Data visualization with ggplot2

Last updated on 2024-05-24 | Edit this page

Estimated time: 94 minutes

Overview

Questions

  • How do we create graphs using R?

Objectives

  • Learn how to create bar and line charts using ggplot2
  • Learn how to customize the appearance of the charts

R 

library(ggplot2)
library(readr)
library(dplyr)
library(lubridate)

Creating graphs

We are going to be using functions from the ggplot2 package to create visualizations. ggplot plots are built step by step by adding new layers, which allows for extensive customization of plots.

We call ggplot() function, and pass in data and mappings. Then we call a geom_ function to create the plot.

ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>()

Setup

First, read data from the cleaned iNaturalist observation file.

R 

inat <- read_csv('data/cleaned/observations.csv')

OUTPUT 

Rows: 93950 Columns: 39
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (23): observed_on_string, time_observed_at, time_zone, user_login, user...
dbl  (10): id, user_id, num_identification_agreements, num_identification_di...
lgl   (5): captive_cultivated, private_place_guess, private_latitude, privat...
date  (1): observed_on

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Bar chart

Create a bar chart that shows the number of observations per year.

First, add year column to iNaturalist data.

R 

inat_year <- inat %>% 
  mutate(year = year(observed_on))

Pass the data to ggplot.

R 

ggplot(data = inat_year)

We need tell ggplot how to process the data. We tell ggplot how to map the data to various plot elements, such as x/y axis, size, or color by using the aes() function.
For bar charts, we need to tell what column to use for the x axis. We want to create a plot with years on the x axis so we use mapping = aes(x = year). ggplot will count the number of rows for each year, and use the count for y axis.

R 

ggplot(data = inat_year, mapping = aes(x = year))

Next we need to specify how we want the data to be displayed. We do this using geom_ functions, which specify the type of geometry we want, such as points, lines, or bars. We use geom_bar() to create a vertical bar plot.

We can add a geom_bar() layer to our plot by using the + sign. We indent onto a new line to make it easier to read, and we have to end the first line with the + sign.

R 

ggplot(data = inat_year, mapping = aes(x = year)) +
  geom_bar()

If we want year on x axis, and count on y axis, use coord_flip()

R 

ggplot(data = inat_year, mapping = aes(x = year)) +
  geom_bar() +
  coord_flip()

Line chart

Create a line chart that shows the number of observations per year.

For line charts, we need both x and y variables. Create a dataframe that count the number of observations by year.

R 

inat_year_count <- inat %>% 
  mutate(year = year(observed_on)) %>%
  count(year, name='obs_count')  

inat_year_count

OUTPUT 

# A tibble: 8 × 2
   year obs_count
  <dbl>     <int>
1  2016      5791
2  2017      9354
3  2018     10855
4  2019     17950
5  2020     10659
6  2021     13051
7  2022     11924
8  2023     14366

We use year on the x axis and obs_count on the y axis. And we use geom_line() for to create a line chart.

R 

ggplot(data = inat_year_count, 
       mapping = aes(x = year, y=obs_count)) +
  geom_line()

More bar plots

To create bar chart when we already have x and y, use geom_col()

We want year on the x axis, and count on the y axis.

R 

ggplot(data = inat_year_count, 
       mapping = aes(x = year, y = obs_count)) +
  geom_col()

Exercise 1

Create a chart with all your observations for each year - Use mutate() and filter() -

R 

my_year  <- inat %>% 
  mutate(year = year(observed_on))  %>% 
  filter(user_login == 'natureinla')

  
ggplot(data = my_year, mapping = aes(x = year)) +
  geom_bar()

Changing aesthetics

Building ggplot plots is often an iterative process, so we’ll continue developing the column plot we just made. We can change the color of the bars using fill='color'.

Use colors() to get a list of the 657 colors in R.

