IN THIS LAB YOU WILL LEARN:
1.) How to apply basic plotting functions to new data
2.) How to add color, themes, and other aesthetics
3.) How to panel plots w/ patchwork
4.) How to identify good data visualizations and how to make them!
Additional Tutorials and Resources for graphs in ggplot
Let’s spend some time talking about this and brainstorming ideas on the board. We will even draw out some graphs and then take a look at some published graphs to get ideas and critique!
2: Load any packages we may need
We learned about packages last week. This week, we will use them! Making nice looking graphs is a key feature of R and of data science in general. The best way to do this in R is through use of the ggplot2 package. This package is the most user friendly and flexible way to make nice plots in R. We will use ggplot2 exclusively for making plots in this class. Notably, ggplot2 is a package that is contained within the tidyverse package, which is more of a style of R usage than a package. So, let’s load tidyverse and a few other useful packages for today.
ggplot2 is the preferred graphics package for most R users. It allows users to build a graph piece by piece from your own data through mapping of aesthetics. It is much easier to make pretty (publication and presentation quality) plots with ggplot2 than it is with the base plot function in R. If you prefer base plot() that is ok. You can use whatever you’d like but when we talk about graphs we will be using the language of ggplot.
Attached here are the Tidyverse Cheat Sheets for ggplot2
The ggplot() function is the base of the ggplot2 package. Using it creates the space that we use to build a graph. If we run just the ggplot() function we will get a gray rectangle. This is the space (and background) of our plot!
ggplot()
To build a plot on the background, we must add to the ggplot call. First, we need to tell it what data to use. Next, we need to tell it where in the data frame to pull data from to build the axes and data points. The part of the ggplot() function we use to build a graph is called aes() or aesthetics.
Here is an example using penguins: I am telling ggplot that the data we are using is ‘penguins’ and then defining the x and y axis in the aes() call with column names from penguins
head(penguins)
# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex year
<fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
2 Adelie Torgersen 39.5 17.4 186 3800 fema… 2007
3 Adelie Torgersen 40.3 18 195 3250 fema… 2007
4 Adelie Torgersen NA NA NA NA <NA> 2007
5 Adelie Torgersen 36.7 19.3 193 3450 fema… 2007
6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
# … with abbreviated variable names ¹flipper_length_mm, ²body_mass_g
Before we get too excited about making perfect graphs, let’s take a look at the types of graphs we have available to us…
Histograms are used to explore the frequency distribution of a single variable. We can check for normality (a bell curve) using this feature. We can also look for means, skewed data, and other trends.
A boxplot is a really useful plot to assess median and range of data. It can also identify outliers! The defaults for a boxplot in ggplot produce a median and interquartile range (IQR). The 1st quartile is the bottom of the box and the 3rd quartile is the top. The whiskers show the spread of the data where the ends of the whiskers represent the data points that are the furthest from the median in either direction. Notably, if a data point is 1.5 * IQR from the box (either the 1st or 3rd quartile) it is an outlier. Outliers are excluded from whiskers and are presented as points. There
We can use geom_violin to combine boxplot with a density plot (similar to a histogram) Here we can see the distribution of values within bill length by species.
We can make bar graphs in ggplot using geom_bar(). There are some tricks to getting bar graphs to work exactly right, which I will try to detail below. NOTE Bar graphs are very rarely useful. If we want to show means, why not just use points + error bars? What does the bar actually represent? There aren’t that many cases where we really need bar graphs. There are exceptions, like when we have a population and we want to see the demographics of that population by count or percentage (see example below)
Here is a simple bar chart.
ggplot(data=penguins, aes(species)) +geom_bar()
Here is a more elaborate boxplot that shows species breakdown by island! Note that we use an aes() call within geom_bar to define a fill. That means fill by species, or add a color for each species.
We learned when the best (only) times to use bar graphs are. Do you remember what those were? Are the examples above representative of that?
A line graph can be extremely useful, especially if we are looking at time series data or rates!
