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Course Notes: Intermediate R

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& 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 been pre-defined for you. & and | (2) 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? 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.

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 variables have been pre-defined for you. 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 to use. It's now up to you to extend them with the appropriate else statements! 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! 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:

If number is set to 6, "small" gets printed to the console. If number is set to 100, R prints out "medium". If number is set to 4, "extra small" gets printed out to the console. If number is set to 2500, R will generate an error, as result will not be defined. Select the option that lists all the true statements.

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

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

# Is last between 15 (exclusive) and 20 (inclusive)?
last > 15 & last <= 20
# The social data (linkedin, facebook, views) has been created for you

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

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

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

# When is views less than or equal to 14?
views<=14
# The linkedin and last variable are already defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
last <- tail(linkedin, 1)

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

# Is last between 15 (exclusive) and 20 (inclusive)?
last > 15 & last <= 20
# The social data (linkedin, facebook, views) has been created for you

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

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

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

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



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

# 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!")
}
# Variables related to your last day of recordings
li <- 15
fb <- 9

# Code the control-flow construct
if (li >=15 & fb >= 15) {
  sms <- 2 * (li + fb)
} else if (li <10 & fb <10) {
  sms <- 0.5 * (li + fb)
} else {
  sms <- li + fb
}

# Print the resulting sms to the console
print(sms)
# Initialize the speed variable
speed <- 64

# Code the while loop
while (speed > 30) {
  print("Slow down!")
  speed <- speed -7
}

# Print out the speed variable
speed
# Initialize the speed variable
speed <- 64

# Extend/adapt the while loop
while (speed > 30) {
  print(paste("Your speed is",speed))
  if (speed > 48) { 
    print("Slow down big time!")
    speed <- speed - 11
  } else {
    print("Slow down!")
    speed <- speed - 6
  }
}
# Initialize the speed variable
speed <- 88

while (speed > 30) {
  print(paste("Your speed is", speed))
  
  # Break the while loop when speed exceeds 80
  if (speed >80 ) {
  break
  }
  
  if (speed > 48) {
    print("Slow down big time!")
    speed <- speed - 11
  } else {
    print("Slow down!")
    speed <- speed - 6
  }
}
# Initialize i as 1 
i <- 1

# Code the while loop
while (i <= 10) {
  print(3 * i)
  if (i %% 8 == 0) {
    break
  }
  i <- i + 1
}
# The linkedin vector has already been defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)

# Loop version 1
for (l in linkedin) {
    print(l)
}



# Loop version 2
for (i in 1:length(linkedin)){
    print(linkedin[i])
}
# The nyc list is already specified
nyc <- list(pop = 8405837, 
            boroughs = c("Manhattan", "Bronx", "Brooklyn", "Queens", "Staten Island"), 
            capital = FALSE)

# Loop version 1
for(n in nyc ) {
    print(n)
}



# Loop version 2
for (n in 1:length(nyc)) {
    print(nyc[[n]])
}
# The tic-tac-toe matrix ttt has already been defined for you

# define the double for loop
for (i in 1:nrow(ttt)) {
  for (j in 1:ncol(ttt)) {
    print(paste("On row", i,  "and column", j, "the board contains", ttt[i, j]))
  }
}
# The linkedin vector has already been defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)

# Code the for loop with conditionals
for (li in linkedin) {
  if (li > 10) {
    print("You're popular!")
  } else {
  print("Be more visible!")
  }
  print(li)
}
# The linkedin vector has already been defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)

# Adapt/extend the for loop
for (li in linkedin) {
  if (li > 10) {
    print("You're popular!")
  } else {
    print("Be more visible!")
  }
  
  # Add if statement with break
  if(li > 16){
      print("This is ridiculous, I'm outta here!")
  break
    }
    
  
  # Add if statement with next
  if(li < 5){
    print("This is too embarrassing!")
    next
    }
  
  print(li)
}
# Pre-defined variables
rquote <- "r's internals are irrefutably intriguing"
chars <- strsplit(rquote, split = "")[[1]]
help(sd)

# Initialize rcount
rcount <- 0 

# Finish the for loop
for (char in chars) {
  if(char=="r"){
    rcount <- rcount + 1
  }
  if(char=="u"){
    break
  }
}

# Print out rcount
print(rcount)
# Consult the documentation on the mean() function
?mean

# Inspect the arguments of the mean() function
args(mean)

# 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)

# Calculate average number of views
avg_li <- mean(linkedin)
avg_fb <- mean(facebook)

# Inspect avg_li and avg_fb
avg_li
avg_fb
Hidden output
Unknown integration
Data frameavailable as
brands
variable
SELECT * FROM production.brands
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
Unknown integration
Data frameavailable as
categories
variable
SELECT * FROM production.categories
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
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