Lab 1

R Basics & Data Visualization

To learn to program in R (or any language), you can read about how to do it, and watch someone else do it; but the only way to really learn is to do it yourself. Create some data structures, try some stuff, and see what happens! Here are some practice quiz questions to guide your learning. We will go over the solutions to these in lab.

Tip

More than one answer may be correct!

R Basics

Google Colab

1. True or false? We can start a new R notebook in Google Colab with File > New notebook

   True True, but we need to change the Runtime environment too False

2. For problem sets, how will you submit your colab notebook for grading?

   File > Download > Download .ipynb, then upload to Gradescope File > Download > Download .ipynb, then upload to Canvas File > Download > Download .py, then upload to Gradescope File > Download > Download .py, then upload to Canvas

3. What version of R is Google Colab running? Hint: use sessionInfo().

4. What is the relationship between R and Google Colab?

   R is a programming language. Google Colab is a smaller, specific version of R. R is a programming language. Google Colab is a development environment where you can run R R and Google Colab are both programming languages. R is a paid (proprietary) programming language. Google Colab is a free service to run R.

5. What happens to files you upload to google colab when the Runtime environment is restarted?

   They are saved in your google drive. They are saved in Google Colab's cloud for future use They are deleted They are stored for 7 business days, then deleted.

The very basics

1. Which of the following are expressions?

   10 5 + 10 x <- 5 + 10 x <- y + 10 mean(x)

2. Which of the following are valid variable names in R?

   childAge response_time 1stPlaceWinner 2fast2furious pi

3. Suppose we open a new colab notebook and run the following code block. What will be returned?

   x <- 1 + 2
   y <- 0 + 3
   ls()

   3 x=3 • y=3 'x' • 'y' mean(c(1,3,5)) • median(c(1,3,5))

4. Which of the following will load the emo package into the current environment?

   install.packages('emo') library(emo) data(emo) attributes(emo)

5. Which of the following occur in the code block below?

   # compute the mean of x and y
   mean(c(x,y))

   a message a function a comment an expression

Vectors

1. Which of the following returns the vector 20 22 24 26

   20:26 seq(from=20, to=26, by =2) rep(c(20, 22, 24, 26), times = 4) c(20, 22, 24, 26)

2. Suppose we construct a vector with c(1, "two", 3, 4, 5, 6) and assign it to x. What will the following code block return?

   typeof(x)

   What is the previous question an example of?

   attribute addition explicit coercion implicit coercion none of the above

3. What will the following code block return?

   x <- 1:4
   y <- matrix(x, ncol=2, nrow=2)
   typeof(y)

4. What will the following code block return?

   x <- c()
   length(x)

5. Given the following vector, what will as.logical(x) return?

   x <- c(1, 0, 1, 0)

an error TRUE • FALSE • TRUE • FALSE FALSE FALSE • TRUE • FALSE • TRUE

1. Given the following vector, what will as.double(x) return?

   x <- c("one", "two", "three")

   an warning message 1 • 2 • 3 2 • 4 • 6 'one' • 'two' • 'three'

2. What happens if you add a vector of numbers to a single number in the following expression?

   c(1, 3, 5) + 1

   2 • 3 • 5 Error: length mismatch 1 • 3 • 5 • 1 2 • 4 • 6

3. What happens if you multiply a vector times another vector?

   c(1, 3, 5) * c(10, 100, 1000)

   10 • 300 • 5000 Error: length mismatch 10 • 30 • 50 • 100 • 300 • 500 • 1000 • 3000 • 5000 a 2 x 3 matrix Error: cannot multiply vectors

4. Suppose we run the following code. What will any(x) return?

   x <- c(1, 5, 11) > 10

   TRUE FALSE Error: vector is double but requires logical

Missing Values

Warning

We didn’t cover missing values in class, but they are an important thing for the data scientists to understand! Play around and see what you can find out about them here.

1. Suppose we run the following code. What will is.na(y) return?

   
   y <- c(25, 25, NA, 36)

   3 Error: Non-atomic vector is invalid type TRUE FALSE FALSE TRUE FALSE

2. Suppose we run the following code. What will is.null(y) return?

   y <- c()

3. Suppose we run the following code. What will mean(y) return?

   y <- c()

Visualization

Practice your new ggplot skills with these practice exam questions!

Setup

We will continue working with the ratings dataset from the visualization lecture (part of the languageR package).

Load the libraries:

library(ggplot2)
library(languageR)

And inspect the structure of the ratings dataset:

str(ratings)

'data.frame':   81 obs. of  14 variables:
 $ Word            : Factor w/ 81 levels "almond","ant",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ Frequency       : num  4.2 5.35 6.3 3.83 3.66 ...
 $ FamilySize      : num  0 1.39 1.1 0 0 ...
 $ SynsetCount     : num  1.1 1.1 1.1 1.39 1.1 ...
 $ Length          : int  6 3 5 7 9 7 6 6 3 6 ...
 $ Class           : Factor w/ 2 levels "animal","plant": 2 1 2 2 2 2 1 2 1 1 ...
 $ FreqSingular    : int  24 69 315 26 19 24 53 74 155 37 ...
 $ FreqPlural      : int  42 140 231 19 19 6 78 77 103 14 ...
 $ DerivEntropy    : num  0 0.562 0.496 0 0 ...
 $ Complex         : Factor w/ 2 levels "complex","simplex": 2 2 2 2 2 2 2 2 2 2 ...
 $ rInfl           : num  -0.542 -0.7 0.309 0.3 0 ...
 $ meanWeightRating: num  1.49 3.35 2.19 1.32 1.44 ...
 $ meanSizeRating  : num  1.89 3.63 2.47 1.76 1.87 ...
 $ meanFamiliarity : num  3.72 3.6 5.84 4.4 3.68 4.12 2.12 5.68 3.2 2.2 ...

