R: Data Visualization with R - A Complete Guide!: 3-in-1
4.3 (12 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
107 students enrolled

R: Data Visualization with R - A Complete Guide!: 3-in-1

Advanced visualization techniques in R to visualize 2D and 3D interactive plots!
4.3 (12 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
107 students enrolled
Created by Packt Publishing
Last updated 10/2018
English
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 7.5 hours on-demand video
  • 1 downloadable resource
  • 1 coding exercise
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2.
  • Create a fully-featured website using Shiny with real-time features such as adding and controlling functionalities.
  • Create simple and quick visualizations using the basic graphics tools in R.
  • Introduce users to basic R functions and data manipulation techniques while creating meaningful visualizations.
  • Add elements, text, animation, and colors to your plot to make sense of data.
  • Perform predictive modeling and create animated applications.
Requirements
  • Prior basic understanding of R programming is expected.
Description

Effective visualization helps you get better insights from your data, make better and more informed business decisions! R is one of the most widely used open source languages for data and graph analysis. It is platform-independent and allows users to load various packages as well as develop their own packages to interpret data better. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way. So, if you're a data science professional and want to learn about the powerful data visualization techniques of R, then go for this Learning Path.

This comprehensive 3-in-1 course follows a practical approach, where each recipe presents unique functions of plots, charts, and maps as well as visualization of 2D and 3D interactive plots in a step-by-step manner! You’ll begin with generating various plots in R using the basic R plotting techniques. Utilize R packages to add context and meaning to your data. Finally, you'll design interactive visualizations and integrate them on your website or blog!

By the end of the course, you’ll master the visualization capabilities of R to build interactive graphs, plots, and Pie charts as well as visualize 2D and 3D interactive plots.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learning R for Data Visualization, covers getting to grips with R’s most popular packages and functions to create interactive visualizations for the web. 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. 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 armor full of different visualization techniques, with the capacity to apply these abilities to real-world data sets.

The second course, R Data Visualization - Basic Plots, Maps, and Pie Charts, covers mastering the visualization capabilities of R to build interactive graphs, plots, and Pie. We start - off with the basics of R plots and an introduction to heat maps and customizing them. After this, we gradually take you through creating interactive maps using the googleVis package. Finally, we generate choropleth maps and contouring maps, bubble plots, and pie charts.

The third course, R Data Visualization - Word Clouds and 3D Plots, covers advanced visualization techniques in R to build word clouds, 3D plots, and more. We start off with the basics of R plots and an introduction to heat maps and customizing them. After this, we gradually take you through creating interactive maps using the googleVis package. Finally, we generate choropleth maps and contouring maps, bubble plots, and pie charts.

By the end of the course, you’ll master the visualization capabilities of R to build interactive graphs, plots, and Pie charts as well as visualize 2D and 3D interactive plots.

About the Authors

  • Fabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography, and shaded relief, renewable energy and transmission line siting. During this time Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data.


  • Atmajit Singh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog. He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a Master of Arts degree in economics from the University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time.

Who this course is for:
  • Data Analysts, Data Scientists or Data Journalist, who wants to learn about Data Visualization and represent complex sets of data in an impressive way.
Course content
Expand all 104 lectures 07:28:26
+ Learning R for Data Visualization
31 lectures 01:58:53

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.

   •  Use ggplot2 that can produce beautiful plots with a few lines of code

   •  Plots are fully customizable; to obtain perfect plots every time, use customizable plots.

   •  Create complex results using interactive plots

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.

   •  Introduce the data provider, that is, EPA.

   •  Understand the EPA network of stations

   •  A detailed description of the data structure

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.

   •  Set the working directory

   •  Understand the important setting of the function read.table

   •  Import the data and check the structure

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.

   •  Install the package xlsx

   •  Understand the format of the code to import Excel files

   •  Import and check the data

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.

   •  Firstly, we need to subset our data to have something to export

   •  Then, we can learn how to export data in R

   •  The final step would be exporting data into multiple Excel sheets

Exporting Data
04:18

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.

   •  Load ggplot2 and then import the dataset

   •  Plot a simple histogram, using the default settings.

   •  Plot multiple distributions with faceting

Creating Histograms
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.

   •  Explain what a box plot is and what does it represents

   •  Create multiple box plots with just two lines of code

   •  Order the plot to achieve better results

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.

   •  Learn bar charts

   •  Create simple bar charts in ggplot2

   •  Learn how to automatically order a data.frame and plot ordered bar charts

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.

   •  Describe the importance of scatterplots

   •  Create simple scatterplots in ggplot2

   •  Create more complex visualization by tweaking some basic options

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.

