R Tutorial: Visualizing summaries

Want to learn more? Take the full course at https://learn.datacamp.com/courses/visualizing-big-data-with-trelliscope-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I'm Ryan Hafen, and I'll be your instructor. As a data scientist, I love exploring datasets and finding new insights, particularly with large and complex datasets. In this chapter, you will learn methods for visualizing summaries of large datasets, looking at interactions between variables, and visualizing subsets in detail. The most natural place to begin exploring a large dataset is to find general high-level patterns by plotting distributions and summary statistics of each variable. Summarization reduces the data to a manageable size and can help you get a general understanding of the data before asking more detailed questions. For summaries of one variable, we will focus on three major types of variables - continuous, categorical, and temporal. We'll cover one popular scalable summary method for each variable type. Each of these methods involves greatly reducing the size of the data in a computationally scalable way. That makes it possible to use these methods on very large datasets! To illustrate the methods, we will use the gapminder dataset, which you may have seen in other DataCamp courses. This dataset provides indicators for 142 countries over 12 years. Let's start with the histogram. Histograms provide an easily interpretable way to visualize the distribution of a single continuous variable by splitting the range of the variable into bins and counting the observations that fall into each bin. You can create a histogram using ggplot2's `geom_histogram()` function. Here is a histogram of the Gapminder life expectancy variable. We see a distribution that looks like it has at least two modes. While the underlying dataset is small for this example, we could make a similar plot for a much larger dataset since the co