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Count Layers

count.Rmd

Introduction

Counting things seems simple enough. You look at the data, tally up how many observations fall into each category, and move on. But in clinical reporting, counting is deceptively nuanced. A demographics table needs simple frequencies. An adverse events table needs both the number of subjects who experienced an event and the total number of events. A disposition table might need all of those plus custom column groupings and sorting by frequency.

tplyr2 handles all of these scenarios through group_count(). In this vignette, we will walk through the most common counting patterns you will encounter when building clinical tables, starting with the basics and building up to nested hierarchical summaries.

A Simple Count

The simplest use of group_count() is to count the levels of a single categorical variable. Here we build a basic disposition table showing the reasons subjects discontinued from the study, broken out by planned treatment group.

spec <- tplyr_spec(
  cols = "TRT01P",
  layers = tplyr_layers(
    group_count("DCDECOD")
  )
)

result <- tplyr_build(spec, tplyr_adsl)
kable(result[, c("rowlabel1", "res1", "res2", "res3")])
rowlabel1 res1 res2 res3
ADVERSE EVENT 8 ( 9.3%) 40 (47.6%) 44 (52.4%)
COMPLETED 58 (67.4%) 27 (32.1%) 25 (29.8%)
DEATH 2 ( 2.3%) 0 ( 0.0%) 1 ( 1.2%)
LACK OF EFFICACY 3 ( 3.5%) 1 ( 1.2%) 0 ( 0.0%)
LOST TO FOLLOW-UP 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
PHYSICIAN DECISION 1 ( 1.2%) 2 ( 2.4%) 0 ( 0.0%)
PROTOCOL VIOLATION 2 ( 2.3%) 3 ( 3.6%) 1 ( 1.2%)
STUDY TERMINATED BY SPONSOR 2 ( 2.3%) 3 ( 3.6%) 2 ( 2.4%)
WITHDRAWAL BY SUBJECT 9 (10.5%) 8 ( 9.5%) 10 (11.9%)

By default, tplyr2 formats counts as n (xx.x%), where the denominator is the total number of observations within each treatment column. Every level present in the data gets a row, and zero counts are filled in automatically so that no treatment arm is left with a gap.

Adding a Total Column

In many tables, you need a “Total” column that pools all treatment groups. You can add one at the spec level with total_group().

spec <- tplyr_spec(
  cols = "TRT01P",
  total_groups = list(total_group("TRT01P", label = "Total")),
  layers = tplyr_layers(
    group_count("DCDECOD")
  )
)

result <- tplyr_build(spec, tplyr_adsl)
kable(result[, c("rowlabel1", "res1", "res2", "res3", "res4")])
rowlabel1 res1 res2 res3 res4
ADVERSE EVENT 8 ( 9.3%) 92 (36.2%) 40 (47.6%) 44 (52.4%)
COMPLETED 58 (67.4%) 110 (43.3%) 27 (32.1%) 25 (29.8%)
DEATH 2 ( 2.3%) 3 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
LACK OF EFFICACY 3 ( 3.5%) 4 ( 1.6%) 1 ( 1.2%) 0 ( 0.0%)
LOST TO FOLLOW-UP 1 ( 1.2%) 2 ( 0.8%) 0 ( 0.0%) 1 ( 1.2%)
PHYSICIAN DECISION 1 ( 1.2%) 3 ( 1.2%) 2 ( 2.4%) 0 ( 0.0%)
PROTOCOL VIOLATION 2 ( 2.3%) 6 ( 2.4%) 3 ( 3.6%) 1 ( 1.2%)
STUDY TERMINATED BY SPONSOR 2 ( 2.3%) 7 ( 2.8%) 3 ( 3.6%) 2 ( 2.4%)
WITHDRAWAL BY SUBJECT 9 (10.5%) 27 (10.6%) 8 ( 9.5%) 10 (11.9%)

The total_group() function works by duplicating every row in the data with the treatment variable set to the label you provide (here, “Total”). This means the total column is computed through the same pipeline as every other column, keeping the logic consistent.

