On the one hand, you want to highlight as many meaningful relationships and interesting findings as possible, but on the other you don’t want to include so many as to overwhelm your audience.Īs we will see, plots/graphics also help us to identify patterns and outliers in our data. This does however require a balancing act. The most important thing to know about graphics is that they should be created to make it obvious for your audience to understand the findings and insight you want to get across. We will use the ggplot2 package as it provides an easy way to customize your plots and is rooted in the data visualization theory known as The Grammar of Graphics (Wilkinson 2005).Īt the most basic level, graphics/plots/charts (we use these terms interchangeably in this book) provide a nice way for us to get a sense for how quantitative variables compare in terms of their center (where the values tend to be located) and their spread (how they vary around the center). By visualizing our data, we will be able to gain valuable insights from our data that we couldn’t initially see from just looking at the raw data in spreadsheet form. We begin the development of your data science toolbox with data visualization. 8.10 Theory-based inference for regression.8.9 Resampling-based inference for regression.8.8.1 Example: \(t\)-test for two independent samples.8.8 Building theory-based methods using computation.8.7.3 Sampling \(\rightarrow\) randomization.8.7.2 Comparing action and romance movies.8.6 Example: Revisiting the Lady Tasting Tea.8.4 Types of errors in hypothesis testing.7.4 Review of mosaic simulation functions.6.2.1 Correlation does not imply causation.
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