KEY - {ggplot2} review

Note

This key follows the {ggplot2} review slides. Please be sure to cross-reference the slides, which contain important information and additional context!

Setup

#..........................load packages.........................
library(tidyverse)
library(patchwork)
library(palmerpenguins)

Tidy Data Review

Example untidy / wide data:

#..................create some untidy temp data..................
temp_data_wide <- tribble(
  ~date, ~station1, ~station2,  ~station3,
  "2023-10-01", 30.1, 29.8,  31.2,
  "2023-11-01", 28.6, 29.1,  33.4,
  "2023-12-01", 29.9, 28.5,  32.3
)

# print it out ----
print(temp_data_wide)

# A tibble: 3 × 4
  date       station1 station2 station3
  <chr>         <dbl>    <dbl>    <dbl>
1 2023-10-01     30.1     29.8     31.2
2 2023-11-01     28.6     29.1     33.4
3 2023-12-01     29.9     28.5     32.3

Using pivot_longer() to “lengthen” / tidy our data:

#..................convert data from wide > long.................
temp_data_long <- temp_data_wide |> 
  pivot_longer(cols = starts_with("station"),
               names_to = "station_id",
               values_to = "temp_c")

#..........................print it out..........................
print(temp_data_long)

# A tibble: 9 × 3
  date       station_id temp_c
  <chr>      <chr>       <dbl>
1 2023-10-01 station1     30.1
2 2023-10-01 station2     29.8
3 2023-10-01 station3     31.2
4 2023-11-01 station1     28.6
5 2023-11-01 station2     29.1
6 2023-11-01 station3     33.4
7 2023-12-01 station1     29.9
8 2023-12-01 station2     28.5
9 2023-12-01 station3     32.3

Plot #1

Explore the relationship between penguin bill length and bill depth. Our goals are to review:

initializing a plot object and adding a geometry layer to represent our data
when to define data & map variables globally (e.g. within ggplot()) vs. locally (e.g. within a geom_*())
updating how aesthetic mappings manifest visually, aka scaling
piping into a ggplot

Example 1 (the basics)

initialize a plot object
define data & map aesthetics (also, omitting argument names)
add a geometry layer
update how aesthetic mappings manifest visually (i.e. scaling)
map custom colors

#..........scatterplott with custom categorical colors...........
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() +
  scale_color_manual(values = c("darkorange","purple","cyan4"))

Example 2 (mapping color to continuous variable)

#...........scatterplott with custom continuous colors...........
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + 
  geom_point() +
  scale_color_gradient(low = "#F7DD4C", high = "#132B43")

Example 3 (updating color for all points)

#................scatter plot with all blue points...............
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(color = "blue")

Example 4 (local vs. global data & variable mappings)

define data and mappings within geom_*() (locally) rather than in ggplot() (globally) – helpful if you plan to include multiple geoms with different mappings (e.g. you’re plotting data from multiple data frames):

#..............create a separate penguins_summary df.............
penguins_summary <- penguins |>
  drop_na() |> 
  group_by(species) |> 
  summarize(
    mean_bill_length_mm = mean(bill_length_mm),
    mean_bill_depth_mm = mean(bill_depth_mm)
  )

#..........create ggplot with layers from different dfs..........
ggplot() +
  geom_point(data = penguins, 
             mapping = aes(x = bill_length_mm, y = bill_depth_mm, color = species), 
             alpha = 0.5) +
  geom_point(data = penguins_summary,
             mapping = aes(x = mean_bill_length_mm, y = mean_bill_depth_mm, fill = species),
             size = 5, shape = 24)

Example 5 & 6 (mapping variables globally vs. locally)

global mappings are passed down to all subsequent layers:

#.............scatterplot with lms fitted to species.............
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point() +
  geom_smooth(method = "lm")

local mappings only apply to that particular layer:

#..........scatterplot with lm fitted to entire data set.........
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point(aes(color = species)) +
  geom_smooth(method = "lm")

Example 7 (piping data into a ggplot)

useful if you need to do a small bit of wrangling first, but don’t want to create a whole new df

#........filter for Gentoo penguins, then pipe into ggplot.......
penguins |> 
  filter(species == "Gentoo") |> 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point()

Plot #2

Explore penguin species counts. Our goals are to review:

statistical transformations – what are they, how to identify the default, and how to update them
position adjustments – what are they, how to identify the default, and how to update them
coordinate systems – what are they, how to identify the default, and how to update them
updating non-data elements using pre-built themes and the theme() function

Example 1 (the basics + default stat)

initialize a plot object
map aesthetics
add a geometry layer
explore default statistical transformations (reading documentation is important here!)

