The apply family of functions allow users to easily apply custom vector functions on matrices, lists, and other R data structures. Operations such as summing by rows and columns or more complex ones, such as functions with conditional logic, can be applied using apply commands. These are extremely convenient, not only from an ease-of-use perspective but also from a performance standpoint. In general, vectorised operations will almost always be faster and more efficient than looping, such as with for-next loops in R:

# apply 
 
# Usage 
#  
# apply(X, MARGIN, FUN, ...) 
# Arguments 
#  
# X: an array, including a matrix. 
# MARGIN: a vector giving the subscripts which the function will be applied over. E.g., for a matrix 1 indicates  
# rows, 2 indicates columns, c(1, 2) indicates rows and columns. Where X has named dimnames, it can be a  
# character vector selecting dimension names. 
 
apply(state[,-ncol(state)], 2, sum) # Sum of all values in the numeric columns 
apply(state[,-ncol(state)], 2, mean) # Mean of all values in the numeric columns 

The output is as follows:

lapply, which belongs to the apply family of functions, is used to the apply functions on lists in R:

# lapply - list apply which is similar to apply, but can be also used for other R object types
## Produces the output as a list
lapply(state[,-ncol(state)], function(x) {list(MIN=min(x), MAX=max(x), MEAN=mean(x))})

The output is as follows:

 sapply—it provides the same functionality as lapply but, instead of a list output, sapply returns the result as a vector:

# sapply - simplifies the output (eg., produce a vector instead of a list) 
sapply(state[,-ncol(state)], function(x) {list(MIN=min(x), MAX=max(x), MEAN=mean(x))}) 

The output of the preceding code is as follows: