Presentation of the Survey Data

The data used in this workshop are a time-series for a small mammal community in southern Arizona. This is part of a project studying the effects of rodents and ants on the plant community that has been running for almost 40 years, but we will focus on the years 1996 to 2002 (n=11332 observations). The rodents are sampled on a series of 24 plots, with different experimental manipulations controlling which rodents are allowed to access which plots. This is a simplified version of the full data set that has been used in over 100 publications and was provided by the Data Carpentries (https://figshare.com/articles/dataset/Portal_Project_Teaching_Database/1314459/10). We are going to focus on animal species diversity and weights in this workshop. The dataset is stored as a comma separated value (CSV) file.

Each row holds information for a single animal, and the columns represent (along with some others we will not use):

We’ll read in our data using the read_csv() function, from the tidyverse package readr, instead of read.csv().

surveys <- read_csv("https://raw.githubusercontent.com/saramannheimer/data-science-r-workshops/master/Data%20Visualization/AY%202020-2021/data/surveys2.csv")

You will see the message Parsed with column specification, followed by each column name and its data type. When you execute read_csv on a data file, it looks through the first 1000 rows of each column and guesses the data type for each column as it reads it into R. For example, in this dataset, read_csv reads weight as col_double (a numeric data type), and species as col_character.

## inspect the data
str(surveys)

## Preview the data
View(surveys)

At the top of the str() output, notice that the class of the data is a tibble. Tibbles tweak some of the behaviors of the data frame objects we introduced in the previous workshop. The data structure is very similar to a data frame, so for our purposes the only differences are that:

1. In addition to displaying the data type of each column under its name, it only prints the first few rows of data and only as many columns as fit on one screen.
2. Columns of class character are never converted into factors.

Building Plots Iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = surveys, 
       mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point()

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to the points, to avoid overplotting:

ggplot(data = surveys, 
       mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.2)
    ## alpha reduces the opacity of the points 
    ## 0 is fully transparent
    ## 1 is the original opacity

We can also add colors for all the points:

ggplot(data = surveys, 
       mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.2, color = "blue")

geom_point also accepts aesthetics of size and shape. The size of a point is its width in mm. The shape of a point has five different options for plotting:

• an integer [0, 25] of defined plotting characters – same as base R
• the name of the shape in quotations (e.g. “circle open” or “diamond filled”)
• a single character, to use that character as a plotting symbol
• a “.” to draw the smallest point that is visible – typically 1 pixel
• an NA, to draw nothing

Note: A “filled” shape means that it is filled with another color or is “open” if no fill color is specified. A solid shape has no further specification.

Reference for shapes in integers and characters:
https://ggplot2.tidyverse.org/articles/ggplot2-specs.html

Challenge 1

Copy and paste the code from the previous code chunk and modify it to assign one of these aesthetics to the geom_point aspect of your plot.

What happened?

## Your ggplot code to answer the challenge goes here!

ggplot(data = surveys, 
       mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.2, color = "blue", shape = "diamond")

Piping Data In

Because ggplot2 lives in the tidyverse, it is expected to work well with other packages in the tidyverse. Because of this, the first argument to creating a ggplot() is the dataset you wish to be working with. The pipe operator sends the output of one function directly into the next function, which is useful when you need to do many things to the same dataset. Since the dataset we wish to use is the first argument to ggplot(), we can use the pipe operator to pipe the data into the ggplot() function!

Pipes in R look like %>% and are made available via the magrittr package, installed automatically with the tidyverse. If you use RStudio, you can type the pipe with Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.

Note: There is now (as of R 4.3.0) a Base R pipe |> that works similar to the %>% pipe operator with minor differences that you may encounter, but since we are working in the tidyverse we will stick with their pipe operator (%>%).

This would instead look like this:

surveys %>%  
  ## data to be used in the ggplot  
  ggplot(mapping = aes(x = weight, y = hindfoot_length)) + 
  ## uses the data piped in as the first argument to ggplot() 
    geom_point(alpha = 0.2, color = "blue")

Once we pipe the data in, the first argument becomes the mapping of the aesthetics. Technically, we are using the name of this argument, which is why it looks like:

mapping = aes(<VARIABLES>)

When we pipe our data in, the first argument then becomes this mapping argument.

