Descriptive statistic on dataset

This function provides a summary of a dataset, including both numeric and non-numeric variables. For numeric variables, it calculates basic descriptive statistics such as minimum, maximum, median, mean, and count of non-missing values. Additionally, users can pass custom functions via the fn argument to compute additional statistics for numeric variables. For non-numeric variables, it provides frequency counts and proportions for each unique value.

Usage

ct_describe_df(data, ..., fn = NULL, by_group = TRUE)

Arguments

data

A data frame containing the dataset to be summarized.

...

(Optional) Columns to include in the summary. If no column is specified, all columns in the data will be included.

fn

A list of functions to apply to numeric variables. Each function must accept x as a vector of numeric values and return a single value or a named vector. Additional arguments for these functions can be specified as a list. For example: fn = list('sum' = list(na.rm = TRUE), 'sd').

by_group

Logical, default TRUE. When TRUE and both categorical and numeric variables are selected, each numeric variable is summarised within the groups defined by the categorical variable(s). When FALSE, numeric and categorical variables are summarised independently and stacked into a single table. If only one variable type is present, by_group has no effect.

Value

A tibble whose shape depends on by_group.

With by_group = TRUE (and at least one categorical and one numeric variable), the data are grouped by the selected categorical variable(s) and each numeric variable is summarised within every group. The result has one row per numeric variable and group combination, with columns:

Variable

Name of the numeric variable being summarised.

grouping column(s)

One column per selected categorical variable, each holding the group value.

N

Number of non-missing values of Variable in the group.

Min, Max, Median, Mean

Descriptive statistics of Variable within the group.

CI Left, CI Right

Lower and upper bounds of the 95% t-based confidence interval for the group mean (see ct_ci()).

With by_group = FALSE (also used as a fallback when only numeric or only categorical variables are selected), numeric and categorical summaries are stacked into one tibble with one row per numeric variable and one row per distinct value of each categorical variable; columns that do not apply to a row are NA:

Variable

Name of the summarised column.

Group

For a categorical variable, the distinct value described; NA for numeric variables.

Prop

For a categorical variable, the percentage of its non-missing records falling in Group; NA for numeric variables.

N

Non-missing count: values for a numeric variable, or records in Group for a categorical variable.

Min, Max, Median, Mean

Numeric statistics; NA for categorical variables.

CI Left, CI Right

95% t-based confidence interval bounds for the mean (see ct_ci()); NA for categorical variables.

In both modes, supplying fn appends one extra column per named function, holding that statistic for each numeric variable (or group).

See also

parse_list_fn

Examples

df <- data.frame(x = c(1:3, NA),
                 y = c(3:4, NA, NA),
                 z = c("A", "A", "B", "A"))

# Numeric variables summarised within each group of the categorical variable
ct_describe_df(df, y, x, z)
#> # A tibble: 4 × 9
#>   Variable z         N   Min   Max Median  Mean `CI Left` `CI Right`
#>   <chr>    <chr> <int> <dbl> <dbl>  <dbl> <dbl>     <dbl>      <dbl>
#> 1 y        A         2     3     4    3.5   3.5     -2.85       9.85
#> 2 y        B         0    NA    NA   NA   NaN       NA         NA   
#> 3 x        A         2     1     2    1.5   1.5     -4.85       7.85
#> 4 x        B         1     3     3    3     3       NA         NA   

# Summarise every variable independently
ct_describe_df(df, y, x, z, by_group = FALSE)
#> # A tibble: 4 × 10
#>   Group  Prop     N Variable  Mean   Min   Max Median `CI Left` `CI Right`
#>   <chr> <dbl> <int> <chr>    <dbl> <dbl> <dbl>  <dbl>     <dbl>      <dbl>
#> 1 NA       NA     2 y          3.5     3     4    3.5    -2.85        9.85
#> 2 NA       NA     3 x          2       1     3    2      -0.484       4.48
#> 3 A        75     3 z         NA      NA    NA   NA      NA          NA   
#> 4 B        25     1 z         NA      NA    NA   NA      NA          NA   

# Add custom statistics for the numeric variables
ct_describe_df(df, y, x, z,
               fn = list('sum' = list(na.rm = TRUE), 'sd' = list(na.rm = TRUE)))
#> # A tibble: 4 × 11
#>   Variable z         N   Min   Max Median  Mean `CI Left` `CI Right`   sum
#>   <chr>    <chr> <int> <dbl> <dbl>  <dbl> <dbl>     <dbl>      <dbl> <int>
#> 1 y        A         2     3     4    3.5   3.5     -2.85       9.85     7
#> 2 y        B         0    NA    NA   NA   NaN       NA         NA        0
#> 3 x        A         2     1     2    1.5   1.5     -4.85       7.85     3
#> 4 x        B         1     3     3    3     3       NA         NA        3
#> # ℹ 1 more variable: sd <dbl>