More basic information on plots in R

This is a complement to the other preliminary material on R graphics with a focus on using the ggplot2 package. We have tried to avoid too much overlap with other sessions.

For the videos and other materials, you can visit the UWA LMS page for the unit you're enrolled in. Or, go to the base-R graphics page if you're not interested in using ggplot2.

Graphical Excellence

In Environmental Science at UWA we try to promote the principles of "graphical excellence". This means:

Show the data – that is, the data are the important thing we want to communicate in graphics; "looking good" is only useful if it shows the data better!.
Give your viewer the greatest number of ideas in the shortest time with the least ink in the smallest space (Tufte, 1983)¹
Graphical excellence is
- almost always multivariate...
- ...requires telling the truth about the data (no distortions, fair comparisons, etc.).

Some practical tips:

Increase the default text and symbol sizes
Remove unnecessary lines (e.g. smooth, spread in car::scatterplot())
In RStudio, use the Export▾ button » Copy to clipboard » select ◉Metafile (best pasting into Word, etc.)
Don't use screen shots, and avoid .jpg files, as both can be blurry or grainy
Graph plus caption should be self-contained – we should only need to refer to these to fully understand the graph
Don't include a title above the plot (in many R plots, use the option main="") – any information describing the plot should be in the caption
Transform axes if necessary (using arguments in R plotting functions)
Use appropriate proportions (width/height) for the plot you are creating
Change the default colour palette to a colourblind-friendly palette, e.g. from the R packages viridis or scico (see examples for viridis here and for scico here)

¹ Tufte, E.R., 1983. The visual display of quantitative information. Graphics Press, Cheshire, Connecticut, USA.

Getting started

Before we start we load some packages (which we need to have installed previously). We also need to load a dataset that we may have seen before (the Smith's Lake & Charles Veryard Reserves data from 2017 – we use it a lot for illustrating environmental statistics and plotting – the code below reads it directly from github).

library(ggplot2)  # the plotting package used in this page
library(viridis)  # colourblind-friendly colour palettes
library(scico)    # 'scientific' colour palettes
git <- "https://github.com/Ratey-AtUWA/Learn-R-web/raw/main/"
sv2017 <- read.csv(file=paste0(git,"sv2017_original.csv"), stringsAsFactors = TRUE)
sv2017$Group <- as.factor(sv2017$Group)

The components of a `ggplot` plot

With any plot using ggplot, we add layers or properties to the plot with successive functions; each layer needs the preceding line to finish with +. The data to plot is controlled by options in the initial ggplot() function, particularly data= and aes(). The type of plot is controlled by different geom_... functions, as in the example below (Figure 1).

ggplot(data=sv2017, mapping=aes(x=pH, y=Ca, col=Type, shape=Type)) +
  geom_point(size=2.2) +
  scale_y_log10() + 
  scale_color_viridis_d(option="inferno", end=0.55) + 
  stat_ellipse(level=0.95) +
  theme_bw()

Figure 1: X-Y (scatter) plot of calcium (Ca) vs. pH for different sample types from Smith's Lake and Charles Veryards Reserves in 2017 (sv2017), showing regression lines by Type.

In Figure 1, we use the ggplot2 package to:

specify the data to be plotted in the ggplot() function, including
- a data frame with the data= option
- which variables control the location and appearance of points with the options in mapping=aes(...)
  (aes is short for “aesthetic”)
make an xy-plot layer using the geom_point() function
log₁₀-transform the y-axis scale using scale_y_log10()
use a custom palette by including scale_color_viridis_d()
add a layer of data ellipses using stat_ellipse()
style the plot using the non-default theme_bw()

We will cover these concepts in the examples to follow.

Customising `ggplot2` features

In contrast with base-R plotting, we don't use the par() function to control any plotting features. One of the ideas of ggplot2 is to produce plots which look good without customisation – but we still often need to “tweak” the output. Actually, we have done this with Figure 1 above already.

In the plots that follow, our "default setting" will be to use the theme_bw() theme to set the plot appearance. Otherwise we will build on the usual ggplot2 default settings. The most basic plot is in Figure 2.

