Create Summary Statistics for Specified Variables

Computes a set of summary statistics for numeric and integer variables in a data frame. It allows users to select specific variables for summarization and can calculate statistics for the whole dataset or within groups specified by the by argument. Additional detail levels for quantiles can be included.

Usage

create_summary_statistics(
  data,
  ...,
  by = NULL,
  detail = FALSE,
  drop_na = FALSE
)

Arguments

data

A data frame containing the variables to be summarized.

...

Comma-separated list of unquoted variable names in the data frame to summarize. These variables must be either numeric, integer, or logical.

by

An optional unquoted variable name to group the data before summarizing. If NULL (the default), summary statistics are computed across all observations.

detail

A logical flag indicating whether to compute detailed summary statistics, including additional quantiles. Defaults to FALSE, which computes basic statistics (n, mean, sd, min, median, max). When TRUE, additional quantiles (1%, 5%, 10%, 25%, 75%, 90%, 95%, 99%) are computed.

drop_na

A logical flag indicating whether to drop missing values for each variable (default is FALSE).

Value

A tibble with summary statistics for each selected variable. If by is specified, the output includes the grouping variable as well. Each row represents a variable (and a group if by is used), and each column contains the computed statistics.

Details

The function first checks that all specified variables are of type numeric, integer, or logical. If any variables do not meet this criterion, the function stops and returns an error message indicating the non-conforming variables.

The basic set of summary statistics includes the count of non-NA values (n), mean, standard deviation (sd), minimum (min), median (q50), and maximum (max). If detail is TRUE, the function also computes the 1st, 5th, 10th, 25th, 75th, 90th, 95th, and 99th percentiles.

Summary statistics are computed for each variable specified in .... If a by variable is provided, statistics are computed within each level of the by variable.

See also

Other utility functions: get_available_huggingface_files(), list_supported_datasets(), list_supported_datasets_ff(), list_supported_datasets_ff_legacy(), list_supported_datasets_macro_predictors(), list_supported_datasets_other(), list_supported_datasets_pseudo(), list_supported_datasets_wrds(), list_supported_indexes(), list_supported_jkp_factors(), list_tidy_finance_chapters(), open_tidy_finance_website(), trim(), validate_dates(), winsorize()

Examples

data <- data.frame(
  ret = c(0.01, -0.02, 0.03, NA, 0.005),
  size = c(100, 200, 150, 300, 250),
  group = c("A", "A", "B", "B", "A")
)

# Basic summary across all observations
create_summary_statistics(data, ret, size)
#> # A tibble: 2 × 7
#>   variable     n  mean    sd   min   q50   max
#>   <chr>    <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ret          4    NA  NA      NA    NA    NA
#> 2 size         5   200  79.1   100   200   300

# Grouped summary
create_summary_statistics(data, ret, size, by = group)
#> # A tibble: 4 × 8
#>   variable group     n      mean       sd    min     q50    max
#>   <chr>    <chr> <int>     <dbl>    <dbl>  <dbl>   <dbl>  <dbl>
#> 1 ret      A         3  -0.00167   0.0161  -0.02   0.005   0.01
#> 2 ret      B         1  NA        NA       NA     NA      NA   
#> 3 size     A         3 183.       76.4    100    200     250   
#> 4 size     B         2 225       106.     150    225     300   

# Detailed quantiles
create_summary_statistics(data, ret, detail = TRUE)
#> # A tibble: 1 × 15
#>   variable     n  mean    sd   min   q01   q05   q10   q25   q50   q75   q90
#>   <chr>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ret          4    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> # ℹ 3 more variables: q95 <dbl>, q99 <dbl>, max <dbl>