28.19 Sample Figure
REVIEW To include figures generated by R in our document we simply add plotting commands to the code chunk. Here for example is R code to generate a simple density plot of the 3pm temperature in 4 cities over a year. We use (Wickham et al. 2024) to generate the figure.
# Load packages from the local library into the R session.
library(rattle) # For the weatherAUS dataset.
library(ggplot2) # To generate a density plot.
# Identify cities of interest.
cities <- c("Canberra", "Darwin", "Melbourne", "Sydney")
# Generate the plot.
weatherAUS %>%
subset(Location %in% cities & ! is.na(Temp3pm)) %>%
ggplot(aes(x=Temp3pm, colour=Location, fill=Location)) +
geom_density(alpha=0.55)In the source document (the .Rnw file) the above R code is actually inserted between the chunk begin and end marks within the document itself. Those marks are:
<<myfigure, eval=FALSE>>=
... R code ...
@Notice the use of eval=FALSE, which allows the R
code to be included in the text of the final document, as it is above,
but will not yet generate the plot to be included in the figure. We
leave that for a little later.
The code chunk begins by attaching the requisite packages: (G. Williams 2024) to access the dataset; and (Wickham et al. 2024) for the function to generate the actual plot.
The four cities we wish to plot are then identified, and we generate a
BiocGenerics::subset() of the weatherAUS dataset containing just those
cities. We pass the subset on to ggplot2::ggplot() and identify
Temp3pm for the x-axis, using location to colour and fill the
plot. We add a layer to the figure containing a density plot with a
level of transparency specified as an alpha= value. We can see the
figure below.
<<myfigure, eval=FALSE>>=
... R code ...
@We include the figure in the final document as above simply by
removing the eval=FALSE from the previous code
chunk. Thus the R code is evaluated and a plot is generated. We
have actually replaced the eval=FALSE with
echo=FALSE so as not to print the R code a
second time.
We do not actually need to rewrite the R code again in a second chunk, given the code has already been provided in the first chunk on the previous page. We use a feature of knitr (Xie 2024) where an empty chunk having the same name as a previous chunk is actually a reference to that previous chunk. Thus in our source .Rnw text document we add the following two lines. This is effectively replaced by the R code from the previous block of the same name.
<<myfigure, echo=FALSE}@This is exactly what we included at the beginning of this section in
the actual source document for this page. Noticing that we have
replaced eval=FALSE with echo=FALSE, we cause the
original R code to be executed, generating the plot which is
included as the figure above. Using echo=FALSE simply
ensures we do not include the R code itself in the final output,
this time. That is, the R code is replaced with the figure it
generates.
Notice how the figure takes up quite a bit of space on the page.
References
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
