Session 3: Data visualization in ggplot2

This session introduces the basic functionalities of the ggplot2 package, allowing you to create beautiful graphs using R.

Simon J. Brandl, PhD https://www.fishandfunctions.com/ (The University of Texas at Austin)
2025-04-17

a. 3: Demo



b. 3: Slides

You can access the full slideshow used in the 3-ggplot narration here.

The dataset called ‘coralreefherbivores.csv’ can be downloaded here.

The dataset called ‘reef_fishes.csv’ can be downloaded here.

The dataset called ‘fish_abundance.csv’ can be downloaded here.

The dataset called ‘site_info.csv’ can be downloaded here.

c. 3: Exercises

Start by loading in the two datasets below (which are available for download using the links above).

fish.abundance <- read.csv(file = "data/fish_abundance.csv")
site.info <- read.csv(file = "data/site_info.csv")

Re-familiarize with the “fish_abundance.csv” dataset. The dataset includes many fish species, which were counted across different sites and depths. Fish diversity often decreases with depth on coral reefs, so let’s explore whether there is a relationship between depth and diversity.

Part I

  1. Simplify the dataset to include species, site, depth.
  2. Use distinct() to make sure no species were recorded twice.
  3. Calculate species richness by site and depth.

Part II

  1. Plot depth versus species richness.
  2. Add site as a color variable.

Part III

  1. Produce another plot, only including the families Serranidae, Acanthuridae, Pomacentridae, and Chaetodontidae.
  2. Use with facet_wrap() or facet_grid() to create separate facets for each family.

Part IV

  1. Explore differences in the species richness of the four families (Serranidae, Acanthuridae, Pomacentridae, and Chaetodontidae) using violin or density plots
  2. Color the density plots based on family and include the raw data to create sina plots
  3. Try to sort the violin plot in descending order, starting with the highest species richness
  4. Make it pretty! 😊

Part V

In the last plot from Exercise 1, it appears as though some sites have higher species richness than others. Let’s further examine why species richness across sites using “site_info.csv,” which includes metadata on the exposure of each site.

  1. Create a dataset that summarizes species richness by surveyid and site.
  2. Join this species richness data with the site exposure metadata.

Part VI

  1. Use a violin plot to visualize the species richness of exposed vs. lagoon sites.

Part VII

  1. Calculate the average abundance of each family in a given survey.
  2. Plot the average abundance using density curves.
  3. Bonus: Transform the x-axis to make the plot more useful.

d. 3: Solutions

Part I

  1. Simplify the dataset to include species, site, depth.
  2. Use distinct() to make sure no species were recorded twice.
  3. Calculate species richness by site and depth.
fish.abundance <- read.csv(file = "data/fish_abundance.csv") # load data

fish.sprich <- fish.abundance %>%
  select(site, depth, genspe) %>%
  distinct() %>%
  group_by(site, depth) %>%
  summarize(sprich = n())

fish.sprich2 <- fish.abundance %>%
  select(site, depth, genspe) %>%
  group_by(site, depth) %>%
  summarize(sprich = n_distinct(genspe))


head(fish.sprich)
# A tibble: 6 × 3
# Groups:   site [2]
  site        depth sprich
  <chr>       <dbl>  <int>
1 Bird Islets   2.5    107
2 Bird Islets   3      117
3 Bird Islets   3.1     64
4 Bird Islets  10       40
5 Blue Hole     2       68
6 Blue Hole     3.5     71

Part II

  1. Plot depth versus species richness.
  2. Add site as a color variable.
fish.sprich.plot <- ggplot(data = fish.sprich, aes(x = depth, y = sprich, color = site)) +
  geom_point() +
  scale_color_fish_d(option = "Synchiropus_splendidus") +
  theme_bw()
  
fish.sprich.plot

Part III

  1. Produce another plot, only including the families Serranidae, Acanthuridae, Pomacentridae, and Chaetodontidae.
  2. Use with facet_wrap() or facet_grid() to create separate facets for each family.
fish.sprich.fam <- fish.abundance %>%
  filter(family %in% c("Acanthuridae", "Chaetodontidae", "Serranidae", "Pomacentridae")) %>%
  select(site, depth, family, genspe) %>%
  distinct() %>%
  group_by(site, depth, family) %>%
  summarize(sprich = n()) 
head(fish.sprich.fam)
# A tibble: 6 × 4
# Groups:   site, depth [2]
  site        depth family         sprich
  <chr>       <dbl> <chr>           <int>
1 Bird Islets   2.5 Acanthuridae        9
2 Bird Islets   2.5 Chaetodontidae      6
3 Bird Islets   2.5 Pomacentridae      29
4 Bird Islets   2.5 Serranidae          2
5 Bird Islets   3   Acanthuridae        5
6 Bird Islets   3   Chaetodontidae      9
fish.sprich.fam.plot <- ggplot(fish.sprich.fam, aes(x = depth, y = sprich, color = site)) +
  geom_point() + 
  facet_wrap(.~family, scales = "free_y") +
    scale_color_fish_d(option = "Synchiropus_splendidus") +
  theme_bw()
fish.sprich.fam.plot

