R can create almost any plot imaginable and as with most things in R if you don’t know where to start, try Google. The Introduction to R curriculum summarizes some of the most used plots, but cannot begin to expose people to the breadth of plot options that exist.There are existing resources that are great references for plotting in R:
In base R:
- Breakdown of how to create a plot from R-bloggers
- Another blog breaking down basic plotting from FlowingData
- Basic plots (histograms, boxplots, scatter plots, QQ plots) from University of Georgia
- Intermediate plots (error bars, density plots, bar charts, multiple windows, saving to a file, etc) from University of Georgia
- ggplot2 homepage
- ggplot2 video tutorial
- Website with everything you want to know about ggplot2 by Selva Prabhakaran
- R graphics cookbook site
- ggplot2 cheatsheet
- ggplot2 reference guide
In the Introduction to R class, we have switched to teaching ggplot2 because it works nicely with other tidyverse packages (dplyr, tidyr), and can create interesting and powerful graphics with little code. While
ggplot2 has many useful features, this blog post will explore how to create figures with multiple
You may have already heard of ways to put multiple R plots into a single figure – specifying
mfcol arguments to
layout are all ways to do this. However, there are other methods to do this that are optimized for
Multiple plots in one figure using ggplot2 and facets
When you are creating multiple plots and they share axes, you should consider using facet functions from ggplot2 (
facet_wrap). You write your
ggplot2 code as if you were putting all of the data onto one plot, and then you use one of the faceting functions to specify how to slice up the graph.
Let’s start by considering a set of graphs with a common x axis. You have a data.frame with four columns: Date, site_no, parameter, and value. You want three different plots in the same figure – a timeseries for each of the parameters with different colored symbols for the different sites. Sounds like a lot, but facets can make this very simple. First, setup your ggplot code as if you aren’t faceting.
We will download USGS water data for use in this example from the USGS National Water Information System (NWIS) using the
dataRetrieval package (you can learn more about
dataRetrieval in this curriculum). Three USGS gage sites in Wisconsin were chosen because they have data for all three water quality parameters (flow, total suspended solids, and inorganic nitrogen) we are using in this example.
library(dataRetrieval) library(dplyr) # for `rename` & `select` library(tidyr) # for `gather` library(ggplot2) # Get the data by giving site numbers and parameter codes # 00060 = stream flow, 00530 = total suspended solids, 00631 = concentration of inorganic nitrogen wi_daily_wq <- readNWISdv(siteNumbers = c("05430175", "05427880", "05427927"), parameterCd = c("00060", "00530", "00631"), startDate = "2017-08-01", endDate = "2017-08-31") # Clean up data to have human-readable names + move data into long format wi_daily_wq <- renameNWISColumns(wi_daily_wq, p00530 = "TSS", p00631 = "InorganicN") %>% select(-ends_with("_cd")) %>% gather(key = "parameter", value = "value", -site_no, -Date) # Setup plot without facets p <- ggplot(data = wi_daily_wq, aes(x = Date, y = value)) + geom_point(aes(color = site_no)) + theme_bw() # Now, we can look at the plot and see how it looks before we facet # Obviously, the scales are off because we are plotting flow with concentrations p
Now, we know that we can’t keep these different parameters on the same plot. We could have written code to filter the data frame to the appropriate values and make a plot for each of them, but we can also take advantage of
facet_grid. Since the resulting three plots that we want will all share an x axis (Date), we can imagine slicing up the figure in the vertical direction so that the x axis remains in-tact but we end up with three different y axes. We can do this using
facet_grid and a formula syntax,
y ~ x. So, if you want to divide the figure along the y axis, you put variable in the data that you want to use to decide which plot data goes into as the first entry in the formula. You can use a
. if you do not want to divide the plot in the other direction.
# Add vertical facets, aka divide the plot up vertically since they share an x axis p + facet_grid(parameter ~ .)
The result is a figure divided along the y axis based on the unique values of the
parameter column in the data.frame. So, we have three plots in one figure. They still all share the same axes, which works for the x axis but not for the y axes. We can change that by letting the y axes scale freely to the data that appears just on that facet. Add the argument
facet_grid and specify that they should be “free” rather than the default “fixed”.
# Add vertical facets, but scale only the y axes freely p + facet_grid(parameter ~ ., scales = "free_y")
From here, there might be a few things you want to change about how it’s labelling the facets. We would probably want the y axis labels to say the parameter and units on the left side. So, we can adjust how the facets are labeled and styled to become our y axis labels.
p + facet_grid(parameter ~ ., scales = "free_y", switch = "y", # flip the facet labels along the y axis from the right side to the left labeller = as_labeller( # redefine the text that shows up for the facets c(Flow = "Flow, cfs", InorganicN = "Inorganic N, mg/L", TSS = "TSS, mg/L"))) + ylab(NULL) + # remove the word "values" theme(strip.background = element_blank(), # remove the background strip.placement = "outside") # put labels to the left of the axis text
There are still other things you can do with facets, such as using
space = "free". The Cookbook for R facet examples have even more to explore!
cowplot to create multiple plots in one figure
When you are creating multiple plots and they do not share axes or do not fit into the facet framework, you could use the packages
patchwork (very new!), or the
grid.arrange function from
gridExtra. In this blog post, we will show how to use
cowplot, but you can explore the features of
The package called
cowplot has nice wrapper functions for ggplot2 plots to have shared legends, put plots into a grid, annotate plots, and more. Below is some code that shows how to use some of these helpful
cowplot functions to create a figure that has three plots and a shared title.
Just as in the previous example, we will download USGS water data from the USGS NWIS using the
dataRetrieval package (find out more about
dataRetrieval in this curriculum). This USGS gage site on the Yahara River in Wisconsin was chosen because it has data for all three water quality parameters (flow, total suspended solids, and inorganic nitrogen) we are using in this example.
library(dataRetrieval) library(dplyr) # for `rename` library(tidyr) # for `gather` library(ggplot2) library(cowplot) # Get the data yahara_daily_wq <- readNWISdv(siteNumbers = "05430175", parameterCd = c("00060", "00530", "00631"), startDate = "2017-08-01", endDate = "2017-08-31") # Clean up data to have human-readable names yahara_daily_wq <- renameNWISColumns(yahara_daily_wq, p00530 = "TSS", p00631 = "InorganicN") # Create the three different plots flow_timeseries <- ggplot(yahara_daily_wq, aes(x=Date, y=Flow)) + geom_point() + theme_bw() yahara_daily_wq_long <- gather(yahara_daily_wq, Nutrient, Nutrient_va, TSS, InorganicN) nutrient_boxplot <- ggplot(yahara_daily_wq_long, aes(x=Nutrient, y=Nutrient_va)) + geom_boxplot() + theme_bw() tss_flow_plot <- ggplot(yahara_daily_wq, aes(x=Flow, y=TSS)) + geom_point() + theme_bw() # Create Flow timeseries plot that spans the grid by making one plot_grid # and then nest it inside of a second. Also, include a title at the top # for the whole figure. title <- ggdraw() + draw_label("Conditions for site 05430175", fontface='bold') bottom_row <- plot_grid(nutrient_boxplot, tss_flow_plot, ncol = 2, labels = "AUTO") plot_grid(title, bottom_row, flow_timeseries, nrow = 3, labels = c("", "", "C"), rel_heights = c(0.2, 1, 1))