Renaming is a big factor in the analysis. I have a dataset like the following:
dat1 <- read.table(header=TRUE, text="
ID Age Align Weat
8645 15-24 A 1
6228 15-24 B 1
5830 15-24 C 3
1844 25-34 B 3
4461 35-44 C 4
2119 55-64 C 2
2115 45-54 A 1
")
dat1
ID Age Align Weat
1 8645 15-24 A 1
2 6228 15-24 B 1
3 5830 15-24 C 3
4 1844 25-34 B 3
5 4461 35-44 C 4
6 2119 55-64 C 2
7 2115 45-54 A 1
I want to change column 2 to column 4 into binary variables. My option is:
if in Age column, 15-24=1, otherwise=0
if in Align column, A=1, otherwise=0
if in Weat column, 3=1, otherwise=0
My code is not an easy solution (using plyr function rename). I want an easy to do code for more complex and large data.
library(plyr)
dat1$Age <- revalue(dat1$Age, c("15-24"=1,"25-34"=0,"35-44"=0,"45-54"=0,"55-64"=0))
dat1$Align <- revalue(dat1$Align, c("A"=1,"B"=0,"C"=0))
dat1$Weat <- as.factor(dat1$Weat)
dat1$Weat <- revalue(dat1$Weat, c("3"=1,"1"=0,"2"=0, "4"=0))
dat1
ID Age Align Weat
1 8645 1 1 0
2 6228 1 0 0
3 5830 1 0 1
4 1844 0 0 1
5 4461 0 0 0
6 2119 0 0 0
7 2115 0 1 0
We can use logical operation to determine if the condition is meet and then use as.integer to convert the value to 1 and 0.
dat2 <- dat1 %>%
mutate(Age = as.integer(Age %in% "15-24"),
Align = as.integer(Align %in% "A"),
Weat = as.integer(Weat == 3))
dat2
# ID Age Align Weat
# 1 8645 1 1 0
# 2 6228 1 0 0
# 3 5830 1 0 1
# 4 1844 0 0 1
# 5 4461 0 0 0
# 6 2119 0 0 0
# 7 2115 0 1 0
Use + 0L would also work.
dat2 <- dat1 %>%
mutate(Age = Age %in% "15-24" + 0L,
Align = Align %in% "A" + 0L,
Weat = (Weat == 3) + 0L)
dat2
# ID Age Align Weat
# 1 8645 1 1 0
# 2 6228 1 0 0
# 3 5830 1 0 1
# 4 1844 0 0 1
# 5 4461 0 0 0
# 6 2119 0 0 0
# 7 2115 0 1 0