4 R Tutorial DPLYR Apply Function

R Programming
Sakthi Dasan Sekar
http://shakthydoss.com 1

Apply functions
Apply functions in R
 apply
 lapply
 sapply
 tapply
 vapply
 mapply
These functions usually have apply in there name.
They used to apply a specify function to each column or row to R objects
They are much more helpful than a for or while loops.

Apply functions
apply
It is used to apply a function to a matrix in row wise or column wise.
Returns a vector or array or list.
apply(x, margin, function)
It takes minimum three arguments
1. matrix / array
2. margin
3. function

Apply functions
apply
apply(x, margin, function)
margin - tells whether function need to apply for row or column
margin = 1 indicates function need to apply for row
margin = 2 indicates function need to apply for column
function can be mean, sum, average etc.

Apply functions
apply
Example
m <- matrix( c(1,2,3,4),2,2 )
apply(m,1,sum)
returns a vector containing sum of rows in the matrix in m
returns a vector containing sum of column in the matrix in m
apply(m,2,sum)

Apply functions
lapply
lapply function takes list as argument and apply the function by looping
through each element in the list.
Returns a list.
lapply(list, function)
It takes minimum two argument
1. List
2. function

Apply functions
lapply
Example
list <- list(a = c(1,1), b=c(2,2), c=c(3,3))
lapply(list,sum)
Returns a list containing sum of a,b,c.
lapply(list,mean)
Returns a list containing mean of a,b,c.

Apply functions
sapply
sapply(list, func)
It takes minimum two argument
1. list
2. function
sapply does every thing similar to lappy expect that sapply can simplify retuning object.
If the result is list and every element in list is of size 1 then vector is retuned.
If the restult is list and every element in list is of same size (>1) then matrix is returned.
Other wise result is retuned as a list itself.

Apply functions
sapply
Example
list <- list(a = c(1,1), b=c(2,2), c=c(3,3))
sapply(list,sum)
Returns a vector containing sum of a,b,c.
list <- list(a = c(1,2), b=c(1,2,3), c=c(1,2,3,4))
sapply(list, range)
Returns a matrix containing min and max of a,b,c.

Apply functions
tapply
tapply works on vector, It apply the function by grouping factors inside
the vector.
tapply(x, factor, fun)
1. vector
2. factor of vector
3. function

Apply functions
tapply
Example
age <- c(23,33,28,21,20,19,34)
gender <- c("m","m","m","f","f","f","m")
f <- factor(gender)
tapply(age,f,mean)
Returns the mean age for male and female.

Apply functions
vapply
vapply works just like sapply except that you need to specify the type of
return value (integer, double, characters).
vapply is generally safer and faster than sapply. Vapply can save some time in
coercing returned values to fit in a single atomic vector.
vapply(x, function, FUN.VALUE)
1. list
2. function
3. return value (integer, double, characters)

Apply functions
vapply
Example
list <- list(a = c(1,1), b=c(2,2), c=c(3,3))
vapply(list, sum, FUN.VALUE=double(1))

Apply functions
mapply
mapply is a multivariate version of sapply. mapply applies FUN to the
first elements of each ... argument, the second elements, the third
elements, and so on. Arguments are recycled if necessary.
mapply(FUN, ...)

Apply functions
Example
list(rep(1, 4), rep(2, 3), rep(3, 2), rep(4, 1))
We see that we are repeatedly calling the same function (rep) where the first
argument varies from 1 to 4, and the second argument varies from 4 to 1.
Instead, we can use mapply:
mapply(rep, 1:4, 4:1)
which will produce the same result.

dplyr package
dplyr overview
dplyr is a powerful R-package to transform and summarize tabular data with
rows and columns.
By constraining your options, it simplifies how you can think about common
data manipulation tasks.
It provides simple “verbs”, functions that correspond to the most common data
manipulation tasks, to help you translate those thoughts into code.
It uses efficient data storage backends, so you spend less time waiting for the
computer.

