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

Units ENVT3361, ENVT4461, and ENVT5503

 

Suggested Activities

Using data previously provided (on LMS) and the ggplot2 R package, produce presentation quality graphs of:

 

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.

 

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.)

 

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 – you'll need to download it to your working directory first).

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)

We then make an xy-plot (Figure 1) using the geom_point() function from the ggplot2 package, making a custom palette with scale_color_viridis_d().

spgg <- ggplot(sv2017, aes(x=pH, y=Ca, col=Type, shape=Type)) +
  geom_point(size=2.2) +
  scale_y_log10() + 
  scale_color_viridis_d(end=0.55, 
                      option="inferno") +
  theme_bw()
spgg + geom_smooth(method="lm", se=F, linewidth=0.5)
Figure 1: 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.

Figure 1: 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.

 

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".

 

Histogram with density line

Adding a density line to a histogram like the one in Figure 2 can help us identify bimodal distributions, or see more easily if the distribution is symmetrical or not.

ggh <- ggplot(data=sv2017, mapping=aes(x=log10(EC))) +
  geom_histogram(fill="lightgray", color="black", linewidth=0.3,
                 binwidth = 0.1, boundary=0) + 
  theme_bw()
ggh + geom_density(col="blue3")
Figure 2: Histogram of EC from the sv2017 data with density line plot added.

Figure 2: 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 3 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")
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)
ggbx + geom_hline(yintercept = log10(65), col="red3",size=0.3,
                  linetype="dashed") + 
  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 3: Boxplot of Cu by Type created using `ggplot2`, showing mean values as blue plus (+) This version is drawn to resemble a `base-R` plot.

Figure 3: Boxplot of Cu by Type created using ggplot2, showing mean values as blue plus (+) This version is drawn to resemble a base-R plot. 

# 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 4: 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.

Figure 4: 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 5) 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 5: Map-style plot of water sampling points in Smith's Lake in 2018.

Figure 5: 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

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 some of the examples above already: e.g. Figures 1, 4, and 5.

First we read some more data to illustrate some ideas:

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

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.

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

ggplot(data=sv18, mapping=aes(x=pH, y=Ca)) +
    theme_bw() +
    geom_point()
Figure 6: Default ggplot of Ca *vs*. pH.

Figure 6: 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.

ggplot(data=sv18, mapping=aes(x=pH, y=Ca)) +
  geom_point() + 
  theme_bw() +
  theme(axis.title = element_text(size=18, colour="red2")) # text size in points
Figure 7: 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).

Figure 7: 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

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.

ggplot(data=sv18, 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 8: Plot of Ca *vs*. pH using `ggplot2`, showing how to resize axis labels (and symbol sizes).

Figure 8: 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 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=sv18, 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 9: Plot of Ca *vs*. pH using `ggplot2`, showing how to log10-transform an axis scale.

Figure 9: 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 10 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=sv18, 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 10: Plot of Ca *vs*. pH using `ggplot2`, showing how to separate points into different groups based on factor levels.

Figure 10: Plot of Ca vs. pH using ggplot2, showing how to separate points into different groups based on factor levels.

 

5. 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 11 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=sv18, 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 11: Plot of Ca *vs*. pH using `ggplot2`, showing how to separate points into different groups based on factor levels.

Figure 11: Plot of Ca vs. pH using ggplot2, showing how to separate points into different groups based on factor levels.

 

5. 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 12.

library(ggpubr)
scatter <- ggplot(data=sv18, mapping=aes(x=Al, y=Fe)) + geom_point()
boxes <- ggplot(data=sv18, mapping = aes(x=Type, y=Fe)) +   geom_boxplot()
histo <- ggplot(data=sv18, mapping=aes(x=Al)) + geom_histogram()
ggarrange(scatter, boxes, histo, nrow=1)
Figure 12: Example of multiple `ggplot2` plots per frame using `ggarrange()` from the `ggpubr` package.

