# Calculate 3 + 4
3 + 4## [1] 7# Calculate 6 + 12
6 + 12## [1] 18# An addition
5 + 5 ## [1] 10# A subtraction
5 - 5 ## [1] 0# A multiplication
3 * 5## [1] 15 # A division
(5 + 5) / 2 ## [1] 5# Exponentiation
2 ^ 5## [1] 32# Modulo
# 28 %% 6
28 %% 5## [1] 3# Assign the value 42 to x
x <- 42
# Print out the value of the variable x
x## [1] 42# Assign the value 5 to the variable my_apples
my_apples <- 5
# Print out the value of the variable my_apples
my_apples## [1] 5# Assign a value to the variables my_apples and my_oranges
my_apples <- 5
my_oranges <- 6
# Add these two variables together
my_apples + my_oranges## [1] 11# Create the variable my_fruit
my_fruit <- my_apples + my_oranges# Assign a value to the variable my_apples
my_apples <- 5
# Fix the assignment of my_oranges
my_oranges <- 6
# Create the variable my_fruit and print it out
my_fruit <- my_apples + my_oranges
my_fruit## [1] 11# Change my_numeric to be 42
my_numeric <- 42
# Change my_character to be "universe"
my_character <- "universe"
# Change my_logical to be FALSE
my_logical <- FALSE# Declare variables of different types
my_numeric <- 42
my_character <- "universe"
my_logical <- FALSE
# Check class of my_numeric
class(my_numeric)## [1] "numeric"# Check class of my_character
class(my_character)## [1] "character"# Check class of my_logical
class(my_logical)## [1] "logical"# Define the variable vegas
vegas <- "Go!"numeric_vector <- c(1, 10, 49)
character_vector <- c("a", "b", "c")
# Complete the code for boolean_vector
boolean_vector <- c(TRUE, FALSE, TRUE)# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Assign days as names of poker_vector
names(poker_vector) <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
# Assign days as names of roulette_vector
names(roulette_vector) <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# The variable days_vector
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
# Assign the names of the day to roulette_vector and poker_vector
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vectorA_vector <- c(1, 2, 3)
B_vector <- c(4, 5, 6)
# Take the sum of A_vector and B_vector
total_vector <- A_vector + B_vector
# Print out total_vector
total_vector## [1] 5 7 9# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Assign to total_daily how much you won/lost on each day
total_daily <- poker_vector + roulette_vector# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Total winnings with poker
total_poker <- sum(poker_vector)
# Total winnings with roulette
total_roulette <- sum(roulette_vector)
# Total winnings overall
total_week <- total_poker + total_roulette
# Print out total_week
total_week## [1] -84# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Calculate total gains for poker and roulette
total_poker <- sum(poker_vector)
total_roulette <- sum(roulette_vector)
# Check if you realized higher total gains in poker than in roulette
total_poker > total_roulette## [1] TRUE# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Define a new variable based on a selection
poker_wednesday <- poker_vector[3]# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Define a new variable based on a selection
poker_midweek <- poker_vector[c(2, 3, 4)]# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Define a new variable based on a selection
roulette_selection_vector <- roulette_vector[2:5]# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Select poker results for Monday, Tuesday and Wednesday
poker_start <- poker_vector[c("Monday", "Tuesday", "Wednesday")]
# Calculate the average of the elements in poker_start
mean(poker_start)## [1] 36.66667# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Which days did you make money on poker?
selection_vector <- poker_vector > 0
# Print out selection_vector
selection_vector## Monday Tuesday Wednesday Thursday Friday
## TRUE FALSE TRUE FALSE TRUE# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Which days did you make money on poker?
selection_vector <- poker_vector > 0
# Select from poker_vector these days
poker_winning_days <- poker_vector[selection_vector]# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Which days did you make money on roulette?
selection_vector <- roulette_vector > 0
# Select from roulette_vector these days
roulette_winning_days <- roulette_vector[selection_vector]# Construct a matrix with 3 rows that contain the numbers 1 up to 9
matrix(1:9, byrow = TRUE, nrow = 3)## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9# Box office Star Wars (in millions!)
new_hope <- c(460.998, 314.4)
empire_strikes <- c(290.475, 247.900)
return_jedi <- c(309.306, 165.8)
# Create box_office
box_office <- c(new_hope, empire_strikes, return_jedi)
# Construct star_wars_matrix
star_wars_matrix <- matrix(box_office, byrow = TRUE, nrow = 3)# Box office Star Wars (in millions!)
