In the editor on the right, write R code to see if TRUE equals FALSE.
Likewise, check if -6 * 14 is not equal to 17 - 101.
Next up: comparison of character strings. Ask R whether the strings "useR" and "user" are equal.
Finally, find out what happens if you compare logicals to numerics: are TRUE and 1 equal?
# Comparison of logicals
TRUE==FALSE
# Comparison of numerics
-6*14 != 17-101
# Comparison of character strings
'useR'=='user'
# Compare a logical with a numeric
TRUE==1
#3-Greater and less than
Write R expressions to check whether:
-6 * 5 + 2 is greater than or equal to -10 + 1.
"raining" is less than or equal to "raining dogs".
TRUE is greater than FALSE.
# Comparison of numerics
-6*5+2>=-10+1
# Comparison of character strings
'raining'<='raining dogs'
# Comparison of logicals
TRUE>FALSE
#4-Compare vectors
Using relational operators, find a logical answer, i.e. TRUE or FALSE, for the following questions: On which days did the number of LinkedIn profile views exceed 15? When was your LinkedIn profile viewed only 5 times or fewer? When was your LinkedIn profile visited more often than your Facebook profile?
# 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
linkedin>15
# Quiet days
linkedin<=5
# LinkedIn more popular than Facebook
linkedin>facebook
#5-Compare matrices
Using the relational operators you've learned so far, try to discover the following: When were the views exactly equal to 13? Use the views matrix to return a logical matrix. For which days were the number of views less than or equal to 14? Again, have R return a logical matrix.
# 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)
print(views)
# When does views equal 13?
views==13
# When is views less than or equal to 14?
views<=14
#Write R expressions to solve the following
#questions concerning the variable last:
#Is last under 5 or above 10?
#Is last between 15 and 20, excluding 15 but including 20?
# 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?
last<5 | last>10
# Is last between 15 (exclusive) and 20 (inclusive)?
last>15 & last<=20
#8-& and | (AND & OR) (2)
#When did LinkedIn views exceed 10 and did Facebook views-
#fail to reach 10 for a particular day?
#Use the linkedin and facebook vectors.
#When were one or both of your LinkedIn
#and Facebook profiles-
#visited at least 12 times?
#When is the views matrix equal to a number
#between 11 and 14,
#excluding 11 and including 14?
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)
# The social data (linkedin, facebook, views)
#has been created for you
# linkedin exceeds 10 but facebook below 10
linkedin>10 & 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
#9-Reverse the result: !
x <- 5
y <- 7
!(!(x < 4) & !!!(y > 12))
#10-Blend it all together
#Select the entire second column, named day2,
#from the li_df data frame
#as a vector and assign it to second.
#Use second to create a logical vector, that contains TRUE
#if the corresponding number of views is strictly
#greater than 25
#or strictly lower than 5 and FALSE otherwise.
#Store this logical vector as extremes.
#Use sum() on the extremes vector to calculate
#the number of TRUEs in
#extremes (i.e. to calculate the number of
#employees that are
#either very popular or very low-profile).
#Simply print this number to the console.
employee<-c("employee_1","employee_2","employee_3",
"employee_4","employee_5")
day1<-c(2,19,24,22,25)
day2<-c(3,23,18,18,25)
li_df<-data.frame(employee,day1,day2)
# li_df is pre-loaded in your workspace
li_df
# Select the second column, named day2, from li_df: second
second<-li_df$day2
second
# Build a logical vector, TRUE if value in second
#is extreme: extremes
extremes<-second>25 | second<5
extremes
# Count the number of TRUEs in extremes
sum(extremes)
# Solve it with a one-liner
sum(li_df$day2 > 25 | li_df$day2 < 5)
#Examine the if statement that prints out
#"Showing LinkedIn information"
#if the medium variable equals "LinkedIn".
#Code an if statement that prints "You're popular!"
#to the console if the num_views variable exceeds 15.
# 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're popular!")
}
#13-Add an else
#Add an else statement to both control structures, such that
#"Unknown medium" gets printed out to the console when the
#if-condition on medium does not hold.
#R prints out "Try to be more visible!"
#when the if-condition on num_views is not met.
# 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!")
}
#14-Customize further: else if
#Add code to both control structures such that:
#R prints out "Showing Facebook information"
#if medium is equal to "Facebook". Remember that
#R is case sensitive!
#"Your number of views is average" is printed if num_views
#is between 15 (inclusive) and 10 (exclusive).
#Feel free to change the variables medium and num_views
#to see how the control structure respond.
#In both cases, the existing code should be extended
#in the else if statement.
#No existing code should be modified.
# 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!")
}
#15-Else if (2)
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.
#Select the option that lists all the true statements.
#Answer: 1 and 3
#15-Else if (3)
#Finish the control-flow construct with the
#following behavior:
#If both li and fb are 15 or higher, set sms equal
#to double the sum of li and fb.
#If both li and fb are strictly below 10, set
#sms equal to half the sum of li and fb.
#In all other cases, set sms equal to li + fb.
#Finally, print the resulting sms variable to the console.
# 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
sms
