Spring 2025 - Coding workshop: Week 4

Workshop dates: April 24 (Thursday), April 25 (Friday)

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

The shark incident data is from Riley et al.

The tornado data is from the NOAA National Weather Service Storm Prediction Center.

2. Code

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.

Resources

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

# copy paste this into the console
?theme

You can do this with any function - whenever you want to know what it does, just look it up in the Console by hitting ? then the function name.

Read about your function. Make sure you understand the arguments, and decide which ones are relevant to you.

Second, see these resources for some explanation of themes and customization:
- ggplot2tor
- Jumping Rivers
- theme elements from ggplot2 handbook
- built in themes from ggplot2 handbook

Code

1. Set up

Packages and data

# packages
library(tidyverse)

# data
# salmon data
salmon <- read_csv("adultdaily_1745380588_196.csv")

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

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

Cleaning

Salmon

# 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)

Tornados

# create new clean object from tornados
tornados_clean <- tornados |> 
  # group by year
  group_by(yr) |>
  # 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()

Sharks

# 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"
  ))

2. Basic visualization

a. 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

b. Total property loss (in dollars) due to tornados in US from 1950-2022

                              # base layer: ggplot
tornado_property_loss_plot <- ggplot(data = tornados_clean,
                                     # aesthetics: x-axis, y-axis
                                     aes(x = yr,
                                         y = total_property_loss_bil)) +
  # first layer: points
  geom_point() +
  # second layer: line
  geom_line() +
  # labels
  labs(x = "Year",
       y = "Total property loss (billions of dollars)")

# display the plot
tornado_property_loss_plot

c. Total annual tornados in US from 1950-2022

                      # base layer: ggplot
tornado_count_plot <- ggplot(data = tornados_clean,
                             # aesthetics: x-axis, y-axis
                             aes(x = yr,
                                 y = number_tornados)) +
  # first layer: points
  geom_point() +
  # second layer: line
  geom_line() +
  # labels
  labs(x = "Year",
       y = "Total annual tornados")

# display the plot
tornado_count_plot

d. 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

3. Plot components

As everyone is going through their plot components, take notes in each section.

a. `strip` in `theme()`

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

Note: you may want to use the shark_plot for this theme element.

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

b. `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

Note: you will have to add a title, subtitle, and caption to the plot you choose to manipulate.

Code for your own independent exploration:

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

c. `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

Code for your own independent exploration:

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

d. `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

Note: you may want to use the salmon_plot for this theme element.

Code for your own independent exploration:

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

e. `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

Code for your own independent exploration:

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

f. `scale_color` or `scale_fill` functions

Note: use the shark plot for scale_fill and any other plot for scale_color

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

Code for your own independent exploration:

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

g. 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

Code for your own independent exploration:

# insert code here for your individual plot

Code for the plot your group made:

# insert code here for your group plot

3. Extra stuff

Thursday 4 PM plot

Code

ggplot(data = sharks_clean,
                     # aesthetics: x-axis, y-axis
                     aes(x = n,
                         y = shark_common_name,
                         # fill columns by shark common name
                         fill = shark_common_name)) +
  # first layer: columns to represent counts
  geom_col() +
  # faceting by state, putting the strip on the bottom of the plot
  facet_wrap(~ state,
             strip.position = "bottom") +
  # labels
  labs(x = "Total number of shark incidents",
       y = "Species") +
  # manually manipulate fill colors
  scale_fill_manual(values = c("bull shark" = "pink",
                               "whaler shark" = "chartreuse")) +
  # built in theme
  theme_dark() +
  # theme elements
  theme(
    # strip elements
    strip.placement = "outside",
    strip.background = element_rect(color = "red", linetype = 4, linewidth = 7),
    strip.text = element_text(color = "hotpink"),
    strip.text.x.top = element_text(size = 20, face = "italic"),
    # plot elements
    plot.background = element_rect(fill = "turquoise", color = "#EC4899", linewidth = 4, linetype = "dashed"),
    # panel elements
    panel.grid.major.x = element_line(color = "hotpink", linewidth = 3),
    panel.grid.major.y = element_line(color = "dodgerblue", linewidth = 1),
    panel.background = element_rect(fill = "lavender"),
    # legend elements
    legend.background = element_rect(fill = "chartreuse"),
    legend.key = element_rect(color = "orange"),
    legend.text = element_text(size = 30),
    # axis elements
    axis.line.x = element_line(color = "yellowgreen", linewidth = 4),
    axis.line.y = element_line(color = "deeppink1"),
    axis.text.x.bottom = element_text(color = "skyblue"),
    axis.text.y.left = element_text(color = "chocolate")
  )