new_hope <- c(460.998, 314.4)
empire_strikes <- c(290.475, 247.900)
return_jedi <- c(309.306, 165.8)
# Construct matrix
star_wars_matrix <- matrix(c(new_hope, empire_strikes, return_jedi), nrow = 3, byrow = TRUE)
# Vectors region and titles, used for naming
region <- c("US", "non-US")
titles <- c("A New Hope", "The Empire Strikes Back", "Return of the Jedi")
# Name the columns with region
colnames(star_wars_matrix) <- region
# Name the rows with titles
rownames(star_wars_matrix) <- titles
# Print out star_wars_matrix
star_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8# Construct star_wars_matrix
box_office <- c(460.998, 314.4, 290.475, 247.900, 309.306, 165.8)
star_wars_matrix <- matrix(box_office, nrow = 3, byrow = TRUE,
dimnames = list(c("A New Hope", "The Empire Strikes Back", "Return of the Jedi"),
c("US", "non-US")))
# Calculate worldwide box office figures
worldwide_vector <- rowSums(star_wars_matrix)# Construct star_wars_matrix
box_office <- c(460.998, 314.4, 290.475, 247.900, 309.306, 165.8)
star_wars_matrix <- matrix(box_office, nrow = 3, byrow = TRUE,
dimnames = list(c("A New Hope", "The Empire Strikes Back", "Return of the Jedi"),
c("US", "non-US")))
# The worldwide box office figures
worldwide_vector <- rowSums(star_wars_matrix)
# Bind the new variable worldwide_vector as a column to star_wars_matrix
all_wars_matrix <- cbind(star_wars_matrix, worldwide_vector)# star_wars_matrix and star_wars_matrix2 are available in your workspace
star_wars_matrix ## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8star_wars_matrix2 ## US non-US
## The Phantom Menace 474.5 552.5
## Attack of the Clones 310.7 338.7
## Revenge of the Sith 380.3 468.5# Combine both Star Wars trilogies in one matrix
all_wars_matrix <- rbind(star_wars_matrix, star_wars_matrix2)# all_wars_matrix is available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5# Total revenue for US and non-US
total_revenue_vector <- colSums(all_wars_matrix)
# Print out total_revenue_vector
total_revenue_vector## US non-US
## 2226.279 2087.800# all_wars_matrix is available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5# Select the non-US revenue for all movies
non_us_all <- all_wars_matrix[,2]
# Average non-US revenue
mean(non_us_all)## [1] 347.9667# Select the non-US revenue for first two movies
non_us_some <- non_us_all[1:2]
# Average non-US revenue for first two movies
mean(non_us_some)## [1] 281.15# all_wars_matrix is available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5# Estimate the visitors
visitors <- all_wars_matrix / 5
# Print the estimate to the console
visitors## US non-US
## A New Hope 92.1996 62.88
## The Empire Strikes Back 58.0950 49.58
## Return of the Jedi 61.8612 33.16
## The Phantom Menace 94.9000 110.50
## Attack of the Clones 62.1400 67.74
## Revenge of the Sith 76.0600 93.70# all_wars_matrix and ticket_prices_matrix are available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5ticket_prices_matrix## US non-US
## A New Hope 5.0 5.0
## The Empire Strikes Back 6.0 6.0
## Return of the Jedi 7.0 7.0
## The Phantom Menace 4.0 4.0
## Attack of the Clones 4.5 4.5
## Revenge of the Sith 4.9 4.9# Estimated number of visitors
visitors <- all_wars_matrix / ticket_prices_matrix
# US visitors
us_visitors <- visitors[,1]
# Average number of US visitors
mean(us_visitors)## [1] 75.01339# Assign to the variable theory what this chapter is about!
theory <- "factors for categorical variables"# Sex vector
sex_vector <- c("Male", "Female", "Female", "Male", "Male")
# Convert sex_vector to a factor
factor_sex_vector <- factor(sex_vector)
# Print out factor_sex_vector
factor_sex_vector## [1] Male Female Female Male Male
## Levels: Female Male# Animals
animals_vector <- c("Elephant", "Giraffe", "Donkey", "Horse")
factor_animals_vector <- factor(animals_vector)
factor_animals_vector## [1] Elephant Giraffe Donkey Horse
## Levels: Donkey Elephant Giraffe Horse# Temperature
temperature_vector <- c("High", "Low", "High","Low", "Medium")
factor_temperature_vector <- factor(temperature_vector, order = TRUE, levels = c("Low", "Medium", "High"))
factor_temperature_vector## [1] High Low High Low Medium
## Levels: Low < Medium < High# Code to build factor_survey_vector
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
# Specify the levels of factor_survey_vector
levels(factor_survey_vector) <- c("Female", "Male")
factor_survey_vector## [1] Male Female Female Male Male
## Levels: Female Male# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")
factor_survey_vector## [1] Male Female Female Male Male
## Levels: Female Male# Generate summary for survey_vector
summary(survey_vector)## Length Class Mode
## 5 character character# Generate summary for factor_survey_vector
summary(factor_survey_vector)## Female Male
## 2 3# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")
# Male
male <- factor_survey_vector[1]
# Female
female <- factor_survey_vector[2]
# Battle of the sexes: Male 'larger' than female?
male > female## Warning in Ops.factor(male, female): '>' not meaningful for factors## [1] NA# Create speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")# Create speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")
# Convert speed_vector to ordered factor vector
factor_speed_vector <- factor(speed_vector, ordered=TRUE, levels = c("slow", "medium", "fast"))
# Print factor_speed_vector
factor_speed_vector## [1] medium slow slow medium fast
## Levels: slow < medium < fastsummary(factor_speed_vector)## slow medium fast
## 2 2 1# Create factor_speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")
factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "medium", "fast"))
# Factor value for second data analyst
da2 <- factor_speed_vector[2]
# Factor value for fifth data analyst
da5 <- factor_speed_vector[5]
