![]() ![]() It also lets you easily prototype your figures as you create them.Īnd that’s it! Remember that if you want to learn more about visualization, be sure to check out my complete course “Intro to data visualization in R (for ecologists)”. However, I find that this simple system using Quartz windows is the perfect intermediary between hard coding everything and total point-and-click exporting. Remember that from the Quartz window you still have to go to File > Save in order to export the plot. That’s not to say that there aren’t other (even fancier) ways to save plots directly from the R code. These are the tools I’ve always used for the majority of all my visualization work in R. So those are the basics of prototyping and saving your plots in R. Now if we go to our files and click on the plot that we saved, we can see it in PDF form. The disadvantages of doing this versus using the Quartz window is that you aren’t really able to visualize what your sizing might look like, and if you want to share reproducible code with someone, they won’t know what size to save the figure as. When you select either option, a window will pop up that will allow you to choose your figure height and width. You also have the option to export your figure from the R figure viewer pane, either as an image or as a PDF. png whenever you need a smaller file type. This also means that the file size ends up being pretty large, so you can just convert it to a. PDFs are actually one of the best file formats for figures because they have a virtually infinite resolution (try to keep zooming in on a figure you save as a PDF and you’ll see what I mean!). This will prompt you to save your figure as a. On a Windows computer, you might go to “File” or a similar menu tab. Now we have a plot that we’re happy with! To save the figure from the Quartz window, go to the “RStudio” menu tab and click “Save”. Since 1"x 1" was too small, let’s set our plot size at height = 4.5, width = 4.5. You can keep playing around with the window size until you find something that works for you. But also watch out if you make the figure too small, because you might receive an error about the figure margins being too large to fit the figure itself.įor example, if I set the window to be 1 inch by 1 inch and then try to run the plot code, the console says Error in plot.new() : figure margins too large When you’re assigning values to height and width, you should generally use values ranging from 1 to 10. This creates at best a very unappealing visual, and at worst a figure that is very hard to read or interpret in the first place. My biggest pet peeve is the common tendency of saving figures with a size that is way too big relative to the font and point size. For example, it’s quite clear that the smaller 4x4 figure looks a bit better, aesthetically speaking, than the 7x7. If you don’t specify a height or width, the default size for quartz() is height = 7 and width = 7, measured in inches as you can see in the image above, our h = 4 and w = 4 Quartz window is much smaller than the default behind it.īut notice that the font sizes and other graphical elements such as line widths or point sizes remain the same size! This is why it’s important to prototype the sizing. # Set a standard plot size quartz(h = 4, w = 4) If you run quartz(), it will open up a blank graphics device window like this one: You can do this using the quartz() function on a Mac. Then I begin prototyping the different sizing and aspect ratio of the figure by writing out the width and height right in the code until I find something that I like. So the general workflow that I use for creating figures is to first create something that looks more or less good in the viewer window. As a result, it can be hard to come up with figures that have consistent and correct sizing and proportions, especially if you’re making several figures that need to have consistent sizing. If you drag the size of that viewer, you can make the plot have whatever proportions you want. ![]() In other words, your figure is plotted in, and conforms to, the Viewer tab in R Studio. When we just create a plot like this in R Studio, the visual proportions of the plot aren’t set automatically. # Create the plot plot(weight ~ group, data = PlantGrowth, ![]()
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