Assigning More Variables to Aesthetics

To color each species in the plot differently, you could use a vector as an input to the argument color. ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with species_id:

surveys %>% 
  ggplot(mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.2, aes(color = species_id)) 

Note: When specifying an alpha for a scatterplot, it automatically uses that same alpha in the legend. To remedy this you can add:

guides(color = guide_legend(override.aes = list(alpha = 1)))

to your plot. This customizes the legend appearance, similar to what we will see in the customization section.

We can also specify the colors directly inside the mapping provided in the ggplot() function. This will be seen by any geom layers and the mapping will be determined by the x- and y-axis set up in aes().

surveys %>% 
  ggplot(mapping = aes(x = weight, y = hindfoot_length, color = species_id)) +
    geom_point(alpha = 0.2)

Notice that we can change the geom layer and colors will still be determined by species_id

Local Aesthetics versus Global Aesthetics

When you define aesthetics in the ggplot() function, those mappings hold for every aspect of your plot.

For example, if you chose to add a smoothing line to your plot of weight versus hindfoot length, you would get different lines depending on where you define your color aesthetics.

Globally

surveys %>% 
  ggplot(mapping = aes(x = weight, y = hindfoot_length, color = species_id)) +
  geom_jitter(alpha = 0.2) + 
  geom_smooth()
  ## smoothing line for each species_id -- because color is defined globally

Locally

surveys %>% 
  ggplot(mapping = aes(x = weight, y = hindfoot_length)) +
  geom_jitter(aes(color = species_id), alpha = 0.2) + 
  geom_smooth()
  ## one smoothing line -- no color defined globally

Challenge 2 (Part 1)

Inspect the geom_point help file (either go to https://ggplot2.tidyverse.org/reference/geom_point.html or run ?geom_point) to see what other aesthetics are available. Map a new variable from the dataset to another aesthetic in your plot. What happened? Does the aesthetic change if you use a continuous variable versus a categorical/discrete variable?

## Your ggplot() code for the challenge goes here!

surveys %>% 
  ggplot(mapping = aes(x = weight, y = hindfoot_length)) +
  geom_jitter(aes(color = plot_type), alpha = 0.2) + 
  geom_smooth()

Challenge 2 (Part 2)

Use what you just learned to create a scatter plot of weight over plot_id with data from different plot types being shown in different colors. Is this a good way to show this type of data?

## Your ggplot() code for the challenge goes here!

surveys %>% 
  ggplot(mapping = aes(x = plot_id, y = weight)) +
  geom_jitter(aes(color = plot_type), alpha = 0.2) + 
  geom_smooth()

Boxplots & Violin Plots

Boxplots provide a visualization of a quantitative variable across different levels of a categorical (grouping) variable. For example, we can use boxplots to visualize the distribution of weight within each species:

surveys %>% 
  ggplot(mapping = aes(x = species_id, y = weight)) +
    geom_boxplot()

By adding points to boxplot, we can have a better idea of the number of measurements and their distribution:

surveys %>% 
  ggplot(mapping = aes(x = species_id, y = weight)) +
    geom_boxplot(alpha = 0) + 
    ## alpha = 0 eliminates the black (possible outlier) points, so they're not plotted twice
    geom_jitter(alpha = 0.2, color = "tomato")
    ## alpha = 0.2 decreases the opacity of the points, to not be too busy

Did you notice how the boxplot layer is behind the jitter layer? What would you change in the code to put the boxplot in front of the points? (We’ll come back to this…)

Challenge 3 (Part 1)

Boxplots are useful summaries, but hide details of the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. A superior density plot is the violin plot, where the shape (of the density of points) is drawn.

