• R Plot Parabola
• R Plot Par Margins
• R Plot Par

R also allows combining multiple graphs into a single image for our viewing convenience using the par function. We only need to set the space before calling the plot function in our graph. We only need to set the space before calling the plot function in our graph. The most used plotting function in R programming is the plot function. It is a generic function, meaning, it has many methods which are called according to the type of object passed to plot. In the simplest case, we can pass in a vector and we will get a scatter plot of magnitude vs index. But generally, we pass in two vectors and a scatter plot of these points are plotted. Welcome the R graph gallery, a collection of charts made with the R programming language. Hundreds of charts are displayed in several sections, always with their reproducible code available. The gallery makes a focus on the tidyverse and ggplot2. Feel free to suggest a. Par allows us to customize the graphical parameters (title, axis, font, color, size) for a particular session. For combining multiple plots, we can use the graphical parameters mfrow and mfcol. These two parameters create a matrix of plots filled by rows and columns respectively. Let us combine plots using both the above parameters. Graphical Parameters. The value of adj determines the way in which text strings are justified in text, mtext and title.A value of 0 produces left-justified text, 0.5 (the default) centered text and 1 right-justified text. (Any value in (0, 1) is allowed, and on most devices values outside that interval will also work.).

Plotting is different to the other types of things you do with R –even when done as “nicely” as possible it might require many lines ofcode. This is because making really nice figures is something thatrequires some finesse as you move between science and art. Goodfigures carry a lot of impact, and to deliver this, you often have tothink carefully about each line, about relative sizes of everything,about the spacing of the figures, amongst other things.

Before going into how to do everything in R, it is worth consideringif you should use R for everything. Sometimes a graphics editingprogram (like Illustrator or Inkscape) will befaster, especially for adding pictures, making maps, or doingcomplicated graphics composed of many pieces. Fundamentally, R justgives you a blank device as a canvas, so anything is possible, but itmay just be very awkward or time consuming. If you do persevere, thebenefits include:

• Reproducibility – can go from raw data to final figure.
• Helps you rework figure after review.
• Helps you rework figure for different talk (colour scheme, etc).
• Eventually faster, and time spent learning pays back later.
• Build up a library of tricks.

A common workflow for making figures is to

1. Plot a bunch of stuff to screen
2. Resize the device until it looks about right
3. Save the output (File: Save, or similar)

There are several problems with this approach:

• The figure dimensions are only appoximate
• The figure is not very reproducible, because slight changes in sizechange the appearance
• Resizing the plot to get the right dimensions can be awkward
• It’s hard to generate many similar figures for a talk, or togenerate different format output from the same piece of code.

We don’t encourage this approach.

Instead, we suggest this workflow:

1. Open an appropriate plotting device (e.g., with pdf – seebelow)
2. Plot a bunch of stuff, which goes to the device
3. Close the device

In practice, you’ll tweak the figure (step 2) on the screen untilyou’re happy with it first.

What type of device?

There are two basic types of graphic – “vector graphics”, wherethe logical units are lines and shapes, and “bitmap graphics”(also called raster graphics, not to be confused withrasta graphics.

To see the difference: here are screenshots of part of a figure zoomedin about 4 times. The first image is taken from a pdf, with crisplines, and the second is a png showing the underlying matrix of pixelsthat make up the image:

If you’re making plots for a paper, or for a talk, you almostcertainly want vector graphics. These render nicely at all scales -you can zoom in and lines and text stay crisp (some journals still askfor high resolution bitmaps at the final stage of processing –sometimes a “tiff” file). For vector graphics, the PDF device isgreat for most things, and pdf files incorporate nicely into mostdocument formats now.

If you’re making plots for the web, you probably want raster graphics,as there are few widely supported formats for vector graphics (svg isgetting better support). For raster graphics,

In general, you want to make figures in png(Portable Network Graphics) format. Don’t make figures in jpegformat.

Here is a zoom of the same region of a plot as the figures above,showing “jpeg artefacts”:

This is because jpeg is a lossy compression algorithm; it’sdesigned to work well with images that are like photos, and doesspectacularly badly with line art. Jpegs are great forcat pictures but not good atall for plots. Unfortunately, many journals will still butcher yourbeautiful vector figures by making them into jpegs at some point intheir production workflow, so be vigilant.

Using the workflow

To make a PDF figure, you open the pdf device, add some plottingcommands, and close the device:

The width/height arguments are in inches (you can use the functioncm to get dimensions in centimeters, e.g., cm(10) returns thenumber of inches required for a 10 cm plot), and at that size thetext will be in 12 point type. You can tweak that with thepointsize arguments. This makes it really simple to work out whatthe plot will look like on a page (e.g., in a journal). When you runthese commands, you will end up with a file called my_plot.pdf inthe current directory. If you’re using a project layout likewe suggested in this post,then the filename might be figs/my_plot.pdf to put the figures intothe figs directory.

As the number of lines of code between pdf and dev.off grows, thisgets tedious. We find that it works well to make a function perfigure:

and then use it like this:

There are a number of advantages and extensions to this approach thatwe discuss in this post.

There are a lot of things that can be tweaked about figures using thepar command. The help page is very terse, but worth looking throughto get an idea of what can be tweaked. Obviously, we can’t deal withall possible parameters, as it’s a huge set.

Margins

R Plot Parabola

There are two ways of specifying margins; by physical size (in inchesagain) and in lines of text. Because you’re mostly going to beputting text into the margins, it is much easier to scale margins bythe text size. The commands par('mai') and par('mar')return the size of the margins in inches and lines of textrespectively. By default,

R Plot Par Margins

This vector is refers to the edges of the plot from the bottom (firstelement) clockwise (i.e., bottom, left, top, right). You canvisualise the lines of text this way:

There are other relevant dimension attributes for multipanel plots:

In class, we’ll work through a number of tricks to convert thisfigure:

into something nicer:

R Plot Par

(both of these actually look substantially nicer in somethingscalable, so here is a svg output for people with browsers who canrender them)

Code that steps the development process is available asa gist.

Text layout

You can easily tweak the alignment of text, using the adj parameter(many graphical functions involving text accept this)

Scientific notation in axes

This is tricky, but with trial and error usually gives reasonableresults. See demo(plotmath) for a reasonably comprehensive tour ofthe feature.

• Paul Murrell has written a great book on R Graphics, and also provides code from the book online here.
• You may also like to check out some of Paul’s talks, such as this one.
• Here is another intro to graphing in R, by Christoph Scherber.