Week 06: Communicate

[06a] Code Style / Code Chunks

Topics

Style your code to improve organization and clarity
Manage the behavior of individual code chunks in Quarto

Readings

R4DS (2E) Chapter 4: Code Style
R4DS (2E) Section 28.5: Code Chunks
R4DS (2E) Section 28.6: Figures

Slides

*Note that you can click the three-line (hamburger) icon on the bottom-left of the slides to access a navigation menu. You can click inside the slides region and press the left and right arrow keys on your keyboard to advance and reverse the slides and animations.

Practice

1) Code Style

The following code works, but it violates several principles of the tidyverse style guide. Using consistent code style makes collaboration easier and smoother. Rewrite this code to improve its style.

Use a consistent snake case style for the object name.
Put spaces around the relational operators, arithmetic operators, and after commas.
Follow each pipe with a line break and indent appropriately.

Clean_Sleep_Data<-msleep|>filter(order=="Carnivora",sleep_total>10)|>mutate(sleep_ratio=sleep_rem/sleep_total)|>select(name,sleep_ratio)|>arrange(desc(sleep_ratio))

Answer key

clean_sleep_data <- 
  msleep |> 
  filter(order == "Carnivora", sleep_total > 10) |> 
  mutate(sleep_ratio = sleep_rem / sleep_total) |> 
  select(name, sleep_ratio) |> 
  arrange(desc(sleep_ratio))

2) Chunk Options

Create a new code chunk that generates a simple plot (e.g., plot(1:10)). Use hash pipes (#|) to configure the chunk options. You can combine multiple options by putting each on a new line.

Give the chunk a unique label called simple-plot.
Run the code without showing it in the final document.
Prevent any messages or warnings from rendering.
Add a figure caption that reads “A simple plot of numbers.”.

Answer key

#| label: simple-plot
#| echo: false
#| message: false
#| warning: false
#| fig-cap: "A simple plot of numbers."

plot(1:10)

[06b] Table Anatomy / Table Styling

Topics

Understand the structural anatomy of a gt table
Format and style table data for clear communication

Readings

gt Vignette: Introduction to Creating gt Tables
gt Vignette: Creating Summary Lines
gt Vignette: Case Study–gtcars

Slides

Practice

1) Table Structure

Using the msleep dataset (from the {ggplot2} package), create a base display table using the gt package.

First, filter the data to only include animals where the vore is “carni”, and select the name, sleep_total, and sleep_rem columns.
Pass this data to gt() to create the base table.
Use tab_header() to add a title (“Carnivore Sleep Patterns”) and a subtitle (“Total and REM sleep”).
Use cols_label() to rename the columns to “Animal”, “Total Sleep”, and “REM Sleep”.

Answer key

library(tidyverse)
library(gt)

msleep |> 
  filter(vore == "carni") |> 
  select(name, sleep_total, sleep_rem) |> 
  gt() |> 
  tab_header(
    title = md("**Carnivore Sleep Patterns**"),
    subtitle = "Total and REM sleep"
  ) |> 
  cols_label(
    name = "Animal",
    sleep_total = "Total Sleep",
    sleep_rem = "REM Sleep"
  )
Carnivore Sleep Patterns

Total and REM sleep

Animal Total Sleep REM Sleep

Cheetah 12.1 NA

Northern fur seal 8.7 1.4

Dog 10.1 2.9

Long-nosed armadillo 17.4 3.1

Domestic cat 12.5 3.2

Pilot whale 2.7 0.1

Gray seal 6.2 1.5

Thick-tailed opposum 19.4 6.6

Slow loris 11.0 NA

Northern grasshopper mouse 14.5 NA

Tiger 15.8 NA

Jaguar 10.4 NA

Lion 13.5 NA

Caspian seal 3.5 0.4

Common porpoise 5.6 NA

Bottle-nosed dolphin 5.2 NA

Genet 6.3 1.3

Arctic fox 12.5 NA

Red fox 9.8 2.4

Carnivore Sleep Patterns
Cheetah	12.1	NA
Northern fur seal	8.7	1.4
Dog	10.1	2.9
Long-nosed armadillo	17.4	3.1
Domestic cat	12.5	3.2
Pilot whale	2.7	0.1
Gray seal	6.2	1.5
Thick-tailed opposum	19.4	6.6
Slow loris	11.0	NA
Northern grasshopper mouse	14.5	NA
Tiger	15.8	NA
Jaguar	10.4	NA
Lion	13.5	NA
Caspian seal	3.5	0.4
Common porpoise	5.6	NA
Bottle-nosed dolphin	5.2	NA
Genet	6.3	1.3
Arctic fox	12.5	NA
Red fox	9.8	2.4

2) Spanners and Styling

Modify your table from question 1 to add grouping and formatting options.

