There are several ways you can do this. Here is one starting with concat.split.multiple
from my "splitstackshape" package:
## SAMPLE DATA
mydf <- data.frame(ID = LETTERS[1:3], vals = c("700-800", "600-750", "100-220"))
mydf
# ID vals
# 1 A 700-800
# 2 B 600-750
# 3 C 100-220
First, split the "vals" column, rename them if required (using setnames
), and add a new column with the rowMeans
.
library(splitstackshape)
mydf <- concat.split.multiple(mydf, "vals", "-")
setnames(mydf, c("vals_1", "vals_2"), c("min", "max"))
mydf$mean <- rowMeans(mydf[c("min", "max")])
mydf
# ID min max mean
# 1 A 700 800 750
# 2 B 600 750 675
# 3 C 100 220 160
For reference, here's a more "by-hand" approach:
mydf <- data.frame(ID = LETTERS[1:3], vals = c("700-800", "600-750", "100-220"))
SplitVals <- sapply(sapply(mydf$vals, function(x)
strsplit(as.character(x), "-")), function(x) {
x <- as.numeric(x)
c(min = x[1], mean = mean(x), max = x[2])
})
cbind(mydf, t(SplitVals))
# ID vals min mean max
# 1 A 700-800 700 750 800
# 2 B 600-750 600 675 750
# 3 C 100-220 100 160 220