![]() You’ll learn how to add labels for multiple stacks later, but let’s start with the basics. The reason is simple - ggplot2 uses stacked bar charts by default, and there are two products in the stack for each quarter. For the first example, you’ll need to filter the dataset so only product A is shown. You’ll learn how to put labels on top of bars. It also makes it more user-friendly, as you don’t have to divert your view to the y-axis constantly. That solves the problem of reading values from the chart. You can put text somewhere near the top of each bar to show the exact value. If the y-axis is on a scale of millions, reading values from a chart becomes an approximation (at best). Knowing the exact value is often a requirement. You can create a simple bar chart with this code: ggplot(data, aes(x = quarter, y = profit)) + geom_col()īar charts can be hard to look at. With the first option, you need to specify stat = "identity" for it to work, so the ladder is used throughout the article. The geom_bar and geom_col layers are used to create bar charts. That’s declared in the first layer (data), and the second layer (visualization) specifies which type of visualization you want. To start, you’ll make a bar chart that has the column quarter on the x-axis and profit on the y-axis. You’ll see later how additional layers can make charts more informative and appealing. These two are mandatory for any visualization. For example, you first declare a data layer and then a visualization layer. ![]() R’s standard library for data visualization is ggplot2. The reasoning is simple - you’re here to learn how to make bar charts, not how to aggregate data. There are plenty of datasets built into R and thousands of others available online.
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