Replace the box plot with a violin plot. For help see geom_violin(). Start with the boxplot we created:

surveys %>%
  ggplot(mapping = aes(x = species_id, y = weight)) +
    geom_boxplot(alpha = 0) +
    geom_jitter(alpha = 0.2, color = "tomato")

##  Start with the boxplot we created
##  1. Replace the boxplot with a violin plot. For help, see geom_violin().
##  You might need to decrease opacity even more to see the violins (try 0.03)

surveys %>% 
  ggplot(mapping = aes(x = species_id, y = weight)) +
    geom_violin() + 
    geom_jitter(alpha = 0.03, color = "tomato")

Challenge 3 (Part 2)

So far, we’ve looked at the distribution of weight within species. Let’s try making a new plot to explore the distribution of another variable within each species.

Create a boxplot for hindfoot_length. This time overlay the boxplot layer over a jitter layer that shows the actual measurements.

##  First: create boxplot for hindfoot_length` overlaid on a jitter layer.

surveys %>% 
  ggplot(mapping = aes(x = species_id, y = hindfoot_length)) +
    geom_jitter(alpha = 0.3, color = "tomato") +
    geom_boxplot(alpha = 0) 

Challenge 3 (Part 3)

Now, add color to the data points on your boxplot according to the plot from which the sample was taken (plot_id).

Hint: Check the class for plot_id. If plot_id was a character instead, how would the graph be different?

## Next: add color to the data points on your boxplot according to the
## plot from which the sample was taken (plot_id).

## Hint: Check the class for plot_id`. If plot_id was a character instead, 
## how would the graph be different? 

surveys %>% 
  ggplot(mapping = aes(x = species_id, y = hindfoot_length)) +
    geom_jitter(alpha = 0.3, mapping = aes(color = plot_id)) +
    geom_boxplot(alpha = 0) 

## Checking the data type for plot_id
class(surveys$plot_id)

## Creating a new variable named plot_id_chr
## which is the character version of plot_id
surveys <- surveys %>% 
  mutate(plot_id_chr = as.character(plot_id))

## Using new character plot_id to make a boxplot
surveys %>% 
  ggplot(mapping = aes(x = species_id, y = hindfoot_length)) +
    geom_jitter(alpha = 0.3, mapping = aes(color = plot_id_chr)) +  
    geom_boxplot(alpha = 0)  

Bonus violin plot example (DatasauRus!)

The previous example doesn’t fully illustrate the power of violin plots. This example from the datasauRus package (https://www.autodeskresearch.com/publications/samestats) shows five different distributions that have exactly the same summary statistics and boxplots but very different shapes:

data(box_plots)

box_plots_long <- box_plots %>%
  pivot_longer(cols=1:5)
  
box_plots_long <- box_plots_long %>%
  rename(Example=name, Response=value)

box_plots_long <- box_plots_long %>% 
  mutate(Example = factor(Example))

ggplot(box_plots_long, aes(x=Example, y=Response)) +
  geom_violin(fill="bisque") +
  geom_boxplot(alpha=.6) +
  geom_jitter(alpha=0.15, col="tomato") +
  theme_minimal() + 
  scale_x_discrete(labels=c('Trimodal, Right Skew', 'Clumpy', 'Normal', 'Trimodal, Left Skew',  'Bimodal'))

Distribution Plots (Quantitative Variables)

If we wish to visualize the distribution of a single quantitative variable, our plot changes a bit. Unfortunately, the geom_violin() function only accepts groups, so we cannot make a violin plot with no groups. Darn it!

But, a violin is simply a density plot that’s been reflected across the y-axis. So, we could likely suffice with a density plot.

To visualize the distribution of rodent weights we could aggregate over all species, years, plots, etc. and produce a single density plot:

surveys %>% 
  ggplot(mapping = aes(x = weight)) + 
  geom_density()

The default is an empty density plot, which is largely unsatisfying. By adding a fill = <COLOR> argument to geom_density() we can produce a nicer looking plot:

surveys %>% 
  ggplot(mapping = aes(x = weight)) + 
  geom_density(fill = "sky blue")

Another frequently used plot for a single quantitative variable is the histogram. The same plot as above can be recreated using geom_histogram() instead of geom_density(). However, when you use geom_histogram() it gives you a warning.

surveys %>% 
  ggplot(mapping = aes(x = weight)) + 
  geom_histogram()

What warning do you get and why? Do you get an error like this when you use hist() in base R?

