The aes() function enables you to create a set of mappings from data (in your dataframe) to the aesthetic attributes of the plot. The hardest thing to understand in ggplot2 is the aes() function. Keep in mind that there are dozens of geoms in the ggplot2 system, but all of them are essentially just types of shapes that we can draw in a data visualization. “Geoms” (aka, geometric objects) are the geometric objects that get drawn in the data visualization things like lines, bars, points, and tiles. It’s short for “geometric object.” Once you understand that “geoms” are actually “geometric objects,” they become easier to understand. You might be asking … “what the hell is a geom?”Ī geom is something you draw.
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In the example above, the second line of code has a geom, specifically geom_line. It enables you to specify the dataframe that contains the variables that you want to visualize. Having said that, the data parameter enables you to specify the dataframe that contains your data. ggplot2 almost exclusively operates on dataframes. More specifically, ggplot visualizes data that is contained inside of dataframes.
Remember that ggplot2 is essentially a tool for visualizing data in the R programming language. The data that you plot is specified by the data = parameter. The ggplot() function initiates plotting.īut what exactly you’re going to create is determined by the other parts of the syntax. When you use this function, you’re basically telling ggplot that you’re going to plot something. The ggplot() function is the core function of the ggplot2 data visualization system. There are 4 basic parts of a simple data visualization in ggplot2: the ggplot() function, the data parameter, the aes() function, and the geom specification. Let’s quickly break down the ggplot2 syntax to see how it works. That means that you should first have a good understanding of the ggplot2 syntax. To use facet_wrap and create small multiple charts, you first need to be able to create basic data visualizations with ggplot.
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Here, I’m going to quickly review the syntax of ggplot2, and then I’ll explain how to use facet_wrap. A quick review of ggplot2 syntaxĬreating small multiple charts is surprisingly easy in ggplot2, once you understand the syntax. With that in mind, let’s look at how to create this sort of small multiple plot in ggplot2. However, it’s rather easy to do in ggplot2 with facet_wrap. So ultimately, facet_wrap lays out the panels like a “ribbon” that wraps around (and downward) from one row to the next.Ĭreating this sort of small multiple chart is hard in most software. When it reaches the final column of the layout, facet_wrap “wraps” the panels downward to the next row. Each successive panel is placed to the right until it reaches the final column of the panel layout. The primary difference between facet_wrap and facet_grid is in how they lay out the panels of the small multiple chart.Įssentially, facet_wrap places the first panel in the upper right hand corner of the small multiple chart. Ggplot2 has a two primary techniques for creating small multiple charts: facet_wrap and facet_grid. facet_wrap “wraps” the panels like a ribbon Many other data visualization tools can’t create them at all.īut creating a small multiple chart is relatively easy in R’s ggplot2. Creating them in Excel is a bit of a pain in the a$$. Small multiple charts are often hard to create. “Small multiple.” That’s where the name comes from.īecause this design breaks the visualization into separate panels, it is sometimes called the “panel chart.” You might also hear it called a trellis chart. So there are multiple small versions of the same type of chart.
Each small map (one for every year) is broken out into a separate panel.Įach panel is a “small” version of the overall data visualization technique. In this example, the map of the United States has been re-created for every year. The small multiple chart is a chart where a data visualization is repeated in several small panels.įor example, here’s an example of a small multiple chart from the New York Times: facet_wrap creates small multiple charts in ggplot2 And finally, the tutorial will show you a few examples, so you can see how the technique works. After that, it will show you the syntax to create small multiple charts with facet_wrap. Having said that, this tutorial will explain exactly how to create small multiple charts with facet_wrap.įirst, the tutorial will quickly explain small multiple charts.
It makes it easy to create small multiple charts. The small multiple design is an incredibly powerful (and underused) data visualization technique.įacet_wrap is great, because it enables you to create small multiple charts easily and effectively. This tutorial will teach you how to use facet_wrap to create small multiple charts in ggplot2.
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