Lecture 1.3 KEY

Note

This template follows lecture 1.3 slides.

Setup

# load packages ----
library(tidyverse)
library(palmerpenguins)

Tidy Data Review

Example untidy / wide data:

# create some untidy temperature 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:

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

Example 1 (the basics)

• initialize a plot object
• map aesthetics
• show that you can omit argument names
• add a geometry layer
• update how aesthetic mappings manifest visually (i.e. scaling)

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point()

Example 2 (mapping custom 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 3 (mapping color to continuous variable)

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + 
  geom_point() +
  scale_color_gradient(low = "#132B43", high = "#F7DD4C")

Example 4 (updating color for all points)

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(color = "blue")

Example 5 (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 6 & 7 (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 8 (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:

1. statistical transformations – what are they, how to identify the default, and how to update them
2. position adjustments – what are they, how to identify the default, and how to update them
3. coordinate systems – what are they, how to identify the default, and how to update them
   
4. 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!

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) 

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

Example 3 (override default stat mapping)

• e.g. we can display the same data but with y-axis values as proportions rather than counts

ggplot(data = penguins, mapping = aes(x = species, y = after_stat(prop), group = 1)) +
  geom_bar()

Example 4 (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):

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

Example 5 & 6 (override default position adjustments)

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

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):

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

Example 7 (alternatively, use position_*())

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

Example 8 (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 9 & 10 (alternative coordinate systems)

• flip x & y axes:

ggplot(penguins, aes(x = species)) +
  geom_bar() +
  coord_flip()

• polar coordinates:

ggplot(penguins, aes(x = species)) +
  geom_bar() +
  coord_polar()

Example 11 (pre-made themes)

ggplot(penguins, aes(x = species)) +
  geom_bar() +
  theme_light() 

Example 12 (customize further with 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:

1. more position adjustments and scales
2. updating plot labels
3. 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

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)