Add a spanner using tab_spanner() with the label “Hours” over the sleep_total and sleep_rem columns.
Center the alignment of the data columns using cols_align().
Format the sleep values to only show one decimal place using fmt_number().

Answer key

msleep |> 
  filter(vore == "carni") |> 
  select(name, sleep_total, sleep_rem) |> 
  gt() |> 
  tab_header(
    title = md("**Carnivore Sleep Patterns**"),
    subtitle = "Total and REM sleep"
  ) |> 
  cols_label(
    name = "Animal",
    sleep_total = "Total Sleep",
    sleep_rem = "REM Sleep"
  ) |> 
  tab_spanner(
    label = "Hours",
    columns = c(sleep_total, sleep_rem)
  ) |> 
  cols_align(
    align = "center",
    columns = c(sleep_total, sleep_rem)
  ) |> 
  fmt_number(
    columns = c(sleep_total, sleep_rem),
    decimals = 1
  )
Carnivore Sleep Patterns

Total and REM sleep

Animal
Hours

Total Sleep REM Sleep

Cheetah 12.1 NA

Northern fur seal 8.7 1.4

Dog 10.1 2.9

Long-nosed armadillo 17.4 3.1

Domestic cat 12.5 3.2

Pilot whale 2.7 0.1

Gray seal 6.2 1.5

Thick-tailed opposum 19.4 6.6

Slow loris 11.0 NA

Northern grasshopper mouse 14.5 NA

Tiger 15.8 NA

Jaguar 10.4 NA

Lion 13.5 NA

Caspian seal 3.5 0.4

Common porpoise 5.6 NA

Bottle-nosed dolphin 5.2 NA

Genet 6.3 1.3

Arctic fox 12.5 NA

Red fox 9.8 2.4

Carnivore Sleep Patterns
Cheetah	12.1	NA
Northern fur seal	8.7	1.4
Dog	10.1	2.9
Long-nosed armadillo	17.4	3.1
Domestic cat	12.5	3.2
Pilot whale	2.7	0.1
Gray seal	6.2	1.5
Thick-tailed opposum	19.4	6.6
Slow loris	11.0	NA
Northern grasshopper mouse	14.5	NA
Tiger	15.8	NA
Jaguar	10.4	NA
Lion	13.5	NA
Caspian seal	3.5	0.4
Common porpoise	5.6	NA
Bottle-nosed dolphin	5.2	NA
Genet	6.3	1.3
Arctic fox	12.5	NA
Red fox	9.8	2.4

[06c] Plot Labels / Plot Scales

Topics

Customize the axis and plot labels in a figure
Customize the appearance of scales in a figure

Readings

R4DS (2E) Section 11.2: Labels
R4DS (2E) Section 11.4: Scales

Slides

Practice

1) Customizing Labels

Create a scatterplot using the iris dataset (from the {datasets} package).

Map Sepal Length (Sepal.Length) to the x-axis and Sepal Width (Sepal.Width) to the y-axis.
Map Species (Species) to the color aesthetic.
Use labs() to add a clear title, x-axis label, y-axis label, and legend title.

Answer key

library(tidyverse)

iris |> 
  ggplot(aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3) +
  labs(
    title = "Sepal dimensions vary by iris species",
    x = "Sepal Length (cm)",
    y = "Sepal Width (cm)",
    color = "Iris Species"
  )

2) Customizing Scales

Modify your plot from question 1 to customize its scales.

Use scale_y_continuous() to set the limits from 2 to 5, and add breaks every 0.5 units.
Use scale_color_manual() to assign specific colors (“darkorange”, “purple”, and “cyan4”) to the three iris species.

Answer key

iris |> 
  ggplot(aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3) +
  labs(
    title = "Sepal dimensions vary by iris species",
    x = "Sepal Length (cm)",
    y = "Sepal Width (cm)",
    color = "Iris Species"
  ) +
  scale_y_continuous(
    limits = c(2, 5),
    breaks = seq(2, 5, by = 0.5)
  ) +
  scale_color_manual(
    values = c(
      "setosa" = "darkorange",
      "versicolor" = "purple",
      "virginica" = "cyan4"
    )
  )

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