   •  Understand the structure of time-series plots

   •  Plot a simple time-series plot in ggplot2

   •  Customize the plots with color and size

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.

   •  Understand how to handle uncertainty

   •  Present simple ways to include uncertainty in bar-charts

   •  Present the scatterplots with double error bars

Handling Uncertainty
04:14

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.

   •  Explain the graphical elements of the standard theme

   •  Change the default theme

   •  Explore the differences between the default theme and the others

Changing Theme
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.

   •  Change the default two colors for plotting continuous variables

   •  Explore ways to include more colors in the color scale

   •  Present discrete color scale for categorical variables

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.

   •  Add a title for the plot

   •  Change the title of the legend

   •  Change the axes labels

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.

   •  Add the trend lines to scatterplots

   •  Learn how to add vertical and horizontal lines to plots

   •  Customize the lines

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.

   •  Add fixed text labels

   •  Add dynamic textual labels

   •  Add text outside the plot and change the axis labels

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.

   •  Review the facet_wrap function

   •  Install the gridExtra package

   •  Create the multi plots

Multi-plots
03:59

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.

   •  Create an object with the plot we want to save

   •  Learn the basics of the ggsave function

   •  Change the size of the image

Exporting Plots as Images
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.

   •  Specify the page size

   •  Rotate the page

   •  Specify other options

Adjusting the Page Size
02:32

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.

   •  Explain the rCharts package

   •  Install devtools

   •  Install rCharts from GitHub

Getting Started with Interactive Plotting
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.

   •  Explain the syntax of rCharts

   •  Include more details

   •  Add JavaScript functions for more flexibility

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.

   •  Plot basic interactive bar charts

   •  Add axis labels

   •  Use a template for an elegant finish

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.

   •  Create basic interactive scatterplots

   •  Understand the interactivity

   •  Add elements and controls

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.

   •  Set the data in the correct format

   •  Plot a basic time-series plot

   •  Add elements

Developing Interactive Time-series Plots and Saving
03:47

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.

   •  Introduce the Shiny package

   •  Explain the tutorial and examples

   •  Understand the basic structure of a Shiny website

Getting Started with Shiny
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.

   •  Understand the basic structure of Shiny

   •  Add elements to UI and Server

   •  Test the website

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.

   •  Importing files in Shiny

   •  Simple code to do it

   •  Add a separator for more flexibility

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.

   •  Explain conditional panels

   •  Understand UI modifications

   •  Apply server modifications

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.

   •  Modify server side modifications

   •  Keep track of the IDs

   •  Recognize variables automatically

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.

   •  Separate ui.r and server.r

   •  Add plots to the script

   •  Finally, we deploy the site.

Deploying the Site
05:37
+ R Data Visualization - Basic Plots, Maps, and Pie Charts
37 lectures 03:00:43

This video gives an overview of the entire course.

Preview 03:24

R comes loaded with some basic packages, but the R community is rapidly growing and active R users are constantly developing new packages for R.

  • Install a package

  • Load the package

Installing Packages and Getting Help in R
05:34

Everything in R is in the form of objects. Objects can be manipulated in R.

  • Generate a vector numeric, character and complex

  • Detect missing values

  • Remove the NA values

Data Types and Special Values in R
04:46

We will dive into R's capability with regard to matrices and edit (add, delete, or replace) elements of a matrix.

  • Define a vector using the c() notation

  • Generate a transpose

  • Edit (add, delete, or replace) the elements

Matrices and Editing a Matrix in R
04:28

One of the useful and widely used functions in R is the data.frame() function.

  • Create a matrix and convert it into a data frame

  • Add and remove columns and rows

  • Reorder the columns of a data frame

Data Frames and Editing a Data Frame in R
03:56

Once we have processed our data, we need to save it to an external device or send it to our colleagues. It is possible to export data in R in many different formats.

  • Import a CSV fie

  • Export data from R

Importing and Exporting Data in R
04:35

Most of the tasks in R are performed using functions. A function in R has the same utility as functions in Arithmetic.

  • Open a new R script by navigating to File

  • Write a function that accepts two values and adds these values

  • Write a function that makes use of the if else logic

Writing a Function and if else Statement in R
03:12

If we want to perform an action repeatedly in R, we can utilize the loop functionality.

  • Multiply each element of x and y

  • Define a matrix in R

Basic and Nested Loops in R
02:16

R has some very handy functions, such as apply, sapply, tapply, and mapply, that can be used to reduce the task of writing complicated statements.