1. Using xy-plot (`geom_point`) to explore ways of changing plot features

ggplot(data=sv2017, mapping=aes(x=pH, y=Ca)) +
    geom_point() +
    theme_bw()

Figure 2: Default ggplot of Ca vs. pH.

2. Resizing axis labels in `ggplot2`

For many plot features in ggplot2 we need to customise the plot theme. An axis label is referred to as an axis.title in ggplot2. The resized (and recoloured) axis titles are shown in Figure 3.

ggplot(data=sv2017, mapping=aes(x=pH, y=Ca)) +
  geom_point() + 
  theme_bw() +
  theme(axis.title = element_text(size=18, colour="red2")) # size in points

Figure 3: Plot of Ca vs. pH using ggplot2, showing how to resize axis labels (we've also made them a different colour to be really obvious).

3. Resizing axis labels, tick labels, and symbol sizes in `ggplot2`

To get the larger symbol sizes shown in Figure 4, we have included size=2.5 in geom_point(), outside aes() so it applies to all symbols.

ggplot(data=sv2017, mapping=aes(x=pH, y=Ca)) +
  geom_point(size=2.5) + # default size = 1.5 
  theme_bw() +
  theme(axis.title = element_text(size=18), # axis.title is axis label
        axis.text = element_text(size=16)) # axis.text is the tick labels

Figure 4: Plot of Ca vs. pH using ggplot2, showing how to resize axis labels (and symbol sizes).

4. Transforming plot axes in `ggplot2`

To log₁₀-transform axis scales in ggplot2 (Figure 5) we add a statement using the scale_x_log10() and/or the scale_y_log10() functions. There are several different types of scaling function available, e.g., scale_x_sqrt(), scale_y_reverse().

ggplot(data=sv2017, mapping=aes(x=pH, y=Ca)) +
  geom_point(colour="dodgerblue") + # changing the default symbol colour too! 
  scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(face="bold")) # # axis.title is axis label

Figure 5: Plot of Ca vs. pH using ggplot2, showing how to log10-transform an axis scale.

5. Plots in `ggplot2` which show grouping by factor levels

To group points or other plot features by a factor, we need to associate that factor with the property we want to change (e.g. colour, shape, size) in the aes() statement. In Figure 6 below, we plot zinc (Zn) vs. iron (Fe), showing points in different sample types by including colour=Type, shape=Type in the aes() statement.

If we associate a variable with size, we can create a bubble plot – this is useful for making distribution maps of variables. Try it!

ggplot(data=sv2017, mapping=aes(x=Fe, y=Zn, colour=Type, shape=Type)) +
  geom_point() + 
  scale_x_log10() + scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(face="bold"))

Figure 6: Plot of Zn vs. Fe using ggplot2, showing how to separate points into different groups based on factor levels.

Plot symbols

R has a set of 26 built-in plotting symbols which can be specified using the shape = argument in many ggplot2 functions.
In base-R, the outline colour is set for symbols 0-20 using the argument col =. For symbols 21-25 col = sets the border colour, and the fill colour is set with bg =.

For all symbols above, col="navy" and for pch 21-25 bg="gold".

6. Using custom colours in `ggplot2`

If we're not happy with the default colours, there are many different ways to customise plot colours in ggplot2. The statements needed are all of the type scale_color_xxxxx(), depending on what type of colour scale or palette is needed.

The code for the plot in Figure 7 uses scale_color_manual to specify custom colours for the points. If we didn't include enough colours for the levels in the factor (i.e. 3 levels in Type), we would get an error message.

ggplot(data=sv2017, mapping=aes(x=Fe, y=Zn, colour=Type, shape=Type)) +
  geom_point(size=2) + 
  scale_color_manual(values=c("darkred","gold3","royalblue")) +
  scale_x_log10() + scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(face="bold"))

Figure 7: Plot of Zn vs. Fe using ggplot2, showing how to separate points into different groups based on factor levels and define custom colours.

Colours in R

The palette() function needs a vector of colour names or codes, which we can then refer to by numbers in subsequent functions. R has over 600 built-in colours, some with nifty names like "burlywood", "dodgerblue", and "thistle" – to see these, run the function colors() or run demo("colors").