R 

colors()

OUTPUT 

  [1] "white"           "aliceblue"       "antiquewhite"    "antiquewhite1"  
  [5] "antiquewhite2"   "antiquewhite3"   "antiquewhite4"   "aquamarine"     
  [9] "aquamarine1"     "aquamarine2"     "aquamarine3"     "aquamarine4"    
 [13] "azure"           "azure1"          "azure2"          "azure3"         
 [17] "azure4"          "beige"           "bisque"          "bisque1"        
 [21] "bisque2"         "bisque3"         "bisque4"         "black"          
 [25] "blanchedalmond"  "blue"            "blue1"           "blue2"          
 [29] "blue3"           "blue4"           "blueviolet"      "brown"          
 [33] "brown1"          "brown2"          "brown3"          "brown4"         
 [37] "burlywood"       "burlywood1"      "burlywood2"      "burlywood3"     
 [41] "burlywood4"      "cadetblue"       "cadetblue1"      "cadetblue2"     
 [45] "cadetblue3"      "cadetblue4"      "chartreuse"      "chartreuse1"    
 [49] "chartreuse2"     "chartreuse3"     "chartreuse4"     "chocolate"      
 [53] "chocolate1"      "chocolate2"      "chocolate3"      "chocolate4"     
 [57] "coral"           "coral1"          "coral2"          "coral3"         
 [61] "coral4"          "cornflowerblue"  "cornsilk"        "cornsilk1"      
 [65] "cornsilk2"       "cornsilk3"       "cornsilk4"       "cyan"           
 [69] "cyan1"           "cyan2"           "cyan3"           "cyan4"          
 [73] "darkblue"        "darkcyan"        "darkgoldenrod"   "darkgoldenrod1" 
 [77] "darkgoldenrod2"  "darkgoldenrod3"  "darkgoldenrod4"  "darkgray"       
 [81] "darkgreen"       "darkgrey"        "darkkhaki"       "darkmagenta"    
 [85] "darkolivegreen"  "darkolivegreen1" "darkolivegreen2" "darkolivegreen3"
 [89] "darkolivegreen4" "darkorange"      "darkorange1"     "darkorange2"    
 [93] "darkorange3"     "darkorange4"     "darkorchid"      "darkorchid1"    
 [97] "darkorchid2"     "darkorchid3"     "darkorchid4"     "darkred"        
 [ reached getOption("max.print") -- omitted 557 entries ]

R 

ggplot(data = inat_year, mapping = aes(x = year)) +
  geom_bar(fill='aquamarine')

We can also use 6 digit hex color. You can use online tools to get hex colors. https://html-color.codes

R 

ggplot(data = inat_year, mapping = aes(x = year)) +
  geom_bar(fill='#75cd5e')

Adding another variable

iNaturalist has af field called iconic_taxon_name that assigns each taxa name to a some commonly known groups.

R 

unique(inat$iconic_taxon_name)

OUTPUT 

 [1] "Mollusca"       "Insecta"        "Reptilia"       "Aves"          
 [5] "Mammalia"       "Plantae"        "Animalia"       "Arachnida"     
 [9] "Amphibia"       "Fungi"          "Chromista"      "Actinopterygii"
[13] NA               "Protozoa"

Create charts that show the observations per year, and subdivide each year by iconic_taxon_name. Give each iconic_taxon_name a different color.

Since we’re now mapping a variable (iconic_taxon_name.) to a component of the ggplot2 plot (fill), we need to put the argument inside aes().

Create a bar chart that shows iconic_taxon_name by color.

R 

ggplot(data = inat_year, mapping = aes(x = year, fill=iconic_taxon_name)) +
  geom_bar()

We create a new dataframe that counts the number observations per year and iconic_taxon_name. Use mutate() and year() to add a year column. We want count by both year and iconic_taxon_name. We want the column to be called obs_count.

R 

inat_year_iconic_count <- inat %>% 
  mutate(year = year(observed_on))  %>%
  count(year, iconic_taxon_name, name='obs_count') 

inat_year_iconic_count

OUTPUT 

# A tibble: 107 × 3
    year iconic_taxon_name obs_count
   <dbl> <chr>                 <int>
 1  2016 Actinopterygii            1
 2  2016 Amphibia                 87
 3  2016 Animalia                 87
 4  2016 Arachnida                99
 5  2016 Aves                    976
 6  2016 Chromista                 9
 7  2016 Fungi                    24
 8  2016 Insecta                1325
 9  2016 Mammalia                192
10  2016 Mollusca                183
# ℹ 97 more rows

Create a line chart that shows iconic_taxon_name by color.