There is no single right answer for the number of bins. There are some “plug-in” choices for the number of bins that can be used, but you are always welcome to explore different numbers of bins to see if features you are seeing persist when you choose more or less bins.

Challenge 4

Use the bins argument in geom_histogram() to play around with the number of bins in your histogram. Try different numbers of bins to explore how that changes the results!

## Your code to answer the challenge goes here!

surveys %>% 
  ggplot(aes(x = weight)) + 
  geom_histogram(fill = "sky blue" , bins = 50)

Bar Charts (Categorical Variables)

At first glimpse, you would think that a bar plot would be simple to create, but bar plots reveal a subtle nuance of the plots we have created thus far. The following bar chart displays the total number of rodents in the surveys dataset, grouped by their species ID.

surveys %>% 
  ggplot(mapping = aes(x = species_id)) + 
  geom_bar()

The x-axis displays the levels of species_id, a variable in the surveys dataset. On the y-axis count is displayed, but count is not a variable in our dataset! Where did count come from? Graphs, such as the scatterplots, display the raw values of your data. Other graphs, like bar charts and boxplots, calculate new values (from your data) to plot.

• Bar charts and histograms bin your data and then plot the number of observations that fall in each bin.

• Boxplots find summaries of your data (min, max, quartiles, median) and plot those summaries in a tidy box, with “potential outliers” (data over 1.5*IQR from Q1 or Q3) plotted as points.

• Smoothers (as used in geom_smooth) fit a model to your data (you can specify, but we used the gam (generalized additive model from the mgcv package) default) and then plot the predicted means from that model (with associated 95% confidence intervals).

To calculate each of these summaries of the data, R uses a different statistical transformation, or stat for short. With a bar chart this looks like the following process:

1. geom_bar first looks at the entire data frame
   
2. geom_bar then transforms the data using the count statistic
   
3. the count statistic returns a data frame with the number of observations (rows) associated with each level of species_id
   
4. geom_bar uses this summary data frame, to build the plot – levels of species_id are plotted on the x-axis and count is plotted on the y-axis

Generally, you can use geoms and stats interchangeably. This is because every geom has a default stat and vice versa. For example, the following code produces the same output as above:

surveys %>% 
  ggplot(mapping = aes(x = species_id)) + 
  stat_count()

If you so wish, you could override the default stat for that geom. For example, if you wanted to plot a bar chart of proportions you would use the following code to override the count stat:

surveys %>% 
  ggplot(mapping = aes(x = species_id)) + 
  geom_bar(aes(y = after_stat(prop), group = 1))

Challenge 5

Why do we need to set group = 1 in the above proportion bar chart? In other words, what is wrong with the plot below?

## What is wrong with this plot? 
surveys %>% 
  ggplot(mapping = aes(x = species_id)) + 
  geom_bar(aes(y = after_stat(prop)))

Colored and/or Stacked Bar Charts

Another piece of visual appeal to creating a bar chart is the ability to use colors to differentiate the different groups, or to plot two different variables in one bar chart (stacked bar chart). Let’s start with adding color to our bar chart.

surveys %>% 
  ggplot(mapping = aes(x = species_id, color = species_id)) + 
  geom_bar()

surveys %>% 
  ggplot(mapping = aes(x = species_id, color = species_id)) + 
  geom_bar()

surveys %>% 
  ggplot(mapping = aes(x = species_id, fill = species_id)) + 
  geom_bar()

surveys %>% 
  ggplot(mapping = aes(x = species_id, fill = sex)) + 
  geom_bar()

surveys %>% 
  ggplot(mapping = aes(x = species_id, fill = sex)) + 
  geom_bar(position = "dodge")

surveys %>% 
  ggplot(mapping = aes(x = species_id, fill = sex)) + 
  geom_bar(position = "fill")

Faceting

ggplot2 has a special technique called faceting that allows the user to split one plot into multiple plots based on a categorical variable included in the dataset.