Part IV

  1. Explore differences in the species richness of the four families (Serranidae, Acanthuridae, Pomacentridae, and Chaetodontidae) using violin or density plots
  2. Color the density plots based on family and include the raw data to create sina plots
  3. Try to sort the violin plot in descending order, starting with the highest species richness
  4. Make it pretty! 😊
fish.family.plot <- ggplot(fish.sprich.fam, aes(x = fct_reorder(family, -sprich, .fun = mean), 
                                                y = sprich, fill = family)) +
  geom_violin(draw_quantiles = c(0.05, 0.5, 0.95), color = "grey23", alpha = 0.8, lwd = 0.5) +
  geom_jitter(aes(shape = family), alpha = 0.5, color = "black", size = 3, width = 0.2) +
  theme_bw() +
  scale_fill_fish_d(option = "Bodianus_rufus", name = "Fish family") +
  scale_shape_manual(values = c(21:24), name = "Fish family") +
  ylab("Number of species per site") +
  xlab("Fish family") 
  
fish.family.plot

Part V

  1. Create a dataset that summarizes species richness by surveyid and site.
  2. Join this species richness data with the site exposure metadata.
site.info <- read.csv(file = "data/site_info.csv")

fish.sprich.site <- fish.abundance %>%
  select(surveyid, site, genspe) %>%
  distinct() %>%
  group_by(surveyid, site) %>%
  summarize(sprich = n()) %>%
  left_join(site.info, by = "site")

head(fish.sprich.site) 
# A tibble: 6 × 4
# Groups:   surveyid [6]
  surveyid site                sprich exposure
     <int> <chr>                <int> <chr>   
1  4000720 Watsons Bay north       87 lagoon  
2  4000721 Watsons Bay north       84 lagoon  
3  4000722 Watsons-Turtle Reef     71 lagoon  
4  4000723 Watsons-Turtle Reef     82 lagoon  
5  4000724 Horseshoe Reef          60 lagoon  
6  4000725 Horseshoe Reef          68 lagoon

Part VI

  1. Use a violin plot to visualize the species richness of exposed vs. lagoon sites.
fish.sprich.site.plot <- ggplot(fish.sprich.site, aes(x = exposure, y = sprich, fill = exposure)) +
  geom_violin(draw_quantiles = c(0.025, 0.5, 0.975)) +
  geom_jitter(width = 0.1) +
    scale_fill_fish_d(option = "Synchiropus_splendidus") +
  theme_bw()
fish.sprich.site.plot

Part VII

  1. Calculate the average abundance of each family in a given survey (surveyid).
  2. Plot the average abundance using density curves.
  3. Bonus: transform the x-axis to make the plot more useful.
# 1) Calculate the average abundance of each family in a given survey.
fish.abun.survey <- fish.abundance %>%
  group_by(surveyid, family) %>%
  summarize(total.fish = sum(total))
head(fish.abun.survey)
# A tibble: 6 × 3
# Groups:   surveyid [1]
  surveyid family         total.fish
     <int> <chr>               <int>
1  4000720 Acanthuridae           37
2  4000720 Blenniidae              9
3  4000720 Carangidae              2
4  4000720 Chaetodontidae         33
5  4000720 Gobiidae                5
6  4000720 Haemulidae              3
# 2) Plot the average abundance using density curves.
fish.abun.plot <- ggplot(fish.abun.survey, 
                        aes(x = total.fish, y = family)) +
                          geom_density_ridges(alpha = 0.5, fill = "forestgreen") +
  theme_bw()
fish.abun.plot
# 3) Bonus: Transform the x-axis to make the plot more useful.
# use rel_min_height() to cut the tails
fish.abun.plot2 <- ggplot(fish.abun.survey, 
                        aes(x = log10(total.fish), #use log10 transformation
                            y = fct_reorder(family, total.fish, .fun = sum))) + # use fct_reorder() to reorder the y-variable as descending based on the total sum of fish in each family
                          geom_density_ridges(alpha = 0.5, rel_min_height = 0.005, fill = "forestgreen") +
  xlab("Abundance (log)") +
  ylab("Fish family") +
  theme_bw()
fish.abun.plot2

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY-NC 4.0. Source code is available at https://github.com/simonjbrandl/marinecommunityecology, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".