R 

inat_year_iconic_count %>% 
  ggplot(aes(x = year, y = obs_count, color = iconic_taxon_name)) +
  geom_line()

Changing scales

The default color scheme isn’t friendly to viewers with colorblindness. ggplot2 comes with quite a few other color scales, including viridis scales, which are designed to be colorblind and grayscale friendly. See a list of color scales. https://ggplot2.tidyverse.org/reference/scale_colour_continuous.html

We can change scales by adding scale_ functions to our plots:

R 

ggplot(data = inat_year, mapping = aes(x = year, fill=iconic_taxon_name)) +
  geom_bar() +
  scale_fill_viridis_d()

Changing themes

we can assign a plot to an object

R 

myplot <- ggplot(data = inat_year, mapping = aes(x = year)) +
  geom_bar(fill='#75cd5e')

myplot

We can change the overall appearance using theme_ functions. Let’s try a black-and-white theme by adding theme_bw() to our plot:

R 

myplot + 
  theme_bw()

To see a list of available themes in ggplot, visit https://ggplot2.tidyverse.org/reference/index.html#themes

To individually change parts of a plot, we can use the theme() function, which can take many different arguments to change things about the text, grid lines, background color, and more.

Let’s try changing the size of the text on our axis titles. We can do this by specifying that the axis.title should be an element_text() with size set to 14.

R 

myplot +
  theme_bw() +
  theme(axis.title = element_text(size = 14))

Another change we might want to make is to remove the vertical grid lines. To do this, inside theme(), we will change the panel.grid.major.x to an element_blank().

R 

myplot +
  theme_bw() +
  theme(axis.title = element_text(size = 14), 
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank())

Because there are so many possible arguments to the theme() function, it can sometimes be hard to find the right one. Here are some tips for figuring out how to modify a plot element:

  • type out theme(), put your cursor between the parentheses, and hit Tab to bring up a list of arguments
    • you can scroll through the arguments, or start typing, which will shorten the list of potential matches
  • like many things in the tidyverse, similar argument start with similar names
    • there are axis, legend, panel, plot, and strip arguments
  • arguments have hierarchy
    • text controls all text in the whole plot
    • axis.title controls the text for the axis titles
    • axis.title.x controls the text for the x axis title

Changing labels

We customize the axis labels and add a chart title labs() function.

R 

myplot +
  theme_bw() +
  theme(axis.title = element_text(size = 14), 
        plot.title = element_text(face = "bold", size = 20)) +
  labs(title = "CNC Los Angeles",
       subtitle="Observations per year",
       x = "Year",
       y = "Observations")

Exercise 2: Customizing a plot

Take the chart from exercise 1 (your observations for each year) and assign it to an object.

Customize the appearance. Here are some ideas. - add a title - choose the one of the built in themes - change the axis titles - change the colors of the bar or line

R 

my_yearly_plot  <- inat %>% 
  mutate(year = year(observed_on))  %>% 
  filter(user_login == 'natureinla') %>% 
  ggplot(data = my_year, mapping = aes(x = year)) +
  geom_bar(fill='#E0A90A')

my_yearly_plot +
  theme_classic() +
  labs(title = "CNC Los Angeles",  x = "Year",  y = "Observations")

Exporting plots

Once we are happy with ourplot, we can export the plot.

Assign the plot to an object. Then run ggsave() to save our plot. The first argument we give is the path to the file we want to save, including the correct file extension. You can save as jpb, pdf, tiff, png. Next, we tell it the name of the plot object we want to save. We can also specify things like the width and height of the plot in inches.

R 

finalplot <- myplot +
  theme_bw() +
  theme(axis.title = element_text(size = 14), 
        plot.title = element_text(face = "bold", size = 20)) +
  labs(title = "CNC Los Angeles",
       subtitle="Observations per year",
       x = "Year",
       y = "Observations")

R 

ggsave(filename = 'data/cleaned/observations_per_year.jpg', plot = finalplot,  height = 6, width = 8)