filter( )

Keep rows that match conditions. filter() is from the dplyr package, which is part of the tidyverse.

Original Documentation ↗ dplyr Cheatsheet ↗

Required Library

install.packages("tidyverse")
library(tidyverse)

Syntax

# Pipe style
df %>% filter(condition)

# Non-pipe style
filter(df, condition)

df %>% filter(condition) and filter(df, condition) return only the rows where the condition is TRUE. Rows where the condition is FALSE or NA are removed.

Use == to test equality and %in% to match several allowed values. When checking for missing values, use is.na(x) instead of x == NA.

Examples

Filter condition on one column

Filter rows where weight is outside the 190–210 mg range

Filter rows where batch is B01, B03, or B05

Filter condition on multiple columns
Filtering with a negated condition
Filtering on missing values

Keep rows with missing dissolution readings

Keep rows with complete dissolution readings

Argument Overview

Required arguments must be included when using a function while optional arguments can be included on demand.

.data data frame | tibble Required

The data frame to filter. In a pipe (%>%), this is passed automatically from the left-hand side.

capsules %>% filter(pct_label >= 85)

Data Types: data.frame | tibble

… — filtering expressions logical expression(s) Required

One or more logical conditions. Keep rows where every condition is TRUE. Multiple conditions separated by commas behave like AND.

filter(capsules, pct_label >= 85, pct_label <= 115)
# Keep rows that satisfy multiple conditions
# (all conditions must be TRUE)
df %>% filter(condition1, condition2)

# Keep rows that satisfy at least one of several conditions
# (any condition must be TRUE)
df %>% filter(condition1 | condition2)

Use commas to combine multiple conditions with implicit AND logic. For OR logic, combine conditions with | inside a single expression.

Data Types: logical expressions that return TRUE/FALSE/NA per row

.by — group temporarily for this call tidy-select Optional

Group by selected columns only for this filter() call, without creating a permanently grouped data frame.

filter(capsules, weight_mg > mean(weight_mg), .by = batch)

Data Types: column names or tidy-select grouping expression · Default: NULL

.preserve — keep original grouping metadata logical Optional

When filtering an already grouped data frame, controls whether grouping structure is recalculated based on remaining rows. Most users can keep the default.

filter(grouped_data, value > 0, .preserve = TRUE)

Data Types: logical · Default: FALSE