R has some very handy functions such as
apply, sapply, tapply, and mapply, that can be used to reduce the task of writing complicated statements. Also, using them makes our code look cleaner. The apply() function is similar to writing a loop statement.
The lapply() function is very similar to the apply() function but can be used on lists; this will return a list. The sapply() function is very similar to lapply() but returns a vector and not a list.
The apply() function can be used as follows:
mat= matrix(1:25, 5,5) apply(mat,1,sd)
The lapply() function can be used in the following way:
j = list(x = 1:4, b = rnorm(100,1,2)) lapply(j,mean)
The tapply() function is useful when we have broken a vector into factors, groups, or categories:
tapply(mtcars$mpg,mtcars$gear,mean)
The first argument in the apply() function is the data. The second argument takes two values: 1 and 2; if we state 1, R will perform a row-wise computation; if we mention 2, R will perform a column-wise computation. The third argument is the function. We would like to calculate the standard deviation of each row in R; hence we use the sd function as the third argument. Note that we can define our own function and replace it with the sd function.
With regard to the lapply() function, we have defined J as a list and would like to calculate the mean. The first argument in the lapply() function is the data and the second argument is the function used to process the data.
The first argument in the tapply() function is the data; in our case it is mpg. The second argument is the factor or the grouping; in this case it would be gears. The last argument is the function used to process the data. We would like to calculate the mean of mpg for each unique gear (3, 4, and 5 gears) in the mtcars data.