How to Filter Data in R

Filtering data is essential for data analysis in R. Here are the main methods.

Method 1: Base R with Brackets

Use logical indexing:

df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana"),
  age = c(25, 30, 35, 28),
  city = c("Paris", "London", "Paris", "Berlin")
)

# Filter by single condition
adults <- df[df$age >= 30, ]
print(adults)

# Filter by multiple conditions
paris_young <- df[df$city == "Paris" & df$age < 30, ]
print(paris_young)

Method 2: subset() Function

A more readable base R approach:

# Single condition
adults <- subset(df, age >= 30)

# Multiple conditions
result <- subset(df, city == "Paris" & age < 30)

# Select specific columns too
result <- subset(df, age > 25, select = c(name, age))

Method 3: dplyr filter() (Recommended)

The tidyverse approach:

library(dplyr)

df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana"),
  age = c(25, 30, 35, 28),
  city = c("Paris", "London", "Paris", "Berlin")
)

# Single condition
adults <- df %>% filter(age >= 30)

# Multiple conditions (AND)
result <- df %>% filter(city == "Paris", age < 30)

# Multiple conditions (OR)
result <- df %>% filter(city == "Paris" | city == "Berlin")

Method 4: Filter with %in% for Multiple Values

library(dplyr)

# Filter where city is in a list
cities <- c("Paris", "Berlin")
result <- df %>% filter(city %in% cities)
print(result)

Method 5: Filter with String Matching

library(dplyr)
library(stringr)

# Filter names starting with "A"
result <- df %>% filter(str_starts(name, "A"))

# Filter names containing "li"
result <- df %>% filter(str_detect(name, "li"))

Summary

• Use base R brackets for quick filtering
• Use subset() for readable base R code
• Use dplyr filter() for tidyverse workflows (recommended)
• Use %in% for checking multiple values