Winter 2026 - Coding workshop: Week 4

Workshop date: Friday 30 January

1. Summary

Packages

tidyverse

Operations

Review

read in data using read_csv()
visualize data using ggplot()
modify theme elements using theme()
use built-in themes using theme_()
modify colors using scale_color_() and scale_fill_() functions
create multi-panel plots using facet_wrap()
make line plots using geom_point() and geom_line()
group data using group_by()
summarize data using summarize()
chain functions together using |>
filtering observations using filter()
manipulate columns using mutate() and case_when()

Data sources

The fish migration data (salmon.csv) is from the Columbia River DART (Data Access in Real Time) on fish migration through the Columbia River Basin in 2023.

The Dungeons and Dragons data (dnd.csv) is from Tidy Tuesday.

The palm tree data (palmtrees.csv) is from the PalmTraits database via Tidy Tuesday.

The Pokémon data (pokemon.csv) is from the {pokemon} package via Tidy Tuesday.

The shark incident data (sharks.csv) is from Riley et al.

The tornado data (tornados.csv) is from the NOAA National Weather Service Storm Prediction Center.

The data from Mt. Vesuvius (vesuvius.csv) is from the Italian Istituto Nazionale di Geofisica e Vulcanologia via Tidy Tuesday.

2. Today’s topic

We’re going to be exploring different ways to customize plots in ggplot2 (remember that ggplot2 is a package within the tidyverse, which you already have installed).

Each group will be responsible for creating a figure manipulating your assigned plot component. You will spend some time exploring each part and creating a figure together. After you are done, you’ll compile slides to teach the class
a) what your assigned plot component is (what it does) and
b) how to manipulate it (in code).

Your challenge is to make the ugliest plot you can! Change the colors, line types, line widths, etc. - whatever your heart desires to make a fundamentally ugly plot.

Before you start customizing

1. Look up the `theme()` function.

In the Console, type a question mark, then your function name. For example:

# copy paste this into the console
?theme

# if you are working on scale_color or scale_fill, type ?scale_color or ?scale_fill into the console and pick one of the options in the pop-up menu
?scale_color
?scale_fill

Read about your function. Make a list of the arguments that are relevant to you given your plot element.

2. Read about the `ggplot2` manual section about your plot element.

If your element is strip, plot, panel, legend, or axis, read the section in “Modifing theme components” that is relevant to you.

If your element is scale_color or scale_fill, read the “Manual scales” section and this guide to using palettes.

If your topic is built-in themes combined with custom theme elements, read the “Complete themes” section.

3. See your elements in action.

If your element is strip, plot, panel, legend, axis, or if you are combining built-in themes with theme elements, see this tutorial from ggplot2tor.

If your element is scale_color or scale_fill, see this tutorial from the R Graph Gallery about dealing with colors.

3. Code

1. Set up

Packages and data

You will need the tidyverse. Insert a code chunk to read that in. Name it packages.

library(tidyverse)

You will also need to read in your data. Insert a code chunk to read that in. Name it data.

Only read in the data you are working with!

You do not need to read in all the datasets. Choose the code for the one you are working with.

# shark data (group 1)
sharks <- read_csv("sharks.csv")

# dungeons and dragons data (group 2)
dnd <- read_csv("dnd.csv")

# palm tree data (group 3)
palmtrees <- read_csv("palmtrees.csv")

# mt. vesuvius data (group 4)
vesuvius <- read_csv("vesuvius.csv")

# pokemon data (group 5)
pokemon <- read_csv("pokemon.csv")

# tornado data (group 6)
tornados <- read_csv("tornados.csv")

# salmon data (group 7)
salmon <- read_csv("salmon.csv")

2. Cleaning

For some datasets, you will need to do some cleaning and summarizing.

If you are working with the salmon, tornados, or sharks data frames, you will need to include the following cleaning/summarizing lines in your .qmd file. Insert a new code chunk called cleaning and wrangling in your file. Copy paste the appropriate cleaning code below into your code chunk and run it.

If you are working with the dnd, pokemon, or vesuvius datasets, you will not need any additional cleaning and wrangling. You can skip to the next section.

