Part 2: R for Data Science – Chapter 1 Exercises
Exercise 1.2.5 – Run ggplot(data = mpg). What do you see?
# Q1: Running ggplot with no aesthetics produces a blank canvas.
ggplot(data = mpg)
# Q2: How many rows and columns does mpg have?
nrow(mpg)
## [1] 234
ncol(mpg)
## [1] 11
# Q3: What does the drv variable describe?
# drv describes the drive type: f = front-wheel, r = rear-wheel, 4 = 4WD.
?mpg
## starting httpd help server ... done
# Q4: Make a scatterplot of hwy vs cyl.
ggplot(data = mpg) +
geom_point(mapping = aes(x = cyl, y = hwy))
# Q5: What happens if you make a scatterplot of class vs drv?
# Both variables are categorical, so the plot shows discrete combinations
# with no useful trend information.
ggplot(data = mpg) +
geom_point(mapping = aes(x = drv, y = class))
Exercise 1.4.3 – Aesthetic Mappings
# Q1: What's wrong with this code? Why are the points not blue?
# The color argument is inside aes(), treating it as a variable rather
# than a fixed aesthetic. Move it outside aes() to fix it.
# Broken (points not blue):
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
# Fixed:
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
# Q2: Which variables in mpg are categorical vs continuous?
# Categorical: manufacturer, model, trans, drv, fl, class
# Continuous: displ, year, cyl, cty, hwy
str(mpg)
## tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
## $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
## $ model : chr [1:234] "a4" "a4" "a4" "a4" ...
## $ displ : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr [1:234] "f" "f" "f" "f" ...
## $ cty : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr [1:234] "p" "p" "p" "p" ...
## $ class : chr [1:234] "compact" "compact" "compact" "compact" ...
# Q3: Map a continuous variable to color, size, and shape.
# Color and size work; shape does not accept continuous variables.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = cty))
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, size = cty))
# shape = cty → would throw an error (continuous variable)
# Q4: What happens if you map the same variable to multiple aesthetics?
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = drv, shape = drv))
# Q5: What does the stroke aesthetic do?
# stroke controls the width of the border for shapes that have a border (e.g., shape 21).
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), shape = 21, stroke = 2)
# Q6: What happens if you map an aesthetic to something other than a variable name?
# ggplot evaluates the expression and uses the result.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = displ < 5))
Exercise 1.5.5 – Facets
# Q1: What happens if you facet on a continuous variable?
# ggplot converts the continuous variable to a factor, creating one panel
# per unique value. With many unique values this can be unreadable.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ cty)
# Q2: What do empty cells in facet_grid(drv ~ cyl) mean?
# Empty cells indicate no observations exist for that combination.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ cyl)
# Q3: What does . do in facet_grid?
# A dot (.) means "no variable on this axis", effectively collapsing
# the grid to a single row or single column.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ .)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ cyl)
# Q4: First faceted plot from the chapter – advantages and disadvantages.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2)
# Advantage: easier to see patterns within each class.
# Disadvantage: harder to compare across classes; more difficult with large datasets.
# Q5: facet_wrap vs facet_grid
# facet_wrap wraps a 1-D sequence of panels into 2-D.
# facet_grid forms a matrix of panels based on two variables.
# Use facet_grid when you want to see interactions between two categorical variables.
# Q6: When would you use faceting instead of color aesthetics?
# Faceting is better when there are many categories or when individual
# panels need to be examined closely. Color works better for quick
# comparisons with few categories.
Exercise 1.6.1 – Geometric Objects
# Q1: What geom would you use for a line chart? Box plot? Histogram? Area chart?
# Line chart → geom_line()
# Box plot → geom_boxplot()
# Histogram → geom_histogram()
# Area chart → geom_area()
# Q2: Run this code in your head, then execute it. Does the output match?
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Q3: What does show.legend = FALSE do?
# It hides the legend for that specific layer.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = drv), show.legend = FALSE)
# Q4: What does the se argument to geom_smooth() do?
# se controls whether the confidence interval ribbon is displayed (TRUE by default).
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy), se = TRUE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy), se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Q5: Will these two graphs look different? Why/why not?
# They will look the same. In the first, aesthetics are set globally in ggplot();
# in the second, each geom inherits the same local aesthetics. The result is identical.
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot() +
geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Q6: Recreate the six plots from the textbook.
# Plot 1
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Plot 2
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth(mapping = aes(group = drv), se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Plot 3
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Plot 4
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(mapping = aes(color = drv)) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Plot 5
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(mapping = aes(color = drv)) +
geom_smooth(mapping = aes(linetype = drv), se = FALSE)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Plot 6
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(shape = 21, color = "white", stroke = 2,
mapping = aes(fill = drv))