Custom Column Groups

Sometimes you need to combine existing treatment arms into a new group. For example, you might want a “Treated” column that pools all active dose groups. The custom_group() function handles this.

spec <- tplyr_spec(
  cols = "TRT01P",
  custom_groups = list(
    custom_group("TRT01P",
      "Treated" = c("Xanomeline High Dose", "Xanomeline Low Dose")
    )
  ),
  layers = tplyr_layers(
    group_count("DCDECOD")
  )
)

result <- tplyr_build(spec, tplyr_adsl)
kable(result[, c("rowlabel1", "res1", "res2", "res3", "res4")])
rowlabel1 res1 res2 res3 res4
ADVERSE EVENT 8 ( 9.3%) 84 (50.0%) 40 (47.6%) 44 (52.4%)
COMPLETED 58 (67.4%) 52 (31.0%) 27 (32.1%) 25 (29.8%)
DEATH 2 ( 2.3%) 1 ( 0.6%) 0 ( 0.0%) 1 ( 1.2%)
LACK OF EFFICACY 3 ( 3.5%) 1 ( 0.6%) 1 ( 1.2%) 0 ( 0.0%)
LOST TO FOLLOW-UP 1 ( 1.2%) 1 ( 0.6%) 0 ( 0.0%) 1 ( 1.2%)
PHYSICIAN DECISION 1 ( 1.2%) 2 ( 1.2%) 2 ( 2.4%) 0 ( 0.0%)
PROTOCOL VIOLATION 2 ( 2.3%) 4 ( 2.4%) 3 ( 3.6%) 1 ( 1.2%)
STUDY TERMINATED BY SPONSOR 2 ( 2.3%) 5 ( 3.0%) 3 ( 3.6%) 2 ( 2.4%)
WITHDRAWAL BY SUBJECT 9 (10.5%) 18 (10.7%) 8 ( 9.5%) 10 (11.9%)

Like total_group(), custom groups duplicate the matching rows with the treatment variable set to the new label. Custom groups and total groups can be combined in a single spec when you need both.

Custom Format Strings

The default n (xx.x%) format works for many tables, but you have full control over how counts are displayed through the f_str() function. Format strings use x characters to define field widths: each x reserves one character position.

spec <- tplyr_spec(
  cols = "TRT01P",
  layers = tplyr_layers(
    group_count("DCDECOD",
      settings = layer_settings(
        format_strings = list(
          n_counts = f_str("xxx (xxx.x%)", "n", "pct")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adsl)
kable(result[, c("rowlabel1", "res1", "res2", "res3")])
rowlabel1 res1 res2 res3
ADVERSE EVENT 8 ( 9.3%) 40 ( 47.6%) 44 ( 52.4%)
COMPLETED 58 ( 67.4%) 27 ( 32.1%) 25 ( 29.8%)
DEATH 2 ( 2.3%) 0 ( 0.0%) 1 ( 1.2%)
LACK OF EFFICACY 3 ( 3.5%) 1 ( 1.2%) 0 ( 0.0%)
LOST TO FOLLOW-UP 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
PHYSICIAN DECISION 1 ( 1.2%) 2 ( 2.4%) 0 ( 0.0%)
PROTOCOL VIOLATION 2 ( 2.3%) 3 ( 3.6%) 1 ( 1.2%)
STUDY TERMINATED BY SPONSOR 2 ( 2.3%) 3 ( 3.6%) 2 ( 2.4%)
WITHDRAWAL BY SUBJECT 9 ( 10.5%) 8 ( 9.5%) 10 ( 11.9%)

The available count statistics are:

  • n – the number of observations
  • pct – the percentage of observations (using the column total as denominator)
  • total – the denominator used for the percentage calculation
  • distinct_n – the number of distinct subjects (requires distinct_by)
  • distinct_pct – the percentage of distinct subjects
  • distinct_total – the distinct denominator

You can combine any of these in a single format string. We will use the distinct variants in the next section.

Total Rows

Many tables require a “Total” row at the bottom of each count block. Set total_row = TRUE in layer_settings() to add one.

spec <- tplyr_spec(
  cols = "TRT01P",
  layers = tplyr_layers(
    group_count("DCDECOD",
      settings = layer_settings(
        total_row = TRUE,
        total_row_label = "Overall Total"
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adsl)
kable(result[, c("rowlabel1", "res1", "res2", "res3")])
rowlabel1 res1 res2 res3
ADVERSE EVENT 8 ( 9.3%) 40 (47.6%) 44 (52.4%)
COMPLETED 58 (67.4%) 27 (32.1%) 25 (29.8%)
DEATH 2 ( 2.3%) 0 ( 0.0%) 1 ( 1.2%)
LACK OF EFFICACY 3 ( 3.5%) 1 ( 1.2%) 0 ( 0.0%)
LOST TO FOLLOW-UP 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
PHYSICIAN DECISION 1 ( 1.2%) 2 ( 2.4%) 0 ( 0.0%)
PROTOCOL VIOLATION 2 ( 2.3%) 3 ( 3.6%) 1 ( 1.2%)
STUDY TERMINATED BY SPONSOR 2 ( 2.3%) 3 ( 3.6%) 2 ( 2.4%)
WITHDRAWAL BY SUBJECT 9 (10.5%) 8 ( 9.5%) 10 (11.9%)
Overall Total 86 (100.0%) 84 (100.0%) 84 (100.0%)