There are two types of facet functions:

• facet_wrap() arranges a one-dimensional sequence of panels to allow them to cleanly fit on one page – used for one variable
• facet_grid() allows you to form a matrix of rows and columns of panels – used for two variables

Both geometries allow you to specify faceting variables using formula notation or the vars() function. We will use the formula notation, as you will use this notation when creating models (not covered in this workshop).

This looks like: facet_wrap(facets = ~ facet_variable) or facet_grid(row_variable ~ col_variable).

Let’s start by using facet_wrap() to make a time series plot for each species:

yearly_counts %>% 
  ggplot(mapping = aes(x = year, y = n)) +
    geom_line() +
    facet_wrap(facets = ~ genus)

Now we would like to split the line in each plot by the sex of the rodent captured. To do that we need to make counts in the data frame grouped by year, species_id, and sex:

yearly_sex_counts <- surveys %>%
  count(year, species_id, sex)
## counts the number of observations (rows) for each year, species, sex combination

yearly_sex_counts

We can now make the faceted plot by splitting further by sex using color (within each panel):

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(facets =  ~ species_id)

You can also organize the panels only by rows (or only by columns), using the optional nrow and ncol arguments:

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(~ species_id, ncol = 1)
  # One column, facet by rows

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(~ species_id, nrow = 1)
  # One row, facet by columns

Now let’s use facet_grid() to control how panels are organized by both rows and columns:

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_grid(sex ~ species_id)

Challenge 7

Use what you just learned to create a plot that depicts how the average weight of each species changes through the years. Play around with which variable you facet by versus plot by!

## To get you started:  
yearly_species_weight <- surveys %>% 
  group_by(year, species_id) %>%
  ## Variables to group by
  summarize(avg_weight = mean(weight))

## Edit the following ggplot() code for the plot here:
yearly_species_weight %>%
  ggplot(mapping = aes(x = year, y = n, color = avg_weight)) +
    geom_line() +
    facet_wrap(facets =  ~ species_id)
## Your ggplot() code for the plot goes here!

## To get you started:  
yearly_species_weight <- surveys %>% 
  ## Variables to group by:
  group_by(year, species_id) %>%
  summarize(avg_weight = mean(weight))

## Coloring by species id
yearly_species_weight %>% 
ggplot(mapping = aes(x = year, y = avg_weight, color = species_id)) +
    geom_line()

## Faceting by species id
yearly_species_weight %>% 
ggplot(mapping = aes(x = year, y = avg_weight, color = species_id)) +
    geom_line() + 
  facet_wrap(~ species_id)

Plot Labels

Now, let’s change the names of axes to something more informative than ‘year’ and ‘n’ and add a title to the figure. Label customizations are done using the labs() function like so:

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(~ species_id) +
  theme_bw() +
  labs(title = "Observed Species Through Time",
       x = "Year of Observation",
       y = "Number of Rodents",  
       color = "Sex") 

Tip: Wrapping Titles

Sometimes the titles we wish to have for our plots are longer than the space originally allotted. If you create a title and the text is running off the plot you can add a \n inside your title to force a line break (\n stands for new line).

Label & Plot Fonts

Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the extrafont package, and follow the instructions included in the README for this package.

In the last plot, the axes have more informative names, but their readability can be improved by increasing the font size. This can be done with the generic theme() function.

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(~ species_id) +
  theme_bw() +
  labs(title = "Observed Species Through Time",
      x = "Year of Observation",
      y = "Number of Rodents", 
      color = "Sex") +
  theme(text = element_text(size = 16))
    ## sets ALL the text on the plot to be size 16

Note:
theme_bw() is a function for a specific theme and theme() is a generic function for a variety of different themes!

After our manipulations, you may notice that the values on the x-axis are still not properly readable. Let’s swap the orientation of the labels, so the reader doesn’t have to tilt their head when reading our plot! The coord_flip() function easily changes the x- and y-axis.

