There are multiple ways to replace column values based on conditions in an R DataFrame. Conditionally updating columns is a very basic thing we do all the time while manipulating data.
In this article, I will explain how to replace values based on single/multiple logical conditions, and conditions on numeric and character columns in the R dataframe
First, Let’s create an R DataFrame.
# Create dataframe with numeric columns
df = data.frame(id=c(25,40,30,30),
name=c('Chris','Scott','Anna','Ramana'),
gender=c('m','m','f','m'),
marks1=c(99,30,50,NA),
marks2=c(80,99,60,45))
df
Yields below output.
# Output
id name gender marks1 marks2
1 25 Chris m 99 80
2 40 Scott m 30 99
3 30 Anna f 50 60
4 30 Ramana m NA 45
1. Replace Values Based on Condition in R
Replace column values based on checking logical conditions in R DataFrame is pretty straightforward. All you need to do is select the column vector you want to update and use the condition within [].
The following example demonstrates how to update DataFrame column values by checking conditions on a numeric column. It updates column id to 55 when its value is equal to 40.
# Replace Values Based on Condition
df$id[df$id == 40] <- 55
df
Yields below output. You can also use this approach to replace NA with 0 or replace NA with an empty string in R.
# Output
id name gender marks1 marks2
1 25 Chris m 99 80
2 55 Scott m 30 99
3 30 Anna f 50 60
4 30 Ramana m NA 45
2. Check the Condition of the Character Column
Similarly, you can also update the column value by checking the condition of the character column. The following example replaces the name column with the Jeni string when it finds the name value is equal to Chris.
# Check Condition on Character Column
df$name[df$name == "Chris"] <- "Jeni"
df
Yields below output.
# Output
id name gender marks1 marks2
1 25 Jeni m 99 80
2 55 Scott m 30 99
3 30 Anna f 50 60
4 30 Ramana m NA 45
3. Replace Values in Column Based on Multiple Conditions
Now, let’s see how to replace column values by checking multiple conditions in R. The following example demonstrates using & operator with two conditions. It updates column id with value 60 when id is equal to 55 and gender is equal to 'm'.
# Replace by Checking Multiple Conditions
df$id[df$id == 55 & df$gender == 'm'] <- "60"
df
Yields below output.
# Output
id name gender marks1 marks2
1 25 Jeni m 99 80
2 60 Scott m 30 99
3 30 Anna f 50 60
4 30 Ramana m NA 45
Replace All DataFrame Columns Conditionally
The below example updates all column values in a DataFrame to 95 when the existing value is 99. Here, marks1 and marks2 have 99 value hence, these two values are updated with 95.
# Replace all columns by condition
df[df==99] <- 95
df
Yields below output.
# Output
id name gender marks1 marks2
1 25 Jeni m 95 80
2 60 Scott m 30 95
3 30 Anna f 50 60
4 30 Ramana m NA 45
4. Using data.table to Replace Values Conditionally
If you have data.table, then use the following approach to replace values Conditionally. This performs much faster than the traditional approach.
First, you need to load the library using library("data.table“). In case you don’t have this package, install it using install.packages("data.table“).
#Load dplyr package
library("data.table")
# Replace conditionally using data.table.
df2 = as.data.table(df)
df2[id==30, id := 60]
df2
Yields below output.
# Output
id name gender marks1 marks2
1: 25 Chris m 99 80
2: 40 Scott m 30 99
3: 60 Anna f 50 60
4: 60 Ramana m NA 45
5. Replace Column Based on Condition Using dplyr Package
To use this mutate() method, first, you need to load its library using library("dplyr"). In case you don’t have this package, install it using install.packages("dplyr"). The dplyr package provides a set of functions to work with strings as easily as possible.
All previous examples use the Base R built-in functions that can be used on a smaller dataset but, for bigger data sets, you have to use methods from the dplyr package as they perform 30% faster. This package uses C++ code to evaluate.
Let’s see how we can write the above examples using dplyr::mutate()
#Load dplyr package
library(dplyr)
# Create dataframe with numeric columns
df=data.frame(id=c(25,40,30,30,45,40),
marks1=c(99,30,50,NA,40,50),
marks2=c(80,99,60,45,NA,60))
df
# Output
# id marks1 marks2
#1 25 99 80
#2 40 30 99
#3 30 50 60
# Replace using mutate() function and checking condition
# Replaces when id==30
df <- mutate(df, id = case_when(
id == 30 ~ 40,
TRUE ~ id
))
df
#Output
# id marks1 marks2
#1 25 99 80
#2 40 30 99
#3 40 50 60
6. Complete Examples of Replace Values Based on Condition
Following is a complete example of how to replace column values based on conditions in R DataFrame.
# Create dataframe with numeric columns
df = data.frame(id=c(25,40,30,30),
name=c('Chris','Scott','Anna','Ramana'),
gender=c('m','m','f','m'),
marks1=c(99,30,50,NA),
marks2=c(80,99,60,45))
df
# Example 1 - Replace Column Value Based on Condition
df$id[df$id == 40] <- 55
df
# Example 2 - Replace by Checking Condition on Character Column
df$name[df$name == "Chris"] <- "Jeni"
df
# Example 3 - Replace Column Value by Checking Multiple Conditions
df$id[df$id == 55 & df$gender == 'm'] <- "60"
df
# Example 4 - Replace all DataFrame columns by condition
df[df==99] <- 95
df
# Example 5 - Using data.table
library('data.table')
df2 = as.data.table(df)
df2[id==30, id := 60]
df2
# Create dataframe with numeric columns
df=data.frame(id=c(25,40,30,30,45,40),
marks1=c(99,30,50,NA,40,50),
marks2=c(80,99,60,45,NA,60))
df
# Example 6 - Using dplyr
# Using this only on numeric columns df.
library('dplyr')
df <- mutate(df, id = case_when(
id == 30 ~ 40,
TRUE ~ id
))
df
Conclusion
In this article, I have explained how to replace values based on a single logical condition, multiple conditions, conditions on numeric and character columns etc. Also covered using the data.table and dplyr packages.
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- How to Replace Character in a String
- How to Replace Column Value with Another Column
- How to Replace String with Another String or Character
- R – str_replace() to Replace Matched Patterns in a String.
- How to Replace Empty String with NA
- How to Replace Zero (0) with NA on Dataframe Column