We always start by loading up and looking at the dataset we want to analyze and visualize. We will use the famous mtcar s dataset available as one of the pre-loaded datasets in plotnine. The mtcars dataset consists of data that was extracted from the Motor Trend US magazine, and depicts fuel consumption and 10 other attributes of automobile design and performance for 32 automobiles —74 models. The details of each attribute are depicted in the figure above.
We can now visualize data up to two dimensions using some of the components from our layered grammar of graphics framework including data, scale, aesthetics and geoms. We choose a dot or scatter plot in this case for our geometric object to represent each data point. We can clearly see from the above visualization that mpg has a negative correlation with the cat wt. To visualize three dimensions from our dataset, we can leverage color as one of our aesthetic components to visualize one additional dimension besides our other two dimensions as depicted in the following example.
In the above visualization, we depict the cars with different number of gears as separate categories using the color aesthetic along with our other two data dimensions variables. It is quite clear that cars with a smaller number of gears on average tend to have higher wt and lower mpg. To visualize four dimensions from our dataset, we can leverage color as well as size as two of our aesthetics besides other regular components including geoms, data and scale. The visualization shows us how powerful aesthetics can be in helping us visualize multiple data dimensions in a single plot. It is quite clear that cars with higher number of cyl cylinders have lower number of gears and in turn, their wt is higher and mpg lower.
Alternatively, we can also use color and facets to depict data in four dimensions instead of size as depicted in the following example. Facets are definitely one of the most powerful components towards building an effective data visualization as shown in the visualization above, where we can clearly see cars with higher cyl count have lower gear count and similar trends as the previous visualization with color and size. To visualize data dimensions and some relevant statistics like fitting a linear model , we can leverage statistics along with the other components in our layered grammar.
ggplot2 - the grammar of graphics
This enables us to see linear model trends for mpg based on wt due to the statistics component. To visualize data in five dimensions, you know the drill by now! We will leverage the power of aesthetics including color, size and facets. Here we use am as the facet where 0 indicates cars with automatic transmission and 1 indicates cars with manual transmission. The plot shows that cars with manual transmission have higher number of gears as compared to cars with automatic transmission. Also, majority of cars with a higher number of cylinders cyl have automatic transmission.
Other insights are similar to what we have observed in the previous plots. To visualize data in six dimensions, we can add in an additional facet on the y-axis along with a facet on the x-axis , and color and size as aesthetics. We represent transmission am as 0 automatic and 1 manual as a facet on the y-axis and number of carburetors carb as a facet on the x-axis besides the other dimensions represented using other aesthetics similar to our previous plots.
An interesting insight from the above visualization is that cars with higher number of gears have manual transmission am and higher number of carburetors carb. Do you notice any other interesting insights? The pressing question is, can we go higher than six dimensions? Well, it definitely becomes more and more difficult to hack our way around the limitations of a two-dimensional rendering device to visualize more data dimensions.
One method is to use more facets and subplots. Besides this, you can also use the notion of time if your dataset has a temporal aspect as depicted in the following example.
This should give you a good perspective on how to leverage the layered grammar of graphics to visualize multi-dimensional data. Like I have mentioned time and again, data visualization is an art as well as a science. This article should give you enough motivation and examples to get started with understanding and leveraging a layered grammar of graphics framework towards building effective visualizations of your own on multi-dimensional data.
All the code used in this article is available as a Jupyter notebook along with other content and slides in my GitHub repository. I do cover how to visualize multi-dimensional data using state-of-the-art data visualization frameworks in Python like seaborn and matplotlib on slightly more complex data. The following article should help you get started on the same if you are interested.
A major portion of these articles was covered in one of my recent conference talks in ODSC, You can check out the full talk agenda and slides here. Have feedback for me? Or interested in working with me on research, data science, artificial intelligence or even publishing an article on TDS? You can reach out to me on LinkedIn.
Thanks to Durba for editing this article. Sign in. Get started. Learn effective strategies for leveraging a layered Grammar of Graphics framework for effective data visualization. Dipanjan DJ Sarkar Follow. Introduction Visualizing multi-dimensional data is an art as well as a science. Motivation Data visualization and storytelling has always been one of the most important phases of any data science pipeline involving extracting meaningful insights from data, regardless of the complexity of the data or the project.
