Learning R for Data Visualization
Publisher: Packt Publishing
Final Release Date: April 2016
Run time: 1 hour 58 minutes

Get to grips with R’s most popular packages and functions to create interactive visualizations for the web

The course is structured in simple lessons so that the learning process feels like a step-by-step guide to plotting. We start by importing data in R from popular formats such as CSV and Excel tables. Then you will learn how to create basic plots such as histograms, scatterplots, and more, with the default options, which guarantees stunning results.

The second part of the course is dedicated to interactive plots. Static plots, in fact, are extremely important for scientific manuscripts, but nowadays most of our work is done online on websites and blogs, where static plots do not harness the full potential of the technology. Interactive plots, on the other hand, can improve that and allow us to present our results in more appealing and informative ways, by using the native language of the web. Do not worry though, you will not need to learn an additional programming language because this course will show you how to create stunning web plots directly from R.

In the final part of the course, the Shiny package will be extensively discussed. This allows you to create fully-featured web pages directly from the R console, and Shiny also allows it to be uploaded to a live website where your peers and colleagues can browse it and you can share your work. You will see how to build a complete website to import and plot data, plus we will present a method to upload it for everybody to use. Finally, you will revise all the concepts you've learned while having some fun creating a complete website.

By the end of the course, you will have an armour full of different visualization techniques, with the capacity to apply these abilities to real-world data sets.

Who this course is for

This course is primarily intended for researchers and data analysts at every stage of their career. It covers some theoretical aspects of scientific plotting, which makes it ideal for undergraduates to improve on the skills they learned at college or university. More advanced viewers who already have a good understanding of scientific plots will benefit from a practical introduction to the statistical programming language R.

What you will learn from this course

  • See how to plot a distribution with histograms and box-plot
  • Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2
  • Enhance the user experience using dynamic visualisation
  • Save your work for publication, in tiff, at a good resolution
  • Test your coding limits by creating stunning interactive plots for the web
  • Create a fully-featured website using Shiny with real-time features such as adding and controlling functionalities
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