- Relational Operators in R
- Logical Operators in R
- Conditional Statements in R
- For Loop in R Programming
- While and Repeat Loop in R Programming
- Functions in R Programming
- Creating Functions in R
- Apply Functions in R
- Importing Data from External Data Sources in R
- Importing Data Using read.csv in R
- Import Data using read.table in R
- Importing Data Using data.table – fread in R
- Importing Data from Excel in R
- Using XLConnect in R Programming
- Importing Data from a Database in R
- SQL Queries from R
- Importing Data from Web in R
Apply Functions in R
In the earlier lessons, we learned about how we can use the for
loop to iterate over various R objects. R also provides other functions for implicit looping such as apply
, lapply
and sapply
which are even easier to use. It is a whole family of functions that are commonly referred to as 'Apply' family of functions.
The apply
functions are not just for compactness of code, but also for speed. If speed is an issue, such as when working with large data sets or long-running simulations, one must avoid explicit loops (read 'for' loop, or 'while' loop) as much as possible, because with apply()
function and its variants, R can do them a lot faster than you can.
The apply
Function
The apply()
function applies a simple function over dimensions of a data structure. The apply()
function has the following structure:
> args(apply)
function (X, MARGIN, FUN, ...)
- X is any R structure with dimensions (matrix, data frame, array .. NOT lists or vectors)
- MARGIN is the dimension number (1 = rows, 2 = columns)
- FUN is the function to apply (example:
mean()
) - ... represents additional arguments to the function
Example
Let's define a simple matrix myMatrix
which we will use to understand the apply()
function. The rpois()
function is a built-in function that can be used to simulate N independent Poisson random variables. For example, we can generate 30 Poisson random numbers with parameter λ = 3 as follows: > rpois(30, 3)
> myMatrix <- matrix(rpois(30,3),5)
> myMatrix
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 3 1 7 4 4 3
[2,] 2 3 5 5 2 3
[3,] 2 5 1 2 2 1
[4,] 3 5 1 4 5 3
[5,] 0 0 4 2 1 2
>
Now let's take a few examples to understand how we can use the apply()
function.
Calculate the sum
of each row.
> apply(myMatrix,1,sum)
[1] 22 20 13 21 9
The apply
function applied the function sum
to each row (specified with MARGIN=1) of the matrix myMatrix
.
We can do the same column-wise by changing MARGIN=2, as shown below:
> apply(myMatrix,2,sum)
[1] 10 14 18 17 14 12
The following two examples show row minima and column maxima:
> apply(myMatrix,1,min) #row minima
[1] 1 2 1 1 0
> apply(myMatrix,2,max) #col maxima
[1] 3 5 7 5 5 3
>
The apply
function can be used with matrixes, arrays, and data frames. There are other functions which can be applied to lists and vectors.
The lapply()
Function
The analog of apply()
for lists is lapply()
. It applies the given function to all elements of the specified list.
Example
> lapply(list(1:5,20:26),median)
[[1]]
[1] 3
[[2]]
[1] 23
>
As you can see, the lapply()
function returns a list (always). You can use the unlist()
to flatten the list and produce a simple vector.
> unlist(lapply(list(1:5,20:26),median))
[1] 3 23
We can also use the apply
family of functions on our own custom functions as shown below:
#The custom oilCost function - oil cost for two gallons
oilCost <− function ( x=0 ) {
output <− x*2
}
#sample vector containing oil prices for the past 5 days (per gallon)
oil_price <- c(10,9,8,9,11)
#Use apply function to calculate two gallon oil cost each day
lapply(oil_price,oilCost)
Result will be a list with cost for two gallons.
> lapply(oil_price,oilCost)
[[1]]
[1] 20
[[2]]
[1] 18
[[3]]
[1] 16
[[4]]
[1] 18
[[5]]
[1] 22
>
Let's say our custom function allows us to specify the quantity (instead of the fixed quantity of 2). We can modify our function as follows:
#The custom oilCost function - oil cost for a specified quantity of gallons
oilCost <− function ( x,qty ) {
output <− x*qty
}
#sample vector containing oil prices for the past 5 days (per gallon)
oil_price <- c(10,9,8,9,11)
#Use apply function to calculate three gallon oil cost each day
unlist(lapply(oil_price,oilCost,qty=3))
#Use apply function to calculate four gallon oil cost each day
unlist(lapply(oil_price,oilCost,qty=4))
The function oilCost
is applied to the vector oil_price
and the qty
is supplied as the third argument.
Result:
> #Use apply function to calculate three gallon oil cost each day
> unlist(lapply(oil_price,oilCost,qty=3))
[1] 30 27 24 27 33
>
> #Use apply function to calculate three gallon oil cost each day
> unlist(lapply(oil_price,oilCost,qty=4))
[1] 40 36 32 36 44
The sapply()
Function
As we saw, the lapply
function takes a list or a vector, but always returns a list. This is because the input list can have different elements of different classes. That's why the output must be a list to accomodate output elements having different classes. However, there are many scenarios where all the elements of the output are of the same class (e.g. integer), and we would rather get the output as a vector. Earlier we achieved it using the unlist
function. However, the sapply
is a better choice as it automatically does this for us. It takes a list or a vector as an input and returns a vector or a matrix when possible. internally, it calls lapply
and simplifies the output. The following example shows the difference between lapply
and sapply
.
> myList <- list(Pois = rpois(10,3), Norm = rnorm(5), Unif = runif(5,1,10))
> myList
$Pois
[1] 7 5 3 3 2 2 0 1 4 4
$Norm
[1] 1.56840563 -0.08843082 0.19965929 0.20645959 0.85374249
$Unif
[1] 8.729636 8.109225 7.884665 9.558795 1.016197
> lapply(myList,mean)
$Pois
[1] 3.1
$Norm
[1] 0.5479672
$Unif
[1] 7.059704
> sapply(myList,mean)
Pois Norm Unif
3.1000000 0.5479672 7.0597036
>
In this lesson, we saw three functions in the apply
family, namely, apply()
, lapply()
, and sapply()
. The entire family is made up of the apply()
, lapply()
, sapply()
, vapply()
, mapply()
, rapply()
, and tapply()
functions.
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