Colour codes are a character string with 6 or 8 digits or letters after the # symbol, like "#A1B2C3". In the 6-digit version (the most common), the first 2 characters after # are a hexadecimal number specifying the intensity of the red component of the colour, the next 2 specify green , and the next 2 blue . #rrggbb

The greatest 2-digit hexadecimal number is FF, equal to the decimal number 255. Since we can include zero, this means there will be 256³ = 16,777,216 unique colours in R.

Optionally we can use an 8-character colour code, such as "#A1B2C399", where the last 2 characters define the alpha value, or colour transparency. "#rrggbb00" would be fully transparent, and "#rrggbbFF" would be fully opaque. We can also use the colour name "transparent" in R which can sometimes be useful.

Warning: semi-transparent colours (i.e. alpha < 1; anything other than "#rrggbb" or "#rrggbbFF") are not supported by metafiles in R. To use semi-transparent colours, save as .png or .tiff, or copy as a bitmap.

(Some of this information refers to base-R, but can also be useful for fine-tuning colours using ggplot2.)

7. Adding a regression (“best fit”) line

Sometimes adding a regression line is useful to confirm trends (in a later session we'll cover linear regression in more detail). We use the function geom_smooth(method="lm") in ggplot to add a regression line (Figure 8):

ggplot(data=sv2017, aes(x= Fe, y = Zn)) +
  geom_point() +
  geom_smooth(method="lm") +
  labs(x="Fe (mg/kg)", y="Zn (mg/kg)") +
  scale_x_log10() + scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(face="bold"))

Figure 8: Plot of Zn vs. Fe, with no grouping and an added regression line.

The grey shaded area around the regression line in Figure 8 is the 95% confidence interval, which is included by default.

We can also include trend lines for grouped scatterplots (Figure 9):

ggplot(data=sv2017, aes(x=Fe, y=Zn, colour=Type, shape=Type)) +
  geom_point(size=2.5) +
  geom_smooth(method="lm") +
  labs(x="Fe (mg/kg)", y="Zn (mg/kg)") +
  scale_x_log10() + scale_y_log10() +
  theme_bw() +
  theme(axis.title = element_text(face="bold"))

Figure 9: Plot of Zn vs. Fe, with added regression lines for data grouped by a sample Type.

8. Multiple-frame plots in `ggplot2`

If we want to plot multiple plots on a page, we can't do this in ggplot2 using the mfrow argument in par().

Instead, we would need to use another R package in the gg family called ggpubr, which has the ggarrange function allowing multiple ggplots on the same page. An example is shown in Figure 10.

library(ggpubr)
scatter <- ggplot(data=sv2017, mapping=aes(x=Al, y=Fe)) + scale_x_log10() +
  scale_y_log10() + geom_point()
boxes <- ggplot(data=sv2017, mapping = aes(x=Type, y=Fe)) + scale_y_log10() +
  geom_boxplot()
histo <- ggplot(data=sv2017, mapping=aes(x=Al)) + geom_histogram()
ggarrange(scatter, boxes, histo, nrow=1)

Figure 10: Example of multiple ggplot2 plots per frame using ggarrange() from the ggpubr package.

Histogram with density line

Adding a density line to a histogram like the one in Figure 11 can help us identify bimodal distributions, or see more easily if the distribution is symmetrical or not.

We use geom_histogram(), including the option mapping = aes(y=after_stat(density)) so that the y-axis scale is changed from frequency to density (~ relative frequency).

ggh <- ggplot(data = sv2017, mapping = aes(x=log10(EC))) +
  geom_histogram(mapping=aes(y = after_stat(density)), 
                 fill="lightgray", color="black", linewidth=0.3,
                 binwidth = 0.1, boundary=0) + 
  theme_bw()
ggh + geom_density(col="blue3")

Figure 11: Histogram of EC from the sv2017 data with density line plot added.

Boxplot by groups

We also made a grouped box plot like the one in Figure 12 in a previous session.

