I have 2 functions that I use inside a mutate call. One produces per row results as expected while the other repeats the same value for all rows:
library(dplyr)
df <- data.frame(X = rpois(5, 10), Y = rpois(5,10))
pv <- function(a, b) {
fisher.test(matrix(c(a, b, 10, 10), 2, 2),
alternative='greater')$p.value
}
div <- function(a, b) a/b
mutate(df, d = div(X,Y), p = pv(X, Y))
which produces something like:
X Y d p
1 9 15 0.6000000 0.4398077
2 8 7 1.1428571 0.4398077
3 9 14 0.6428571 0.4398077
4 11 15 0.7333333 0.4398077
5 11 7 1.5714286 0.4398077
ie the d column varies, but v is constant and its value does not actually correspond to the X and Y values in any of the rows.
I suspect this relates to NSE, but I do not undertand how from what litlle I have been able to find out about it.
What accounts for the different behaviours of div and pv? How do I fix pv?
We need rowwise
df %>%
rowwise() %>%
mutate(d = div(X,Y), p = pv(X,Y))
# X Y d p
# <int> <int> <dbl> <dbl>
#1 10 9 1.111111 0.5619072
#2 12 8 1.500000 0.3755932
#3 9 8 1.125000 0.5601923
#4 11 16 0.687500 0.8232217
#5 16 10 1.600000 0.3145350
In the OP's code, the pv is taking the 'X' and 'Y' columns as input and it gives a single output.
Or as @Frank mentioned, mapply can be used
df %>%
mutate(d = div(X,Y), p = mapply(pv, X, Y))