Population Data: Getting the Denominator Right

Everything so far has summarized tplyr_adsl, which has one row per subject, so the column total is the number of subjects in each arm. Adverse event data is different, and this is where denominators most often go wrong.

tplyr_adae only contains subjects who experienced at least one event, with many rows per subject. If you count it directly, the denominator becomes “subjects who had any adverse event” rather than the number of subjects at risk – and every incidence percentage comes out too large. For an adverse event table (and for most tables built from a findings or events dataset), the denominator must come from the population dataset – typically ADSL, the set of subjects in the analysis population.

You supply population data in two places: a pop_data() mapping in the spec, and the population data frame itself at build time. Here is the same simple adverse-event count built both ways:

# WITHOUT population data: denominator = subjects present in ADAE
spec_no_pop <- tplyr_spec(
  cols = "TRTA",
  layers = tplyr_layers(
    group_count("AEDECOD", settings = layer_settings(distinct_by = "USUBJID"))
  )
)
res_no_pop <- tplyr_build(spec_no_pop, tplyr_adae)

# WITH population data: denominator = full safety population from ADSL
spec_pop <- tplyr_spec(
  cols = "TRTA",
  pop_data = pop_data(cols = c("TRTA" = "TRT01A")),
  layers = tplyr_layers(
    group_count("AEDECOD", settings = layer_settings(distinct_by = "USUBJID"))
  )
)
res_pop <- tplyr_build(spec_pop, tplyr_adae, pop_data = tplyr_adsl)

# Column Ns: subjects-with-events vs. the true population
sapply(c("res1", "res2", "res3"), function(c) attr(res_no_pop[[c]], "label"))
#>                          res1                          res2 
#>              "Placebo (N=47)" "Xanomeline High Dose (N=77)" 
#>                          res3 
#>  "Xanomeline Low Dose (N=76)"
sapply(c("res1", "res2", "res3"), function(c) attr(res_pop[[c]], "label"))
#>                          res1                          res2 
#>              "Placebo (N=86)" "Xanomeline High Dose (N=84)" 
#>                          res3 
#>  "Xanomeline Low Dose (N=84)"

The header N tells the story: without population data the Placebo column is N=47 (only the subjects who had an event), and with it the column is the true N=86. Because the numerator is unchanged, every percentage roughly halves once the denominator is correct – a cell that read 6 (14.0%) becomes 6 (7.1%). Getting this wrong silently inflates every incidence in the table, so for adverse event tables population data is effectively mandatory.

The named vector in pop_data(cols = c("TRTA" = "TRT01A")) maps the treatment column in the analysis data (TRTA, actual treatment in ADAE) to the matching column in the population data (TRT01A in ADSL). The mapping is needed because the two datasets frequently name the treatment variable differently.

Population data also unlocks related features – a “no events reported” row for subjects who are in the population but absent from the analysis data (missing_subjects), a separate denominator filter (denom_where), and per-subgroup denominators (denoms_by). Those are covered in depth in vignette("denom"). Every adverse event example in the rest of this vignette uses population data.

Distinct Versus Event Counts

When summarizing adverse events, the distinction between events and subjects matters enormously. A single subject might experience the same event multiple times. If you simply count rows, you are counting events. If you want the number of subjects who experienced each event at least once, you need distinct counts.

The distinct_by parameter in layer_settings() tells tplyr2 which variable identifies unique subjects. Once set, the distinct_n and distinct_pct statistics become available for use in format strings.

