R HELP CPLOT MULTINOM HOW TOWhy the small multiples technique is important (and how to use it) We'll cover more of those in future tutorials. R HELP CPLOT MULTINOM FULLThe above examples will allow you to create basic small multiples charts, but the full options in ggplot2 allow for many possible layouts. (note: again, we've also flipped the axis labels 90 degrees, for readability) We can also choose the number of columns in our layout using the ncol= parameter.įor example, we could modify our layout to have 6 charts per row ( ncol=6). R HELP CPLOT MULTINOM CODE(we've also added a few lines of "theme" code to add a title and perform some formatting.)įacet_wrap(~month) creates one bar chart for each month, laying them out in a grid, one-by-one, starting from 'Jan' (in the upper right hand corner) and then continuing in a 'ribbon' that wraps around until all of the charts are placed. The main difference is we've added facet_wrap(~month). The first few lines of code are essentially the same as the code for a simple bar chart: Ggplot(data=df.facet_data, aes(x=region,y=sales)) + We'll facet our dummy dataset on 'month.' What facet_wrap() does is it treats your multiple charts as a "ribbon." It lays them out one-after-another in a ribbon. Ggplot also allows you to wrap your small multiples charts using facet_wrap(). and lay out one small multiple chart on a separate row. with 1 small multiple chart for each region So we can read the line of code facet_grid(region ~. " character indicates that we want to make a 1-dimensional grid (i.e., no column facet in our grid layout). Inside of the facet_grid() call, the " ~" character means "by", and the ". ).įacet_grid() specifies that we want to create small multiples of our line chart, and lay them out in a row-by-column grid layout. Notice that the code is exactly the same as the code to create a basic line chart, but with the addition of one new line of code: facet_grid(region ~. Here, we're specifying that we want to make a line chart. Ggplot(data=df.facet_data, aes(x=month,y=sales, group=1)) + facet_grid()įacet_grid() lays out multiple charts in a row-by-column grid. To demonstrate, we'll look at facet_grid(). Granted, we could add color to differentiate the lines, but I'm not a big fan of that solution line charts get uglier the more lines you add (for a good example, see the line chart that Phil Simon criticized in a recent blog post).Ī better solution is to create small multiples. The chart only has 3 lines on it, yet it's difficult to read. Ggplot(data=df.facet_data, aes(x=df.facet_data$month,y=sales, group=region)) + You could start out by creating a line chart:ĭf.facet_data$month <- factor(df.facet_data$month, levels=month.abb) You have 12 months of sales data for 3 separate sales regions and you want to do some data exploration. To illustrate the faceting technique in R, let’s imagine the following scenario: In R’s ggplot2 package, we call the small multiples technique faceting. In contrast, R’s GGPlot2 package makes small multiples extraordinarily easy to create. In my mind, this is a vastly under-used technique, if for no other reason than the small multiples design is difficult to implement with most tools like Excel, SAS, and even Tableau. The small multiples technique is a powerful data visualization method for comparing across groups or comparing over time. For a wide range of problems in data presentation, small multiples are the best design solution.” Tufte as well notes in Envisioning Information:Īt the heart of quantitative reasoning is a single question: Compared to what? Small multiple designs, multivariate and data bountiful, answer directly by visually enforcing comparisons of changes, of the differences among objects, of the scope of alternatives. Small Multiples: Comparing data across groupsĪs Hadley Wickham points out in his book ggplot2, the small multiples technique facilitates comparison by creating the same chart for multiple subsets of your data. (This is also known as the “trellis chart,” “lattice chart”). This is where the “small multiple” design comes into play. In practice, this typically means starting at a high level with a single chart, and then “zooming into” the data by replicating that chart for specific subsets of the dataset.Īnd, even more valuable is being able to compare these multiple subsetted charts against each other. An important principle in analyzing data is “overview first, zoom and filter, then details on demand” (quote: Ben Shneiderman)
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