Most analytical work relies on a small set of core calculations.

Master these 10 operations in R and you’ll cover 80% of real‑world dashboards, reports, and automation tasks.

Below is a clean, practical guide with minimal theory and maximum usefulness.

Sum, Mean, Median r x <- c(10, 20, 30, 40)

sum(x)

mean(x)

median(x)

Grouped Calculations (dplyr) r library(dplyr)

df %>%

group_by(region) %>%

summarise(

avg_sales = mean(sales),

total = sum(sales)

)

Percent Change r percent_change <- (new - old) / old * 100

Rolling Calculations r library(zoo)

rollmean(df$value, k = 7, fill = NA)

Cumulative Calculations r cumsum(x) cumprod(x)

Ranking & Percentiles r rank(x) quantile(x, probs = 0.9)

Row‑wise Calculations r rowSums(df) rowMeans(df)

Conditional Calculations r df %>% mutate(score = case_when( value > 90 ~ "High", value > 70 ~ "Medium", TRUE ~ "Low" ))

Correlation & Covariance r cor(df$x, df$y) cov(df$x, df$y)

Mini KPI Block r df %>% summarise( total = sum(value), avg = mean(value), pct_change…