  • Implement the apply function

  • Use the lapply function

  • Perform the tapply function

The apply, lapply, sapply, and tapply Functions
03:32

One quick and easy way to edit a plot is by generating the plot in R and then using Inkspace or any other software to edit it.

  • Set the background color

  • Change the orientation of the labels

  • Specify the color of the labels

Using and Saving par to Beautify a Plot in R
03:51

Scatter plots are used primarily to conduct a quick analysis of the relationships among different variables in our data.

  • Attach the data using the attach() function

  • View the data and plot the data

  • Add a legend using the legend() function

Introducing a Scatter Plot with Texts, Labels, and Lines
13:12

We will display multivariate data on a scatter plot and also introduce interactive scatter plots.

  • Install and load the googleVis package and import data

  • Construct a new data frame

  • Generate an interactive scatter plot

Connecting Points and Generating an Interactive Scatter Plot
08:21

The advantage of using the Google Chart API in R is the flexibility it provides in making interactive plots.

  • Install the googleVis package

  • Load a library and import data

  • Generate an interactive bar plot

A Simple and Interactive Bar Plot
10:49

Line plots are simply lines connecting all the x and y dots. They are very easy to interpret and are widely used to display an upward or downward trend in data.

  • Generate a line plot

  • Import the fertility data

  • Construct a line object

Introduction to Line Plot and Its Effective Story
08:09

Gantt charts are used to track the progress of a project displayed against time.

  • Import data

  • Redefine a new data frame

  • Generate an object

Generating an Interactive Gantt/Timeline Chart in R
02:57

Plot a histogram using the googleVis package and merge more than one histogram on the same page.

  • Install the googleVis package

  • Generate the histograms

  • Combine the two gvis objects in one browser

Merging Histograms
04:11

The advantage of the Google Chart API is the interactivity and the ease with which they can be attached to a web page.

  • Install and load the googleVis package

  • Construct our bubble chart

  • View both the bubble plots side by side

Making an Interactive Bubble Plot
04:30

The waterfall plots or staircase plots are observed mostly in financial reports.

  • Import the waterf.csv data file

  • Construct the Waterfall plot

Constructing a Waterfall Plot in R
03:01

This video helps you get introduced to the concept of dendrograms.

  • Define the start of random numbers

  • Bind the column

  • Generate the scatterplot and dendrogram

Constructing Simple Dendrogram
06:47

This video teaches you to create a plot which is easy to study and more informative.

  • Use the dendroextras package

  • Change the view from rectangle to triangle

  • Plot a three-dimensional dendrogram

Creating Dendrograms with Colors and Labels
05:13

Heat maps are a visual representation of data, wherein each value in a matrix is represented with a color. This video shows you how to create a heat map.

  • Install and load the pheatmap package

  • Import data in the matrix form

  • Plot heatmap

Creating Heat Maps
04:35

This video dives into plotting a heat map by customizing colors.

  • Install the packages

  • Construct the color palette

  • Define the color scheme

Generating a Heat Map with Customized Colors
02:20

This video teaches you to integrate a dendrogram and heat map into a single plot.

  • Install and import packages

  • Import data and scale it

  • Perform PCA in R

Generating an Integrated Dendrogram and a Heat Map
02:34

R allows us to plot three-dimensional interactive heat maps using the heat map package.

  • Install Neatmap

  • Clean the data

  • Plot stereo

Creating a Three-Dimensional Heat Map and Stereo Map
02:35

Tree maps are basically rectangles placed adjacent to each other. The size of each rectangle is directly proportional to the data being used in the visualization.

  • Install and load the package

  • Import data and store it as data frame

  • Create a tree object in R

Constructing A Tree Map in R
05:22

We encounter maps on a daily basis, be it for directions or to infer information regarding the distribution of data. Maps have been widely used to plot various types of data in R.

  • Install and load the googleVis package

  • Import the debt.csv data file

  • Generate the visualization

Introducing Regional Maps
04:28

Choropleth maps can be state level as well as county level. In this video, we will plot well-being data on a state level.

  • Install and load the googleVis package

  • Import the well-being.csv data file

  • Generate the plot

Introducing Choropleth Maps
04:31

Contour maps are used to display data related to temperature or topographic information.

  • Specify the range of colors

  • Define our color palette

  • Generate a contour map

A Guide to Contour Maps
05:16

For each region, a bubble or a pie chart is used that represents percentage.

  • Import the ghg.csv fie

  • Examine the data

  • Specify the size of the bubble

Constructing Maps with bubbles
05:07

Overlaying maps with text is not a very prominent medium of displaying information.

