Data Aggregation and Summarization

R provides tools to summarize and aggregate data efficiently. The dplyr package makes this easy using functions like group_by() and summarize().

Summarizing Data

library(dplyr)

# Sample data
sales <- data.frame(
  region = c("North", "South", "North", "East", "South"),
  revenue = c(100, 150, 200, 130, 170)
)

# Summarize total revenue by region
sales_summary <- sales %>%
  group_by(region) %>%
  summarize(total_revenue = sum(revenue))

sales_summary

Calculating Summary Statistics

# Calculate mean, max, and min revenue
sales %>%
  summarize(
    mean_revenue = mean(revenue),
    max_revenue = max(revenue),
    min_revenue = min(revenue)
  )

These techniques are essential for analyzing patterns and understanding trends in your dataset.

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