Quiz 1

Data Science for Studying Language & the Mind

Estimated time: 30 minutes

You may need more time if programming is completely new to you, or less if you have some experience already.

Instructions

1 R basics: general

  1. Suppose you run the following code. Which command could you run to remove the y variable from the current environment? Choose all that apply.

    x <- 1 + 2 
y <- 3 + 4
z <- 0
    • ls(y)
    • rm(list = ls())
    • rm(y)
    • remove(y)

  2. Write an expression that would assign the value 10 to the variable name my_var?

  3. Which of the following would install the praise package? Choose all that apply.

    • library(praise)
    • install(praise)
    • install.packages("praise")
    • install.packages(praise)

  4. Suppose you run the following code. Which functions would return the structure of the object you defined? Choose one.

    x <- c("bus", "stop")
    • length(x)
    • print(x)
    • str(x)
    • attributes(x)
    • None of the above

2 R basics: vectors, operations, subsetting

  1. Suppose you run the following code. What will length(x) return? Write your answer in the box below.

    x <- 1:5

  2. Suppose you run the following code. What will y > 4 return? Write your answer in the box below and show your work.

    x <- seq(2, 8, by = 2)
y <- x[c(-4)]

  3. Suppose you run the following code. What will typeof(x) return?

    x <- c("true", "false", "true", "false")
    • double
    • integer
    • character
    • logical

  4. Suppose you create the following data frame and assign it to the df variable. What will sum(df$antique) return?

      antique age show
1       1   2    a
2       2   3    b
3       3   4    c
    • NULL
    • Error: no columns include value “antique”
    • 6
    • 9
    • 15

3 Data importing

  1. Which of the following will load the readr package into the current environment? Choose all that apply.

    • library(tidyverse)
    • library(readr)
    • install.packages("tidyverse")
    • install.packages("readr")
    • import(tidyverse)
    • import(readr)

  2. Suppose print(x) returns the following. What will is.data.frame(x) return? Write your answer in the box below.

    # A tibble: 4 × 3
      x     y     z
  <int> <int> <int>
1     1     5     9
2     2     6    10
3     3     7    11
4     4     8    12

  3. Suppose you import “junesales.csv”, shown below, with the following code. What would data$Sale return? Choose one.

    Year, Month, Day, Sale
2023, June, 1, 0
2023, June, 2, 1
2023, June, 3, 0
2023, June, 4, 1
    data <- read_csv("junesales.csv",
    col_types = list(Sale = col_logical())
)
    • A double vector with values 0 1 0 1
    • A logical vector with values FALSE TRUE FALSE TRUE
    • A double vector with values NA NA NA NA
    • NULL

  4. Suppose you import a dataset with readr, but when you print(data) you notice that the age column was identified as character when you were expecting double. Given the resulting tibble, which of the following arguments could you include in blank in the code below to solve this problem?

    # A tibble: 4 × 3
  age   graduated   gpa
  <chr> <lgl>     <dbl>
1 18    FALSE      NA  
2 na    FALSE       3.8
3 25    TRUE        2.9
4 21    TRUE        3.1
    data <- read_csv("data.csv", ________)
    • .drop = NA
    • skip = 1
    • guess_max = Inf
    • na = c("na")
    • col_names = FALSE

4 Data visualization: basics

Section 5 makes use of the durationsGe dataset and plots A, B, and C in the appendix.

  1. Which of the plots above (A and B) did the code blocks below generate?

    # code 1
ggplot(durationsGe, aes(x = DurationOfPrefix, fill = Sex)) +
    geom_density(fill = "lightgray") +
    theme_classic(base_size = 12) +
    labs(y = "") +
    scale_fill_manual(values = c("white", "gray", "black"))
# code 2
ggplot(durationsGe, aes(x = DurationOfPrefix)) +
    geom_density(fill = "lightgray") +
    theme_classic(base_size = 12) +
    labs(y = "")
    • Code 1 generates plot A, code 2 generates plot B
    • Code 2 generates plot A, code 1 generates plot B
    • Code 1 and 2 both generate plot A
    • Code 1 and 2 both generate plot B
    • Code 1 and 2 generate neither plot A nor plot B

  2. Which geoms could be depicted in plots A and B? Choose all that apply.

    • geom_histogram()
    • geom_smooth()
    • geom_line()
    • geom_density()
    • geom_bar()

  1. True or false, the following code blocks generate the same figure. Write your answer in the following box and explain why.

