Multiple conditions within if_else, if else and if statements

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Consider the following data:

Names <- c("name1", "name2", "name3", "name4", "name1")
Methods <- c("method1","method2", "method2", "method1", "method2")
Amount <- c(5, 10, 15, 20, 25)
df <- data.frame(Names, Methods, Amount)

I want to create a new variable for the dataframe based on a condition within a condition of the df as a new column. I tried it based on 1 conditions and that worked:

df <- mutate(df, new_column = if_else(Methods=="method2", 0.05 + Amount * 1.5, NULL))

However how can I get a condition within this condition, to get for example:

name1 with method 2 and then give the new_column a value of 0.25 + amount * 0.5 ?

I tried the following code:

df <- mutate(df, new_column = if_else(Names=="name1",
if_else(Methods=="method2", 0.25 + Amount * 0.5, NULL)))

Can anybody help me please?

multiple-conditions conditional-statements if-statement r
1 Answers

You could use:

mutate(df, new_column = ifelse(Methods=="method2" & Names=="name1", 0.05 + Amount * 1.5, 
                                ifelse(Names=="name2", 0.05 + Amount * 1.5, NA
                                )))

  Names Methods Amount new_column
1 name1 method1      5       7.55
2 name2 method2     10      15.05
3 name3 method2     15      22.55
4 name4 method1     20         NA
5 name1 method2     25      37.55

The difference between ifelse and if_else is that (from ?if_else)

compared to the base ifelse(), this function is more strict. It checks that true and false are the same type. This strictness makes the output type more predictable, and makes it somewhat faster.

It also makes it less flexible.

If you have many of these conditions, you should use case_when() as @SamR mentioned.

mutate(df, new_column = case_when(
                                  Methods=="method2"& Names=="name1"~ 0.05 + Amount * 1.5, 
                                  Names=="name2"~ 0.05 + Amount * 1.5, 
                                  TRUE ~ NA_real_
                               ))
  Names Methods Amount new_column
1 name1 method1      5       7.55
2 name2 method2     10      15.05
3 name3 method2     15      22.55
4 name4 method1     20         NA
5 name1 method2     25      37.55
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