#...................bar plot with default stat...................
ggplot(penguins, aes(x = species)) +
  geom_bar(stat = "count") # this is the default stat, so you don't actually need to type this out!

Example 2 (override default stat)

let’s say we have a df that already contains calculated species count values, and we want the height of the bars to be based on those count values:

#........................new summarized df.......................
penguin_summary <- penguins |> 
  count(species) 

#..create bar heights based on `identity` of values in `n` col...
ggplot(penguin_summary, aes(x = species, y = n)) +
  geom_bar(stat = "identity")

Note: See the slides for an example of overriding the default stat mapping (e.g. displaying these y-axis values as proportions, rather than counts)

Example 3 (default position adjustment)

position adjustments tweak position of elements to resolve overlapping geoms
all geoms have a default position (e.g. barplots have position = "stack") – see documentation
let’s say we now want to visualize penguin counts by species (bar height) and by island (color):

#........................default position........................
ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = "stack") # this is the default, so you don't actually need to specify!

Example 4 & 5 (override default position adjustments)

position = "fill" creates stacked bars of the same height (easier to compare proportions):

#..........................position fill.........................
ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = "fill")

position = "dodge" places overlapping bars directly beside one another (easier to compare individual values):

#.........................position dodge.........................
ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = "dodge")

Example 6 (alternatively, use `position_*()`)

#.......................position functions.......................
ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = position_dodge2(preserve = "single"))

Example 7 (default coordinate system)

#....................default coordinate system...................
ggplot(penguins, aes(x = species)) +
  geom_bar() +
  coord_cartesian() # you don't need to explicitly include `coord_cartesian()` since it's the default

Example 8 & 9 (alternative coordinate systems)

flip x & y axes:

#............................flip axes...........................
ggplot(penguins, aes(x = species)) +
  geom_bar() +
  coord_flip()

polar coordinates:

#........................polar coordinates.......................
ggplot(penguins, aes(x = species)) +
  geom_bar() +
  coord_polar()

Example 10 (pre-made themes + futher customization with `theme()`)

#..................premade theme + custom theme..................
ggplot(penguins, aes(x = species)) +
  geom_bar() +
  theme_light() +
  theme(
    axis.title = element_text(size = 17, color = "purple")
  )

Plot #3

Explore penguin flipper lengths. Our goals are to review:

more position adjustments and scales
updating plot labels
faceting

Example 1 (all in one go!)

initialize a plot object
map aesthetics
add a geometry layer
map color to species
update position adjustment
update labels
facet by species

#......................labels & facet wrap.......................
ggplot(penguins, aes(x = flipper_length_mm, fill = species)) + 
  geom_histogram(position = "identity", alpha = 0.5) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) + 
  labs(x = "Flipper Length (mm)",
       y = "Frequency",
       fill = "Species",
       title = "Penguin Flipper Lengths") +
  facet_wrap(~species, ncol = 1)

Plot #4

Combine individual plots into a single graphic using {patchwork}:

Save plots to object names

#.................save scatter plot to obj `p1`..................
p1 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
  geom_point() +
  scale_color_gradient(low = "#F7DD4C", high = "#132B43") +
  theme(
    legend.position = "top" # note: this is newly added from earlier!
  )

#....................save bar plot to obj `p2`...................
p2 <- ggplot(penguins, aes(x = species)) +
  geom_bar() +
  theme_light()

#...................save histogram to obj `p3`...................
p3 <- ggplot(penguins, aes(x = flipper_length_mm, fill = species)) + 
  geom_histogram(position = "identity", alpha = 0.5) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4"))

Assemble single graphic

#..........................stacked plots.........................
p1 / p2

#.......................side-by-side plots.......................
p1 + p2

#..........side-by-side & stacked plots + graphic title..........
(p1 + (p2 / p3)) +
  plot_annotation(title = "All the info you'd ever want about penguins")

Setup

Tidy Data Review

Plot #1

Example 1 (the basics)

Example 2 (mapping color to continuous variable)

Example 3 (updating color for all points)

Example 4 (local vs. global data & variable mappings)

Example 5 & 6 (mapping variables globally vs. locally)

Example 7 (piping data into a ggplot)

Plot #2

Example 1 (the basics + default stat)

Example 2 (override default stat)

Example 3 (default position adjustment)

Example 4 & 5 (override default position adjustments)

Example 6 (alternatively, use position_*())

Example 7 (default coordinate system)

Example 8 & 9 (alternative coordinate systems)

Example 10 (pre-made themes + futher customization with theme())

Plot #3

Example 1 (all in one go!)

Plot #4

Example 6 (alternatively, use `position_*()`)

Example 10 (pre-made themes + futher customization with `theme()`)