Figure 12: Example of multiple ggplot2 plots per frame using ggarrange() from the ggpubr package.

 

Plots of variable distributions

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

ggplot(sv18, aes(pH)) + stat_ecdf(geom="step") + 
  labs(y="Proportion of samples with pH \u2264 x")
Figure 13: Cumulative distribution of pH in the sv18 dataset.

Figure 13: Cumulative distribution of pH in the sv18 dataset.

 

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

ggplot(data=sv18, aes(sample=pH)) + geom_qq() + geom_qq_line() + 
  labs(x="Theoretical normal distribution quantiles", y="pH") + theme_bw()
Figure 14: Normal quantile (QQ) plot of soil pH in the sv18 dataset.

Figure 14: Normal quantile (QQ) plot of soil pH in the sv18 dataset.

 

We can group q-q–plots by a factor:

ggplot(data=sv18, aes(sample=Fe, col=Type)) + geom_qq() + geom_qq_line() + 
  labs(x="Theoretical normal distribution quantiles", 
       y="pH") + theme_bw()
Figure 15: Normal quantile (QQ) plots of soil pH for different sample types in the sv18 dataset.

Figure 15: Normal quantile (QQ) plots of soil pH for different sample types in the sv18 dataset. 

# with(sv18, qqPlot(Na ~ Type, 
#        xlab="Theoretical normal distribution quantiles",
#        ylab="Soil pH"))

 

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

ggplot(data=sv18, 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(450), y = 10, label = "Sediment DGV\nZn 410 mg/kg", 
           hjust = 0, col="red3", size=3) +
  theme_classic()
## Warning: Removed 1 row containing non-finite outside the scale range (`stat_bin()`).
Figure 16: Histogram of Zn in the sv18 data with a vertical line representing an environmental threshold.

Figure 16: Histogram of Zn in the sv18 data with a vertical line representing an environmental threshold.

 

Depth profile scatter plots in ggplot2

In this plot 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, shape=Group to have colours and symbols differ by group
  • use of the scico 'lajolla' palette for nice soil-y colours!
  • scale_shape_manual(values=seq(0:12)) so we have enough plot symbols available
  • scale_y_reverse() so depth increases downwards
  • scale_x_continuous(position="top") gets the x-axis on the top
sv18$Depth.mean <- (sv18$Depth_upper+sv18$Depth_lower)/2
ggplot(data=sv18, mapping=aes(x=Fe, y=Depth.mean, col=Group, shape=Group)) + 
  labs(x="Fe (mg/kg)", y="Mean sample depth (cm)") +
  geom_point(stroke=1.2) +
  scale_color_scico_d(palette="lajolla", direction=-1, begin=0.2, end=0.8) +
  scale_shape_manual(values=seq(0:12)) +
  scale_y_reverse(limits=c(100,0)) +
  scale_x_continuous(position="top") + 
  theme_bw() + theme(axis.title = element_text(face="bold"))
## Warning: Removed 12 rows containing missing values or values outside the scale range
## (`geom_point()`).
Figure 17: Plots of Fe vs. depth measurements in the sv18 data.

Figure 17: Plots of Fe vs. depth measurements in the sv18 data.

 

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 18, 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(sv18, columns = c("Al","Ca","Fe","S")) +
  scale_x_log10() + scale_y_log10() + theme_bw()
Figure 18: 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 2018. Note the log10-transformed axes.

Figure 18: 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 2018. 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 19), 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 scico package, use col = c("#EDB256", "#AC4A44", "#191900") for the same result.]

ggpairs(sv18, columns = c("Al","Ca","Fe","S"), aes(color=Type, shape=Type)) +
  scale_color_viridis_d(option="plasma", begin=0.2, end=0.7) +
  scale_x_log10() + scale_y_log10() + theme_bw()
Figure 19: 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 2018 and grouped by sample type (sediment, soil, or street dust). Note the log10-transformed axes.

Figure 19: 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 2018 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")

 

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