Intermediate of R - Conditionals and Control Flow

Equality

The most basic form of comparison is equality. Let’s briefly recap its syntax. The following statements all evaluate to TRUE (feel free to try them out in the console).

3 == (2 + 1)
"intermediate" != "r"
TRUE != FALSE
"Rchitect" != "rchitect"

Notice from the last expression that R is case sensitive: “R” is not equal to “r”. Keep this in mind when solving the exercises in this chapter!

# Comparison of logicals
TRUE == FALSE

## [1] FALSE

# Comparison of numerics
-6*14 != 17-101

## [1] FALSE

# Comparison of character strings
"useR" == "user" 

## [1] FALSE

# Compare a logical with a numeric
TRUE == 1

## [1] TRUE

Greater and less than

Apart from equality operators, Filip also introduced the less than and greater than operators: < and >. You can also add an equal sign to express less than or equal to or greater than or equal to, respectively. Have a look at the following R expressions, that all evaluate to FALSE:

(1 + 2) > 4
"dog" < "Cats"
TRUE <= FALSE

Remember that for string comparison, R determines the greater than relationship based on alphabetical order. Also, keep in mind that TRUE is treated as 1 for arithmetic, and FALSE is treated as 0. Therefore, FALSE < TRUE is TRUE.

# Comparison of numerics
-6 * 5 + 2 >= -10 + 1

## [1] FALSE

# Comparison of character strings
"raining" <= "raining dogs"

## [1] TRUE

# Comparison of logicals
TRUE > FALSE

## [1] TRUE

Compare vectors

You are already aware that R is very good with vectors. Without having to change anything about the syntax, R’s relational operators also work on vectors.

Let’s go back to the example that was started. You want to figure out whether your activity on social media platforms have paid off and decide to look at your results for LinkedIn and Facebook. The sample code in the editor initializes the vectors linkedin and facebook. Each of the vectors contains the number of profile views your LinkedIn and Facebook profiles had over the last seven days.

# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

# Popular days, On which days did the number of LinkedIn profile views exceed 15
linkedin > 15

## [1]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE

# Quiet days, When was your LinkedIn profile viewed only 5 times or fewer
linkedin <= 5

## [1] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE

# LinkedIn more popular than Facebook, When was your LinkedIn profile visited more often than your Facebook profile?
linkedin > facebook

## [1] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE

Compare matrices

R’s ability to deal with different data structures for comparisons does not stop at vectors. Matrices and relational operators also work together seamlessly!

Instead of in vectors (as in the previous exercise), the LinkedIn and Facebook data is now stored in a matrix called views. The first row contains the LinkedIn information; the second row the Facebook information. The original vectors facebook and linkedin are still available as well.

# The social data has been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
views <- matrix(c(linkedin, facebook), nrow = 2, byrow = TRUE)

# When does views equal 13?
views == 13

##       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]
## [1,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE

# When is views less than or equal to 14?
views <= 14

##       [,1] [,2] [,3]  [,4] [,5]  [,6] [,7]
## [1,] FALSE TRUE TRUE  TRUE TRUE FALSE TRUE
## [2,] FALSE TRUE TRUE FALSE TRUE  TRUE TRUE

& and |

Before you work your way through the next exercises, have a look at the following R expressions. All of them will evaluate to TRUE:

TRUE & TRUE
FALSE | TRUE
5 <= 5 & 2 < 3
3 < 4 | 7 < 6

Watch out: 3 < x < 7 to check if x is between 3 and 7 will not work; you’ll need 3 < x & x < 7 for that.

In this exercise, you’ll be working with the last variable. This variable equals the last value of the linkedin vector that you’ve worked with previously. The linkedin vector represents the number of LinkedIn views your profile had in the last seven days, remember? Both the variables linkedin and last have already been defined in the editor.

# The linkedin and last variable are already defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
last <- tail(linkedin, 1)
last

## [1] 14

# Is last under 5 or above 10?
last < 5 | last > 10

## [1] TRUE

# Is last between 15 (exclusive) and 20 (inclusive)?
last > 15 & last <= 20  

## [1] FALSE

Like relational operators, logical operators work perfectly fine with vectors and matrices.

Both the vectors linkedin and facebook are available again. Also a matrix - views - has been defined; its first and second row correspond to the linkedin and facebook vectors, respectively. Ready for some advanced queries to gain more insights into your social outreach?