Here is an example of CO2 uptake vs concentration in plants. Each color represents a different plant. NOTE: the dataset called ‘CO2’ is built into R, so we can just use it without loading anything :)
We can change the aesthetics of the lines using color, linetype, size, etc. Here I am changing the linetype based on the plant species and increasing the size of ALL lines to 2. This is a good example of how aes() works. Anything within the aes() call is conditional. That means, I give it a name (such as a column or variable name) and it changes based on that column or variable. To change an aesthetic across all lines, points, etc, I just put the code outside of the aes(). As I did for size. That makes the size of ALL lines = 2.
Importantly, we can use the data= and aes() calls within geom_point() or any other geom instead of within ggplot() if needed. Why might this be important?
We often want to present means and error in our visualizations. This can be done through the use of geom_boxplot() or through combining geom_point() with geom_errorbar()
Here is an example of the later…
#First, we need to calculate a mean bill length for our penguins by species and islandsumpens<- penguins %>%group_by(species, island) %>%na.omit() %>%#removes rows with NA values (a few rows may otherwise have NA due to sampling error in the field)summarize(meanbill=mean(bill_length_mm), sd=sd(bill_length_mm), n=n(), se=sd/sqrt(n))sumpens
# A tibble: 5 × 6
# Groups: species [3]
species island meanbill sd n se
<fct> <fct> <dbl> <dbl> <int> <dbl>
1 Adelie Biscoe 39.0 2.48 44 0.374
2 Adelie Dream 38.5 2.48 55 0.335
3 Adelie Torgersen 39.0 3.03 47 0.442
4 Chinstrap Dream 48.8 3.34 68 0.405
5 Gentoo Biscoe 47.6 3.11 119 0.285
# Now we can plot! ggplot(data=sumpens, aes(x=species, y=meanbill, color=island))+geom_point()+geom_errorbar(data=sumpens, aes(x=species, ymin=meanbill-se, ymax=meanbill+se), width=0.2)
And if we want to be extra fancy (and rigorous), we can plot the raw data behind the mean+error This is considered a graphical best practice as we can see the mean, error, and the true spread of the data!
ggplot()+geom_jitter(data= penguins, aes(x=species, y=bill_length_mm, color=island), alpha=0.5, width=0.2)+#this is the raw datageom_point(data=sumpens, aes(x=species, y=meanbill, color=island), size=3)+#this is the averagesgeom_errorbar(data=sumpens, aes(x=species, ymin=meanbill-se, ymax=meanbill+se), width=0.1)
An alternative to geom_jitter, which doesn’t always work, is to use geom_point but force the points to not overlap with position_dodge. Here is an example
#first we should define the distance of our position_dodgepd<-position_dodge(width=0.2)ggplot(data=sumpens, aes(x=species, y=meanbill, color=island))+geom_point(data= penguins, aes(x=species, y=bill_length_mm, color=island), alpha=0.2, width=0.2, position=pd)+#raw datageom_point(size=3, position=pd)+#averagesgeom_errorbar(aes(ymin=meanbill-se, ymax=meanbill+se), width=0.2, position=pd)
This code will produce the same graph as above. Note that in geom_jitter we just replaced width = with position =
ggplot(sumpens, aes(x=species, y= meanbill, color=island))+geom_jitter(data= penguins, aes(x=species, y=bill_length_mm, color=island), alpha=0.5, position=pd)+#this is the raw datageom_point(size=3,position=pd)+#this is the averagesgeom_errorbar(aes(ymin=meanbill-se, ymax=meanbill+se), width=0.2, position=pd)
ggplot gives us options to change point shape using the aesethic option ‘shape’ We can either change shape based on a characterstic of the data (‘cyl’, for example), make all the shapes the same, or manually control shape
ggplot(data=mtcars, aes(x=cyl, y=mpg, color=cyl))+geom_point(shape=17) #Here 'shape=' is inside the settings for geom_point. Note that it is outside the aes() function, as that applied aesethics conditionally)
#example 2, same w/ different syntaxggplot()+geom_point(data=mtcars, aes(x=cyl, y=mpg, color=cyl), shape=17)
Manual shape changes
ggplot(data=penguins, aes(x=species, y=bill_length_mm, color=island, shape=island))+geom_jitter(size=2)+theme_classic()+scale_shape_manual(values=c(2,3,4)) #scale_shape_manual allows us to choose shapes for each group (cyl in this case). c stands for concatenate, as we've seen before
ggplot(data=mtcars, aes(x=cyl, y=mpg, color=cyl, size=cyl))+#note that we added 'size=' to our aes. geom_point()
#note the warning message that using size for a discrete variable is not best practice. #Instead, let's use the size to five us an idea of hp (a 3rd variable)ggplot(data=mtcars, aes(x=cyl, y=mpg, color=cyl, size=hp))+#note that we added 'size=' to our aes. geom_point()
Change size of all points (all points must be same size)
ggplot(data=mtcars, aes(x=cyl, y=mpg, color=cyl))+geom_point(size=5) #as w/ shape, point needs to be outside the aes() here.