  • Import the name.csv data file

  • Use the [i] notation to plot

  • Use a loop to plot all the 50 names

Integrating Text with Maps
03:41

The shapefile package in R can be used to read a shapefile, add the processed data to our shapefile, and then save it in the shapefile format.

  • Open our dataset

  • Select all the columns

  • Select the Format option

Introducing Shapefiles
06:12

The idea of a cartogram is to show the gravity of the issue or data being studied.

  • Click on Add layer

  • Navigate to the folder

  • Click on Create cartogram

Creating Cartograms
04:44

Pie charts are a great visualization technique to represent data and help viewers understand statistical data.

  • Create a vector and calculate percentages

  • Create a pie using the pie() function

Generating a Simple Pie Chart
05:48

Labels are important as they give the information about the sections of the pie chart. We will include labels inside the pie chart in this video.

  • Create a label and pie chart using the floating.pie() function

  • Add legends and view the pie chart

Constructing Pie Charts with Labels
04:54

Donut charts have advantages over pie charts with respect to the area and efficiency in visualizing information.

  • Install the plotrix package

  • Create a pie chart and draw a circle in the center to convert it into a donut chart

Creating Donut Plots and Interactive Plots
05:31

Instead of using multiple pie charts for comparing data, we can use slope charts.

  • Import data

  • Create a slope chart using the bumpchart() function

Generating a Slope Chart
03:28

Fan plots are an alternative to pie charts and are useful in differential and comparative analysis.

  • Construct a fan plot using the fan.plot() function

  • Make changes to the chart and view the final plot

Constructing a Fan Plot
02:53
Test Your Knowledge
3 questions
+ R Data Visualization - Word Clouds and 3D Plots
36 lectures 02:28:50

This video gives overview of the entire course.

Preview 03:25

Adding a third dimension to the existing plot helps in revealing information and portraying data from a newer angle.

  • Install Plot3D package

  • Load the package

  • Generate a scatter plot

Constructing a 3D Scatter Plot
04:29

Applying text to a plot is the additional functionality of the plot3D package

  • Install and load the plot3D package

  • Define the column names as row names

  • Plot the actual text in 3D

Generating a 3D Scatter Plot with Text
03:25

In this video we will generate simple 3D Pie Chart using Plotrix package.

  • Install and load the plotrix package

  • Define a character vector – col

  • Apply pie 3D labels function

A Simple 3D Pie Chart
02:52

In this video, we will plot a 3D Histogram.

  • Generate data for the x and y values

  • Generate the histogram using hist3D function

  • Plot a ribbon plot

A Simple 3D Histogram
02:43

In this video, we will explore about implementation of 3D contours in R.

  • Create a layout for our image

  • Generate dataset for the x and y values

  • Construct four different contour plots

Generating a 3D Contour Plot
02:31

In this video, we will learn to plot a contour map in 3D using the plot3D package in R.

  • Generate some data to construct our plots

  • Generate four different plots

  • Plot an interactive contour map

Integrating a 3D Contour and a Surface Plot
02:39

In this video, we will learn to surface plots and animation in R.

  • Use an animation package

  • Define the m, n, and o variables

  • Use the surf3D() function to construct

Animating a 3D Surface Plot
05:46

When the density of data increases in a particular region of a plot, it becomes hard to read. So in this video, the sunflower plots are used as variants of scatter plots to display bivariate distribution.

  • Load the Galton data

  • Examine the headers and first six observations

  • Construct a sunflower plot

Constructing a Sunflower Plot
03:45

The hexagon-shaped bins were introduced to plot densely packed sunflower plots.

  • Implement the rnorm() function

  • Generate a hexbin plot

  • Use the generic R plot() function

Creating a Hexbin Plot
03:06

Calendar plots have been used to display data on a daily or monthly basis, where each square represents a data point.

  • Create a character vector

  • Use the data.frame() function

  • Plot the calendar plot

Generating Interactive Calendar Maps
04:41

In this video, we will implement the alternative methods to visualize multivariate data that is by using Chernoff faces.

  • Load the ‘aplpack’ data file

  • Examine the format and column headers and explore the data

  • Generate Chernoff faces

Creating Chernoff Faces in R
03:38

In this video, we will construct the coxcomb plot.

  • Utilize the HistData and plotrix packages

  • Load the Nightingale data file

  • Generate the coxcomb plot

Constructing a Coxcomb Plot in R
03:41

In this video, we will study the basics of creating a network plot using a random dataset.

  • Install the igraph package

  • Generate fake data and import it

  • Create a network graph object

Constructing Network Plots
03:12

In this video, we will use oil prices in USA as an example to construct the radial plot.

