Learning R for Data Visualization
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Learning R for Data Visualization

Get to grips with R’s most popular packages and functions to create interactive visualizations for the web
4.3 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
60 students enrolled
Created by Packt Publishing
Last updated 5/2016
English
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Current price: $10 Original price: $75 Discount: 87% off
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Includes:
  • 2 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
Requirements
  • More advanced viewers who already have a good understanding of scientific plots will benefit from a practical introduction to the statistical programming language R.
  • This course presents the theoretical and technical aspects necessary to master the art of scientific plotting in R in a colloquial manner. Unlike many other guides, this one is heavily practical, featuring bite-sized chunks of information as well detailed explanations and real results.
Description

R is on the rise and showing itself as a powerful option in many software development domains. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis, creating high-level graphics, and machine learning. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way.

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.

About The Author

Dr. Fabio Veronesi obtained a PhD in digital soil mapping from Cranfield University and then moved to Zurich, where he has been working for the past three years as a postdoc at ETH. There, he is working on Geoinformation topics, ranging from the application of mathematical techniques to the improvement of shaded relief representations to the use of machine learning to increase the accuracy of wind speed maps.

During his PhD, he needed to learn a programming language, because commercial applications did not provide the ideal platforms to pursue his research work. Since R has a series of packages created specifically for the application of statistical techniques to soil science, he decided to teach himself this powerful language. Since then, he has been using R every day for his work.

Who is the target audience?
  • 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.
Students Who Viewed This Course Also Viewed
Curriculum For This Course
31 Lectures
01:58:53
+
Introducing Scientific Plotting in R
6 Lectures 24:39

This video provides an overview of the entire course.

Preview 05:31

Creating professional looking plots, both static and interactive, may seem hard; however, with R we can create and fully customize plots with a few lines of code. 

Preview of R Plotting Functionalities
03:15

Often, beginners fail to properly understand their dataset before analyzing it. However, a good understanding of the origin and structure of the data is of primary importance. 

Introducing the Dataset
03:21

It is not always good to import data in R using the default settings. For doing it successfully, several parameters need to be set. 

Loading Tables and CSV Files
04:41

Importing Excel tables in R may be tricky. However, with the right explanation the proper package can be installed and everything should work out fine. 

Loading Excel Files
03:33

Exporting data in R may seem difficult, since we have many options to choose from. However, R has powerful exporting functions that with few options can do the job successfully. 

Exporting Data
04:18
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Scientific Plotting in ggplot2
6 Lectures 21:26

Producing elegant plots in ggplot2 may seem difficult but it is actually quite easy to do. In fact, ggplot2 takes care, by default, of most of the graphical design of the plot, meaning that we can produce beautiful histograms with just a few lines of code. 

Preview 05:01

Histograms are useful for certain tasks, but for comparing several variables at once they are not the best. Box plots can be used instead, since they allow the comparison of the distribution of multiple variables side by side. 

The Importance of Box Plots
03:44

Categorical variables are invariably difficult to visualize in meaningful ways. Bar charts are important for plotting categorical variables and defining their characteristics. 

Plotting Bar Charts
02:43

In many cases, we are interested in comparing multiple variables at once and checking their correlation. Scatterplots allow us to do just that and are an important tool in a data analyst's toolbox. 

Plotting Multiple Variables – Scatterplots
03:06

In many cases, the variable time is underestimated. However, time-series are extremely useful to determine the temporal pattern of a variable. 

Dealing with Time – Time-series Plots
02:38

Many datasets are affected by uncertainty and people not always know how to show this in plots. This video will present ways to solve this and take uncertainty into account. 

Handling Uncertainty
04:14
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Customizing Plots
6 Lectures 22:14

By default, ggplot2 creates plots with a grayish background, and without axes lines and white gridlines. This is not the standard look you normally find in scientific manuscripts. 

Preview 03:06

The default color scale is not always appropriate to spot all the differences in the data we are trying to plot. In many cases, we have to change it so that our plots can become more informative. 