Sharks (group 1)

# create new clean object from sharks
sharks_clean <- sharks |> 
  # drop any observations where shark common name is missing
  drop_na(shark_common_name) |> 
  # group by shark species and state
  group_by(shark_common_name, state) |> 
  # count observations (easy way to get total number of observations from groups)
  count() |> 
  # ungroup the data frame (good to do if you're doing any cleaning steps after this)
  ungroup() |> 
  # filter to only include New South Wales, Queensland, and Western Australia
  filter(state %in% c("NSW", "QLD", "WA")) |> 
  # mutate state column to show full names for each state
  mutate(state = case_when(
    state == "NSW" ~ "New South Wales",
    state == "QLD" ~ "Queensland",
    state == "WA" ~ "Western Australia"
  ))

Mt. Vesuvius (group 4)

# create a new object called vesuvius_clean from vesuvius
vesuvius_clean <- vesuvius |> 
  # make sure each year is read as a character instead of a number
  mutate(year = as.character(year))

Tornados (group 6)

# create a new object called tornados_states from tornados
tornados_states <- tornados |> 
  # group by year
  group_by(yr, st) |>
  # calculate total property loss in dollars, sum number of tornados, calculate total property loss in billions of dollars
  summarize(total_property_loss = sum(loss, na.rm = TRUE),
            number_tornados = length(yr),
            total_property_loss_bil = total_property_loss/1000000000) |> 
  # ungroup the data frame (useful if you're going to do any further summarizing steps)
  ungroup() |> 
  # filter state to only include OK, TX, LA, KS
  filter(st %in% c("OK", "TX", "LA", "KS"))

Salmon (group 7)

# create new clean object from salmon
salmon_clean <- salmon |> 
  # making sure the date is read as a date
  mutate(Date = mdy(Date)) |> 
  # selecting date and 3 salmonid species
  select(Date, Chin, Stlhd, Coho) |> 
  # making the data frame longer
  pivot_longer(cols = Chin:Coho,
               names_to = "species",
               values_to = "daily_count") |> 
  # mutating species column to display species names in full
  mutate(species = case_when(
    species == "Chin" ~ "Chinook",
    species == "Stlhd" ~ "Steelhead",
    TRUE ~ species
  )) |> 
  # filter to only include dates after December 31st 2023
  filter(Date > as_date("2023-12-31")) |> 
  # take out any missing values
  drop_na(daily_count)

3. Plot customization

Find the plot that accompanies your dataset.

Create a new code chunk in your .qmd file called plot customization.

Copy paste the code for your plot into your .qmd file to start working with it.

a. Sharks

This plot depicts the total number of shark incidents in New South Wales and Queensland from 1791-2022

              # base layer: ggplot
shark_plot <- ggplot(data = sharks_clean,
                     # aesthetics: x-axis, y-axis
                     aes(x = n,
                         y = shark_common_name)) +
  # first layer: columns to represent counts
  geom_col() +
  # faceting by state
  facet_wrap(~ state) +
  # labels
  labs(x = "Year",
       y = "Total number of shark incidents")

# display the plot
shark_plot

b. Dungeons and Dragons

                   # base layer: ggplot
monster_ratings <- ggplot(data = dnd,
                          # aesthetics: monsters on the y-axis, challenge rating
                          # on the x-axis
                          # y-axis (type) ordered by median challenge rating (cr)
                          aes(y = fct_reorder(type, cr, .fun = median),
                              x = cr)) +
  # first player: boxplot
  geom_boxplot(outliers = FALSE) +
  # second layer: jitter to represent individual mosters
  geom_jitter(width = 0,
              height = 0.1) +
  # relabelling x- and y-axes
  labs(x = "Challenge rating",
       y = "Monster type")

# display the plot
monster_ratings

c. Palm trees

                # base layer: ggplot
palm_heights <- ggplot(palmtrees,
                       # aesthetics: palm subfamily on x-axis, stem height on
                       # y-axis, color by subfamily
                       aes(x = palm_subfamily,
                           y = max_stem_height_m,
                           color = palm_subfamily)) +
  # first layer: points to represent each species of palm
  geom_point() +
  # second layer: quick way of showing means in the middle of each set of points
  stat_summary(fun = mean,
               geom = "point",
               color = "red",
               size = 2)

# displaying the plot
palm_heights

d. Salmon

This plot depicts daily counts of salmon through Bonneville Dam in Columbia River Basin, Oregon in 2024.

