Practice 8 Answers

PUBH 523/623

pacman::p_load(
  tidyverse,
  here,
  rio
)

hrs_data <- import(here("data", "hrs_data.rds"))

Questions

Question 1: Grammar of ggplot2

In Lesson 31, we saw that every ggplot2 plot is built from the same basic grammar: data, a mapping, and a layer.

Part A: The three required pieces

WarningTask

Fill in the table below with what each of the data, mapping, and layer contribute to a plot, and which function we use to supply each one.

Answer:

Example for one row, fill in the rest yourself following this style:

Component	Contribution	Function supplied
Data
Mapping
Layer	Determines how the mapped data actually gets drawn: as points, bars, boxplots, and so on	a geom_*() (or stat_*()) function, added with +

Part B: Start with just the data

WarningTask

Call ggplot() with just hrs_data as the data (don’t add a mapping or a layer yet). Assign the result to hrs_01_plot_data and display it. What do you see in the plot area?

Answer:

Here’s an example to show you the pattern: the rest of your answers should follow this same style, with code chunks and #| ... chunk options as needed.

hrs_01_plot_data <- ggplot(hrs_data)

hrs_01_plot_data

Part C: Add a mapping

WarningTask

Now rebuild the plot, this time adding a mapping: aes(x = height). Assign the result to hrs_02_plot_mapped and display it. Compare it to hrs_01_plot_data. What showed up this time, and what is still missing?

Answer:

Not given.

Part D: Add a layer

WarningTask

Finally, add a layer to hrs_02_plot_mapped by adding geom_histogram(). Assign the complete plot to hrs_03_plot_hist and display it.

Answer:

Your plot should look like this:

Part E: Same plot, different code style

WarningTask

Recreate hrs_03_plot_hist again, but this time move aes(x = height) out of ggplot() and into geom_histogram() instead. Assign the result to hrs_04_plot_aes_in_geom. Confirm it looks identical to hrs_03_plot_hist.

Answer:

Not given.

Question 2: Basic plotting and aesthetics

In Lesson 32, we covered different geom_*() functions and how to set or map aesthetics like color, size, and transparency.

Part A: A density plot

WarningTask

Using hrs_data, create a plot of income and add geom_density() to show its distribution. Assign the plot to hrs_05_plot_density and display the plot.

Answer:

Not given.

Part B: A boxplot of the same variable

WarningTask

Now make the same plot of income, but use geom_boxplot() instead of geom_density(). Assign the plot to hrs_06_plot_boxplot.

Answer:

Not given.

Part C: A scatterplot of two continuous variables

WarningTask

Create a plot of ed (years of education, x-axis) vs. income (y-axis) from hrs_data using geom_point(). Assign it to hrs_07_plot_scatter and display it.

Answer:

Your plot should look like this:

Part D: A line plot of the same two variables

WarningTask

Now make the same plot of ed vs. income, but use geom_smooth() instead of geom_point() to show the overall trend as a line. Assign the plot to hrs_08_plot_smooth and display it.

Answer:

Not given.

Part E: A boxplot of a categorical and a continuous variable

WarningTask

Create a plot of act_vig (vigorous physical activity frequency, x-axis) vs. income (y-axis) from hrs_data using geom_boxplot(). Assign it to hrs_09_plot_box.

Answer:

Start with something like:

hrs_09_plot_box <- ggplot(hrs_data, aes(x = ______, y = ______)) +
  geom_boxplot()

Part F: Layer jitter on top

WarningTask

Starting from the same mapping, add a geom_jitter() layer on top of the boxplot layer so you can see the individual data points as well. Set alpha = 0.2 inside geom_jitter() so the points don’t overwhelm the boxplot. Assign the result to hrs_10_plot_box_jitter.

Answer:

Not given.

Part G: Setting an aesthetic

WarningTask

Create a plot of cond_count (x-axis) vs. act_vig (y-axis) using geom_jitter(), and set the color of the points to "darkblue" (this goes outside of aes()). Also set alpha lower so the individual dots show up better where they overlap. Assign the plot to hrs_11_plot_color_set.

Answer:

Start with something like:

hrs_11_plot_color_set <- ggplot(hrs_data, aes(x = _______, y = ______)) +
  geom_jitter(color = ______, alpha = ______)

Part H: Mapping an aesthetic

WarningTask

Now recreate the same plot, but this time map color to the smoke_ever variable inside aes() instead of setting a fixed color. Also set alpha lower so the dots show up better. Assign the plot to hrs_12_plot_color_map.

In 1-2 sentences, explain the difference between setting an aesthetic outside aes() and mapping it inside aes().

Answer:

Not given.

Question 3: Design choices for labels, facets, and themes

In Lesson 33, we covered how to polish a plot using labs(), facet_wrap()/facet_grid(), theme(), and how to save a finished plot with ggsave().

Part A: Labels

WarningTask

Starting from hrs_10_plot_box_jitter (Question 2 Part F), add a labs() layer with an x-axis label, a y-axis label, and a title of your choice. Assign the result to hrs_13_plot_labs and display it.

Answer:

Not given.

Part B: Facets

WarningTask

Using hrs_data, create a histogram of age_yr, faceted by sex with facet_wrap(). Assign the result to hrs_14_plot_facet.

Looking at the faceted plot, does the age distribution appear to differ between males and females?

Answer:

Your plot should look like this:

Part C: Color

WarningTask

Recreate the same distribution as hrs_14_plot_facet (a histogram of age_yr), but this time show the comparison by mapping fill to sex instead of faceting. Assign the result to hrs_15_plot_fill and display it.

Which approach, facet or fill, feels more appropriate for comparing the age distributions of males and females here? Why?

Answer:

Not given.

Part D: Themes

WarningTask

Take hrs_13_plot_labs from Part A and add a complete theme of your choice (e.g., theme_minimal(), theme_bw(), or theme_classic()). Then, use theme() to additionally increase the size of the plot title to 16. Assign the final plot to hrs_16_plot_final and display it.

Answer:

Not given.

Part E: Saving your plot

WarningTask

Use ggsave() to save hrs_16_plot_final as a .png file into your images folder (use here() to build the file path). Set the width to 6 inches, the height to 4 inches, and the dpi to 200.

Answer:

Start with something like:

ggsave(
  filename = here(______, ______),
  plot = ______,
  width = ______,
  height = ______,
  units = "in",
  dpi = ______
)

Part F: Insert your saved plot

WarningTask

In a text section of your document (not a code chunk), insert the image you just saved in Part E using Markdown image syntax.

Answer:

Not given.