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Removing rows with any zeros in R can be done using the subset() function. This function allows you to specify which rows you would like to keep, based on a logical expression. For example, you can use subset() to keep only those rows that have all non-zero values by specifying that all columns have values greater than 0. This can be done by writing subset(dataset, dataset > 0). This will return a new dataset containing only those rows that have all non-zero values.
You can use one of the following methods to remove rows with any zeros in a data frame in R:
Method 1: Remove Rows with Any Zeros Using Base R
df_new <- df[apply(df!=0, 1, all),]
Method 2: Remove Rows with Any Zeros Using dplyr
library(dplyr) df_new <- filter_if(df, is.numeric, all_vars((.) != 0))
The following examples show how to use each method in practice with the following data frame:
#create data frame df <- data.frame(points=c(5, 7, 8, 0, 12, 14, 0, 10, 8), assists=c(0, 2, 2, 4, 4, 3, 7, 6, 10), rebounds=c(8, 8, 7, 3, 6, 5, 0, 12, 11)) #view data frame df points assists rebounds 1 5 0 8 2 7 2 8 3 8 2 7 4 0 4 3 5 12 4 6 6 14 3 5 7 0 7 0 8 10 6 12 9 8 10 11
Example 1: Remove Rows with Any Zeros Using Base R
The following code shows how to remove rows with any zeros by using the function from base R:
#create new data frame that removes rows with any zeros from original data frame df_new <- df[apply(df!=0, 1, all),] #view new data frame df_new points assists rebounds 2 7 2 8 3 8 2 7 5 12 4 6 6 14 3 5 8 10 6 12 9 8 10 11
Notice that the three rows with zero values in them have been removed.
Example 2: Remove Rows with Any Zeros Using dplyr
The following code shows how to remove rows with any zeros by using the filter_if() function from the dplyr package in R:
#create new data frame that removes rows with any zeros from original data frame df_new <- filter_if(df, is.numeric, all_vars((.) != 0)) #view new data frame df_new points assists rebounds 1 7 2 8 2 8 2 7 3 12 4 6 4 14 3 5 5 10 6 12 6 8 10 11
Notice that the three rows with zero values in them have been removed.
This matches the result that we got using base R.
Note: We used the is.numeric function to specify that all numeric variables in the data frame must be non-zero.
The following tutorials explain how to perform other common tasks in R: