Can the mutate be used when the mutation is conditional (depending on the values of certain column values)?
This example helps showing what I mean.
structure(list(a = c(1, 3, 4, 6, 3, 2, 5, 1), b = c(1, 3, 4,
2, 6, 7, 2, 6), c = c(6, 3, 6, 5, 3, 6, 5, 3), d = c(6, 2, 4,
5, 3, 7, 2, 6), e = c(1, 2, 4, 5, 6, 7, 6, 3), f = c(2, 3, 4,
2, 2, 7, 5, 2)), .Names = c("a", "b", "c", "d", "e", "f"), row.names = c(NA,
8L), class = "data.frame")
a b c d e f
1 1 1 6 6 1 2
2 3 3 3 2 2 3
3 4 4 6 4 4 4
4 6 2 5 5 5 2
5 3 6 3 3 6 2
6 2 7 6 7 7 7
7 5 2 5 2 6 5
8 1 6 3 6 3 2
I was hoping to find a solution to my problem using the dplyr package (and yes I know this not code that should work, but I guess it makes the purpose clear) for creating a new column g:
library(dplyr)
df <- mutate(df,
if (a == 2 | a == 5 | a == 7 | (a == 1 & b == 4)){g = 2},
if (a == 0 | a == 1 | a == 4 | a == 3 | c == 4) {g = 3})
The result of the code I am looking for should have this result in this particular example:
a b c d e f g
1 1 1 6 6 1 2 3
2 3 3 3 2 2 3 3
3 4 4 6 4 4 4 3
4 6 2 5 5 5 2 NA
5 3 6 3 3 6 2 NA
6 2 7 6 7 7 7 2
7 5 2 5 2 6 5 2
8 1 6 3 6 3 2 3
Does anyone have an idea about how to do this in dplyr? This data frame is just an example, the data frames I am dealing with are much larger. Because of its speed I tried to use dplyr, but perhaps there are other, better ways to handle this problem?