# make an object containing a table of means
Cu_means <- data.frame(meanCu=tapply(log10(sv2017$Cu), sv2017$Type, mean, na.rm=T))
# plot the boxplot with nice axis titles etc.
ggbx <- ggplot(data=sv2017, mapping = aes(x=Type, y=log10(Cu))) +
  geom_boxplot(fill="seashell",staplewidth = 0.2)
ggbx <- ggbx + geom_point(data=Cu_means, mapping=aes(x=c(1:nrow(Cu_means)),
                  y=meanCu), shape=3, size=2.5, col="royalblue", stroke=1.5)
ggbx + geom_hline(yintercept = log10(65), col="red3",size=0.3,
                  linetype="dashed") +
  labs(y=expression(paste("log"[10],"(Cu)"))) + 
  annotate("text", x=2.9, y = log10(65), label = "Sediment DGV Cu 65 mg/kg", 
           vjust = -0.5, col="red3", size=3) + 
  annotate("text", x=2.2, y=0.4, label = "+", 
           hjust = 0, col="royalblue", size=6.5) + 
  annotate("text", x=2.2, y=0.4, label = " \u00a0 \u00a0 Mean Cu concentrations", 
           hjust = 0, col="royalblue", size=3.5) +
  theme_bw()

Figure 12: Boxplot of Cu by Type created using ggplot2, showing mean values as blue plus (+) This version is drawn to resemble a base-R plot.

The default box plots made with 'geom_boxplot()' in ggplot (e.g. Figure 13) don't have caps (“staples”) on the lines above and below the interquartile range box.

# make an object containing a table of means
Cu_means <- data.frame(meanCu=tapply(log10(sv2017$Cu), sv2017$Type, mean, na.rm=T))
# plot the boxplot with nice axis titles etc.
cols <- c("Mean"="royalblue", "DGV"="red3")
lineTypes <- c("DGV"=2)
ggplot(data=sv2017, mapping = aes(x=Type, y=log10(Cu))) +
  geom_boxplot(fill="seashell", shape=1) +
  labs(y=expression(paste(log[10],"[Cu]"))) +
  geom_point(data=Cu_means, mapping=aes(x=c(1:nrow(Cu_means)),
                  y=meanCu, col="Mean"), shape=3, size=2.5, stroke=1) +
  geom_hline(yintercept = log10(65), col="red3", size=0.3,
                  linetype=2) + 
  scale_color_manual(name="", values=cols, 
                     guide=guide_legend()) +
  annotate("text", x=2.9, y = log10(65), label = "Sediment DGV\nCu 65 mg/kg", 
           vjust = 0.5, col="red3", size=3.5) + 
  theme_bw()

Figure 13: Boxplot of Cu by Type created using ggplot2, showing mean values as blue plus (+) This version is drawn in a style more closely resembling a ggplot2 style.

Scatterplot of water sample points by group

First read the water data (from Smith's Lake, North Perth, sampled in 2018). We convert to a spatial object to use the geom_sf() function in ggplot2, as this makes the most sense for these data (we need to load the sf package first).

SL_water <- read.csv(file=paste0(git, "SL18.csv"), stringsAsFactors = TRUE)
SL_water$Group <- as.factor(SL_water$Group)
library(sf)
SL_water_sf <- sf::st_as_sf(SL_water, coords=c("Easting","Northing"), 
                            remove=FALSE, crs=sf::st_crs(32750))

We can then use geom_sf() from the ggplot2 package to plot the points. Other important statements in the ggplot2 code in this example (Figure 14) are:

use fill=Group instead of color=Group since we are using fillable symbols (numbers 21-25, see Plot symbols section above)
coord_sf(...) to use UTM Zone 50S coordinates (otherwise geom_sf() will convert to Longitude-Latitude)
scale_shape_manual(...) to manually set which plot symbols we want (otherwise some groups will be omitted by ggplot2)
scale_x_continuous(...) to ‘declutter’ the x-axis labels
annotate(...) to add text labels
scale_color_viridis_d() to choose our colour palette
theme_bw() to use a plot theme other than the ggplot2 default

ggplot(data=SL_water_sf, 
       mapping=aes(x=Easting, y=Northing, fill=Group, shape=Group)) +
  geom_sf(size=2.5) + # set symbol size and outline width 
  coord_sf(datum=st_crs(32750)) +
  scale_shape_manual(values=c(21:25,21:24)) +
  scale_x_continuous(breaks=seq(391325,391450,25)) +
  annotate("text", x=c(391385,391390), y = c(6466515,6466403), 
           label = c("Inlet","Outlet"), 
           vjust = 0.5, col="steelblue4", size=3.5) + 
  scale_fill_viridis_d(alpha=0.8) + theme_bw()

Figure 14: Map-style plot of water sampling points in Smith's Lake in 2018.

