How to subset a data frame by multiple conditions in R

March 14, 2026 · 3 min read · Updated March 14, 2026 · beginner

r subsetting filtering dplyr data.table

Subsetting by multiple conditions lets you filter data based on several criteria at once. This is essential for real-world data analysis.

The Three Boolean Operators

Before diving in, understand the three key operators:

& (AND): Both conditions must be true
| (OR): At least one condition must be true
! (NOT): Negates a condition

With dplyr

The tidyverse filter() function handles multiple conditions clearly:

library(dplyr)

# AND: both conditions must be TRUE
subset <- df %>%
  filter(salary > 50000 & department == "Engineering")

# OR: at least one condition must be TRUE  
subset <- df %>%
  filter(salary > 70000 | years_experience > 5)

# Mix AND and OR with parentheses
subset <- df %>%
  filter((salary > 50000 | bonus > 10000) & department == "Sales")

Using ! (NOT):

# Exclude certain departments
subset <- df %>%
  filter(!department %in% c("HR", "Legal"))

# Keep rows where salary is NOT missing
subset <- df %>%
  filter(!is.na(salary))

With Base R

Base R uses square bracket notation with the same operators:

# AND: both conditions
subset <- df[df$salary > 50000 & df$department == "Engineering", ]

# OR: at least one condition
subset <- df[df$salary > 70000 | df$years_experience > 5, ]

# Complex conditions with parentheses
subset <- df[(df$salary > 50000 | df$bonus > 10000) & df$department == "Sales", ]

# NOT operator
subset <- df[!df$department %in% c("HR", "Legal"), ]

The comma after the conditions is required for data frames—it tells R to select rows.

With data.table

The data.table package uses a similar syntax but is more concise:

library(data.table)

dt <- as.data.table(df)

# AND
subset <- dt[salary > 50000 & department == "Engineering"]

# OR
subset <- dt[salary > 70000 | years_experience > 5]

# NOT
subset <- dt[!department %in% c("HR", "Legal")]

Practical Examples

Multiple Numeric Conditions

library(dplyr)

# Filter between a range
subset <- df %>%
  filter(age >= 25 & age <= 45)

# Equivalent using between()
subset <- df %>%
  filter(between(age, 25, 45))

Multiple String Conditions

library(dplyr)

# Match multiple exact values
subset <- df %>%
  filter(city %in% c("New York", "Los Angeles", "Chicago"))

# Starts with (using grepl)
subset <- df %>%
  filter(grepl("^Eng", department))

Combining Date and Numeric Conditions

library(dplyr)

df$hire_date <- as.Date(df$hire_date)

subset <- df %>%
  filter(
    hire_date > as.Date("2020-01-01"),
    salary > 50000,
    department %in% c("Engineering", "Data")
  )

Common Mistakes

Forgetting parentheses with mixed AND/OR causes unexpected results:

# WRONG - OR binds first, then AND
subset <- df %>% filter(salary > 50000 & department == "Sales" | department == "Engineering")

# RIGHT - use parentheses to control order
subset <- df %>% filter((salary > 50000 & department == "Sales") | department == "Engineering")