Understanding the Grammar of Graphics To understand the Grammar of Graphics, we would need to understand what do we mean by Grammar. A layered grammar of graphics. A grammar of graphics is a tool that enables us to concisely describe the components of a graphic. Such a grammar…. Data : Always start with the data, identify the dimensions you want to visualize. Aesthetics : Confirm the axes based on the data dimensions, positions of various data points in the plot. Also check if any form of encoding is needed including size, shape, color and so on which are useful for plotting multiple data dimensions.
A coordinate system, coord for short, describes how data coordinates are mapped to the plane of the graphic. It also provides axes and gridlines to make it possible to read the graph. We normally use a Cartesian coordinate system, but a number of others are available, including polar coordinates and map projections. A facet ing specification describes how to break up the data into subsets and how to display those subsets as small multiples. A theme which controls the finer points of display, like the font size and background colour.
While the defaults in ggplot2 have been chosen with care, you may need to consult other references to create an attractive plot.
While this book endeavours to promote a sensible process for producing plots of data, the focus of the book is on how to produce the plots you want, not knowing what plots to produce. For more advice on this topic, you may want to consult Robbins , Cleveland b , Chambers et al. It does not describe interactivity: the grammar of graphics describes only static graphics and there is essentially no benefit to displaying them on a computer screen as opposed to a piece of paper. Cook and Swayne provides an excellent introduction to the interactive graphics package GGobi.
GGobi can be connected to R with the rggobi package Wickham et al. How does ggplot2 differ from them? Base graphics were written by Ross Ihaka based on experience implementing the S graphics driver and partly looking at Chambers et al. Base graphics has a pen on paper model: you can only draw on top of the plot, you cannot modify or delete existing content. There is no user accessible representation of the graphics, apart from their appearance on the screen.
Base graphics includes both tools for drawing primitives and entire plots. Base graphics functions are generally fast, but have limited scope. Grid grobs graphical objects can be represented independently of the plot and modified later. A system of viewports each containing its own coordinate system makes it easier to lay out complex graphics.
Grid provides drawing primitives, but no tools for producing statistical graphics.
- The Grammar of Graphics.
- Statistics for the Behavioral Sciences (2nd Edition)?
- Masterclass: Learning, Teaching.
- Introduction to the grammar of graphics: ST/ Topics in Data Visualization.
- Top Authors?
The lattice package, developed by Deepayan Sarkar, uses grid graphics to implement the trellis graphics system of Cleveland b and is a considerable improvement over base graphics. You can easily produce conditioned plots and some plotting details e. However, lattice graphics lacks a formal model, which can make it hard to extend. Lattice graphics are explained in depth in Sarkar The solid underlying model of ggplot2 makes it easy to describe a wide range of graphics with a compact syntax, and independent components make extension easy.
Like lattice, ggplot2 uses grid to draw the graphics, which means you can exercise much low-level control over the appearance of the plot.
Work on ggvis, the successor to ggplot2, started in It takes the foundational ideas of ggplot2 but extends them to the web and interactive graphics. However, ggvis is work in progress and currently can create only a fraction of the plots in ggplot2 can. Stay tuned for updates! The first chapter, Chapter 2 , describes how to quickly get started using ggplot2 to make useful graphics. This chapter introduces several important ggplot2 concepts: geoms, aesthetic mappings and facetting.
Chapters 4 to 8 dive into more details, giving you a toolbox designed to solve a wide range of problems.
The Grammar of Graphics | Leland Wilkinson | Springer
Chapter 10 describes the layered grammar of graphics which underlies ggplot2. The theory is illustrated in Chapter 11 which demonstrates how to add additional layers to your plot, exercising full control over the geoms and stats used within them. Understanding how scales work is crucial for fine-tuning the perceptual properties of your plot. Customising scales gives fine control over the exact appearance of the plot and helps to support the story that you are telling.
Chapter 12 will show you what scales are available, how to adjust their parameters, and how to control the appearance of axes and legends. Coordinate systems and facetting control the position of elements of the plot. These are described in Chapter Facetting is a very powerful graphical tool as it allows you to rapidly compare different subsets of your data. Different coordinate systems are less commonly needed, but are very important for certain types of data. To polish your plots for publication, you will need to learn about the tools described in Chapter There you will learn about how to control the theming system of ggplot2 and how to save plots to disk.
Related The Grammar of Graphics (Statistics and Computing)
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