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Summarize Frequency of Values in a Column

Source: R/summarize.R
summarize_data.Rd

Splits multi-value cells, counts frequencies, and computes percentages.

Usage

summarize_data(
  data,
  col,
  sep = "\r\n",
  study_id = StudyID,
  na.rm = TRUE,
  na_label = "Not reported",
  na_in_percent = TRUE,
  na_last = FALSE
)

Arguments

data

A data frame containing at least the columns specified by col and study_id.

col

Column to summarize (quoted or unquoted).

sep

Character. The separator used to split multi-value cells. Defaults to "\r\n".

study_id

Column containing study identifiers (quoted or unquoted). Defaults to StudyID.

na.rm

Logical. If TRUE (default), rows with missing values (NA or empty strings) in col are dropped. If FALSE, they are kept and labelled with na_label.

na_label

Character. Label used for missing values when na.rm = FALSE. Defaults to "Not reported".

na_in_percent

Logical. If TRUE (default), the denominator for percentages is the total number of rows (including missing). If FALSE, only non-missing rows are counted, so non-missing categories sum to \(\approx 100\%\).

na_last

Logical. If TRUE, the missing-value row (when na.rm = FALSE) is placed at the bottom of the result regardless of its frequency. Defaults to FALSE (sorted by frequency like all other rows).

Value

A data frame with columns for the grouping variable, Studies (comma-separated study IDs), Frequency, and Percent (numeric, 0–100).

Examples

df <- data.frame(
  StudyID = c("S1", "S2", "S3", "S4"),
  Design = c("RCT", "Cohort", NA, "RCT"),
  stringsAsFactors = FALSE
)

# Drop NAs (default)
summarize_data(df, Design)
#> # A tibble: 2 × 4
#>   Design Studies Frequency Percent
#>   <chr>  <chr>       <int>   <dbl>
#> 1 Cohort S2              1      25
#> 2 RCT    S1, S4          2      50

# Keep NAs with a custom label
summarize_data(df, Design, na.rm = FALSE, na_label = "Missing")
#> # A tibble: 3 × 4
#>   Design  Studies Frequency Percent
#>   <chr>   <chr>       <int>   <dbl>
#> 1 Missing S3              1      25
#> 2 Cohort  S2              1      25
#> 3 RCT     S1, S4          2      50

# Percentages of non-missing only
summarize_data(df, Design, na_in_percent = FALSE)
#> # A tibble: 2 × 4
#>   Design Studies Frequency Percent
#>   <chr>  <chr>       <int>   <dbl>
#> 1 Cohort S2              1    33.3
#> 2 RCT    S1, S4          2    66.7