spec <- tplyr_spec(
  cols = "TRTA",
  pop_data = pop_data(cols = c("TRTA" = "TRT01A")),
  layers = tplyr_layers(
    group_count("AEDECOD",
      settings = layer_settings(
        distinct_by = "USUBJID",
        format_strings = list(
          n_counts = f_str("xxx (xx.x%) [xxx]", "distinct_n", "distinct_pct", "n")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adae, pop_data = tplyr_adsl)
kable(head(result[, c("rowlabel1", "res1", "res2", "res3")], 10))
rowlabel1 res1 res2 res3
ABDOMINAL PAIN 0 ( 0.0%) [ 0] 0 ( 0.0%) [ 0] 1 ( 1.2%) [ 1]
AGITATION 0 ( 0.0%) [ 0] 0 ( 0.0%) [ 0] 1 ( 1.2%) [ 1]
ANXIETY 0 ( 0.0%) [ 0] 0 ( 0.0%) [ 0] 1 ( 1.2%) [ 1]
APPLICATION SITE DERMATITIS 1 ( 1.2%) [ 1] 3 ( 3.6%) [ 3] 2 ( 2.4%) [ 2]
APPLICATION SITE ERYTHEMA 0 ( 0.0%) [ 0] 3 ( 3.6%) [ 3] 4 ( 4.8%) [ 4]
APPLICATION SITE IRRITATION 1 ( 1.2%) [ 1] 3 ( 3.6%) [ 4] 2 ( 2.4%) [ 2]
APPLICATION SITE PAIN 0 ( 0.0%) [ 0] 1 ( 1.2%) [ 1] 0 ( 0.0%) [ 0]
APPLICATION SITE PRURITUS 4 ( 4.7%) [ 4] 6 ( 7.1%) [ 7] 5 ( 6.0%) [ 5]
APPLICATION SITE REACTION 1 ( 1.2%) [ 1] 1 ( 1.2%) [ 1] 0 ( 0.0%) [ 0]
APPLICATION SITE URTICARIA 0 ( 0.0%) [ 0] 0 ( 0.0%) [ 0] 1 ( 1.2%) [ 1]

In this output, the first number is the count of distinct subjects, the percentage is based on distinct subjects, and the number in brackets is the total event count. This pattern – xxx (xx.x%) [xxx] for distinct subjects, percent, and events – is one of the most common formats for adverse event tables.

Displaying Statistics in Separate Columns

Some sponsors require the subject and event counts in separate columns rather than packed into one cell – for example, an “n (%)” column and an “E” column under each treatment arm. The stat_columns setting produces this layout directly. Pass a named list of f_str() objects: each entry becomes its own result column per treatment group, and the entry name becomes the column sub-label.

spec <- tplyr_spec(
  cols = "TRTA",
  pop_data = pop_data(cols = c("TRTA" = "TRT01A")),
  layers = tplyr_layers(
    group_count("AEDECOD",
      settings = layer_settings(
        distinct_by = "USUBJID",
        stat_columns = list(
          "n (%)" = f_str("xxx (xx.x%)", "distinct_n", "distinct_pct"),
          "E"     = f_str("xxx", "n")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adae, pop_data = tplyr_adsl)
kable(head(result[, c("rowlabel1", "res1", "res2", "res3", "res4")], 10))
rowlabel1 res1 res2 res3 res4
ABDOMINAL PAIN 0 ( 0.0%) 0 0 ( 0.0%) 0
AGITATION 0 ( 0.0%) 0 0 ( 0.0%) 0
ANXIETY 0 ( 0.0%) 0 0 ( 0.0%) 0
APPLICATION SITE DERMATITIS 1 ( 1.2%) 1 3 ( 3.6%) 3
APPLICATION SITE ERYTHEMA 0 ( 0.0%) 0 3 ( 3.6%) 3
APPLICATION SITE IRRITATION 1 ( 1.2%) 1 3 ( 3.6%) 4
APPLICATION SITE PAIN 0 ( 0.0%) 0 1 ( 1.2%) 1
APPLICATION SITE PRURITUS 4 ( 4.7%) 4 6 ( 7.1%) 7
APPLICATION SITE REACTION 1 ( 1.2%) 1 1 ( 1.2%) 1
APPLICATION SITE URTICARIA 0 ( 0.0%) 0 0 ( 0.0%) 0

The result columns interleave arm-major, so each treatment group’s stat columns sit adjacent: res1/res2 are the first arm’s “n (%)” and “E” columns, res3/res4 the second arm’s, and so on. Column identity is carried on the label attributes, which follow the pattern "<treatment> (N=n) | <stat name>":

attr(result$res1, "label")
#> [1] "Placebo (N=86) | n (%)"
attr(result$res2, "label")
#> [1] "Placebo (N=86) | E"

Renderers can split these labels on " | " to span the treatment group (with its header N) over its stat sub-columns as a two-level header. stat_columns works with nested count layers, by variables, total and missing rows, risk difference, metadata, and serialization. A few things to keep in mind:

  • Stat names may not contain " | " or "(N=", which are reserved by the label grammar.
  • When a spec contains multiple layers, all layers must be count layers using stat_columns with the same statistic names so result columns align; otherwise build separate specs.
  • The empty argument of f_str() applies per format, so a row can display empty text in one stat column and a value in another when their variables differ in missingness.