"...when we look at the graphs of rising ocean temperatures, rising carbon dioxide in the atmosphere and so on, we know that they are climbing far more steeply than can be accounted for by the natural oscillation of the weather ... What people (must) do is to change their behavior and their attitudes ... If we do care about our grandchildren then we have to do something, and we have to demand that our governments do something."

— David Attenborough

Plots of variable distributions

Cumulative Distribution Function using plot.ecdf(). The custom y-axis label explains the plot in Figure 15!
(note use of \u to insert a Unicode character by its 4-digit code [Unicode character 2264 is 'less than or equal to' (≤)] )

ggplot(sv2017, aes(pH)) + stat_ecdf(geom="step") + 
  labs(y="Proportion of samples with pH \u2264 x")

Figure 15: Cumulative distribution of pH in the sv2017 dataset.

A better cumulative plot is the normal quantile or 'q-q' plot (Figure 16). Specially transformed axes mean that a normally distributed variable will plot as a straight line. We use geom_qq() to add the points and geom_qq_line() to show the theoretical line for a normal distribution.

ggplot(data=sv2017, aes(sample=pH)) + geom_qq() + geom_qq_line() + 
  labs(x="Theoretical normal distribution quantiles", y="pH") + theme_bw()

Figure 16: Normal quantile (QQ) plot of soil pH in the sv2017 dataset.

We can group q-q–plots by a factor:

ggplot(data=sv2017, aes(sample=pH, col=Type)) + geom_qq() + geom_qq_line() + 
  labs(x="Theoretical normal distribution quantiles", 
       y="pH") + theme_bw()

Figure 17: Normal quantile (QQ) plots of soil pH for different sample types in the sv2017 dataset.

Here's another customization of histograms – we've seen these in a previous Workshop. Adding a reference concentration like that we see in Figure 18 shows what proportion of samples have concentrations exceeding this value (or not).

ggplot(data=sv2017, aes(x=log10(Zn))) + 
  geom_histogram(fill="lightgray", color="black", linewidth=0.3,
                 binwidth = 0.2, boundary=0) + 
  labs(x = expression(paste(log[10],"Zn")), y = "Frequency") + 
  geom_vline(xintercept = log10(410), col="red3", size=0.3,
                  linetype="dashed") + 
  annotate("text", x=log10(400), y = 15, label = "Sediment DGV\nZn 410 mg/kg", 
           hjust = 1, col="red3", size=3) +
  theme_classic()

Figure 18: Histogram of Zn in the sv2017 data with a vertical line representing an environmental threshold.

Depth profile scatter plots in `ggplot2`

First we read some more data which includes depth as a variable:

sv18 <- read.csv(paste0(git, "sv18.csv"), stringsAsFactors = TRUE)
sv18$Group <- as.factor(sv18$Group)

In the plot in Figure 19, we show one way to informatively plot a concentration vs. sediment/soil depth, with depth displayed vertically, and increasing downwards, as we would expect in the real world. Note:

x=Fe, y=Depth.mean to have our dependent variable (Fe) on the x-axis
col=Group, fill=Group to have colours and symbols differ by group
use of the scico 'lajolla' palette for nice soil-y colours!
scale_shape_manual(values=c(21:25,21:24)) filled plot symbols are 21-25
scale_y_reverse() so depth increases downwards
scale_x_continuous(position="top") gets the x-axis on the top

par(lend="square")
sv18$Depth.mean <- (sv18$Depth_upper+sv18$Depth_lower)/2
ggplot(data=sv18, mapping=aes(x=Fe, y=Depth.mean)) + 
  labs(x="Fe (mg/kg)", y="Mean sample depth (cm)") +
  geom_point(aes(fill=Group, shape=Group), size=2.5, stroke=0.5) +
  scale_shape_manual(values=c(21:25,21:24)) +
  scale_fill_scico_d(palette = "lajolla", direction=-1, begin=0.2, end=0.9) +
  scale_y_reverse(limits=c(100,0)) +
  scale_x_continuous(position="top") + 
  theme_bw() + theme(axis.title = element_text(face="bold"))

Figure 19: ‘Depth profile’ plots of Fe concentration vs. depth measurements in the sv18 data.

