R Tutorial: Visualizing summaries

Key Takeaways

hi I'm Ryan - 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 will 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 data sets to illustrate the methods we will use the gut miner dataset which you may have seen in other data camp courses this dataset provides indicators for 142 countries over 12 years let's start with the histogram histograms provide an easily interpreted 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 sgm histogram function here is a histogram of the Gapminder life expectancy variable we see a distribution that looks like it has at least two peaks or modes while the underlying data set is small for this example we could make a similar plot for a much larger data set since the computation is simply counting how many observations fall into each interval to visualize a single discrete variable we can create a bar chart which counts the number of Records for each unique value of the variable a bar chart can be created using DG plot two's GM bar function here we count how many observations we have from each continent in the data a useful way to visualize a temporal variable is to bin by time and compute the number of observations or some other summary using deep layers group by and summarize functions followed by a plot of the summary here for example we compute the annual median gross domestic product across all countries for each year and then plot it with DG plot to s GM Line at this point you may have noticed a theme of binning a general scalable strategy for many summary visualizations of large data is to use deep layers group by and summarize functions in creative ways deep layer is very fast and even scalable when backed by larger databases speaking of larger datasets let's now look at another data set consisting of records of taxi ridership in New York City this data contains a random sample of 1 million yellow cab taxi rides from July to December of 2016 for each taxi ride data such as the pickup date trip duration and information about the cab fare are available we have chosen a subset of variables as well as a random sample of records for the sake of keeping the data size manageable so that you can receive immediate feedback in the interactive exercises that follow the same code use in this chapter can be applied to the full data set on your own computer let's put what we have learned to work by visualizing summaries of these 1 million