Changing Colors
03:19

ggplot2 uses the names of the columns as labels, meaning that if these are not self-explanatory, the plot will not provide a good framework to understand its meaning. By adding some lines of code, we can customize the plot in order to change the labels and make it clearer. 

Modifying Axis and Labels
02:40

The default plots created by ggplot2 lack several elements that in many cases are useful to provide additional information to viewers. However, there are simple functions that can be used to add supplementary elements to the plot. 

Adding Supplementary Elements
04:08

In many cases, it is crucial to be able to include textual labels on plots to provide viewers with additional information. This can be done in ggplot2 in both static and dynamic ways. 

Adding Text Inside and Outside of the Plot
05:02

With the function facet_wrap, it is only possible to create a grid of plots of the same type. However, in some cases, it is necessary to create side-by-side graphs with diverse plots. This can be done in the package gridExtra. 

Multi-plots
03:59
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Exporting Plots
2 Lectures 05:56

We could easily save our plots as images directly from R Studio. This way of saving however, does not provide much flexibility. If we want to customize our images, we need to learn how to export plots from the R code. 

Preview 03:24

The default size that ggplot2 uses to save plots is ideal for most of our needs, such as embedding plots in Word documents. However, in some cases, we may need to specify a particular page size for our plots, which can be easily done with the option paper. 

Adjusting the Page Size
02:32
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Interactive Plots in rCharts
5 Lectures 17:36

Static plots are the standard for publishing in traditional media, such as journal papers. However, the world is moving towards an internet-based presentation of results and even scientific journals are quickly adapting it. Many now offer the possibility of including interactive plots. In R, we can create plots for the Web with the rCharts package, which is a bit more difficult to install than ggplot2. 

Preview 02:44

rCharts features a syntax more similar to standard plotting in R than what we saw with ggplot2. However, it is easy to pick up by showing simple examples and then including additional details. 

Creating Interactive Histograms and Box Plots
04:55

Even though we know nothing about HTML and CSS, we can still obtain beautiful bar-charts using templates created by other users. 

Plotting Interactive Bar Charts
03:12

If too many data points are present in our dataset, scatterplot visualization may become very confusing in static plots. However, in interactive plots this limitation no longer applies, since we can select to visualize only some datasets. 

Creating Interactive Scatterplots
02:58

Time-series plots are a great way to visualize the temporal pattern of a variable. However, sometimes we cannot fully understand the exact date of each point based only on the values on the x axis. Interactive visualization can solve this problem by adding tooltips in which we can take a look at the raw data. 

Developing Interactive Time-series Plots
03:47
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Creating a Website with Shiny
6 Lectures 27:02

Shiny is a package to build fully featured websites from scratch in R. The way it communicates between the user interface and the server may seem difficult to understand. However, with some explanation, understanding Shiny becomes very easy and intuitive. 

Preview 04:09

Understanding the structure of a Shiny website is very important. However, presenting it from a website is not enough for the viewers to replicate it. Therefore, in this video, we are going to create a simple website with data and plots we already used, to further help viewers. 

Creating a Simple Website
04:52

If we plan to upload our Shiny website on-line, we need to implement a way for users to upload their own data. In this video, we are going to show how to do just that. 

File Input
03:09

One of the key components of a successful website is the ability to respond to users’ interactions. This can be achieved with conditional panels, which change the UI based on users’ interactions. 

Conditional Panels – UI
03:44

One of the key components of a successful website is the ability to respond to users’ interactions. This can be achieved with conditional panels, which change the UI based on users’ interactions. 

Conditional Panels – Servers
05:31

So far, we have looked at ways to create and add elements to a Shiny website. However, sooner or later, this website needs to be deployed on the Internet so that everybody can use it. Here, you will learn how to do it using a free account on shinyapps.io. 

Deploying the Site
05:37
About the Instructor
Packt Publishing
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With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

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