CODEWITHSUNDEEP
rdataframeapply
JenB
JenB

Reputation: 17678

R apply custom vectorised function to row in dataframe, specific columns

This should be simple but I just can't get the apply to communicate with my vectorised function.

Test data is: df <- data.frame(a = 1:3, b1 = c(4:5, NA), b2 = c(5,6,5)) Looks like this:

  a b1 b2
1 1  4  5
2 2  5  6
3 3 NA  5

Custom function checks returns a vector to indicate whether values fall in a given interval.

validScore <- function(x, a, b) {
  is.na(x) == FALSE &
  x%%1 == 0 &
  findInterval(x, c(a,b), rightmost.closed = TRUE) == 1
}

Test of custom function: validScore(c(3, 3.5, 6, NA), 1, 5) returns the logical vector TRUE FALSE FALSE FALSE as expected.

I want to run the custom function on the row defined by the columns b1 and b2. This would return TRUE FALSE FALSE (that is T on (b1=4,b2=5), F on (b1=5,b2=6) and F on (b1=NA,b2=5)).

The answer Call apply-like function on each row of dataframe with multiple arguments from each row for selecting the columns, and how to apply a function to every row of a matrix (or a data frame) in R together suggest the following:

library(dplyr)
apply(select(df, b1:b2), 1, function(x) validScore(x, 1, 5))

but that doesn't actually send the row to the function, instead assessing each value individually, so output is:

   [,1]  [,2]  [,3]
b1 TRUE  TRUE FALSE
b2 TRUE FALSE  TRUE

Sticking a rowwise() into the middle like select(df, b1:b2) %>% rowwise() %>% apply(1, function(x) validScore(x, 1, 5)) makes no difference.

I thought it might by something to do with the form that the dplyr select returned, but apply(df[, c("b1", "b2")], 1, function(x) validScore(x, 1, 5)) also generates the same result.

Upvotes: 2

Views: 1902

Answers (1)

csgillespie
csgillespie

Reputation: 60462

You don't need dplyr or plyr. You can just use base R.

The first thing to do is to make validScore return only a single TRUE or FALSE. This can be done using the all function

validScore <- function(x, a, b) {
  test = is.na(x) == FALSE &
    x %% 1 == 0 &
    findInterval(x, c(a,b), rightmost.closed = TRUE) == 1
  all(test)
}

After that just use the standard apply

## Select columns 2 & 3 
apply(df[, 2:3], 1, validScore, a=1, b=8)

Upvotes: 2

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