## Create a numeric vector with the values of 3, 2, 1 using the `c()` function

## Assign the value to a variable named `num_vector`

## Print the vector

num_vector <- ___

## Create a character vector with the values of "three", "two", "one" "using the `c()` function

## Assign the value to a variable named `char_vector`

## Print the vector

char_vector <- ___

## Create a vector called `week1_sleep` representing how many hours slept each night of the week

## Use the values 6.1, 8.8, 7.7, 6.4, 6.2, 6.9, 6.6

week1_sleep <- ___

## Display the amount of sleep on Tuesday of week 1 by selecting the variable index

week1_sleep[__]

## Create a vector called `week1_sleep_weekdays`

## Assign the weekday values using indice slicing

week1_sleep_weekdays <- week1_sleep[__:__]

## Add the total hours slept in week one using the `sum` function

## Assign the value to variable `total_sleep_week1`

total_sleep_week1 <- ___

## Create a vector called `week2_sleep` representing how many hours slept each night of the week

## Use the values 7.1, 7.4, 7.9, 6.5, 8.1, 8.2, 8.9

week2_sleep <- ___

## Add the total hours slept in week two using the sum function

## Assign the value to variable `total_sleep_week2`

total_sleep_week2 <- ___

## Determine if the total sleep in week 1 is less than week 2 by using the < operator

__ < __

## Calculate the mean hours slept in week 1 using the `mean()` function

mean(__)

## Create a vector called `days` containing the days of the week.

## Start with Sunday and end with Saturday

days <- ___

## Assign the names of each day to `week1_sleep` and `week2_sleep` using the `names` function and `days` vector

names(week1_sleep) <- ___

names(week2_sleep) <- ___

## Display the amount of sleep on Tuesday of week 1 by selecting the variable name

week1_sleep[__]

## Create vector called weekdays from the days vector

weekdays <- days[__:__]

## Create vector called weekends containing Sunday and Saturday

weekends <- ___

## Calculate the mean about sleep on weekdays for each week

## Assign the values to weekdays1_mean and weekdays2_mean

weekdays1_mean <- mean(week1_sleep[weekdays])

weekdays2_mean <- mean(week2_sleep[weekdays])

## Using the weekdays1_mean and weekdays2_mean variables,

## see if weekdays1_mean is greater than weekdays2_mean using the `>` operator

__ > __

## Determine how many days in week 1 had over 8 hours of sleep using the `>` operator

___

## Create a matrix from the following three vectors

student01 <- c(100.0, 87.1)

student02 <- c(77.2, 88.9)

student03 <- c(66.3, 87.9)

students_combined <- __

grades <- matrix(students_combined, byrow = __, nrow = __)

## Add a new student row with `rbind()`

student04 <- c(95.2, 94.1)

grades <- rbind(__, __)

## Add a new assignment column with `cbind()`

assignment04 <- c(92.1, 84.3, 75.1, 97.8)

grades <- cbind(__, __)

## Add the following names to columns and rows using `rownames()` and `colnames()`

assignments <- c("Assignment 1", "Assignment 2", "Assignment 3")

students <- c("Florinda Baird", "Jinny Foss", "Lou Purvis", "Nola Maloney")

rownames(__) <- __

colnames(__) <- __

## Total points for each assignment using `colSums()`

__

## Total points for each student using `rowSums()`

__

## Matrix with 10% and add it to grades

weighted_grades <- grades * 0.1 + grades

## Create a factor of book genres using the genres_vector

## Assign the factor vector to factor_genre_vector

genres_vector <- c("Fantasy", "Sci-Fi", "Sci-Fi", "Mystery", "Sci-Fi", "Fantasy")

factor_genre_vector <- ___

## Use the `summary()` function to print a summary of `factor_genre_vector`

summary(__)

## Create ordered factor of book recommendations using the recommendations_vector

## `no` is the lowest and `yes` is the highest

recommendations_vector <- c("neutral", "no", "no", "neutral", "yes")

factor_recommendations_vector <- factor(

recommendations_vector,

ordered = __,

levels = c(__, __, __)

)

## Use the `summary()` function to print a summary of `factor_recommendations_vector`

summary(factor_recommendations_vector)

## Using the built-in `mtcars` dataset, view the first few rows using the `head()` function

__

## Using the built-in mtcars dataset, view the last few rows using the `tail()` function

__

## Create a dataframe called characters_df using the following information from LOTR

name <- c("Aragon", "Bilbo", "Frodo", "Galadriel", "Sam", "Gandalf", "Legolas", "Sauron", "Gollum")

race <- c("Men", "Hobbit", "Hobbit", "Elf", "Hobbit", "Maia", "Elf", "Maia", "Hobbit")

in_fellowship <- c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE)

ring_bearer <- c(FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE)

age <- c(88, 129, 51, 7000, 36, 2019, 2931, 7052, 589)

characters_df <- data.frame(__, __, __, __, __)

## Sorting the characters_df by age using the order function and assign the result to the sorted_characters_df

sorted_characters_df <- characters_df[order(__),]

## Use `head()` to output the first few rows of `sorted_characters_df`

___

## Select all of the ring bearers from the dataframe and assign it to ringbearers_df

ringbearers_df <- characters_df[characters_df$__ == __,]

## Use `head()` to output the first few rows of `ringbearers_df`