Thursday 5 PM plot

Code

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(size = 2, 
            linetype = "dashed") +
  # labels
  labs(x = "Date",
       y = "Daily fish count",
       title = "Daily fish count per day",
       subtitle = "group 2",
       caption = "thursday 5 pm") +
  # custom color scale
  scale_color_manual(values = c("Chinook" = "hotpink",
                                "Coho" = "chartreuse",
                                "Sockeye" = "pink")) +
  # adding this facet 
  facet_wrap(~species) +
  # built in theme
  theme_dark() +
  theme(
    # strip elements
    strip.text = element_text(family = "Times New Roman"),
    strip.background = element_rect(fill = "red"),
    # plot elements
    plot.title = element_text(size = 30, face = "bold", color = "orange"),
    plot.subtitle = element_text(color = "yellow"),
    plot.margin = margin(t = 100, r = 0.6, b = 20, l = 0.9),
    plot.background = element_rect(fill = "lightgreen"),
    # panel elements
    panel.grid.minor.x = element_line(color = "orange", linewidth = 1),
    panel.grid.major.y = element_line(color = "darkgreen", linewidth = 5),
    panel.background = element_rect(fill = "black", color = NA),
    # legend elements
    legend.direction = "horizontal",
    legend.key.size = unit(3, "line"),
    legend.background = element_rect(fill = "lemonchiffon", color = "grey50", linewidth = 1),
    # axis elements
    axis.text.y.left = element_text(size = 15, color = "deeppink")
  )

Friday 9 AM plot

Code

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,
             strip.position = "bottom") +
  # labels
  labs(x = "Total number of shark incidents",
       y = "Shark species",
       title = "Fishy Salmon Run",
       subtitle = "fishfishyfishyfishyfishy") +
  # built in theme
  theme_dark() +
  theme(
    # strip elements
    strip.text = element_text(color = "brown",
                              family = "Times New Roman",
                              size = 50,
                              face = "bold"),
    strip.background = element_rect(fill = "cornflowerblue",
                                    color = "pink2",
                                    size = 5),
    # plot elements
    plot.background = element_rect(fill = "#EC4899",
                                   color = NA),
    plot.title = element_text(angle = -20, size = 40, color = "blue"),
    plot.subtitle = element_text(angle = 20, color = "gold"),
    plot.margin = unit(rep(2, 4), "cm"),
    # panel elements
    panel.grid.major = element_line(size = 8),
    panel.background = element_rect(fill = "#EC4899"),
    panel.grid.minor = element_line(color = "#32CD32"),
    # legend elements
    legend.background = element_rect(fill = "yellow"),
    legend.key.size = unit(3, "cm"),
    legend.position.inside = c(0.6, 0.5)
  )

