Predictive Hacks

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

```library(dplyr)

url = 'https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/ff414a1bcfcba32481e4d4e8db578e55872a2ca1/titanic.csv'

### Use of 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 🙂