How to add multiple columns to a dataframe from a custom function in R

Question

I've created code that will take an input vector, create a dataframe based on the input, optimise some values and return some of these values. I'm now turning this into a function that will apply the calculations rowwise on an input dataframe. Below is a minimum working example of what I would like to achieve (my actual function would be too long to share here!):

# Randomly generated dataframe
df <-  data.frame(a = rnorm(10, 0, 1), x = rnorm(10, 1, 3), y = rnorm(10, 2, 3))

# Function that takes multiple arguments and returns multiple values in a list
zsummary <- function(x, y) { 
  if (y < 0) return(list(NA, NA))
  z = rnorm(10, x, abs(y))
  return(list(mean(z), sd(z)))
}

# Example of something that works using dplyr
#    However, this results in a lot of function calls...
#    especially if there were a lot of columns in the list...
library(dplyr)
df %>% rowwise() %>%
  mutate(mean = zsummary(x,y)[[1]], sd = zsummary(x,y)[[1]])

As you can see, I can't apply individual functions to each new df$mean and dfsd columns as they depend on a z vector that can only be generated once. I've looked around on SO already, but I haven't been able to find an answer yet. I think a solution would be using one of the apply functions and not something from dplyr , but I've honestly never fully understood apply functions. I would also not like solutions that use for loops with rbind as I've tried this in previous projects and for large dataframes it becomes very slow!

Answer 1

We can use mapply for this. As the zsummary takes two arguments, the mapply would be one option as it take corresponding element of 'x' and 'y' to apply the zsummary .

t(mapply(zsummary, df$x, df$y))

---

We can also change the function slightly and get the output with dplyr

zsummary <- function(x, y) { 
   if (y < 0) return(data.frame(mean = NA, sd = NA))
   z = rnorm(10, x, abs(y))
   data.frame(mean = mean(z), sd = sd(z))
}

 df %>%
     rowwise() %>% 
     do(data.frame(., zsummary(.$x, .$y)))

---

Or as we discussed in the comments, instead of having the function taking multiple arguments, have a single argument and use apply with MARGIN=1 for applying it on each row.

zsummary2 <- function(v1){
      if(v1[2] < 0) return(c(mean = NA, sd = NA))
      z <- rnorm(10, v1[1], abs(v1[2]))
       c(mean = mean(v1), sd= sd(v1))
     }

t(apply(df[-1], 1, zsummary2))
#         mean        sd
# [1,]  1.403066 0.8757504
# [2,]  5.058188 5.1401507
# [3,]  4.288365 1.4194393
# [4,]  1.932829 6.7587054
# [5,] -1.864236 3.7587462
# [6,]        NA        NA
# [7,]  3.328629 1.3711950
# [8,] -2.347699 5.0449958
# [9,]  2.936615 1.7332283
#[10,]        NA        NA

NOTE: The values will be different in each run as we didn't set any seed for the rnorm .