You can use the following syntax to filter data frames by multiple conditions using the dplyr library:

**Method 1: Filter by Multiple Conditions Using OR**

library(dplyr) df %>% filter(col1 == 'A' | col2 > 90)

**Method 2: Filter by Multiple Conditions Using AND**

library(dplyr) df %>% filter(col1 == 'A' & col2 > 90)

The following example shows how to use these methods in practice with the following data frame in R:

#create data frame df <- data.frame(team=c('A', 'A', 'B', 'B', 'C'), points=c(99, 90, 86, 88, 95), assists=c(33, 28, 31, 39, 34), rebounds=c(30, 28, 24, 24, 28)) #view data frame df team points assists rebounds 1 A 99 33 30 2 A 90 28 28 3 B 86 31 24 4 B 88 39 24 5 C 95 34 28

**Method 1: Filter by Multiple Conditions Using OR**

The following code shows how to use the or ( **|** ) operator to filter the data frame by rows that meet one of multiple conditions:

library(dplyr) #filter for rows where team is equal to 'A' or points is greater than 90 df %>% filter(team == 'A' | points > 90) team points assists rebounds 1 A 99 33 30 2 A 90 28 28 3 C 95 34 28

The only rows returned are those where the team is equal to ‘A’ **or** where points is greater than 90.

Note that we can use as many “or” operators as we’d like in the filter function:

library(dplyr) #filter for rows where team is equal to 'A' or 'C' or points is less than 89 df %>% filter(team == 'A' | team == 'C' | points > 90) team points assists rebounds 1 A 99 33 30 2 A 90 28 28 3 B 86 31 24 4 C 95 34 28

**Method 2: Filter by Multiple Conditions Using AND**

The following code shows how to use the and ( **&** ) operator to filter the data frame by rows that meet several conditions:

library(dplyr) #filter for rows where team is equal to 'A' and points is greater than 90 df %>% filter(team == 'A' & points > 90) team points assists rebounds 1 A 99 33 30

Only one row met both conditions in the filter function.

Note that we can also use as many “and” operators as we’d like in the filter function:

library(dplyr) #filter where team is equal to 'A' and points > 89 and assists < 30 df %>% filter(team == 'A' & points > 89 & assists < 30) team points assists rebounds 1 A 90 28 28

**Note**: You can find the complete documentation for the dplyr **filter()** function here.

**Additional Resources**

The following tutorials explain how to perform other common operations in dplyr:

How to Filter by Date Using dplyr

How to Filter for Unique Values Using dplyr

How to Filter by Row Number Using dplyr

why in the below code when condition is points > 90, team B with 86 points is selected

#filter for rows where team is equal to ‘A’ or ‘C’ or points is less than 89

df %>%

filter(team == ‘A’ | team == ‘C’ | points > 90)

team points assists rebounds

1 A 99 33 30

2 A 90 28 28

3 B 86 31 24

4 C 95 34 28