R for Data Science – 7 Control Flow

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

Choices

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

#> [1] "x is smaller than 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?"

Choices

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

Choices

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"

Choices

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"

Choices

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"

Loops

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

Loops

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 a matrix or an array
MARGIN 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 the function to be applied on each MARGIN
A data frame should not be used as an input in apply()

Base functionals

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

Base functionals

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

Base functionals

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