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(~ species_id) +
  theme_bw() +
  labs(title = "Observed Species by \n Year of Observation",
      x = "Year of Observation",
      y = "Number of Rodents", 
      color = "Sex") +
  theme(text = element_text(size = 16)) + 
  coord_flip()

This definitely makes the reader tilt their head less! But, the text on the x-axis is a bit too large to separate the numbers. We can specify the text size for each element of the plot independently, if we so wish. This would look something like this:

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(~ species_id) +
  theme_bw() +
  labs(title = "Observed Species by Year of Observation",
        x = "",
        y = "Number of Rodents", 
        color = "Sex") +
  theme(axis.text.x = element_text(size = 10), 
        axis.text.y = element_text(size = 12), 
        axis.title.x = element_text(size = 14),
        legend.text = element_text(size = 12),
        legend.title = element_text(size = 12),
        plot.title = element_text(size = 16)) + 
  coord_flip()

Legend Position

By default in ggplot2 the legend is positioned on the right hand side. However, you are able to change the position of the legend to the left hand side, the top of the plot, or the bottom of the plot.

This is done by adding a legend.position theme to the plot’s theme()’s.

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(~ species_id) +
    labs(title = "Observed Species by Year of Observation by Sex",
        x = "Year of Observation",
        y = "Number of Rodents", 
        color = "Sex") +
  theme_bw() +
  theme(axis.text.x = element_text(size = 10), 
        axis.text.y = element_text(size = 12), 
        axis.title.x = element_text(size = 14),
        legend.text = element_text(size = 12),
        legend.title = element_text(size = 14),
        plot.title = element_text(size = 14), 
        legend.position = "top") + 
  coord_flip()

Removing Grid Lines

By default, the background of a ggplot() contains both minor and major gridlines. These can make the plot look a bit busy and sometimes difficult for the reader to follow. As you may have guessed, to remove these gridlines, we add another theme to our plot.

This looks like this:

yearly_sex_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(~ species_id) +
  labs(title = "Observed Species by Year of Observation by Sex",
      x = "Year of Observation",
      y = "Number of Rodents", 
      color = "Sex") +
  theme(axis.text.x = element_text(size = 10), 
        axis.text.y = element_text(size = 12), 
        axis.title.x = element_text(size = 14),
        legend.text = element_text(size = 12),
        legend.title = element_text(size = 14),
        plot.title = element_text(size = 14), 
        legend.position = "top", 
        ## New themes for the grid lines
        axis.line = element_line(color = "black"),
        ## 
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(), 
        panel.background = element_blank()) + 
  coord_flip()

Let’s break these options down!

• The axis.line option declares what color the x- and y-axis lines should be. (Change it to a different color, if you don’t believe me!)
• The panel.grid.major removes the major grid (the one associated with the ticks from the x- and y-axis).
  
• The panel.grid.minor removes the minor grid (the one between the x- and y-axis ticks).
  
• The panel.border removes the border around the plot.
  
• The panel.background performs a similar action to theme_bw(), but it keeps the border around the facet labels.

Changing Colors

The built in ggplot() color scheme may not be what you were looking for, but don’t worry! There are many other color palettes available to use!

You can change the colors used by ggplot() a few different ways.

# A color deficient friendly palette with grey:
cbPalette_grey <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", 
                    "#0072B2", "#D55E00", "#CC79A7")

# A color deficient friendly palette with black:
cbPalette_blk <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", 
                   "#0072B2", "#D55E00", "#CC79A7")

surveys %>% 
  ggplot(aes(x = species_id, y = hindfoot_length, color = genus)) +
  geom_boxplot() +
  scale_color_manual(values = cbPalette_grey)

surveys %>% 
  ggplot(aes(x = species_id, y = hindfoot_length, color = genus)) +
  geom_boxplot() +
  scale_color_manual(values = cbPalette_blk)

## your code for the challenge goes here!

color_palette <- c("#ee4444", #DM
                   "#670303", #DO
                   "#6b6b6b", #NL
                   "#07511b", #OL
                   "#00c736", #OT
                   "#9747ec", #PB
                   "#44cafd", #PE
                   "#dfe113", #PF
                   "#091298", #PM
                   "#562887", #PP
                   "#d27502", #RM
                   "#d2028a" #SH
)

ggplot(data = surveys, mapping = aes(x = genus, y = hindfoot_length)) +
  geom_jitter(alpha = 0.2, aes(color = species_id)) +
  geom_violin(alpha = 0) +
  labs(x = "Genus",
       y = "Hindfoot Length",
       color = "Species ID") +
  guides(color = guide_legend(override.aes = list(alpha = 1))) +
  theme_bw() +
  scale_color_manual(values = color_palette) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Interactive Graphics (Bonus Material)

