This function calculates the five-number summary (minimum, first quartile, median, third quartile, maximum) for specified numeric columns in a data frame and returns the results in a long format. It also handles categorical, factor, and logical columns by counting the occurrences of each level or value, and includes the results in the summary. The type column indicates whether the data is numeric, character, factor, or logical.
Source: R/tidy_summary.R
tidy_summary.RdThis function calculates the five-number summary (minimum, first quartile, median, third quartile, maximum) for specified numeric columns in a data frame and returns the results in a long format. It also handles categorical, factor, and logical columns by counting the occurrences of each level or value, and includes the results in the summary. The type column indicates whether the data is numeric, character, factor, or logical.
Usage
tidy_summary(df, columns = names(df), ...)Arguments
- df
A data frame containing the data. The data frame must have at least one row.
- columns
Unquoted column names or tidyselect helpers specifying the columns for which to calculate the summary. Defaults to call columns in the inputted data frame.
- ...
Additional arguments passed to the
min,quantile,median, andmaxfunctions, such asna.rm.
Value
A tibble in long format with columns:
- column
The name of the column.
- n
The number of non-missing values in the column for numeric variables and the number of non-missing values in the group for categorical, factor, and logical columns.
- group
The group level or value for categorical, factor, and logical columns.
- type
The type of data in the column (numeric, character, factor, or logical).
- min
The minimum value (for numeric columns).
- Q1
The first quartile (for numeric columns).
- mean
The mean value (for numeric columns).
- median
The median value (for numeric columns).
- Q3
The third quartile (for numeric columns).
- max
The maximum value (for numeric columns).
- sd
The standard deviation (for numeric columns).
Examples
# Example usage with a simple data frame
df <- tibble::tibble(
category = factor(c("A", "B", "A", "C")),
int_values = c(10, 15, 7, 8),
num_values = c(8.2, 0.3, -2.1, 5.5),
one_missing_value = c(NA, 1, 2, 3),
flag = c(TRUE, FALSE, TRUE, TRUE)
)
# Specify columns
tidy_summary(df, columns = c(category, int_values, num_values, flag))
#> # A tibble: 7 × 11
#> column n group type min Q1 mean median Q3 max sd
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 int_values 4 NA numeric 7 7.75 10 9 11.2 15 3.56
#> 2 num_values 4 NA numeric -2.1 -0.3 2.97 2.9 6.18 8.2 4.71
#> 3 category 2 A factor NA NA NA NA NA NA NA
#> 4 category 1 B factor NA NA NA NA NA NA NA
#> 5 category 1 C factor NA NA NA NA NA NA NA
#> 6 flag 1 FALSE logical NA NA NA NA NA NA NA
#> 7 flag 3 TRUE logical NA NA NA NA NA NA NA
# Defaults to full data frame (note an error will be given without
# specifying `na.rm = TRUE` since `one_missing_value` has an `NA`)
tidy_summary(df, na.rm = TRUE)
#> # A tibble: 8 × 11
#> column n group type min Q1 mean median Q3 max sd
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 int_values 4 NA nume… 7 7.75 10 9 11.2 15 3.56
#> 2 num_values 4 NA nume… -2.1 -0.3 2.97 2.9 6.18 8.2 4.71
#> 3 one_missing_value 3 NA nume… 1 1.5 2 2 2.5 3 1
#> 4 category 2 A fact… NA NA NA NA NA NA NA
#> 5 category 1 B fact… NA NA NA NA NA NA NA
#> 6 category 1 C fact… NA NA NA NA NA NA NA
#> 7 flag 1 FALSE logi… NA NA NA NA NA NA NA
#> 8 flag 3 TRUE logi… NA NA NA NA NA NA NA
# Example with additional arguments for quantile functions
tidy_summary(df, columns = c(one_missing_value), na.rm = TRUE)
#> # A tibble: 1 × 11
#> column n group type min Q1 mean median Q3 max sd
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 one_missing_value 3 NA nume… 1 1.5 2 2 2.5 3 1