# Is data analyst 2 faster than data analyst 5?
da2 > da5## [1] FALSE# Print out built-in R data frame
mtcars ## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2# Call head() on mtcars
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# Investigate the structure of mtcars
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...# Definition of vectors
name <- c("Mercury", "Venus", "Earth", "Mars", "Jupiter", "Saturn", "Uranus", "Neptune")
type <- c("Terrestrial planet", "Terrestrial planet", "Terrestrial planet",
"Terrestrial planet", "Gas giant", "Gas giant", "Gas giant", "Gas giant")
diameter <- c(0.382, 0.949, 1, 0.532, 11.209, 9.449, 4.007, 3.883)
rotation <- c(58.64, -243.02, 1, 1.03, 0.41, 0.43, -0.72, 0.67)
rings <- c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
# Create a data frame from the vectors
planets_df <- data.frame(name, type, diameter, rotation, rings)
planets_df## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 3 Earth Terrestrial planet 1.000 1.00 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSE
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUE# Check the structure of planets_df
str(planets_df)## 'data.frame': 8 obs. of 5 variables:
## $ name : Factor w/ 8 levels "Earth","Jupiter",..: 4 8 1 3 2 6 7 5
## $ type : Factor w/ 2 levels "Gas giant","Terrestrial planet": 2 2 2 2 1 1 1 1
## $ diameter: num 0.382 0.949 1 0.532 11.209 ...
## $ rotation: num 58.64 -243.02 1 1.03 0.41 ...
## $ rings : logi FALSE FALSE FALSE FALSE TRUE TRUE ...# The planets_df data frame from the previous exercise is pre-loaded
# Print out diameter of Mercury (row 1, column 3)
planets_df[1, 3]## [1] 0.382# Print out data for Mars (entire fourth row)
planets_df[4,]## name type diameter rotation rings
## 4 Mars Terrestrial planet 0.532 1.03 FALSE# The planets_df data frame from the previous exercise is pre-loaded
# Select first 5 values of diameter column
planets_df[1:5, "diameter"]## [1] 0.382 0.949 1.000 0.532 11.209# planets_df is pre-loaded in your workspace
# Select the rings variable from planets_df
rings_vector <- planets_df$rings
# Print out rings_vector
rings_vector## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE# planets_df and rings_vector are pre-loaded in your workspace
# Adapt the code to select all columns for planets with rings
planets_df[rings_vector, ]## name type diameter rotation rings
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUE# planets_df is pre-loaded in your workspace
# Select planets with diameter < 1
subset(planets_df, diameter < 1)## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSE# Play around with the order function in the console
order()## NULL# planets_df is pre-loaded in your workspace
# Use order() to create positions
positions <- order(planets_df$diameter)
# Use positions to sort planets_df
planets_df[positions, ]## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 3 Earth Terrestrial planet 1.000 1.00 FALSE
## 8 Neptune Gas giant 3.883 0.67 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 5 Jupiter Gas giant 11.209 0.41 TRUE# Just click the 'Submit Answer' button.# Click 'Submit Answer' to start the first exercise on lists.# Vector with numerics from 1 up to 10
my_vector <- 1:10
# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)
# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]
# Construct list with these different elements:
my_list <- list(my_vector, my_matrix, my_df)# Vector with numerics from 1 up to 10
my_vector <- 1:10
# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)
# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]
# Adapt list() call to give the components names
my_list <- list(vec = my_vector, mat = my_matrix, df = my_df)
# Print out my_list
my_list## $vec
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $mat
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
##
## $df
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4# The variables mov, act and rev are available
# Finish the code to build shining_list
shining_list <- list(moviename = mov, actors = act, reviews = rev)# shining_list is already pre-loaded in the workspace
# Print out the vector representing the actors
shining_list$actors## [1] "Jack Nicholson" "Shelley Duvall" "Danny Lloyd" "Scatman Crothers"
## [5] "Barry Nelson"# Print the second element of the vector representing the actors
shining_list$actors[2]## [1] "Shelley Duvall"# shining_list, the list containing movie name, actors and reviews, is pre-loaded in the workspace
# We forgot something; add the year to shining_list
shining_list_full <- c(shining_list, year = 1980)
# Have a look at shining_list_full
str(shining_list_full)## List of 4
## $ moviename: chr "The Shining"
## $ actors : chr [1:5] "Jack Nicholson" "Shelley Duvall" "Danny Lloyd" "Scatman Crothers" ...
## $ reviews :'data.frame': 3 obs. of 3 variables:
## ..$ scores : num [1:3] 4.5 4 5
## ..$ sources : Factor w/ 3 levels "IMDB1","IMDB2",..: 1 2 3
## ..$ comments: Factor w/ 3 levels "A masterpiece of psychological horror",..: 3 2 1
## $ year : num 1980Michael is a hybrid thinker and doer—a byproduct of being a StrengthsFinder “Learner” over time. With 20+ years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.
Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.
LinkedIn | Twitter | www.michaelmallari.com/data | www.columbia.edu/~mm5470