The split() Function
Introduction to split()
The split() function in R is used to divide data into groups based on a factor or a list of factors. It splits a vector, data frame, or list into subsets according to the values of a factor or factors.
Syntax of split()
The general syntax of split() is:
split(x, f, drop = FALSE, ...)
- x: The object to be split (usually a vector, data frame, or list).
- f: A factor or a list of factors that define the grouping.
- drop: A logical value indicating whether levels that do not occur should be dropped from the result.
- …: Additional arguments to pass to methods.
Basic Example
Let’s start with a simple example where we split a vector based on a factor.
# Numeric vector
values <- c(10, 20, 15, 25, 30, 35)
# Factor vector defining groups
groups <- factor(c("A", "B", "A", "B", "A", "B"))
# Split the vector based on the factor
split_values <- split(values, groups)
print(split_values)
# Output:
# $A
# [1] 10 15 30
# $B
# [1] 20 25 35
In this example, split() divides the values vector into two components: one for each level of groups (A and B).
Advanced Examples
Splitting a Data Frame
You can use split() to divide a data frame into a list of data frames based on a factor.
# Create a data frame
df <- data.frame(
id = 1:6,
value = c(10, 20, 15, 25, 30, 35),
group = factor(c("A", "B", "A", "B", "A", "B"))
)
# Split the data frame by the 'group' column
split_df <- split(df, df$group)
print(split_df)
# Output:
# $A
# id value group
# 1 1 10 A
# 3 3 15 A
# 5 5 30 A
# $B
# id value group
# 2 2 20 B
# 4 4 25 B
# 6 6 35 B
Splitting by Multiple Factors
You can split data by multiple factors by providing a list of factors.
# Additional factor vector
subgroups <- factor(c("X", "Y", "X", "Y", "X", "Y"))
# Split the data frame by both 'group' and 'subgroup'
split_df_multiple <- split(df, list(df$group, df$subgroup))
print(split_df_multiple)
# Output:
# $`A.X`
# id value group subgroup
# 1 1 10 A X
# 5 5 30 A X
# $`A.Y`
# id value group subgroup
# 3 3 15 A Y
# $`B.X`
# id value group subgroup
# 2 2 20 B X
# $`B.Y`
# id value group subgroup
# 4 4 25 B Y
# 6 6 35 B Y
Dropping Unused Levels
You can use the drop argument to control whether unused levels are included in the result.
# Factor vector with levels not in data
f <- factor(c("A", "B", "A", "B", "A", "C"), levels = c("A", "B", "C", "D"))
# Split with unused levels
split_values_with_levels <- split(values, f)
print(split_values_with_levels)
# Output:
# $A
# [1] 10 15 30
# $B
# [1] 20 25 35
# $C
# NULL
# $D
# NULL
Here, split() includes the levels “C” and “D” even though they are not present in the data. If you set drop = TRUE, those levels would be omitted.
Applying Functions to Split Data
You can use lapply() in conjunction with split() to apply functions to each subset.
# Calculate the mean for each subset mean_by_group <- lapply(split(df$value, df$group), mean) print(mean_by_group) # Output: # $A # [1] 18.33333 # $B # [1] 26.66667
In this example, lapply() applies the mean function to each subset of df$value created by split().
Summary
The split() function in R is a versatile tool for dividing data into subsets based on one or more factors. It can be used with vectors, data frames, and lists, and is often combined with other functions to perform more complex data manipulations and analyses. Understanding how to effectively use split() can greatly enhance your ability to handle and analyze grouped data in R.