What is the dplyr way to apply a function rowwise
for some columns. For example I want to Grab all the V,
columns and turn them into percents based on the row sums. I show how to do it in base. What about in a dplyr chain. It'd nice to see in data.table form as well (though preference would go to a dplyr solution here).
x <- data.frame(A=LETTERS[1:5], as.data.frame(matrix(sample(0:5, 25, T), ncol=5)))
data.frame(x[1], x[-1]/rowSums(x[-1]))
## A V1 V2 V3 V4 V5
## 1 A 0.1428571 0.2142857 0.2142857 0.35714286 0.07142857
## 2 B 0.2000000 0.2000000 0.1500000 0.20000000 0.25000000
## 3 C 0.3571429 0.2857143 0.0000000 0.07142857 0.28571429
## 4 D 0.1904762 0.2380952 0.1904762 0.23809524 0.14285714
## 5 E 0.2000000 0.2500000 0.1500000 0.25000000 0.15000000
library(dplyr)
props <- function(x) round(x/sum(x), 2)
# does not work
x %>%
rowwise()
mutate(props(matches("^.{2}$")))
In data.table, you can do
library(data.table)
setDT(x)
x[, grep("^V",names(DT)) := .SD/Reduce(`+`, .SD), .SDcols = V1:V5]
A V1 V2 V3 V4 V5
1: A 0.28571429 0.0000000 0.2857143 0.07142857 0.35714286
2: B 0.23076923 0.2307692 0.3076923 0.15384615 0.07692308
3: C 0.44444444 0.0000000 0.4444444 0.00000000 0.11111111
4: D 0.07142857 0.3571429 0.1428571 0.07142857 0.35714286
5: E 0.00000000 0.2222222 0.3333333 0.44444444 0.00000000
To compute the denominator with NA values ignored, I guess rowSums
is an option, though it will coerce .SD
to a matrix as an intermediate step.
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