Analysis and Group Summary Function Review

During table creation, rtables calculates the contents for rows in normal and marginal summary rows by calling analysis an group summary functions, respectively, on the relevant facet data. Thus, while the split_row_by* and split_cols_by* functions allow us to declare the structure of our desired table, the analyze and summarize_row_groups functions, via arguments afun and cfun, respectively, allow us to declare the contents to appear in each structural facet of our desired table.

Key points to recall about a/cfuns:

  • First argument must be x or df
    • x will be passed a facet data vector for the variable (column) being analyzed/summarized
    • df will be passed the full facet data frame containing all columns of the data subset for the facet
  • Can accept optional special arguments which will be populated by rtables during tabulation
    • Values of these arguments cannot currently be overridden by the user
  • Can accept additional (non-special) arguments as desired
    • These can be passed via the extra_args argument to analyze/summarize_row_groups
  • Should return the result of calling in_rows (a RowsVerticalSection object)
  • Only difference between afuns and cfuns is that the latter must accept labelstr as the second argument
    • labelstr will be automatically populated with the label for the facet being summarized for a cfun and not passed to functions used as an afun

General Analysis/Summary Function Structure

Due to the last key point listed above, we can template a function that can be used as both an analysis and summary function:

library(rtables)

template_acfun <- function(x,
                           labelstr = NULL,
                           ## <optional special args>,
                           ## <additional args>,
                           ...) {
  if (is.null(labelstr)) {
    ## 'calculate' label(s) for afun-usage case
    lbl <- "cool label, bro"
  } else {
    ## calculate label(s) from labelstr for cfun-usage case
    lbl <- labelstr
  }

  ## whatever calculations we want
  out <- rcell(sample(c("what?", "huh?", "eh?"), 1), format = "xx")

  ## return our value(s) via in_rows
  in_rows(.list = list(ok = out), .labels = c(ok = lbl))
}

We can then use this function in either capacity:

lyt <- basic_table() |>
  split_cols_by("ARM") |>
  split_rows_by("STRATA1", split_fun = keep_split_levels(c("A", "B"))) |>
  summarize_row_groups("STRATA1", cfun = template_acfun) |>
  split_rows_by("SEX", split_fun = keep_split_levels(c("F", "M"))) |>
  summarize_row_groups("SEX", cfun = template_acfun) |>
  analyze("AGE", afun = template_acfun)

build_table(lyt, ex_adsl)
#                       A: Drug X   B: Placebo   C: Combination
# —————————————————————————————————————————————————————————————
# A                       what?        eh?           what?     
#   F                      eh?        what?           eh?      
#     cool label, bro     what?        huh?           eh?      
#   M                     what?        eh?           what?     
#     cool label, bro      eh?         eh?            eh?      
# B                       huh?         huh?           huh?     
#   F                      eh?        what?           eh?      
#     cool label, bro     what?        huh?           huh?     
#   M                     huh?        what?          what?     
#     cool label, bro     huh?        what?          what?

In light of the above, we will - without loss of generality - discuss analysis functions exclusively for the remainder of this guide with the exception of any situation where the difference is specifically relevant.

Arguments To Analysis Functions

Beyond .spl_context, which is covered in detail on its own in the next section of this guide, the special arguments (again: those that rtables will populate itself during tabulation) can be categorized into three rough, somewhat overlapping groups:

  • Marginal Counts
  • Facet Data
  • Reference Group Information

afun Special Arguments: Marginal Counts

Among special afun arguments supported by rtables, those which supply marginal counts are the most straightforward. That said, some care is warranted to ensure we understand the values our function will receive, particularly in the cases of .N_row and .N_total, as we will see.

Marginal Column and Row Counts (.N_col and .N_row)

.N_col will receive the column count - as understood by the rtables machinery - for the individual column our analysis function is currently being applied within. .N_row meanwhile, will receive a row count of the facet data for the (full) row facet our function is being applied to.

When an alt_counts_df is provided in the call to build_table .N_col will receive a count calculated based on that data frame, the same as the column counts which can be optionally displayed when rendering our tables.

Unlike .N_col, however, .N_row will always receive a count based on the primary data (df) passed to build_table. Thus in the common case of df being e.g., an ADAE dataset representing individual events while alt_counts_df is the corresponding ADSL dataset corresponding to subjects/patients, .N_row will receive a count of events, while .N_col will receive a count of subjects. This is due to the fact that alt_counts_df is required to contain the variables necessary for all column splitting in our layout, it is not required to contain all variables necessary for the row splitting.

Other Marginal Count Special Argument

.all_col_counts will receive the full vector of individual column counts regardless of which column our afun is operating within. Like .N_col, these counts will be based on alt_counts_df when it is specified within the call to build_table.

It is not advised to use N_total. Its current implementation effectively returns the sum of all column counts; while this will be correct for tables with simple column structure, it does not take into account partially or fully overlapping columns and will be incorrect when those are present in the table structure. In the next chapter of this guide we will use the split context (.spl_context) to derive a robust equivalent to .N_total as a way of illustrating some of the information the split context provides.

Further Topics On Creating Custom Analysis Functions