Calculating 25th, 50th and 75th Percentile of Column Values

Calculating Percentiles

When we have a list of values in a column, how can we determine which values are under/over the 25th percentile, 50th percentile or 75th percentile?

Here the example are countries’ average percentages of the population with, broadly speaking, ICT Skills as determine by the Sustainable Development Goals, Indicator 4.4.1.

There are three methods. First, manually calculating values for the 25th, 50th and 75th percentile with the quantile() function.

# Country mean_values at 25th, 50th and 75th percentile 

data %>%
    select(GeoAreaName, Value, Sex, `Type of skill`, TimePeriod, Units) %>%
    rename(type_of_skill = `Type of skill`) %>%
    mutate(Value = as.numeric(Value)) %>%
    group_by(GeoAreaName) %>%
    summarize(
        mean_value = mean(Value)
    ) %>%
    mutate(
        min_mean = min(mean_value),
        iqr_25_percentile = quantile(mean_value, probs = c(0.25)),
        iqr_50_percentile = quantile(mean_value, probs = c(0.50)),
        iqr_75_percentile = quantile(mean_value, probs = c(0.75)),
        max_mean = max(mean_value)
    ) %>%
    arrange(desc(mean_value)) 

The second approach is to use the ntile() function:

# Creating bins using ntile()

data %>%
    select(GeoAreaName, Value, Sex, `Type of skill`, TimePeriod, Units) %>%
    rename(type_of_skill = `Type of skill`) %>%
    mutate(Value = as.numeric(Value)) %>%
    group_by(GeoAreaName) %>%
    summarize(
        mean_value = mean(Value)
    ) %>%
    mutate(
        mean_value_binned = ntile(mean_value, 4)
    ) %>%
    arrange(desc(mean_value))

The third approach uses the purrr package and the partial function that can be used with dplyr's summarize_at() function. Check out the source

## Using purrr
library(purrr)
    
p <- c(0.25, 0.50, 0.75)

p_names <- map_chr(p, ~paste0(.x*100, "%"))

p_funs <- map(p, ~partial(quantile, probs = .x, na.rm = TRUE)) %>%
    set_names(nm = p_names)

p_funs

data %>%
    select(GeoAreaName, Value, Sex, `Type of skill`, TimePeriod, Units) %>%
    rename(type_of_skill = `Type of skill`) %>%
    mutate(Value = as.numeric(Value)) %>%
    group_by(GeoAreaName) %>%
    summarize(
        mean_value = mean(Value)
    ) %>%
    summarize_at(vars(mean_value), funs(!!!p_funs))