    # code block 1
ggplot(
    data=durationsGe, 
    mapping = aes(y = DurationOfPrefix, x = Sex)) + 
        geom_bar(stat = "identity") 

# code block 2
  ggplot(
    aes(y = DurationOfPrefix, x = Sex),
    durationsGe) + 
        geom_bar(stat = "identity")

  2. The code below makes use of a new geom, geom_rug(), to generate plot C, in which each individual data point is plotted along the x-axis like a “rug”. In the box below, rewrite the code such the color of the rug is mapped to the Sex variable and the bars of the histogram are filled in with the color “lightblue”.

    ggplot(durationsGe, aes(x = DurationOfPrefix)) +
    geom_rug() + 
    geom_histogram() +
    theme_classic(base_size = 12)

5 Data visualization: layers

Section 5 makes use of the durationsGe dataset and plots D, E, and F in the appendix.

  1. Which of the following would add a small amount of random noise around each point in plot D? Choose all that apply.

    • add the argument position = "jitter" to geom_point()
    • add the argument position = "random" to geom_point()
    • add the argument rand_noise = TRUE to geom_point()
    • replace geom_point() with geom_jitter()
    • replace geom_point() with geom_noise()

  2. Which of the following could change plot D to plot E? Choose all that apply

    • add facet_wrap(~Sex)
    • add facet_wrap(~Sex, ncol = 2)
    • add facet_wrap(~Sex, ncol = 3)
    • add facet_grid(Sex~.)
    • add facet_grid(.~Sex)
    • add facet(.by = c(Sex))

  3. Which of the following arguments to geom_histogram() could be present in the code that returned plot F? Choose all that apply.

    • bins=12
    • bins=11
    • binwidth=1
    • binwidth=3
    • stat="identity"

  4. Which of the following layers are required to produce plot F? Note that the plot uses the complete theme theme_minimal() and the font is 20pt Palatino. Choose all that apply.

    • theme_minimal(use=TRUE)
    • theme_minimal(base_size = 20, base_family = "Palatino")
    • labs(title = "Histogram of speech rate")
    • font(size=20, family="Palatino")
    • scale_fill_manual(values = c("gray"))

6 Data wrangling

Section 6 makes use of the durationsGe dataset in the appendix.

  1. The Sex variable in the durationsGe dataset has the following distinct values: "male" "female" NA. How many rows would be in the object returned by the following code block? Write your answer in the box below.

    durationsGe %>% 
    filter(Sex %in% c("female")) %>%
    summarise(minBirthYear=min(YearOfBirth, na.rm=TRUE), .by=c(Sex))

  2. True or false, the following code options are equivalent.

    # option 1
durationsGe %>% 
    select(Frequency) %>% 
    filter(Frequency > 40) %>%
    distinct()

# option 2
just_freq <- select(durationsGe, Frequency)
freq_under_40 <- filter(just_freq, Frequency > 40)
distinct(freq_under_40)
    • True
    • False

  3. Fill in the blank in the code below such that it returns a new column called “count”, which counts of the number of rows in the durationsGe dataset per Sex

    ratings %>% group_by(Sex) %>% summarise(_________)

  4. True or false, the following code options are equivalent.

    # option 1
durationsGe %>% 
    select(Freq=Frequency, Speaker:DurationOfPrefix) %>% 
    mutate(AgeInYears = 2023 - YearOfBirth, .before = Freq)

# option 2
durationsGe %>% 
    select(Frequency:DurationOfPrefix) %>% 
    rename(Freq = Frequency) %>%
    mutate(AgeInYears = 2023 - YearOfBirth, .before = 1)
    • True
    • False

Appendix A: Data

Sections 4-6 make use of durationsGe data in the languageR package. The dataset includes the duration of the prefix ge- in Dutch by various speakers from the Spoken Dutch Corpus.

library(languageR)
glimpse(durationsGe)
Rows: 428
Columns: 8
$ Word                <fct> geprikt, gepresteerd, gevolgd, geprikkeld, gestaak…
$ Frequency           <int> 13, 25, 309, 16, 40, 42, 1301, 10, 73, 19, 39, 6, …
$ Speaker             <fct> N01159, N01077, N01032, N01128, N01204, N01151, N0…
$ Sex                 <fct> male, male, female, female, female, female, male, …
$ YearOfBirth         <int> 1944, 1980, 1939, 1979, 1963, 1956, 1979, 1944, 19…
$ DurationOfPrefix    <dbl> 0.238703, 0.082057, 0.120832, 0.106897, 0.133441, …
$ SpeechRate          <dbl> 3.144654, 6.882591, 6.870229, 7.217848, 5.866667, …
$ NumberSegmentsOnset <int> 2, 2, 1, 2, 2, 1, 2, 2, 1, 3, 1, 2, 1, 2, 3, 1, 2,…

Appendix B: Plots section 4

Appendix C: Plots section 5