Cumulative Proportion Table

Cumulative proportions are useful when categories have a meaningful order—such as ratings or levels of satisfaction. We can compute them with cumsum():

library(dplyr)
songs %>%
  count(THEME) %>%
  mutate(
    prop  = n / sum(n),
    cum_prop = cumsum(prop)
  )
                 THEME   n  prop cum_prop
1           Heartbreak 145 0.145    0.145
2       Life_and_death 131 0.131    0.276
3                 Love 139 0.139    0.415
4          Party_songs 162 0.162    0.577
5    People_and_places 145 0.145    0.722
6 Politics_and_protest 141 0.141    0.863
7                  Sex 131 0.131    0.994
8                 <NA>   6 0.006    1.000

This produces a table showing the distribution and how it accumulates across categories.

If the categories are not already ordered, you may want to arrange() them before computing the cumulative values.

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