               # base layer: ggplot
salmon_plot <- ggplot(data = salmon_clean,
                      # aesthetics: x-axis, y-axis, and color
                      aes(x = Date,
                          y = daily_count,
                          color = species)) +
  # first layer: points
  geom_point() +
  # second layer: line
  geom_line() +
  # labels
  labs(x = "Date",
       y = "Daily fish count")

# display the plot
salmon_plot

e. Pokémon

              # base layer
pokemon_hp <- ggplot(data = pokemon,
                     # aesthetics: hit points (hp) on x-axis,
                     # pokemon type ordered by median hit points on y-axis
                     # color points by type and reorder legend by median
                     aes(y = fct_reorder(type_1, hp, .fun = median),
                         x = hp,
                         color = fct_reorder(type_1, -hp, .fun = median))) +
  # first layer: display points representing each individual pokemon
  geom_point() +
  # second layer: quick way of displaying medians
  stat_summary(fun = median,
               geom = "point",
               size = 2, 
               color = "red") +
  # relabelling x- and y-axes
  labs(x = "Hit points",
       y = "Pokémon type")

# displaying plot
pokemon_hp

f. Tornados

This figure depicts total property loss (in dollars) due to tornados in US from 1950-2022

tornados_by_state_plot <- ggplot(data = tornados_states,
                                 aes(x = number_tornados,
                                     y = total_property_loss_bil,
                                     color = st)) +
  geom_point()


tornados_by_state_plot

g. Mt. Vesuvius

                 # base layer
vesuvius_plot <- ggplot(data = vesuvius_clean,
                        # aesthetics: x-axis is time, y-axis is duration magnitude,
                        # color points by year
                        aes(x = time,
                            y = duration_magnitude_md,
                            color = year)) +
  # first layer: points representing duration magnitude of a seismic event
  geom_point() +
  # second layer: lines to connect points
  geom_line() +
  # relabelling x- and y-axes
  labs(x = "Date and time of seismic event",
       y = "Duration magnitude (Md)")

# displaying plot
vesuvius_plot

4. Plot components to focus on

a. Group 1: `strip` in `theme()`

Demonstrate how to:
- change the background
- change the border
- change the placement
- change the text size and font

b. Group 2: `plot` in `theme()`

Demonstrate how to:
- change the plot margin
- change the plot background
- change the plot title, subtitle, and caption text position and color

c. Group 3: `panel` in `theme()`

Demonstrate how to:
- change the panel border
- change the panel major grid lines (vertically and horizontally, in separate arguments)
- change the panel minor grid lines (vertically and horizontally, in separate arguments)
- change the panel background

d. Group 4: `legend` in `theme()`

Demonstrate how to:
- change the legend frame
- change the legend key size
- change the legend text size
- change the legend position
- change the legend row numbers

e. Group 5: `axis` in `theme()`

Demonstrate how to:
- change the axis text color and font
- change the axis tick length (major and minor ticks)
- change the axis line colors and line types

f. Group 6: `scale_color` or `scale_fill` functions

Demonstrate how to:
- use a color palette package
- apply it to a color scale

g. Group 7: built in themes (`theme_`) with your own customization using `theme()`

Demonstrate how to:

use a built in theme and
change additional components using the theme() elements of your choice (one of each element from a - e)