A Note on Parenthesis Hugging

You may notice that tplyr2 carefully aligns numbers within their format fields, padding with leading spaces so that decimal points and parentheses line up across rows. For even tighter alignment, tplyr2 supports “parenthesis hugging” using uppercase X characters in format strings. When you use X instead of x, leading spaces shift outside the parenthesis so the opening delimiter sits right next to the number. This is a more advanced feature covered in detail in the format strings documentation.

Nested Count Summaries

Adverse event tables in clinical reports almost always use a hierarchical structure: body system (SOC) as the outer grouping and preferred term (PT) as the inner grouping. tplyr2 supports this directly by passing a vector of two variable names to group_count().

spec <- tplyr_spec(
  cols = "TRTA",
  pop_data = pop_data(cols = c("TRTA" = "TRT01A")),
  layers = tplyr_layers(
    group_count(c("AEBODSYS", "AEDECOD"),
      settings = layer_settings(
        distinct_by = "USUBJID",
        format_strings = list(
          n_counts = f_str("xxx (xx.x%)", "distinct_n", "distinct_pct")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adae, pop_data = tplyr_adsl)
kable(head(result[, c("rowlabel1", "rowlabel2", "res1", "res2", "res3")], 15))
rowlabel1 rowlabel2 res1 res2 res3
CARDIAC DISORDERS 4 ( 4.7%) 6 ( 7.1%) 5 ( 6.0%)
CARDIAC DISORDERS ATRIAL FIBRILLATION 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
CARDIAC DISORDERS ATRIAL FLUTTER 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
CARDIAC DISORDERS ATRIAL HYPERTROPHY 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC DISORDERS BUNDLE BRANCH BLOCK RIGHT 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC DISORDERS CARDIAC FAILURE CONGESTIVE 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC DISORDERS MYOCARDIAL INFARCTION 0 ( 0.0%) 1 ( 1.2%) 2 ( 2.4%)
CARDIAC DISORDERS SINUS BRADYCARDIA 0 ( 0.0%) 3 ( 3.6%) 1 ( 1.2%)
CARDIAC DISORDERS SUPRAVENTRICULAR EXTRASYSTOLES 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
CARDIAC DISORDERS SUPRAVENTRICULAR TACHYCARDIA 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
CARDIAC DISORDERS TACHYCARDIA 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC DISORDERS VENTRICULAR EXTRASYSTOLES 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
CONGENITAL, FAMILIAL AND GENETIC DISORDERS 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
CONGENITAL, FAMILIAL AND GENETIC DISORDERS VENTRICULAR SEPTAL DEFECT 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
GASTROINTESTINAL DISORDERS 6 ( 7.0%) 4 ( 4.8%) 3 ( 3.6%)

The first variable in the vector (AEBODSYS) becomes the outer level, and the second (AEDECOD) becomes the inner level. In the output, rowlabel1 holds the body system name and rowlabel2 holds the preferred term. Outer-level summary rows have an empty rowlabel2, while inner-level rows show the preferred term in rowlabel2.

The outer-level counts represent the number of distinct subjects with any event in that body system, and the inner-level counts represent subjects with each specific preferred term. A subject who experienced multiple different preferred terms within the same body system is counted once at the outer level but once for each preferred term at the inner level.

Collapsing Row Labels

For display purposes, you often want a single row label column with indentation indicating the hierarchy rather than two separate columns. The collapse_row_labels() function provides two modes for this.

Default Mode (Stub Rows)

The default mode inserts stub rows for outer-level labels. These stub rows have no numeric results – they serve purely as group headers:

collapsed <- collapse_row_labels(result, "rowlabel1", "rowlabel2", indent = "   ")
kable(head(collapsed[, c("row_label", "res1", "res2", "res3")], 15))
row_label res1 res2 res3
CARDIAC DISORDERS
4 ( 4.7%) 6 ( 7.1%) 5 ( 6.0%)
ATRIAL FIBRILLATION 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
ATRIAL FLUTTER 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
ATRIAL HYPERTROPHY 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
BUNDLE BRANCH BLOCK RIGHT 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC FAILURE CONGESTIVE 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
MYOCARDIAL INFARCTION 0 ( 0.0%) 1 ( 1.2%) 2 ( 2.4%)
SINUS BRADYCARDIA 0 ( 0.0%) 3 ( 3.6%) 1 ( 1.2%)
SUPRAVENTRICULAR EXTRASYSTOLES 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
SUPRAVENTRICULAR TACHYCARDIA 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
TACHYCARDIA 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
VENTRICULAR EXTRASYSTOLES 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
CONGENITAL, FAMILIAL AND GENETIC DISORDERS
0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)