For more advanced options for plotting depth profiles in R, click this link.

Scatterplot matrices

A scatterplot matrix can be a very useful data exploration tool (for the most effective visualization, you might need to change the default colours).

Scatterplot matrices are available using the ggpairs() function from the GGally package.

The most basic implementation is plotting without grouping by a Factor variable. Note the different way in which the variables are specified (i.e. including a vector of columns, in this example columns = c("Al","Ca","Fe","S") – the vector can be of column names or column indices/numbers).

A very useful feature of scatter plot matrices is the display of each variable's distribution along the diagonal of the plot matrix. In the example in Figure 20, we can see from the diagonal of density plots that all variables have approximately symmetrical distributions when log₁₀-transformed). The scatter plot matrix drawn by GGally::ggpairs() also includes another useful feature – the correlation coefficients for each pair of variables in the upper triangle of the matrix.

library(GGally)
ggpairs(sv2017, columns = c("Al","Ca","Fe","S")) +
  scale_x_log10() + scale_y_log10() + theme_bw()

Figure 20: Scatter plot matrix of Al, Ca, Fe, and Na concentrations in sediment + soil + street dust samples from Smith's Lake and Charles Veryard Reserves, North Perth, WA, sampled in 2017. Note the log10-transformed axes.

To distinguish the points according to the categories in a Factor variable, we (as usual) associate the factor with plot properties within aes(...). We also see the distributions separated by group along the diagonal, but this looks a bit weird (Figure 21), unfortunately (probably because we used scale_?_log10() for both x and y axes).

Separating the plots by a factor allows us to see if trends are consistent between groups, or if groups behave very differently. [If you don't want to bother with the viridis package to use the plasma palette, use
scale_color_manual(values = c("#6A00A8", "#BF3984", "#F1844B")) for the same result.]

ggpairs(sv2017, columns = c("Al","Ca","Fe","S"), aes(color=Type, shape=Type)) +
  scale_color_viridis_d(option="magma", begin=0.2, end=0.7) +
  scale_x_log10() + scale_y_log10() + theme_bw()

Figure 21: Scatter plot matrix of Al, Ca, Fe, and Na concentrations in samples from Smith's Lake and Charles Veryard Reserves, North Perth, WA, sampled in 2017 and grouped by sample type (sediment, soil, or street dust). Note the log10-transformed axes.

Simple maps

Maps are [mostly] just scatterplots with a map background – we will spend a separate session on maps in R later making use of the ggmap R package, which has a lot in common with ggplot2 including use of geom_sf().

Pretty palettes

(See the code at the end)

Go to the base-R graphics page here.

References and R Packages

Garnier S, Ross N, Rudis R, Camargo AP, Sciaini M, Scherer C (2021). Rvision - Colorblind-Friendly Color Maps for R. R package version 0.6.2. https://sjmgarnier.github.io/viridis/ (viridis package).

Kahle, D. and Wickham, H. ggmap: Spatial Visualization with ggplot2. The R Journal, 5(1), 144-161. http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf.

Kassambara, A. (2020). ggpubr: ggplot2 Based Publication Ready Plots. R package version 0.4.0, https://CRAN.R-project.org/package=ggpubr.

Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial VectorData. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009 . (package sf)

Pedersen T, Crameri F (2023). scico: Colour Palettes Based on the Scientific Colour-Maps. R package version 1.4.0, https://CRAN.R-project.org/package=scico.

Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2021). GGally: Extension to ggplot2. R package version 2.1.2, https://CRAN.R-project.org/package=GGally.

