Using apply(), tapply(), sapply(), lapply()

Load Required packages

library(tidyverse)
library(datasets)

Load iris dataset

data(iris)

head(iris, 3)

tapply()

The object called ‘iris’ will be in the workspace. In this dataset, what is the mean of ‘Sepal.Width’ for the species setosa?

tapply(iris$Sepal.Width, iris$Species, mean)

    setosa versicolor  virginica 
     3.428      2.770      2.974 

Alternative method

iris %>%
  filter(Species == 'setosa') %>%
  group_by(species = Species) %>%
  summarise(avg_sepal_width = mean(Sepal.Width))

lapply()

Return a list of the means of the variables ‘Sepal.Length’, ‘Sepal.Width’, ‘Petal.Length’, and ‘Petal.Width’?

lapply(iris[, -5], mean)

$Sepal.Length
[1] 5.843333

$Sepal.Width
[1] 3.057333

$Petal.Length
[1] 3.758

$Petal.Width
[1] 1.199333

sapply()

Return a vector of the means of the variables ‘Sepal.Length’, ‘Sepal.Width’, ‘Petal.Length’, and ‘Petal.Width’?

sapply(iris[, -5], mean)

Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
    5.843333     3.057333     3.758000     1.199333 

sapply(iris[, 1:4], mean)

Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
    5.843333     3.057333     3.758000     1.199333 

apply()

Use apply() to find the means of the variables ‘Sepal.Length’, ‘Sepal.Width’, ‘Petal.Length’, and ‘Petal.Width’?

apply(iris[,1:4], 2, mean) #2 indicates columns

Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
    5.843333     3.057333     3.758000     1.199333 

NOTE

RowMeans, ColMeans, RowSums, ColSums are preferred functions since they have been optimized for the tasks.

colMeans(iris[1:4])

Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
    5.843333     3.057333     3.758000     1.199333 

Load mtcars

data(mtcars)

head(mtcars,2)

sapply()-Split() combination, tapply()

Calculate the average weight (wt) by number of cylinders in the car (cyl)?

split()-sapply() combination

split(mtcars$wt, mtcars$cyl)

$`4`
 [1] 2.320 3.190 3.150 2.200 1.615 1.835 2.465 1.935 2.140 1.513 2.780

$`6`
[1] 2.620 2.875 3.215 3.460 3.440 3.440 2.770

$`8`
 [1] 3.440 3.570 4.070 3.730 3.780 5.250 5.424 5.345 3.520 3.435 3.840 3.845 3.170 3.570

sapply(split(mtcars$wt, mtcars$cyl), mean)

       4        6        8 
2.285727 3.117143 3.999214 

tapply yields the same result above with the sapply-split combination

tapply(mtcars$wt, mtcars$cyl, mean)

       4        6        8 
2.285727 3.117143 3.999214 

with(mtcars, tapply(wt, cyl, mean))

       4        6        8 
2.285727 3.117143 3.999214 

Alternative Approach

mtcars %>%
  group_by(cyl) %>%
  summarise(avg_wt = mean(wt))