# linkedin exceeds 10 but facebook below 10
linkedin > 10 & facebook < 10

## [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE

# When were one or both visited at least 12 times?
linkedin >= 12 | facebook >= 12

## [1]  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE

# When is views between 11 (exclusive) and 14 (inclusive)?
views > 11 & views <=14

##       [,1]  [,2]  [,3]  [,4]  [,5]  [,6] [,7]
## [1,] FALSE FALSE  TRUE FALSE FALSE FALSE TRUE
## [2,] FALSE FALSE FALSE FALSE FALSE  TRUE TRUE

Blend it all together

With the things you’ve learned by now, you’re able to solve pretty cool problems.

Instead of recording the number of views for your own LinkedIn profile, suppose you conducted a survey inside the company you’re working for. You’ve asked every employee with a LinkedIn profile how many visits their profile has had over the past seven days. You stored the results in a data frame called li_df. This data frame is available in the workspace; type li_df in the console to check it out.

# li_df is pre-loaded in your workspace
li_df

# Select the second column, named day2, from li_df: second
second <- li_df$day2

# Build a logical vector, TRUE if value in second is extreme: extremes
extremes <- second > 25 | second < 5

# Count the number of TRUEs in extremes
sum(extremes)

# Solve it with a one-liner
sum(li_df$day2 > 25 | li_df$day2 < 5)

The if statement

Before diving into some exercises on the if statement, have another look at its syntax:

if (condition) {
expr
}

Remember your vectors with social profile views? Let’s look at it from another angle. The ‘medium’ variable gives information about the social website; the ‘num_views’ variable denotes the actual number of ‘views’ that particular ‘medium’ had on the last day of your recordings. Both these variables have already been defined in the editor.

# Variables related to your last day of recordings
medium <- "LinkedIn"
num_views <- 14

# Examine the if statement for medium
if (medium == "LinkedIn") {
  print("Showing LinkedIn information")
}

## [1] "Showing LinkedIn information"

# Write the if statement for num_views
if (num_views > 15) {
  print("You're popular!")
}

Add an else

You can only use an else statement in combination with an if statement. The else statement does not require a condition; its corresponding code is simply run if all of the preceding conditions in the control structure are FALSE. Here’s a recipe for its usage:

if (condition) {  
  expr1  
} else {  
  expr2  
}    

It’s important that the else keyword comes on the same line as the closing bracket of the if part!

Both if statements that you coded in the previous exercises are already available in the editor. It’s now up to you to extend them with the appropriate else statements!

# Variables related to your last day of recordings
medium <- "LinkedIn"
num_views <- 14

# Control structure for medium
if (medium == "LinkedIn") {
  print("Showing LinkedIn information")
} else {
  print("Unknown medium")
}

## [1] "Showing LinkedIn information"

# Control structure for num_views
if (num_views > 15) {
  print("You're popular!")
} else {
  print("Try to be more visible!")
}

## [1] "Try to be more visible!"

Customize further: else if

The else if statement allows you to further customize your control structure. You can add as many else if statements as you like. Keep in mind that R ignores the remainder of the control structure once a condition has been found that is TRUE and the corresponding expressions have been executed. Here’s an overview of the syntax to freshen your memory:

if (condition1) {  
  expr1  
} else if (condition2) {  
  expr2  
} else if (condition3) {  
  expr3  
} else {  
  expr4  
}  

Again, It’s important that the else if keywords comes on the same line as the closing bracket of the previous part of the control construct!

# Variables related to your last day of recordings
medium <- "LinkedIn"
num_views <- 14

# Control structure for medium
if (medium == "LinkedIn") {
  print("Showing LinkedIn information")
} else if (medium == "Facebook") {
  # Add code to print correct string when condition is TRUE
  print("Showing Facebook information")
} else {
  print("Unknown medium")
}

## [1] "Showing LinkedIn information"

# Control structure for num_views
if (num_views > 15) {
  print("You're popular!")
} else if (num_views <= 15 & num_views > 10) {
  # Add code to print correct string when condition is TRUE
  print("Your number of views is average")
} else {
  print("Try to be more visible!")
}

## [1] "Your number of views is average"

Else if 2.0

You can do anything you want inside if-else constructs. You can even put in another set of conditional statements. Examine the following code chunk:

if (number < 10) {  
  if (number < 5) {  
    result <- "extra small"  
  } else {  
    result <- "small"  
  }  
} else if (number < 100) {  
  result <- "medium"  
} else {  
  result <- "large"  
}  
print(result)  

Have a look at the following statements:

1. If number is set to 6, “small” gets printed to the console.
   
2. If number is set to 100, R prints out “medium”.
   
3. If number is set to 4, “extra small” gets printed out to the console.
   
4. If number is set to 2500, R will generate an error, as result will not be defined.

Option 1 & 3 are the true statements.