How to Compute Row Percentages of Total in R

March 15, 2026 · 2 min read ·Updated May 28, 2026 ·beginner

rpercentagedplyrdata.table

To compute row percentages of total, divide each value by the sum of all values in its group, then multiply by 100. This is a common summarization task in R, useful for frequency tables, budget breakdowns, and contribution analysis. The pattern works across base R, dplyr, and data.table with only minor syntax differences.

library(dplyr)

df <- data.frame(
  product = c("A", "B", "C", "D"),
  sales   = c(150, 300, 100, 450)
)

df %>% mutate(percentage = sales / sum(sales) * 100)
#   product sales percentage
# 1       A   150      15
# 2       B   300      30
# 3       C   100      10
# 4       D   450      45

For grouped percentages, add group_by() before mutate(): df |> group_by(category) |> mutate(pct = sales / sum(sales) * 100). Use na.rm = TRUE in sum() to handle missing values, and call ungroup() afterward to prevent grouping side effects. The base R equivalent is df$percentage <- df$sales / sum(df$sales) * 100, and data.table uses dt[, percentage := sales / sum(sales) * 100].

# Grouped percentages
df %>% group_by(category) %>% mutate(pct = sales / sum(sales) * 100)

# data.table approach
library(data.table)
dt[, percentage := sales / sum(sales) * 100]

For frequency tables, prop.table(table(x)) converts a count table to proportions in one call. Use margin = 1 for row proportions or margin = 2 for column proportions. The scales::percent() function formats decimal proportions as display-ready percentage strings. For cumulative percentages across ordered categories, cumsum(prop.table(table(x))) * 100 gives a running total that is useful for Pareto charts and contribution analysis.

See also