--- title: "Coding workshop: Week 4" subtitle: "ggplot customization" categories: [tidyverse, read_csv, ggplot, theme, facet_wrap, geom_point, geom_line, group_by, summarize, pipe operators, '|>', filter, mutate] format: html: toc: true toc-depth: 8 --- [Workshop date: Friday 30 January]{style="color: #79ACBD; font-size: 24px;"} ## 1. Summary ### Packages - `tidyverse` ### Operations #### Review - read in data using `read_csv()` - visualize data using `ggplot()` - modify theme elements using `theme()` - use built-in themes using `theme_()` - modify colors using `scale_color_()` and `scale_fill_()` functions - create multi-panel plots using `facet_wrap()` - make line plots using `geom_point()` and `geom_line()` - group data using `group_by()` - summarize data using `summarize()` - chain functions together using ` |> ` - filtering observations using `filter()` - manipulate columns using `mutate()` and `case_when()` ### Data sources The fish migration data (`salmon.csv`) is from the [Columbia River DART (Data Access in Real Time)](https://www.cbr.washington.edu/dart/overview) on fish migration through the Columbia River Basin in 2023. The Dungeons and Dragons data (`dnd.csv`) is from [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday/tree/main/data/2025/2025-05-27). The palm tree data (`palmtrees.csv`) is from the [PalmTraits database](https://doi-org.proxy.library.ucsb.edu/10.1038/s41597-019-0189-0) via Tidy Tuesday. The Pokémon data (`pokemon.csv`) is from the [{pokemon} package](https://github.com/williamorim/pokemon) via Tidy Tuesday. The shark incident data (`sharks.csv`) is from [Riley et al.](https://doi.org/10.1038/s41597-022-01453-9) The tornado data (`tornados.csv`) is from the [NOAA National Weather Service Storm Prediction Center](https://www.spc.noaa.gov/wcm/#data). The data from Mt. Vesuvius (`vesuvius.csv`) is from the Italian Istituto Nazionale di Geofisica e Vulcanologia via [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-05-13/readme.md). ## 2. Today's topic We're going to be exploring different ways to customize plots in `ggplot2` (remember that `ggplot2` is a package within the `tidyverse`, which you already have installed). Each group will be responsible for creating a figure manipulating your assigned plot component. You will spend some time exploring each part and creating a figure together. After you are done, you'll compile slides to teach the class a) what your assigned plot component is (what it does) and b) how to manipulate it (in code). **Your challenge is to make the ugliest plot you can!** Change the colors, line types, line widths, etc. - whatever your heart desires to make a fundamentally ugly plot. ### Before you start customizing #### 1. Look up the `theme()` function. In the Console, type a question mark, then your function name. For example: ```{r looking-up-function} #| eval: false # this makes sure that the code shows up in the rendered document but isn't actually run # copy paste this into the console ?theme # if you are working on scale_color or scale_fill, type ?scale_color or ?scale_fill into the console and pick one of the options in the pop-up menu ?scale_color ?scale_fill ``` Read about your function. Make a list of the arguments that are relevant to you given your plot element. #### 2. Read about the `ggplot2` manual section about your plot element. If your element is strip, plot, panel, legend, or axis, read the section in ["Modifing theme components"](https://ggplot2-book.org/themes.html#modifying-theme-components) that is relevant to you. If your element is `scale_color` or `scale_fill`, read the ["Manual scales" section](https://ggplot2-book.org/scales-other.html#sec-scale-manual) and this [guide to using palettes](https://r-graph-gallery.com/color-palette-finder). If your topic is built-in themes combined with custom theme elements, read the ["Complete themes" section](https://ggplot2-book.org/themes.html#sec-themes). #### 3. See your elements in action. If your element is strip, plot, panel, legend, axis, or if you are combining built-in themes with theme elements, see this tutorial from [ggplot2tor](https://ggplot2tor.com/theme/). If your element is `scale_color` or `scale_fill`, see this tutorial from the [R Graph Gallery about dealing with colors](https://r-graph-gallery.com/ggplot2-color.html). ## 3. Code ### 1. Set up #### Packages and data ```{r read-in-packages-and-data-background} #| echo: false #| message: false # packages library(tidyverse) library(here) # data # salmon data salmon <- read_csv(here("workshop", "data", "adultdaily_1745380588_196.csv")) # tornado data tornados <- read_csv(here("workshop", "data", "tornados.csv")) # shark data sharks <- read_csv(here("workshop", "data", "sharks.csv")) # mt. vesuvius data vesuvius <- read_csv(here("workshop", "data", "vesuvius.csv")) # pokemon pokemon <- read_csv(here("workshop", "data", "pokemon.csv")) # palm trees palmtrees <- read_csv(here("workshop", "data", "palmtrees.csv")) # dnd dnd <- read_csv(here("workshop", "data", "dnd.csv")) ``` You will need the `tidyverse`. Insert a code chunk to read that in. Name it `packages`. ```{r} #| eval: false library(tidyverse) ``` You will also need to read in your data. Insert a code chunk to read that in. Name it `data`. :::{.callout-note title="Only read in the data you are working with!"} You do not need to read in all the datasets. Choose the code for the one you are working with. ::: ```{r read-in-packages-and-data-for-website} #| eval: false # shark data (group 1) sharks <- read_csv("sharks.csv") # dungeons and dragons data (group 2) dnd <- read_csv("dnd.csv") # palm tree data (group 3) palmtrees <- read_csv("palmtrees.csv") # mt. vesuvius data (group 4) vesuvius <- read_csv("vesuvius.csv") # pokemon data (group 5) pokemon <- read_csv("pokemon.csv") # tornado data (group 6) tornados <- read_csv("tornados.csv") # salmon data (group 7) salmon <- read_csv("salmon.csv") ``` ### 2. Cleaning For some datasets, you will need to do some cleaning and summarizing. If you are working with the `salmon`, `tornados`, or `sharks` data frames, you will need to include the following cleaning/summarizing lines in your .qmd file. Insert a new code chunk called `cleaning and wrangling` in your file. Copy paste the appropriate cleaning code below into your code chunk and run it. If you are working with the `dnd`, `pokemon`, or `vesuvius` datasets, you will _not_ need any additional cleaning and wrangling. You can skip to the next section. #### Sharks (group 1) ```{r sharks-cleaning-and-wrangling} # create new clean object from sharks sharks_clean <- sharks |> # drop any observations where shark common name is missing drop_na(shark_common_name) |> # group by shark species and state group_by(shark_common_name, state) |> # count observations (easy way to get total number of observations from groups) count() |> # ungroup the data frame (good to do if you're doing any cleaning steps after