Friday 10 AM plot

Code

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(size = 2, 
            linetype = "dashed") +
  # labels
  labs(x = "Date",
       y = "Daily fish count",
       title = "Ugly plot",
       caption = "super ugly") +
  # custom color scale
  scale_color_manual(values = c("Chinook" = "hotpink",
                                "Coho" = "chartreuse",
                                "Sockeye" = "pink")) +
  # adding this facet 
  facet_wrap(~species) +
  # built in theme
  theme_dark() +
  theme(
    # strip elements
    strip.text = element_text(family = "Courier New",
                              color = "gold",
                              face = "bold"),
    strip.background = element_rect(color = "brown",
                                    fill = "white"),
    # plot elements
    plot.background = element_rect(fill = "yellow", color = "red", size = 20),
    plot.title = element_text(color = "purple", size = 28, face = "italic", hjust = 0),
    plot.caption = element_text(color = "red", size = 14, face = "bold.italic", hjust = 0.5),
    # panel elements
    panel.border = element_rect(fill = NA, color = "chartreuse", size = 1),
    panel.grid.major.x = element_line(color = "blue3"),
    panel.grid.minor.y = element_line(color = "chartreuse4"),
    panel.background = element_rect(fill = "deeppink"),
    # legend elements
    legend.key.height = unit(3, "cm"),
    legend.key.width = unit(5, "cm"),
    legend.background = element_rect(fill = "green"),
    legend.position = "left",
    legend.box.background = element_rect(fill = "red"),
    # axis elements
    axis.text.y.left = element_text(size = 17, color = "yellow"),
    axis.ticks = element_line(color = "red"),
    axis.line = element_line(color = "purple",
                             size = 12)
  )

