
    
    #For categorical data use Fisher's Exact test
    if(some_type(x, "factor")) {
      
      p <- fisher.test(factor(y), factor(x), simulate.p.value = TRUE)$p.value
    
    #Otherwise use Kruskall-Wallis
    } else {
      
      p <- kruskal.test(x, factor(y))$p.value
      
    }
    
    ifelse(p < 0.001, "<0.001", as.character(round(p, 2)))
    
  }

The stratification variable will be placed in the second argument of the function(s) provided. Now you can add it to the function call:

heart_disease %>%
  univariate_table(
    strata = ~ HeartDisease,
    associations = list(`P-value` = pval)
  )

The name of function in the list is what becomes the column label.

The comparison will take place across the number of subgroups there are within the column stratification:

heart_disease %>%
  univariate_table(
    strata = ~ Sex + HeartDisease,
    associations = list(`P-value` = pval)
  )

However, using a row stratification makes the comparisons be within those groups:

heart_disease %>%
  univariate_table(
    strata = Sex ~ HeartDisease,
    associations = list(`P-value` = pval)
  )

In general, there must be at least one column stratification variable in order to use association metrics. See univariate_associations() for more details on the workhorse of this functionality.

Specifying data types

As noted above, one of the columns did not appear in the table by default because it was a logical() type. By default, only factor() and numeric() types are placed into the result, though there are (at least) three ways to include it:

heart_disease %>%
  dplyr::mutate(
    BloodSugar = factor(BloodSugar)
  ) %>%
  univariate_table()

heart_disease %>%
  univariate_table(
    categorical_types = c("factor", "logical")
  )

heart_disease %>%
  univariate_table(
    f_other = list(count = function(x) table(x)),
    other_summary = 
      c(
        Summary = "count"
      )
  )

Adding user-specified functions

You can also add custom functions that can be available for numeric or categorical columns:

heart_disease %>%
  univariate_table(
    categorical_types = NULL,
    f_numeric =
      list(
        cv = ~sd(.x) / mean(.x)
      ),
    numeric_summary = 
      c(
        `Coef. of variation` = "sd / mean = cv"
      )
  )

The names of functions become the patterns that searched in the string templates.

Rendering format

As mentioned above, the default format for the table is HTML, but you could choose an alternative with the format argument:

heart_disease %>%
  univariate_table(
    format = "none"
  )

## # A tibble: 14 × 3
##    Variable                Level              Summary         
##    <chr>                   <chr>              <chr>           
##  1 "Age"                   ""                 56 (48, 61)     
##  2 "Sex"                   "Female"           97 (32.01%)     
##  3 ""                      "Male"             206 (67.99%)    
##  4 "ChestPain"             "Typical angina"   23 (7.59%)      
##  5 ""                      "Atypical angina"  50 (16.5%)      
##  6 ""                      "Non-anginal pain" 86 (28.38%)     
##  7 ""                      "Asymptomatic"     144 (47.52%)    
##  8 "BP"                    ""                 130 (120, 140)  
##  9 "Cholesterol"           ""                 241 (211, 275)  
## 10 "MaximumHR"             ""                 153 (133.5, 166)
## 11 "ExerciseInducedAngina" "No"               204 (67.33%)    
## 12 ""                      "Yes"              99 (32.67%)     
## 13 "HeartDisease"          "No"               164 (54.13%)    
## 14 ""                      "Yes"              139 (45.87%)

There are also options for "latex", "pandoc", "markdown".

Relabeling, releveling and reordering

You can use the labels and levels arguments to add clean text to any of the variable or categorical level names, and the order argument to change the position of the variables in the result:

heart_disease %>%
  univariate_table(
    labels = 
      c(
        Age = "Age (years)",
        ChestPain = "Chest pain"
      ),
    levels = 
      list(
        Sex =
          c(
            Male = "M"
          )
      ),
    order = 
      c(
        "BP",
        "Age",
        "Cholesterol"
      )
  )

Notice you only need to specify values that need to be changed. Also, ordering is done with the original names even when relabeled.

Headers, fill values, and captions

The variableName and levelName arguments are used to change what the headers are for the column names and categorical levels, while fill_blanks determines what goes in empty cells. Finally, the caption argument specifies labels the entire table:

heart_disease %>%
  univariate_table(
    variableName = "THESE ARE VARIABLES",
    levelName = "THESE ARE LEVELS",
    fill_blanks = "BLANK",
    caption = "HERE IS MY CAPTION"
  )