You can use the **subset()** function to remove rows with certain values in a data frame in R:

#only keep rows where col1 value is less than 10 and col2 value is less than 8 new_df <- subset(df, col1<10 & col2<8)

The following examples show how to use this syntax in practice with the following data frame:

#create data frame df <- data.frame(a=c(1, 3, 4, 6, 8, 9), b=c(7, 8, 8, 7, 13, 16), c=c(11, 13, 13, 18, 19, 22), d=c(12, 16, 18, 22, 29, 38)) #view data frame df a b c d 1 1 7 11 12 2 3 8 13 16 3 4 8 13 18 4 6 7 18 22 5 8 13 19 29 6 9 16 22 38

**Example 1: Remove Rows Equal to Some Value**

The following code shows how to remove all rows where the value in column ‘c’ is equal to 13:

#remove rows where column 'c' is equal to 13 new_df <- subset(df, c != 13) #view updated data frame new_df a b c d 1 1 7 11 12 4 6 7 18 22 5 8 13 19 29 6 9 16 22 38

**Example 2: Remove Rows Equal to One of Several Values**

The following code shows how to remove all rows where the value in column ‘b’ is equal to 7 or 8:

#remove rows where value in column b is equal to 7 or 8 new_df <- subset(df, !(b %in% c(7, 8))) #view updated data frame new_df a b c d 5 8 13 19 29 6 9 16 22 38

**Example 3: Remove Rows Based on Multiple Conditions**

The following code shows how to remove all rows where the value in column ‘b’ is equal to 7 or where the value in column ‘d’ is equal to 38:

#remove rows where value in column b is 7 or value in column d is 38 new_df <- subset(df, b != 7 & d != 38) #view updated data frame new_df a b c d 2 3 8 13 16 3 4 8 13 18 5 8 13 19 29

**Additional Resources**

How to Remove Duplicate Rows in R

How to Use %in% Operator in R

How to Recode Values in R