mapply() works with multivariate arrays, and applys a function to a set of vector or list arguments. mapply() also simplifies the output.

Structure of the mapply() function:
mapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE)

Answers to the exercises are available here.

Exercise 1
Beginning level

Required dataframe:
PersonnelData <- data.frame(Representative=c(1:4), Sales=c(95,110,115,90), Territory=c(1:4))

Using mapply(), find the classes of PersonnelData‘s columns.

Exercise 2
Beginning level

Print “PersonnelData” with the mapply() function.

Exercise 3
Beginning level

Use mapply() to inspect “PersonnelData” for numeric values.

Exercise 4
Intermediate level

Use mapply() to sum the vectors “5:10” and “20:25“.

Exercise 5
Intermediate level

Use mapply() to paste the vector “1:4” and “5:8“, with the separator “LETTERS[1:4]“.

Exercise 6
Intermediate level

Use mapply() to paste “PersonnelData$Representative“, “PersonnelData$Sales“, and “PersonnelData$Territory“, with the
“MoreArgs=” argument of “list(sep="-")“.

Exercise 7
Advanced level

Required variable:
NewSales <- data.frame(Representative=c(1:4), Sales=c(104, 97, 112, 94), Territory=c(1:4))

Sum the corresponding elements of PersonnelData$Sales and NewSales$Sales.

Exercise 8
Advanced level

Required function:
merge.function <- function(x,y){return(x+y)}

Use merge.function to combine the Sales totals from PersonnelData and NewSales.

Exercise 9
Advanced level

mcmapply is a parallelized version of mapply.

The structure of mcmapply() is:
mcmapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE, mc.preschedule = TRUE, mc.set.seed = TRUE, mc.silent = FALSE, mc.cores = getOption("mc.cores", 2L), mc.cleanup = TRUE)

Required library:
library(parallel)

Use mcmapply() to generate 5 lists of 1:5 random numbers.

Exercise 10
Advanced level

Using mcmapply(), create a 10 by 10 matrix with 10 rows of the sequence 1:10: