7 Control flow

(AST230) R for Data Science

Md Rasel Biswas

Control flow

• In R, there are two primary tools of control flow: choices and loops

• Choices allow you to run different code depending on the input

  • e.g. if statements and switch

• Loops allow you to repeatedly run code, typically with changing options

  • e.g. for and while

Choices

• The basic form of an if statement in R as follows

if (condition) true_action
if (condition) true_action else false_action

• true_action corresponds to the action for which the condition is true

• false_action corresponds to the action for which the condition is false

x <- sample(1:10, 1)
if (x >= 5) {
  "x is greater than or equals 5"
} else {
  "x is smaller than 5"
}

[1] "x is greater than or equals 5"

When R evaluates the condition inside if statements, it is looking for a logical element, i.e., TRUE or FALSE

x  <-  4 == 3
if (x) {
  "Nothing will print as condition is FALSE"
}

if (TRUE) {"Understand?"}

[1] "Understand?"

• Write a function that takes the raw score obtained in an exam and returns the grade corresponding to the number.

y = \begin{cases} A & \text{if} \;x >= 90 \\ B & \text{if} \;80 \leq x < 90 \\ C & \text{if} \;60 \leq x < 80 \\ F & \text{if}\;x < 60\end{cases}

grade <- function(x) {
  if (x >= 90) y = "A"
  else if (x >= 80) y = "B"
  else if (x >= 60) y = "C"
  else y = "D"
  return(y)
}

grade(77)

[1] "C"

grade(95)

[1] "A"

• The dplyr package has a function case_when() to categorize a variable

library(dplyr)
grade_c <- function(x) {
  y <- case_when(x >= 90 ~ "A",
                 x >= 80 | x < 90 ~ "B",
                 x >= 60 | x < 80 ~ "C",
                 x < 60 ~ "D")
  return(y)
}

grade_c(77)

[1] "B"

grade_c(95)

[1] "A"

• if() function is used for single values.

• To sort a list of values into two categories, you can use the base R function ifelse() or the dplyr package’s if_else() function

• The syntax of if_else()

if_else(condition, true_action, false_action)

x <- 1:10
x

 [1]  1  2  3  4  5  6  7  8  9 10

dplyr::if_else(x %% 2 == 0, "even", "odd")

 [1] "odd"  "even" "odd"  "even" "odd"  "even" "odd"  "even" "odd"  "even"

Loops

• for loops are used to iterate over items in a vector

for (item in vector) {
  perform_action
}

for (i in 1:3) {
  print(i)
}

[1] 1
[1] 2
[1] 3

v <- c("this", "is", "a", "for", "loop")
for(i in v){
  print(i)
}

[1] "this"
[1] "is"
[1] "a"
[1] "for"
[1] "loop"

• The next statement skips the current iteration of the loop and starts the loop from the next iteration

• The break statement terminate the execution of the loop.

for (i in 1:10) {
  if (i < 3) next
  if (i >= 5) break
  print(i)
}

[1] 3
[1] 4

• Sometimes you will find yourself needing to repeat an operation until a certain condition is met, rather than doing it for a specific number of times.

z <- 1
while (z < 4) {
  z <- z + 1
  print(z)
}

[1] 2
[1] 3
[1] 4

Base functionals

A functional is a function that takes a function as an input and returns a vector as output.

• apply() function takes a matrix or array as an input and return a lower dimensional summary

apply(X, MARGIN, FUN)

• X \rightarrow a matrix or an array

• MARGIN \rightarrow a vector giving the subscripts which the function will be applied over, e.g., for a matrix, 1 indicates a row and 2 indicates a column

• FUN \rightarrow the function to be applied on each MARGIN

• A data frame should not be used as an input in apply()

mat <- matrix(1:15, nrow = 3)
mat

     [,1] [,2] [,3] [,4] [,5]
[1,]    1    4    7   10   13
[2,]    2    5    8   11   14
[3,]    3    6    9   12   15

• Row sum using loops

rsum <- rep(NA, nrow(mat))
for (i in 1:nrow(mat)) {
  rsum[i] <- sum(mat[i, ])
}
rsum

[1] 35 40 45

• Row sum using apply()

apply(mat, 1, sum)

[1] 35 40 45

• Similarly, column sum

apply(mat, 2, sum)

[1]  6 15 24 33 42

• lapply() returns a list after applying a function on each element of an R object

lapply(X, FUN, ...)

• sapply() is similar to lapply() but it returns a atomic vector instead of a list

lres <- list(x = 1:3, y = matrix(1:4, 2))
lres

$x
[1] 1 2 3

$y
     [,1] [,2]
[1,]    1    3
[2,]    2    4

lapply(lres, sum)

$x
[1] 6

$y
[1] 10

sapply(lres, sum)

 x  y 
 6 10