We can change colors conditionally or manually.
Conditional Color Change To change colors conditionally, we use color= or fill= within an aes() call.
Here I have changed the outline color (color=) for a series of boxplots based on species
Manual Color Change We can also change colors manually by using one of many options within ggplot. scale_color_manual (or scale_fill_manual) is the easiest. We simply define colors we want to use by name or hexcode.
Here’s a giant table of color options in ggplot You can also make your own color palette and apply that to your figure!
mypal<-c('dodgerblue', 'forestgreen', 'coral') # here I've made a 3 color paletteggplot(data=penguins, aes(x=species, y= bill_length_mm)) +geom_boxplot(aes(fill=species))+scale_fill_manual(values=mypal)
You can use the package RColorBrewer to make palettes as well. I’ll let you explore that one on your own!
Finally, EASY and nice looking palettes with ggsci ggsci is a simple and neat package that allows us to use scientific journal color themes for our data (usually colorblind friendly and nice looking). we simply change our “scale_color_manual” to “scale_color_palname” where “palname” is one of many provided by ggsci. For example, we might use scale_color_aaas()
Titles and axis labels are easy to add and change in ggplot! We simply add another line to our code. NOTE you can also add a subtitle, caption, or change the legend title using labs!
ggplot(data=penguins, aes(x=species, y= bill_length_mm)) +geom_boxplot(aes(fill=species))+scale_fill_aaas()+labs(x ='Species', y='Bill length (mm)', title='Penguin bill length by species', fill='Species') #here I change the x-axis and y-axis labels, add a title, and change the legend label (to capitalize the 'S' in 'species')
Themes allow us to change the background color and most other aspects of a plot. There are a range of theme options within ggplot that will allow us to quickly make clean plots. The two that are most commonly used are theme_bw() and theme_classic()
Default theme (with terrible gray background)
ggplot(data=penguins, aes(x=species, y= bill_length_mm)) +geom_boxplot(aes(fill=species))+scale_fill_aaas()+labs(x ='Species', y='Bill length (mm)', title='Penguin bill length by species')
The theme() function in ggplot is SUPER flexible. You can pretty much do anything with it. This is key for customizing plots. I’d encourage you to play around with this a bit. Here is a great place to learn more and see examples.
Facets allow us to produce multiple graph panels with one ggplot code. We can separate out a variable for easier viewing or even create a grid of graphs using multiple variables.
facet_wrap() allows us to make multiple panels. The panels are aligned in columns and rows. We need to use ‘~’ in our facet_wrap code. The ‘~’ essentially means “by”
Using the simple and wonderful patchwork package, we can place multiple plots on the same page. To do this, we must actually name each plot. Here’s an example.
Patchwork is super easy! Learn more here(with examples)
Now let’s patchwork them together! We make a simple formula to make a patchwork. Addition puts everything in the same row. But we can use division and other symbols to organize.
library(patchwork)p1+p2+p3
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
You can also make your own color palette and apply that to your figure!