this) ungroup() |> # filter to only include New South Wales, Queensland, and Western Australia filter(state %in% c("NSW", "QLD", "WA")) |> # mutate state column to show full names for each state mutate(state = case_when( state == "NSW" ~ "New South Wales", state == "QLD" ~ "Queensland", state == "WA" ~ "Western Australia" )) ``` #### Mt. Vesuvius (group 4) ```{r} # create a new object called vesuvius_clean from vesuvius vesuvius_clean <- vesuvius |> # make sure each year is read as a character instead of a number mutate(year = as.character(year)) ``` #### Tornados (group 6) ```{r tornados-cleaning-and-wrangling} # create a new object called tornados_states from tornados tornados_states <- tornados |> # group by year group_by(yr, st) |> # calculate total property loss in dollars, sum number of tornados, calculate total property loss in billions of dollars summarize(total_property_loss = sum(loss, na.rm = TRUE), number_tornados = length(yr), total_property_loss_bil = total_property_loss/1000000000) |> # ungroup the data frame (useful if you're going to do any further summarizing steps) ungroup() |> # filter state to only include OK, TX, LA, KS filter(st %in% c("OK", "TX", "LA", "KS")) ``` #### Salmon (group 7) ```{r salmon-data-and-wrangling} # create new clean object from salmon salmon_clean <- salmon |> # making sure the date is read as a date mutate(Date = mdy(Date)) |> # selecting date and 3 salmonid species select(Date, Chin, Stlhd, Coho) |> # making the data frame longer pivot_longer(cols = Chin:Coho, names_to = "species", values_to = "daily_count") |> # mutating species column to display species names in full mutate(species = case_when( species == "Chin" ~ "Chinook", species == "Stlhd" ~ "Steelhead", TRUE ~ species )) |> # filter to only include dates after December 31st 2023 filter(Date > as_date("2023-12-31")) |> # take out any missing values drop_na(daily_count) ``` ### 3. Plot customization Find the plot that accompanies your dataset. Create a new code chunk in your .qmd file called `plot customization`. Copy paste the code for your plot into your .qmd file to start working with it. #### a. Sharks This plot depicts the total number of shark incidents in New South Wales and Queensland from 1791-2022 ```{r shark-incident-plot} # base layer: ggplot shark_plot <- ggplot(data = sharks_clean, # aesthetics: x-axis, y-axis aes(x = n, y = shark_common_name)) + # first layer: columns to represent counts geom_col() + # faceting by state facet_wrap(~ state) + # labels labs(x = "Year", y = "Total number of shark incidents") # display the plot shark_plot ``` #### b. Dungeons and Dragons ```{r} # base layer: ggplot monster_ratings <- ggplot(data = dnd, # aesthetics: monsters on the y-axis, challenge rating # on the x-axis # y-axis (type) ordered by median challenge rating (cr) aes(y = fct_reorder(type, cr, .fun = median), x = cr)) + # first player: boxplot geom_boxplot(outliers = FALSE) + # second layer: jitter to represent individual mosters geom_jitter(width = 0, height = 0.1) + # relabelling x- and y-axes labs(x = "Challenge rating", y = "Monster type") # display the plot monster_ratings ``` #### c. Palm trees ```{r} # base layer: ggplot palm_heights <- ggplot(palmtrees, # aesthetics: palm subfamily on x-axis, stem height on # y-axis, color by subfamily aes(x = palm_subfamily, y = max_stem_height_m, color = palm_subfamily)) + # first layer: points to represent each species of palm geom_point() + # second layer: quick way of showing means in the middle of each set of points stat_summary(fun = mean, geom = "point", color = "red", size = 2) # displaying the plot palm_heights ``` #### d. Salmon This plot depicts daily counts of salmon through Bonneville Dam in Columbia River Basin, Oregon in 2024. ```{r salmon-timeseries} # base layer: ggplot salmon_plot <- ggplot(data = salmon_clean, # aesthetics: x-axis, y-axis, and color aes(x = Date, y = daily_count, color = species)) + # first layer: points geom_point() + # second layer: line geom_line() + # labels labs(x = "Date", y = "Daily fish count") # display the plot salmon_plot ``` #### e. Pokémon ```{r} # base layer pokemon_hp <- ggplot(data = pokemon, # aesthetics: hit points (hp) on x-axis, # pokemon type ordered by median hit points on y-axis # color points by type and reorder legend by median aes(y = fct_reorder(type_1, hp, .fun = median), x = hp, color = fct_reorder(type_1, -hp, .fun = median))) + # first layer: display points representing each individual pokemon geom_point() + # second layer: quick way of displaying medians stat_summary(fun = median, geom = "point", size = 2, color = "red") + # relabelling x- and y-axes labs(x = "Hit points", y = "Pokémon type") # displaying plot pokemon_hp ``` #### f. Tornados This figure depicts total property loss (in dollars) due to tornados in US from 1950-2022 ```{r tornado-property-loss} tornados_by_state_plot <- ggplot(data = tornados_states, aes(x = number_tornados, y = total_property_loss_bil, color = st)) + geom_point() tornados_by_state_plot ``` #### g. Mt. Vesuvius ```{r} # base layer vesuvius_plot <- ggplot(data = vesuvius_clean, # aesthetics: x-axis is time, y-axis is duration magnitude, # color points by year aes(x = time, y = duration_magnitude_md, color = year)) + # first layer: points representing duration magnitude of a seismic event geom_point() + # second layer: lines to connect points geom_line() + # relabelling x- and y-axes labs(x = "Date and time of seismic event", y = "Duration magnitude (Md)") # displaying plot vesuvius_plot ``` ### 4. Plot components to focus on #### a. Group 1: `strip` in `theme()` Demonstrate how to: - change the background - change the border - change the placement - change the text size and font #### b. Group 2: `plot` in `theme()` Demonstrate how to: - change the plot margin - change the plot background - change the plot title, subtitle, and caption text position and color #### c. Group 3: `panel` in `theme()` Demonstrate how to: - change the panel border - change the panel major grid lines (vertically and horizontally, in separate arguments) - change the panel minor grid lines (vertically and horizontally, in separate arguments) - change the panel background #### d. Group 4: `legend` in `theme()` Demonstrate how to: - change the legend frame - change the legend key size - change the legend text size - change the legend position - change the legend row numbers #### e. Group 5: `axis` in `theme()` Demonstrate how to: - change the axis text color and font - change the axis tick length (major and minor ticks) - change the axis line colors and line types #### f. Group 6: `scale_color` or `scale_fill` functions Demonstrate how to: - use a color palette package - apply it to a color scale #### g. Group 7: built in themes (`theme_`) with your own customization using `theme()` Demonstrate how to: - use a built in theme and - change additional components using the `theme()` elements of your choice (one of each element from a - e)