  • Install the package and load the library

  • Import the oil.csv data file

  • Construct a radial plot

Constructing a Radial Plot
04:17

You might have seen these plots in news or journal articles and wondered how to create them quickly. This video will help you accomplish this task.

  • Install and load the packages

  • Load the CSV file

  • Create a pyramid plot

Generating a Very Basic Pyramid Plot
04:08

Candlestick plots are widely used to display time series data related to financial markets.

  • Install and load the quantmod package

  • Import MSFT and FB data files

  • Generate a candlestick plot

Generating a Candlestick Plot
06:04

We will learn an easy way to generate an interactive version of the same plot.

  • Download data in XTS format

  • Convert our data to the data frame format

  • Generate candlestick plot object

Generating Interactive Candlestick Plots
03:00

The main objective of this section is to introduce the concept of decomposition.

  • Import the data as a CSV file

  • Decompose a series

  • Change the model to multiplicative

Generating a Decomposed Time Series
07:02

Regression lines are a visual representation of the regression equation.

  • Load data using the ISLR package

  • Generate the intercept and the estimate for horsepower

  • Construct the plot

Plotting a Regression Line
03:39

The Flowing Data website provides a very detailed description on how to read a box plot.

  • Set the margin of the plot

  • Construct a simple box plot

Constructing a Box and Whiskers Plot
02:39

In the violin plot, we can observe the mean, which is displayed using white dots, and the dispersion of various variables.

  • Install and load the ISLR and vioplot packages

  • Construct a plot using the vioplot function

Generating a Violin Plot
01:47

R comes with some basic methods to test for normality, such as the Shapiro test.

  • Test the null hypothesis

  • Generate a simple QQ plot

Generating a Quantile-Quantile Plot (QQ Plot)
03:32

In this video, we will utilize the density() function to generate a plot.

  • Download the data using the getSymbols() function

  • Estimate the kernel density using the density() function

  • Plot a density plot over the histogram

Generating a Density Plot
03:43

Correlation plots are a great tool to visualize correlation data.

  • Download the data as a data frame

  • Generate the correlation matrix

  • Pass the fxcor object as an argument

Generating a Simple Correlation Plot
05:28

In this video, we will study how to quickly generate a word cloud in R.

  • Install and load wordcloud, tm, Rcolorbrewer packages

  • Define the range of color palettes

  • Generate a word cloud

Generating a Word Cloud
03:39

In this video, we will learn how to create a word cloud using an entire document.

  • Apply readline function to read the file that contains the text

  • Clean and structure the text document

  • Create a word cloud by simply using wordcloud package

Constructing a Word Cloud from a Document
05:08

A comparison cloud works on the same principles as a word cloud.

  • Generate a corpus using the Corpus() function

  • Clean our text document and utilize the removeWords() function to eliminate stop words

  • Constructs a term document matrix

Generating a Comparison Cloud
04:44

In this video, we will learn some important matrix functions that allow us to further conduct text analysis and also generate a correlation plot.

  • Clean the text and use the same functions

  • Generate a correlation object

  • Create a correlation plot

Constructing a Correlation Plot and a Phrase Tree
05:02

The main aim of this video is to introduce you to installing fonts and how to use them to label plots.

  • Load the xkcd fonts

  • Create fake data for our chart

  • Display the plot using the plot() function

Generating Plots with Custom Fonts
03:30

The idea behind generating an XKCD-style plot is to bring the same humor to our plot.

  • Customize the fonts

  • Create fake data for the plot

  • Construct the plot by adding the theme

Generating an XKCD-Style Plot
03:14

Animating a visualization brings a new dimension to our visualization.

  • Install and load the googleVis package

  • Download the data and import the data using read.csv() function

  • Generate the googleVis object

Creating Animated Plots in R
04:53

One of the issues while creating presentations using PowerPoint is that we have to manually update the data, contents, and plot.

  • Install and load solidify and devtools packages

  • Create an index.rmd file

  • Open a file called index.rmd

Creating a Presentation in R
07:22

In this section, we will get the basic introduction to API and XML.

  • Click on the API Console section

  • Select Real Estate API from the drop-down menu

  • Create a link with all the information

A basic Introduction to API and XML
06:14

In this video, we will construct a bar plot using XML data.

  • Import the data directly in R

  • Parse the XML data

  • Construct and structure the data, and plot a bar plot

Constructing a Line Plot Using JSON in R
03:42

The shiny package allows us to create applications in R.

  • Install and load the shiny package

  • Execute the runApp("shiny") function

  • Generate the main panel with the output plot

Creating a Very Simple Shiny App in R
06:10
Example
1 question