In certain situations, static displays can limit the sorts of information available and do not allow easy interrogation for information on individual aspects of plots. Obviously, most print journals do not have a way to have readers interact with the printed page, but in digital venues there are some possibilities. Of particular interest here are interactive graphics that can function on websites and in blog posts or even in certain presentation formats. One way to do this that leverages the previous work in making ggplot-style graphics is using the ggplotly function from the plotly R package (Sievert, 2020). You can access the 2020 book that goes into more detail on plotly-style graphics at https://plotly-r.com/.

To use ggplotly, we wrap that function around a ggplot object and it will render it in an interactive fashion when the viewer hovers over individual plot components. There are also ways of making plotly graphs directly using plot_ly and that may prove easier for some things, for example for making interactive three-dimensional graphs.

Here are two examples that we worked with earlier converted into ggplotly objects that allow further interrogation of the information displayed:

spp_weight_boxplot <- surveys %>% 
  ggplot(aes(x = genus, y = weight)) +
  geom_violin() +
  geom_jitter(color = "tomato", width = 0.2, alpha = 0.2) + 
  scale_y_log10() +
  ## log (base 10) transforms the y-axis variable
  ## (helps to make the plot less skewed)
  labs(x = "", 
       ## removes the y-axis label
       y = "log10-Weight") +
  coord_flip() + #Switches x and y axes
  theme(axis.text.y = element_text(size = 12), 
        axis.text.x = element_text(size = 12), 
        text = element_text(size = 16)) +
  theme_bw()

spp_count_plot <- yearly_counts %>% 
  ggplot(aes(x = year, y = n, color = genus)) +
  geom_line() + 
  labs(x = "Year", 
       y = "Abundance", 
       color = "Genus") +
  theme(axis.title.x = element_text(face = "bold", size = 12)) +
  theme_bw()

ggplotly(spp_weight_boxplot);ggplotly(spp_count_plot)

One note about using plotly graphics in R-markdown is that they will not knit into word or PDF formats, only into HTML (in word or PDF, you are stuck with static images but you could incorporate a link to a website containing the interactive version of the plot). You can also interact with plots when working in markdown or running code in the console in RStudio. For presentations, you can also record a video of interactions with plot to remove some challenges of live presentations using this format. But if you are looking to wow your viewers in a digital format, need to dig into some details of what is displayed in a plot quickly, or when static graphics are limiting your story-telling ability, remember that there might be another option!

Happy plotting!

Ava Yazdian

• Ava Yazdian is a current sophomore studying conservation biology and ecology. She enjoys helping others learn the language of statistics and data analysis through her experience in her statistics coursework. Her interests include plant ecology, skiing, and ceramics.

Greta Linse

• Greta Linse is the Facility Manager of Social Data Collection and Analysis Services (https://www.montana.edu/socialdata/) among other on campus roles. Greta has been teaching, documenting and working with statistical software including R and RStudio for over 10 years.

Sally Slipher

• Sally Slipher is a research statistician for Social Data. She has taught statistics in the past and uses R extensively (and sometimes other coding languages) to explore data and put together analyses.

Sara Mannheimer

• Sara Mannheimer is an Associate Professor and Data Librarian at Montana State University, where she helps shape practices and theories for curation, publication, and preservation of data. Her research examines the social, ethical, and technical issues of a data-driven world. She is the project lead for the MSU Dataset Search and the Responsible AI in Libraries and Archives project. Her 2024 book, Scaling Up, explores how data curation can address epistemological, ethical, and legal issues in qualitative data reuse and big social research.

The materials have also been modified and improved by:

• Dr. Mark Greenwood
• Harley Clifton
• Eliot Liucci
• Dr. Allison Theobold