R Language Grouping And Aggregating Data Complete Guide

1. How can I group data by a single factor and compute the mean of all numeric columns in R?

Answer: You can use the dplyr package, which makes grouping and summarizing data straightforward. Here’s example code:

# Install and load dplyr package if not already installed
install.packages("dplyr")
library(dplyr)

# Sample data frame
dat <- data.frame(
  Group    = factor(rep(1:3, each = 10)),
  Value1   = runif(30),
  Value2   = rnorm(30),
  stringsAsFactors = FALSE
)

# Group by 'Group' and compute mean for all numeric columns
result <- dat %>%
  group_by(Group) %>%
  summarise(across(where(is.numeric), mean))

print(result)

2. How do I aggregate data using multiple criteria in R?

Answer: Use dplyr again, and this time group by multiple columns.

# Create a dataframe
dat <- data.frame(
  Category = c("A", "B", "A", "B", "A"),
  SubCategory = c("X", "X", "Y", "Y", "Z"),
  Score = rnorm(5, 85, 5)
)

# Group by 'Category' and 'SubCategory', compute mean of Score
result <- dat %>%
  group_by(Category, SubCategory) %>%
  summarise(meanScore = mean(Score))

print(result)

3. How can I count the occurrences of each group in a dataset?

Answer: Use count or summarise with n() in dplyr.

# Count occurrences of each 'Group'
result <- dat %>%
  count(Group)

# Using summarise
result_summarise <- dat %>%
  group_by(Group) %>%
  summarise(count = n())

print(result)
print(result_summarise)

4. How do I aggregate data using the aggregate function in R?

Answer: You can use aggregate for summarizing data without depending on dplyr.

# Aggregate data using 'aggregate'
result_aggregate <- aggregate(Value1 ~ Group, data = dat, FUN = mean)

print(result_aggregate)

5. How do I perform weighted averaging of a variable grouped by factor in R?

Answer: Use weighted.mean inside dplyr or apply the weighted mean function directly within aggregate.

# Add a weight column
dat$Weight <- rnorm(30, 1, 0.5)

# Use dplyr
result_weighted_dplyr <- dat %>%
  group_by(Group) %>%
  summarise(weighted_mean = weighted.mean(Value1, Weight, na.rm = TRUE))

# Use aggregate
result_weighted_aggregate <- aggregate(Value1 ~ Group, data = dat, 
                                       FUN = function(x) weighted.mean(x, dat$Weight, na.rm = TRUE))

print(result_weighted_dplyr)
print(result_weighted_aggregate)

6. How do I apply multiple aggregate functions at once using dplyr?

Answer: You can use summarise to call multiple aggregate functions.

# Compute mean and sum for Value1 in each group
result_summarise <- dat %>%
  group_by(Group) %>%
  summarise(
    mean_value1 = mean(Value1),
    sum_value1 = sum(Value1)
  )

print(result_summarise)

7. How do I aggregate data by date in R?

Answer: Create a Date column and use it in conjunction with group_by.

# Create a sample data frame
dat_time <- data.frame(
  Date     = seq(as.Date('2021-01-01'), by = 'day', length.out = 30),
  Sales    = rpois(30, lambda = 10)
)

# Group by week and compute sum of Sales
result_weekly <- dat_time %>%
  mutate(Week = yearweek(Date)) %>%
  group_by(Week) %>%
  summarise(total_sales = sum(Sales))

print(result_weekly)

8. How can I use ddply from the plyr package to perform complex aggregations?

Answer: ddply is useful for more complex operations. It splits a data frame, applies a function, and then combines the results.

# Install plyr if not installed
install.packages("plyr")
library(plyr)

# Sample data frame
dat <- data.frame(
  Category = rep(c("A", "B"), each = 10),
  Type = rep(c("I", "II"), times = 10),
  Score = rnorm(20, 75, 10)
)

# Use ddply
result_ddply <- ddply(dat, .(Category, Type),
                     summarise,
                     Mean_Score = mean(Score),
                     Min_Score = min(Score),
                     Max_Score = max(Score))

print(result_ddply)

9. How can I perform aggregations and also keep the grouping variables in the result with data.table?

Answer: data.table offers a very efficient way to handle large datasets.

# Install data.table and create a data frame
install.packages("data.table")
library(data.table)
dat <- data.table(
  Category = rep(c("X", "Y"), each = 15),
  Value = rnorm(30, 50, 10)
)

# Perform aggregation with data.table
result_dt <- dat[, .(mean_value = mean(Value)), by = Category]
result_dt_with_original <- dat[, .(Category, mean_value = mean(Value))][, unique()]

print(result_dt_with_original)

10. How can I pivot a data frame to summarize data by grouping and then reshape it using tidyr?

Answer: Use pivot_table style summarization and reshape with tidyr::pivot_wider.