dplyr package
dplyr is grammar for data manipulation.
It provides five verbs, basically function that can be applied on the
data set
1. select - used to select rows in table or data.frame
2. filter - used to filter records in table or data.frame
3. arrange - used for re arranging the table or data.frame
4. mutate - used for adding new data
5. summarize - states the summary of data

dplyr package
dplyr installation
dplyr is not one among the default package, you have to install them separately
install.packages("dplyr")
loading dplyr into memory
library(dplyr)

dplyr package
dplyr - Select
Often you work with large datasets with many columns but only a few are
actually of interest to you.
select function allows you to rapidly select only the interest columns in your
dataset.
To select columns by name
select(mtcars, mpg, disp)
To select a range of columns by name, use the “:” (from:to) operator
select(mtcars, mpg:hp)

dplyr package
dplyr - Select
To select with columns and row with string match.
select(iris, starts_with("Petal"))
select(iris, ends_with("Width"))
select(iris, contains("etal"))
select(iris, matches(".t."))

dplyr package
dplyr - Select
You can rename variables with select() by using named arguments.
Example
select(mtcars, miles_per_gallon = mpg)

dplyr package
dplyr - filter
Filter function in dplyr allows you to easily to filter, zoom in and zoom
out of data your are interested.
filter(data, condition,..)
Simple filter
filter(mtcars, cyl == 8)
filter(mtcars, cyl < 6)

dplyr package
dplyr - filter
Multiple criteria filter
filter(mtcars, cyl < 6 & vs == 1)
filter(mtcars, cyl < 6 | vs == 1)
Comma separated arguments are equivalent to "And" condition
filter(mtcars, cyl < 6, vs == 1)

dplyr package
dplyr - arrange
arrange function basically used to arrange the data in specify order.
You can use desc to arrange the data in descending order.
arrange(data, ordering_column )

dplyr package
dplyr - arrange
Example
Range the data by cyl and disp
arrange(mtcars, cyl, disp)
Range the data by descending order of disp
arrange(mtcars, desc(disp))

dplyr package
dplyr - mutate
mutate function helps to adds new variables to existing data set.
Example
mutate(mtcars, my_custom_disp = disp / 1.0237)
my_custom_disp will be added to mtcars dataset.

dplyr package
dplyr - summarise
dplyr summarise function help to Summarise multiple values to a single
value in the dataset.
summarise(mtcars, mean(disp))
summarize with group function
summarise(group_by(mtcars, cyl), mean(disp))
summarise(group_by(mtcars, cyl), m = mean(disp), sd = sd(disp))

dplyr package
dplyr - summarise
List of Summary function that can be used inside dplyr summarise
mean, median, mode, max, min, sun, var, length, IQR
First - returns the first element of vector
last - returns the last element of vector
nth(x,n) - The 'n' the element of vector
n() - the number of rows in the data.frame
n_distinct(x) - the number of unique value in vector x

DPLYR & APPLY FUNCTION
Knowledge Check

Apply functions in R used to apply a specify function to each column or
row to R objects.
A. TRUE
B. FALSE
Answer A

Which one of the following is true about function apply(x, margin,
function)
A. When margin = 2 it indicates function need to apply for row.
B. When margin = 1, it indicates function need to apply for row.
C. x must be of type list.
D. only arithmetic functions can be passed into apply function.
Answer B

Define lapply.
A. lapply function takes list as argument and apply the function by looping
B. lapply function takes list, array or matrix and apply the function by looping
C. lapply is not standalone. it should with apply function.
D. lapply is used when latitude and longitude comes into to picture.
Answer A

dplyr is a powerful R-package to transform and summarize tabular data
with rows and columns. It also refered as grammar for data
manipulation.
A. TRUE
B. FALSE
Answer A

How do you rearrange the order of column in data set using dplyr
functions.
A. order_data(data, ordering_column)
B. sort_data(data,ordering_column)
C. dplyr(data,ordering_column)
D. arrange(data, ordering_column)
Answer D

4 R Tutorial DPLYR Apply Function

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