# R: How To Assign Values Based On Multiple Conditions Of Different Columns

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In the previous post, we showed how we can assign values in Pandas Data Frames based on multiple conditions of different columns.

Again we will work with the famous `titanic`

dataset and our scenario is the following:

- If the
`Age`

is`NA`

and`Pclass`

=1 then the Age=40 - If the
`Age`

is`NA`

and`Pclass`

=2 then the Age=30 - If the
`Age`

is`NA`

and`Pclass`

=3 then the Age=25 - Else the
`Age`

will remain as is

**Load the Data**

library(dplyr) url = 'https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/ff414a1bcfcba32481e4d4e8db578e55872a2ca1/titanic.csv' df = read.csv(url, sep="\t")

**Use of ***case_when *function of *dplyr*

*case_when*function of

*dplyr*

For this task, we will use the `case_when`

function of `dplyr`

as follows:

df<-df%>%mutate(New_Column = case_when( is.na(Age) & Pclass==1 ~ 40, is.na(Age) & Pclass==2 ~ 30, is.na(Age) & Pclass==3 ~ 25, TRUE~Age ))

Let’s have a look at the Age, Pclass and the New_Column that we created.

df%>%select(Age, Pclass, New_Column)

Age Pclass New_Column 1 22.00 3 22.00 2 38.00 1 38.00 3 26.00 3 26.00 4 35.00 1 35.00 5 35.00 3 35.00 6 NA 3 25.00

As we can see we get the expected results

To

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