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),…