Welcome to the Third Course in Data Chem Series: Data Visualization with ggplot2

This course assumes you are familiar with the functions and concepts in data visualization that we introduced in our first two courses. Those courses should have given you a pretty good idea about what good data visualization is and how to achieve it. Since this is an advanced course, we will dig a bit deeper into some more advanced ggplot2 functions, but before we get to that, we'll round out your knowledge with some niche topics.

We'll begin by exploring two kinds of specialized plots in Chapter one. The first type of specialized plot is suited for a data savvy audience and includes statistical plots like box plots and density plots. We will consider how to combine several variables in these plots. On the other hand, we have another kind of specialized plot that is suited for very specific data types, such as canary plots, networks, and diagnostic plots. This topic will continue into Chapter three when we consider two main classes of maps: coral plants and cartographic maps.

Finally, we'll see many concepts come together with the last type of specialized plot animations, which adds video frames as another mapping aesthetic. Depending on your area of expertise, you may find that you seldom have the need to use these specialized plot types but it's still useful to know what's possible within the ggplot2 framework.

In the fourth chapter, we'll get under the hood by digging into the internals of ggplot2 objects. For this, we'll begin by looking at the basics of the grid package on which ggplot2 is built. The next step is to begin manipulating graphical objects that we made with ggplot2 and look at more efficient ways of doing that with some built-in functions in ggplot2, such as ggplot(). In the last part, we'll make use of a useful accessory package called gridExtra.

In the fifth chapter, we'll bring our series on ggplot2 to a close with two case studies. The first case study will look at a feature that was introduced in ggplot2 release 2.0 making extensions well understand how to build the new or stats function from scratch. This will allow us to use ggplot2 to create exactly the statistics and visualizations we want in a more straightforward manner. In the second case study, we'll reproduce a classic plot by Edward Tufte using data familiar to everyone: the weather. Using ggplot2, we will create a unique plot drawing on what we've learned over all three courses. The capital off is to combine our Tufting weather plot with ggplot2 extensions and turn our unique plot type into a flexible efficient and reproducible plot type that we can use with any data set of the appropriate type.

Before we get into all of that, let's get started on some brief refresher exercises.

"WEBVTTKind: captionsLanguage: enwelcome to the third course in data chem series on data visualization with ggplot2 this course assumes you're familiar with the functions and concepts in data visualization that I introduced in my first two courses those courses should have given you a pretty good idea about what good data visualization is and how to achieve it since this is an advanced course we're going to dig a bit deeper into some more advanced ggplot2 functions but before we get to that we'll cap off the material we began in the first two courses by rounding out your knowledge with some niche topics we'll begin by exploring two kinds of specialized plots in Chapter one we'll consider the first type of specialized plots those suited for data savvy audience these are statistical plots that you wouldn't normally see in the popular press like box plots and density plots plus we'll consider how to combine several variables in Chapter two we'll move on to the second type of specialized plots those that are suited for very specific data types we'll begin by putting some fundamental concepts of working with large data sets into perspective and then we'll see some specific cases like cannery plots networks and diagnostic plots this topic will continue into Chapter three when we consider two main classes of maps coral plants and cartographic maps finally we'll see many concepts come together with the last type of specialized plot animations which adds video frames as another mapping aesthetic depending on your area of expertise you may find that you seldom have the need to use these specialized plant types but it's still useful to know what's possible within the ggplot2 framework in the fourth chapter we're going to get under the hood by digging into the internals of ggplot2 objects for this we'll begin by looking at the basics of the grid package on which ggplot2 is built the next step is to begin manipulating graphical objects that we made with ggplot2 and look at more efficient ways of doing that with some built-in functions in ggplot2 such as ggplot built in the last part we'll make use of a useful accessory package called grid extra in the fifth chapter we'll bring our series on ggplot2 to a close with two case studies and the first case study we'll look at a feature that was introduced in ggplot2 release 2.0 making extensions well understand how to build the new or stats function from scratch this will allow us to use ggplot2 to create exactly the statistics and visualizations we want in a more straightforward manner in the second case study we're going to reproduce a classic plot by Edward Tufte using data familiar to everyone the weather using ggplot2 will create a unique plot drawing on what we've learned over all three courses and the capital off we're going to combine our tufting weather plot with ggplot2 extensions and turn our unique plot type into a flexible efficient and reproducible plot type that we can use with any data set of the appropriate type all right before we get into all of that let's get started on some brief refresher exerciseswelcome to the third course in data chem series on data visualization with ggplot2 this course assumes you're familiar with the functions and concepts in data visualization that I introduced in my first two courses those courses should have given you a pretty good idea about what good data visualization is and how to achieve it since this is an advanced course we're going to dig a bit deeper into some more advanced ggplot2 functions but before we get to that we'll cap off the material we began in the first two courses by rounding out your knowledge with some niche topics we'll begin by exploring two kinds of specialized plots in Chapter one we'll consider the first type of specialized plots those suited for data savvy audience these are statistical plots that you wouldn't normally see in the popular press like box plots and density plots plus we'll consider how to combine several variables in Chapter two we'll move on to the second type of specialized plots those that are suited for very specific data types we'll begin by putting some fundamental concepts of working with large data sets into perspective and then we'll see some specific cases like cannery plots networks and diagnostic plots this topic will continue into Chapter three when we consider two main classes of maps coral plants and cartographic maps finally we'll see many concepts come together with the last type of specialized plot animations which adds video frames as another mapping aesthetic depending on your area of expertise you may find that you seldom have the need to use these specialized plant types but it's still useful to know what's possible within the ggplot2 framework in the fourth chapter we're going to get under the hood by digging into the internals of ggplot2 objects for this we'll begin by looking at the basics of the grid package on which ggplot2 is built the next step is to begin manipulating graphical objects that we made with ggplot2 and look at more efficient ways of doing that with some built-in functions in ggplot2 such as ggplot built in the last part we'll make use of a useful accessory package called grid extra in the fifth chapter we'll bring our series on ggplot2 to a close with two case studies and the first case study we'll look at a feature that was introduced in ggplot2 release 2.0 making extensions well understand how to build the new or stats function from scratch this will allow us to use ggplot2 to create exactly the statistics and visualizations we want in a more straightforward manner in the second case study we're going to reproduce a classic plot by Edward Tufte using data familiar to everyone the weather using ggplot2 will create a unique plot drawing on what we've learned over all three courses and the capital off we're going to combine our tufting weather plot with ggplot2 extensions and turn our unique plot type into a flexible efficient and reproducible plot type that we can use with any data set of the appropriate type all right before we get into all of that let's get started on some brief refresher exercises\n"