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

# first load and run code to make UWAcolgrad() function
# for help download the UWAcolgrad.R file itself and look inside!
source("https://github.com/Ratey-AtUWA/Learn-R/raw/main/UWAcolgrad.R")
np <- 32
par(mar=rep(0.5,4), xpd=TRUE)
plot(0:1, 0:1, ann=F, axes = F, type="n", ylim=c(-0.1,1))
points(seq(0,1,l=np),rep(1,np),pch=22,bg=viridis(np),cex=4)
text(0.5,0.98,labels=paste0("viridis::viridis(",np,")"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(0.85,np),pch=22,bg=plasma(np),cex=4)
text(0.5,0.83,labels=paste0("viridis::plasma(",np,")"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(0.7,np),pch=22,bg=cividis(np),cex=4)
text(0.5,0.68,labels=paste0("viridis::cividis(",np,")"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(0.55,np),pch=22,bg=scico(np, palette = "hawaii"),cex=4)
text(0.5,0.53,labels=paste0("scico(",np,", palette='hawaii')"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(0.4,np),pch=22,bg=scico(np, palette = "lajolla"),cex=4)
text(0.5,0.38,labels=paste0("scico(",np,", palette='lajolla')"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(0.25,np),pch=22,bg=scico(np, palette = "batlow"),cex=4)
text(0.5,0.23,labels=paste0("scico(",np,", palette='batlow')"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(0.1,np),pch=22,bg=UWAcolgrad(np),cex=4)
text(0.5,0.08,labels=paste0("UWAcolgrad(",np,")"),pos=1, cex=1.4, family="mono")
points(seq(0,1,l=np),rep(-0.05,np),pch=22,bg=UWAcolgrad(np, saturated = TRUE),cex=4)
text(0.5,-0.07,labels=paste0("UWAcolgrad(",np,", saturated=TRUE)"),pos=1, cex=1.4, family="mono")

Material to support teaching in Environmental Science at The University of Western Australia

Units ENVT3361, ENVT4461, and ENVT5503

RStudio graphics and plotting

The ggplot version

Andrew Rate

2025-08-19

Suggested Activities

More basic information on plots in R

Graphical Excellence

Getting started

The components of a `ggplot` plot

Customising `ggplot2` features

1. Using xy-plot (`geom_point`) to explore ways of changing plot features

2. Resizing axis labels in `ggplot2`

3. Resizing axis labels, tick labels, and symbol sizes in `ggplot2`

4. Transforming plot axes in `ggplot2`

5. Plots in `ggplot2` which show grouping by factor levels

Plot symbols

6. Using custom colours in `ggplot2`

Colours in R

7. Adding a regression (“best fit”) line

8. Multiple-frame plots in `ggplot2`

Histogram with density line

Boxplot by groups

Scatterplot of water sample points by group

Plots of variable distributions

Depth profile scatter plots in `ggplot2`

Scatterplot matrices

Simple maps

Pretty palettes

References and R Packages

Material to support teaching in Environmental Science at The University of Western Australia

Units ENVT3361, ENVT4461, and ENVT5503

RStudio graphics and plotting

The ggplot version

Andrew Rate

2025-08-19

Suggested Activities

More basic information on plots in R

Graphical Excellence

Getting started

The components of a ggplot plot

Customising ggplot2 features

1. Using xy-plot (geom_point) to explore ways of changing plot features

2. Resizing axis labels in ggplot2

3. Resizing axis labels, tick labels, and symbol sizes in ggplot2

4. Transforming plot axes in ggplot2

5. Plots in ggplot2 which show grouping by factor levels

Plot symbols

6. Using custom colours in ggplot2

Colours in R

7. Adding a regression (“best fit”) line

8. Multiple-frame plots in ggplot2

Histogram with density line

Boxplot by groups

Scatterplot of water sample points by group

Plots of variable distributions

Depth profile scatter plots in ggplot2

Scatterplot matrices

Simple maps

Pretty palettes

References and R Packages

The components of a `ggplot` plot

Customising `ggplot2` features

1. Using xy-plot (`geom_point`) to explore ways of changing plot features

2. Resizing axis labels in `ggplot2`

3. Resizing axis labels, tick labels, and symbol sizes in `ggplot2`

4. Transforming plot axes in `ggplot2`

5. Plots in `ggplot2` which show grouping by factor levels

6. Using custom colours in `ggplot2`

8. Multiple-frame plots in `ggplot2`

Depth profile scatter plots in `ggplot2`