Using paranthesis

'( )' operator is used to specify input for a function in R. Every function must be called using the round brackets. Example

# Creating vector
Example=c(1,2,34)

# Finding sum
sum(Example)

## [1] 37

# loading package 
library(dplyr)

# 

Square brackets

Let us look at first few rows of our data

# Consider iris data
data("iris")
kbl(head(iris))

Any data structure in R having more than one element requires use of '[ ]' for accessing one or more of its elements, it makes use of indexing. For example let us get 3rd element of the 'Example' vector

Example[3]

## [1] 34

We can get multiple elements from a vector using vector of indices. Let us obtain 3rd, 5th, 9th and 55th values of Sepal.Length

iris$Sepal.Length[c(3,5,9,55)]

## [1] 4.7 5.0 4.4 6.5

In a data frame and matrix we can use indexing in form '[m,n]' to access particular element where 'm' represents row number and 'n' represents column number.

# Obtaining 5th row 3rd column value
iris[5,3]

## [1] 1.4

We can also subset more than one row and column using vector to specify the indices

iris[1:15,c(2,3)]

##    Sepal.Width Petal.Length
## 1          3.5          1.4
## 2          3.0          1.4
## 3          3.2          1.3
## 4          3.1          1.5
## 5          3.6          1.4
## 6          3.9          1.7
## 7          3.4          1.4
## 8          3.4          1.5
## 9          2.9          1.4
## 10         3.1          1.5
## 11         3.7          1.5
## 12         3.4          1.6
## 13         3.0          1.4
## 14         3.0          1.1
## 15         4.0          1.2

We can use "-" sign while specifying indices to leave out a row/column or multiple rows/columns

iris[-c(2:150),-c(2,3)]

##   Sepal.Length Petal.Width Species
## 1          5.1         0.2  setosa

Curly braces

While defining functions, executing conditional statements we make use of '{}'. Example of this can be seen in next topic 'Functions'.

if

if statement helps us check for a condition and executes the given instruction if condition is met and does nothing if condition is not met.

if (test_expression) 
      {
    statement
            }   

if(length(iris$Sepal.Length)>2){
  print("Sepal.Length is having more than two elements")
}

## [1] "Sepal.Length is having more than two elements"

if else

An if else statement is a programming conditional statement that , if test expression is satisfactory , performs a function or displays information .Otherwise it performs a statement of else. So it is used when an alternate option is available for a situation.

if (test_expression) 
      {
    statement 1
            }else{
            statement 2
            }   

if(length(iris$Sepal.Width)>115){
  print("Sepal.Length is having more than 115 elements")
}else{
  print("false")
}

## [1] "Sepal.Length is having more than 115 elements"

if else if

An if-else-if ladder statement is the addition of one or more if else statements one after the other. It is used when multiple responses are possible and outcome for each response is different.

if(test_expression1)
  {
  statement1
  } else if(test_expression2)
    {
    statement2
    } else if(test_expression3)
      {
      statement3
      } else
      {
          statement4
        }

An Example is shown below

if(length(iris$Sepal.Length)<10){
  print("small vector")
}else if(length(iris$Sepal.Length)>10 & length(iris$Sepal.Length)<30 )
      {
      print("moderate vector")
      } else
      {
          print("Sepal.Length of iris is a long vector")
        }

## [1] "Sepal.Length of iris is a long vector"

ifelse

We have a convenient functionifelse() in R for performing the above conditional statement. The following is the syntax for the function

ifelse(conditional expression,
task to be executed if the condition is true, 
task to be executed if the condition is false)

Let us see an example. Here we shall create a new factor variable called PL_type in iris based on values of Petal.Length ( if it is less than 4.3 PL_type is "small" otherwise it is "large")

iris$PL_type=ifelse(iris$Petal.Length<4.3,'small','large')
iris$PL_type=as.factor(iris$PL_type)

let us look at first few rows of modified data

kbl(head(iris))

We can add more ifelse conditions in the 3rd argument of the function which specifies the task to be done if the conditional expression is false. Let us see an example. we shall create a new factor variable PL_type3 in iris based on values of Petal.Length ( if it is less than 1.6 PL_type is "small", if it is greater than or equal to 1.6 and less than 5.1 as "medium" otherwise it is "large")

iris$PL_type3=ifelse(iris$Petal.Length<1.6,'small',ifelse(iris$Petal.Length>=1.3&iris$Petal.Length<5.1,'medium','large'))
iris$PL_type=as.factor(iris$PL_type3)

Let us see the first few lines of iris

kbl(head(iris))

for

A for loop is a repetition control structure that allows to efficiently write a loop that needs to execute a specific number of times.

for (value in vector) {
   statements
}

# Considering Species variable of iris and using index of elements
for(i in 1:5)
{
  print(iris$Species[i])
}

## [1] setosa
## Levels: setosa versicolor virginica
## [1] setosa
## Levels: setosa versicolor virginica
## [1] setosa
## Levels: setosa versicolor virginica
## [1] setosa
## Levels: setosa versicolor virginica
## [1] setosa
## Levels: setosa versicolor virginica

for loops are particularly flexible that they are not limited to integers, or even numbers in the input. We can pass character vectors, logical vectors, lists or expressions.

# Considering Species variable of iris
for(species in iris$Species[c(47:53,108)])
  {
  print(species)
} 

## [1] "setosa"
## [1] "setosa"
## [1] "setosa"
## [1] "setosa"
## [1] "versicolor"
## [1] "versicolor"
## [1] "versicolor"
## [1] "virginica"

repeat

The Repeat loop executes the same code again and again until a stop condition is met.

repeat { 
   commands 
   if(condition) {
      break
   }
}

v <- "Repeating statement for "
cutoff <- 1

repeat {
   cat(v,cutoff," time \n")
   cutoff <- cutoff+1
   
   if(cutoff > 5) {
      break
   }
}

## Repeating statement for  1  time 
## Repeating statement for  2  time 
## Repeating statement for  3  time 
## Repeating statement for  4  time 
## Repeating statement for  5  time

print("Came out of loop to next statement")

## [1] "Came out of loop to next statement"

while

The While loop executes the code for all elements for which the condition is met.

while (test_expression) {
   statement
}

x=20
while(x<50){
  print("Re do the exam")
  x= x+5
}

## [1] "Re do the exam"
## [1] "Re do the exam"
## [1] "Re do the exam"
## [1] "Re do the exam"
## [1] "Re do the exam"
## [1] "Re do the exam"

print(x)

## [1] 50

break

When the break statement is encountered inside a loop, the loop is immediately terminated and program control resumes at the next statement following the loop.

var=1
repeat {
  statement
   var <- var + 1
    
   if(var > 5) {
      break
   }
}

we have seen example for break statement before in repeat statement

next

The next statement in R programming language is useful when we want to skip the current iteration of a loop without terminating it. On encountering next, the R parser skips further evaluation and starts next iteration of the loop.

for (value in vector) {
   
   if (test_expression) 
      {
    next
            }
     statement
}

v <- LETTERS[1:6]
for ( i in v) {
   
   if (i == "D") {
      next
   }
   print(i)
}

## [1] "A"
## [1] "B"
## [1] "C"
## [1] "E"
## [1] "F"

We have used 'next' for element 'D' that's why it was skipped.