Nest Mode

With nest = TRUE, the row labels are collapsed in place without adding new rows. Outer-level rows keep their results, and inner rows are indented. This matches the behavior of set_nest_count(TRUE) in Tplyr v1:

nested <- collapse_row_labels(result, nest = TRUE, indent = "   ")
kable(head(nested[, c("row_label", "res1", "res2", "res3")], 15))
row_label res1 res2 res3
CARDIAC DISORDERS 4 ( 4.7%) 6 ( 7.1%) 5 ( 6.0%)
ATRIAL FIBRILLATION 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
ATRIAL FLUTTER 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
ATRIAL HYPERTROPHY 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
BUNDLE BRANCH BLOCK RIGHT 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC FAILURE CONGESTIVE 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
MYOCARDIAL INFARCTION 0 ( 0.0%) 1 ( 1.2%) 2 ( 2.4%)
SINUS BRADYCARDIA 0 ( 0.0%) 3 ( 3.6%) 1 ( 1.2%)
SUPRAVENTRICULAR EXTRASYSTOLES 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
SUPRAVENTRICULAR TACHYCARDIA 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
TACHYCARDIA 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
VENTRICULAR EXTRASYSTOLES 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
CONGENITAL, FAMILIAL AND GENETIC DISORDERS 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
VENTRICULAR SEPTAL DEFECT 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
GASTROINTESTINAL DISORDERS 6 ( 7.0%) 4 ( 4.8%) 3 ( 3.6%)

The indent parameter controls the string used for each level of nesting. Here we use three spaces, but you can use any string that suits your output format.

Nested Counts with Total Rows

Total rows work with nested counts as well. When total_row = TRUE is set on a nested layer, the total row reflects the overall count across all body systems.

spec <- tplyr_spec(
  cols = "TRTA",
  pop_data = pop_data(cols = c("TRTA" = "TRT01A")),
  layers = tplyr_layers(
    group_count(c("AEBODSYS", "AEDECOD"),
      settings = layer_settings(
        distinct_by = "USUBJID",
        total_row = TRUE,
        total_row_label = "Any adverse event",
        format_strings = list(
          n_counts = f_str("xxx (xx.x%)", "distinct_n", "distinct_pct")
        )
      )
    )
  )
)

result <- tplyr_build(spec, tplyr_adae, pop_data = tplyr_adsl)
collapsed <- collapse_row_labels(result, "rowlabel1", "rowlabel2", indent = "   ")
kable(head(collapsed[, c("row_label", "res1", "res2", "res3")], 15))
row_label res1 res2 res3
CARDIAC DISORDERS
4 ( 4.7%) 6 ( 7.1%) 5 ( 6.0%)
ATRIAL FIBRILLATION 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
ATRIAL FLUTTER 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
ATRIAL HYPERTROPHY 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
BUNDLE BRANCH BLOCK RIGHT 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
CARDIAC FAILURE CONGESTIVE 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
MYOCARDIAL INFARCTION 0 ( 0.0%) 1 ( 1.2%) 2 ( 2.4%)
SINUS BRADYCARDIA 0 ( 0.0%) 3 ( 3.6%) 1 ( 1.2%)
SUPRAVENTRICULAR EXTRASYSTOLES 1 ( 1.2%) 0 ( 0.0%) 1 ( 1.2%)
SUPRAVENTRICULAR TACHYCARDIA 0 ( 0.0%) 0 ( 0.0%) 1 ( 1.2%)
TACHYCARDIA 1 ( 1.2%) 0 ( 0.0%) 0 ( 0.0%)
VENTRICULAR EXTRASYSTOLES 0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)
CONGENITAL, FAMILIAL AND GENETIC DISORDERS
0 ( 0.0%) 1 ( 1.2%) 0 ( 0.0%)

Where to Go from Here

Count layers in tplyr2 cover a wide range of clinical table patterns, but there is much more to explore. Here are some related topics covered in other vignettes: