![]() Let’s work through an example of how to highlight data in ggplot2. Example: how to highlight data in a ggplot bar chart So for example, if you wanted to highlight data where price is greater than $25, you would use ggplot2::scale_color_manual() to set the highlight color for the different TRUE or FALSE values of the new indicator variable that you just created.įair enough. Next, you’ll need to use ggplot2::scale_fill_manual() or ggplot2::scale_color_manual() to modify the color of the data based on your new indicator variable. ![]() Use scale_fill_manual or scale_color_manual to set the highlight color ![]() If this doesn’t make sense, don’t worry … I’ll show you a concrete example in a minute. Essentially, you’re going to use dplyr::mutate() to create a TRUE/ FALSE indicator variable based on some condition or conditions.įor example, if you’re working with sales data and you want to highlight all of the observations where price is greater than $25, you would use mutate() to create a flag for observations where price > 25. To highlight data in a ggplot visualization, the first thing you need to do is create a new indicator variable. Use dplyr::mutate to create an indicator “flag” I’ll show you how to use these in your code in a moment, but first, I’ll briefly explain these tools and how they fit into our highlighting strategy. So to highlight data in a ggplot2 plot, you need two core tools: dplyr::mutate() and ggplot2::scale_fill_manual() (or something similar). Use color to highlight those specific items of interest, based on the “indicator” variable.Create an “indicator” variable that identifies the items of interest.There are a few ways to do this, but I want to show you how to do this in ggplot2. You can highlight particular elements of interest in a chart or graph. ![]() One technique you can use to communicate more clearly with your visualizations is highlighting data. You want people to look at your chart and immediately “get it.” How to highlight data in ggplot2 But you’ll also need to modify charts and graphs to communicate clearly and precisely. You need to be able to select the right chart to communicate a particular message. In practice, communicating or storytelling with data actually means using charts and graphs. One of the most important skills you’ll need as a data scientist (and a junior data scientist in particular) is telling stories with data.Ĭompanies, hiring managers, and recruiters not only need you to be able to find insights in data they also need you to be able to communicate with data. ![]()
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