--- 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 dates: April 24 (Thursday), April 25 (Friday)]{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 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 shark incident data is from [Riley et al.](https://doi.org/10.1038/s41597-022-01453-9) The tornado data is from the [NOAA National Weather Service Storm Prediction Center](https://www.spc.noaa.gov/wcm/#data). ## 2. Code ### 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. ### Resources First, look up the 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 ``` You can do this with any function - whenever you want to know what it does, just look it up in the Console by hitting `?` then the function name. Read about your function. Make sure you understand the arguments, and decide which ones are relevant to you. Second, see these resources for some explanation of themes and customization: - [ggplot2tor](https://ggplot2tor.com/theme/) - [Jumping Rivers](https://www.jumpingrivers.com/blog/intro-to-theme-ggplot2-r/) - [theme elements from `ggplot2` handbook](https://ggplot2-book.org/themes.html#sec-theme-elements) - [built in themes from `ggplot2` handbook](https://ggplot2-book.org/themes.html#sec-themes) ### 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")) ``` ```{r read-in-packages-and-data-for-website} #| eval: false # packages library(tidyverse) # data # salmon data salmon <- read_csv("adultdaily_1745380588_196.csv") # tornado data tornados <- read_csv("tornados.csv") # shark data sharks <- read_csv("sharks.csv") ``` ##### Cleaning ###### Salmon ```{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) ``` ###### Tornados ```{r tornados-cleaning-and-wrangling} # create new clean object from tornados tornados_clean <- tornados |> # group by year group_by(yr) |> # 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() ``` ###### Sharks ```{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" )) ``` #### 2. Basic visualization ##### a. 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 ``` ##### b. Total property loss (in dollars) due to tornados in US from 1950-2022 ```{r tornado-property-loss} # base layer: ggplot tornado_property_loss_plot <- ggplot(data = tornados_clean, # aesthetics: x-axis, y-axis aes(x = yr, y = total_property_loss_bil)) + # first layer: points geom_point() + # second layer: line geom_line() + # labels labs(x = "Year", y = "Total property loss (billions of dollars)") # display the plot tornado_property_loss_plot ``` ##### c. Total annual tornados in US from 1950-2022 ```{r tornado-count-plot} # base layer: ggplot tornado_count_plot <- ggplot(data = tornados_clean, # aesthetics: x-axis, y-axis aes(x = yr, y = number_tornados)) + # first layer: points geom_point() + # second layer: line geom_line() + # labels labs(x = "Year", y = "Total annual tornados") # display the plot tornado_count_plot ``` ##### d. 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 ``` #### 3. Plot components As everyone is going through their plot components, take notes in each section. ###### a. `strip` in `theme()` Demonstrate how to: - change the background - change the placement - change the text size and font **Note:** you may want to use the `shark_plot` for this `theme` element. ```{r strip-elements-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r strip-elements-group} # insert code here for your group plot ``` ###### b. `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 **Note:** you will have to add a title, subtitle, and caption to the plot you choose to manipulate. Code for your own independent exploration: ```{r plot-elements-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r plot-elements-group} # insert code here for your group plot ``` ###### c. `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 Code for your own independent exploration: ```{r panel-elements-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r panel-elements-group} # insert code here for your group plot ``` ###### d. `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 **Note:** you may want to use the `salmon_plot` for this `theme` element. Code for your own independent exploration: ```{r legend-elements-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r legend-elements-group} # insert code here for your group plot ``` ###### e. `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 Code for your own independent exploration: ```{r axis-elements-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r axis-elements-group} # insert code here for your group plot ``` ###### f. `scale_color` or `scale_fill` functions **Note: use the shark plot for scale_fill and any other plot for scale_color** Demonstrate how to: - use a color palette package - apply it to a color scale Code for your own independent exploration: ```{r color-or-fill-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r color-or-fill-group} # insert code here for your group plot ``` ###### g. 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 Code for your own independent exploration: ```{r theme-customization-individual} # insert code here for your individual plot ``` Code for the plot your group made: ```{r theme-customization-group} # insert code here for your group plot ``` ## 3. Extra stuff ### Thursday 4 PM plot ```{r thursday-plot} #| fig-width: 12 #| fig-height: 7 #| code-fold: true ggplot(data = sharks_clean, # aesthetics: x-axis, y-axis aes(x = n, y = shark_common_name, # fill columns by shark common name fill = shark_common_name)) + # first layer: columns to represent counts geom_col() + # faceting by state, putting the strip on the bottom of the plot facet_wrap(~ state, strip.position = "bottom") + # labels labs(x = "Total number of shark incidents", y = "Species") + # manually manipulate fill colors scale_fill_manual(values = c("bull shark" = "pink", "whaler shark" = "chartreuse")) + # built in theme theme_dark() + # theme elements theme( # strip