Basic ggplot

1. Fill in the blanks below with one of the following words: data, aesthetics, geom.

   The basic ggplot involves: (1) using your , (2) defining how variables are mapped to visual properties (), and (3) determining the geometrical object that a plot uses to represent data ()

2. When ggplot2 maps a categorical variable to an aesthetic, it automatically assigns a unique value of the aesthetic to each level of the variable. What is this process called?

   level assignment variable aestheticization autofill scaling

3. The code below generated which of the following figures?

   ggplot(
       data = ratings,
       mapping = aes(x = Frequency, y = meanFamiliarity)
   ) + 
       geom_point(mapping = aes(color = Class)) +
       geom_smooth(method = "lm") +
       theme_classic(base_size=20)

   

   A B C

4. Suppose we want to map the variable Complex to the color aesthetic in a scatterplot. Which of the following arguments could we pass to geom_point()?

   color = Complex mapping=aes(color = Complex) color = mapping(Complex) aes(color=Complex)

5. To adjust the size of the font to 20pt in the complete theme theme_minimal(), what argument should we include?

   base_size=20 size=20 font_family_size=20 None of the above

6. Which geoms are depicted in the following figure?

   

   geom_histogram() geom_density() geom_bar() geom_smooth() geom_point()

7. Which geoms are depicted in the following figure?

   

   geom_histogram() geom_density() geom_bar() geom_smooth() geom_point()

Plot-to-Code Matching

Exercise 1

Given code blocks a, b, and c; and the plot below:

# CODE BLOCK a ---------------------------#
ggplot(
    data = ratings, 
    mapping = aes(x = Frequency, y = meanFamiliarity)
    ) +
    geom_point(color = "blue")

# CODE BLOCK b ---------------------------#
ggplot(
    data = ratings, 
    mapping = aes(x = Frequency, y = meanFamiliarity, color = "blue")
    ) 

# CODE BLOCK c ---------------------------#
ggplot(
    data = ratings, 
    mapping = aes(x = Frequency, y = meanFamiliarity)
    ) +
    geom_point()

1. Which of the code blocks above generate the plot ?

   a b c

2. In the plot above, is the color aesthetic mapped, set, or both?

   mapped set both

3. In the plot above, which of the following aesthetics should we set to make the points more transparent?

   color fill alpha shape

4. In plot A above, which of the following would change the x axis label to “FQ”?

   add a labs() layer with x="FQ" argument add a labs() layer with y="FQ" argument change the mapping argument from x="Frequency" to x="FQ"

Exercise 2

Given the following plot:

1. In the plot above, which geom(s) are used to represent the data?

   geom_histogram() geom_density() geom_bar() geom_point() geom_smooth()

2. True or false, the blue line in the plot above is mapped to the Class aesthetic?

   True False

3. In the plot above, which of the following variables is mapped to the x aesthetic?

   Frequency meanFamiliarity FreqSingular FreqPlural

4. True or false, in the plot above, the default statistical transformation in the geom responsible for the red dots is “identity”.

   True False

Exercise 3

Suppose we run the following code.

ggplot(
    data = ratings, 
    mapping = aes(x = Frequency, y = meanFamiliarity, color = Class)
    ) +
    geom_point() +
    geom_smooth(method = "lm", color = "red") 

1. Which of the following plots will be returned?

   A B

2. Which aesthetic is mapped and which is set?

   Class is set to color and the smoothing line is mapped to red Class is mapped to color and the smoothing line is set to red Both are mapped Both are set

3. Which aesthetic is global and which is local?

   Color is mapped to the class variable globally, and set to red locally Color is mapped to the class variable locally, and set to red globally Both are global Both are local

Exercise 4

Suppose we run the following code block

ggplot(
    data = ratings, 
    mapping = aes(x = Class, fill = Complex)
    ) +
    geom_bar() 

1. Which plot will be returned?

   A B C D

2. What would happen if we added the layer scale_fill_manual(values = c("green", "orange")) to the following plot?

   error: no fill aesthetic specified complex would turn green and simplex would turn orange simplex would turn green and complex would turn orange No change

3. What argument could we add to geom_bar() to add a black border around the bars?

   fill = black border_color = 'black' linetype = 'black' color = 'black'

Exercise 5

Consider the following plot

1. To generate the facets in the plot above, which of the following lines of code must be included?

   facet_grid(Complex ~ Class) facet_grid(Class ~ Complex) facet_grid(.~ Complex) facet_wrap(Class~Complex, ncol = 2)

2. Which of the following geoms are added to the plot above?

   geom_histogram() geom_density() geom_bar() geom_smooth() geom_point()

3. Which built-in theme is applied to the following plot?

   theme_grey() theme_classic() theme_void() theme_bw()