Evaluation order inconsistency with dplyr mutate

Question

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?

akrun · Accepted Answer · 2016-08-16T05:23:53.020

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))

Evaluation order inconsistency with dplyr mutate

1 Answers1