elements strip.placement = "outside", strip.background = element_rect(color = "red", linetype = 4, linewidth = 7), strip.text = element_text(color = "hotpink"), strip.text.x.top = element_text(size = 20, face = "italic"), # plot elements plot.background = element_rect(fill = "turquoise", color = "#EC4899", linewidth = 4, linetype = "dashed"), # panel elements panel.grid.major.x = element_line(color = "hotpink", linewidth = 3), panel.grid.major.y = element_line(color = "dodgerblue", linewidth = 1), panel.background = element_rect(fill = "lavender"), # legend elements legend.background = element_rect(fill = "chartreuse"), legend.key = element_rect(color = "orange"), legend.text = element_text(size = 30), # axis elements axis.line.x = element_line(color = "yellowgreen", linewidth = 4), axis.line.y = element_line(color = "deeppink1"), axis.text.x.bottom = element_text(color = "skyblue"), axis.text.y.left = element_text(color = "chocolate") ) ``` ### Thursday 5 PM plot ```{r} #| fig-width: 12 #| fig-height: 7 #| code-fold: true 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(size = 2, linetype = "dashed") + # labels labs(x = "Date", y = "Daily fish count", title = "Daily fish count per day", subtitle = "group 2", caption = "thursday 5 pm") + # custom color scale scale_color_manual(values = c("Chinook" = "hotpink", "Coho" = "chartreuse", "Sockeye" = "pink")) + # adding this facet facet_wrap(~species) + # built in theme theme_dark() + theme( # strip elements strip.text = element_text(family = "Times New Roman"), strip.background = element_rect(fill = "red"), # plot elements plot.title = element_text(size = 30, face = "bold", color = "orange"), plot.subtitle = element_text(color = "yellow"), plot.margin = margin(t = 100, r = 0.6, b = 20, l = 0.9), plot.background = element_rect(fill = "lightgreen"), # panel elements panel.grid.minor.x = element_line(color = "orange", linewidth = 1), panel.grid.major.y = element_line(color = "darkgreen", linewidth = 5), panel.background = element_rect(fill = "black", color = NA), # legend elements legend.direction = "horizontal", legend.key.size = unit(3, "line"), legend.background = element_rect(fill = "lemonchiffon", color = "grey50", linewidth = 1), # axis elements axis.text.y.left = element_text(size = 15, color = "deeppink") ) ``` ### Friday 9 AM plot ```{r} #| fig-width: 12 #| fig-height: 7 #| code-fold: true 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, strip.position = "bottom") + # labels labs(x = "Total number of shark incidents", y = "Shark species", title = "Fishy Salmon Run", subtitle = "fishfishyfishyfishyfishy") + # built in theme theme_dark() + theme( # strip elements strip.text = element_text(color = "brown", family = "Times New Roman", size = 50, face = "bold"), strip.background = element_rect(fill = "cornflowerblue", color = "pink2", size = 5), # plot elements plot.background = element_rect(fill = "#EC4899", color = NA), plot.title = element_text(angle = -20, size = 40, color = "blue"), plot.subtitle = element_text(angle = 20, color = "gold"), plot.margin = unit(rep(2, 4), "cm"), # panel elements panel.grid.major = element_line(size = 8), panel.background = element_rect(fill = "#EC4899"), panel.grid.minor = element_line(color = "#32CD32"), # legend elements legend.background = element_rect(fill = "yellow"), legend.key.size = unit(3, "cm"), legend.position.inside = c(0.6, 0.5) ) ``` ### Friday 10 AM plot ```{r} #| fig-width: 12 #| fig-height: 7 #| code-fold: true 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(size = 2, linetype = "dashed") + # labels labs(x = "Date", y = "Daily fish count", title = "Ugly plot", caption = "super ugly") + # custom color scale scale_color_manual(values = c("Chinook" = "hotpink", "Coho" = "chartreuse", "Sockeye" = "pink")) + # adding this facet facet_wrap(~species) + # built in theme theme_dark() + theme( # strip elements strip.text = element_text(family = "Courier New", color = "gold", face = "bold"), strip.background = element_rect(color = "brown", fill = "white"), # plot elements plot.background = element_rect(fill = "yellow", color = "red", size = 20), plot.title = element_text(color = "purple", size = 28, face = "italic", hjust = 0), plot.caption = element_text(color = "red", size = 14, face = "bold.italic", hjust = 0.5), # panel elements panel.border = element_rect(fill = NA, color = "chartreuse", size = 1), panel.grid.major.x = element_line(color = "blue3"), panel.grid.minor.y = element_line(color = "chartreuse4"), panel.background = element_rect(fill = "deeppink"), # legend elements legend.key.height = unit(3, "cm"), legend.key.width = unit(5, "cm"), legend.background = element_rect(fill = "green"), legend.position = "left", legend.box.background = element_rect(fill = "red"), # axis elements axis.text.y.left = element_text(size = 17, color = "yellow"), axis.ticks = element_line(color = "red"), axis.line = element_line(color = "purple", size = 12) ) ```

1. Summary

Packages

Operations

Review

Data sources

2. Code

Today’s topic

Resources

Code

1. Set up

Packages and data

Cleaning

Salmon

Tornados

Sharks

2. Basic visualization

a. Daily counts of salmon through Bonneville Dam in Columbia River Basin, Oregon in 2024

b. Total property loss (in dollars) due to tornados in US from 1950-2022

c. Total annual tornados in US from 1950-2022

d. Total number of shark incidents in New South Wales and Queensland from 1791-2022

3. Plot components

a. strip in theme()

b. plot in theme()

c. panel in theme()

d. legend in theme()

e. axis in theme()

f. scale_color or scale_fill functions

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

3. Extra stuff

Thursday 4 PM plot

Thursday 5 PM plot

Friday 9 AM plot

Friday 10 AM plot

a. `strip` in `theme()`

b. `plot` in `theme()`

c. `panel` in `theme()`

d. `legend` in `theme()`

e. `axis` in `theme()`

f. `scale_color` or `scale_fill` functions

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