1. Summary

Packages

Operations

Review

Data sources

2. Today’s topic

Before you start customizing

1. Look up the theme() function.

2. Read about the ggplot2 manual section about your plot element.

3. See your elements in action.

3. Code

1. Set up

Packages and data

2. Cleaning

Sharks (group 1)

Mt. Vesuvius (group 4)

Tornados (group 6)

Salmon (group 7)

3. Plot customization

a. Sharks

b. Dungeons and Dragons

c. Palm trees

d. Salmon

e. Pokémon

f. Tornados

g. Mt. Vesuvius

4. Plot components to focus on

a. Group 1: strip in theme()

b. Group 2: plot in theme()

c. Group 3: panel in theme()

d. Group 4: legend in theme()

e. Group 5: axis in theme()

f. Group 6: scale_color or scale_fill functions

g. Group 7: built in themes (theme_) with your own customization using theme()

1. Look up the `theme()` function.

2. Read about the `ggplot2` manual section about your plot element.

a. Group 1: `strip` in `theme()`

b. Group 2: `plot` in `theme()`

c. Group 3: `panel` in `theme()`

d. Group 4: `legend` in `theme()`

e. Group 5: `axis` in `theme()`

f. Group 6: `scale_color` or `scale_fill` functions

g. Group 7: built in themes (`theme_`) with your own customization using `theme()`