Graphics in R Data Visualization and Data Analysis with R

Advance your data visualization skills using r packages. Master ggplot2, lattice, interactive plots with ggvis package
Graphics in R Data Visualization and Data Analysis with R
File Size :
5.49 GB
Total length :
13h 16m



Nkosingimele Ngcobo, hi-mathstats


Last update




Graphics in R Data Visualization and Data Analysis with R

What you’ll learn

Visualize real world datasets in the professional industries such as finance, health, insurance, marketing, sales
How to visualize data with ggplot2 package
Statistical Data visualizations with qplot function
Statistical Data visualizations with ggplot function
Master themes in R using ggplot2 package
Master Faceting with facet_wrap() and facet_grid()
Master scaling and guides R using scaling functions from ggplot2 package
Use plot function in R to create histograms, box and whisker plots, scatterplots, pie charts, barplots
Intermediate Data Visualization with the lattice package
How to use lattice package to create grouped scatterplots, barcharts
Master panel functions and high level functions in lattice package
How to switch between Graphics devices in R
Learn how to use ggvis package
How to create interactive plots with ggvis package from shiny
Create interactive scatterplots, histograms, boxplots with input slider from ggvis package
Data Analysis with dplyr package
Data Analysis with tidyr package
Data Analysis with reshape package
Factors in R and regular expressions
Master 3D Scatterplots in R
Use ggplot2 to visualize real world datasets
Use lattice package to visualize real world datasets
Create interactive plots from real world datasets with ggvis package

Graphics in R Data Visualization and Data Analysis with R


No R programming experience everything is explained
Internet connection for installing R 4.2 and R Studio
Eager to learn data visualization with R
Not being in a rush to master everything at once!


Learn data visualizations by projects that use real world datasets in the professional industries such as finance, marketing, sales etc.This course will help you master data visualizations techniques and create graphics in R using packages such as ggplot2, lattice package and ggvis package from shiny for adding interactivity into you R graphics.Real world datasets are used for projects. So, not only will you master the graphics in r, you will also be able to interpret your graphics and make an impressive plots. All done by yourself. Why learn data visualization with R?Data Visualization helps people see, interact with, and better understand the data. Whether simple or complex, the right visualization can bring everyone on the same page, regardless of their level of expertise.Almost all the professional industries benefit from making data more understandable. Every STEM field benefits from data analysts that are able to understand data—and so do fields in government, finance, marketing, history, consumer goods, service industries, education, sports, and so on.As the “age of Big Data” and “Artificial Intelligence (AI)”  kicks into high gear, visualization is an increasingly key tool to make sense of the trillions of rows of data generated every day. Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting useful information. With R tools such as ggplot2 , lattice package, we can create visually appealing graphics and data visualizations by writing few lines of code. For this purpose R is widely used and it is easy to use and understand when it comes to data visualizations, good appealing graphics, data analysis (dplyr) etc. Through R, we can easily customize our data visualization by changing axes, fonts, legends, annotations, and labels.In this data visualization course you will learn the following:R for beginners: Vectors, Matrices, Arrays, Data frames and ListsFactors in R: Create factors, understand factor levelsregular expressions in r: grep and gsub functionsreshape package for data analysis: melt and casting functionstidyr package for data analysis: gather and spread functionsdplyr package for data analysis: merge functions, filter, select, sort, arrange, pipe operator etcAfter Mastering R Programming for beginners and Data Analysis, you will begin creating graphics with r and visualizations. Here is the summary overview of what you will learn:Graphics in R: Beginner LevelGraphic Devices & ColorsThe Plot FunctionLow Level FunctionsData Visualization in R: Beginner LevelBarplots & Pie ChartsHistograms in rBox and Whisker PlotsScatterplotsIntermediate Data Visualization & Graphics in RWhat is ggplot2?qplot() functionggplot() functionData Visualization with Lattice PackageLattice GraphicsHigh Level Functions in lattice packageLattice Package panel functionsGoing further with data visualizationHow to Handle and switch between graphicsControlling layout with layout functionggplot2 scales and guides: scale_x_continous, scale_y_continous, scale_color_manual,scale_fill_manualscale_shape_manual,scale_shape_manual,scale_alpha_continousguide_legend, gudei_colorbarggplot2 faceting: facet_wrap() vs facet_grid()ggplot2 themesggvis package: scatterplot with layers, interactive plots with input_slider(), add_legend(), add_axis etcAfter completing the course you will receive the electronic certificate that you can add on your resume or CV and LinkedIn profile from Udemy. The access to this course is also lifetime, hence you will learn at your own pace. The course is also updated regularly to ensure it meets all the students demands and students enrolled are learning latest version of r and r studioI am certain with all the material covered in this course you will be able to advance you Data visualization and Data Analysis skills! See you in the first lecture!


Section 1: Introduction

Lecture 1 Introduction to the Course

Section 2: R and R Studio set up

Lecture 2 R 4.2.2 and R Studio download and installation

Lecture 3 R studio walkthrough

Section 3: R for Beginners: Data Structures Crash

Lecture 4 Creating vectors with c function

Lecture 5 Creating named vectors with names() function

Lecture 6 Vectors: Attributes

Lecture 7 Matrices: Creating matrices with rbind and cbind

Lecture 8 Matrices: Creating matrices with matrix function

Lecture 9 Matrices: creating matrices with names

Lecture 10 Arrays: Creating Arrays in r

Lecture 11 Arrays Attributes & subsets

Lecture 12 Creating lists

Lecture 13 subscripting lists: subsets of a list

Lecture 14 Referencing elements in a list

Lecture 15 Appending elements in a list

Lecture 16 Creating dataframe

Lecture 17 Querying data frames attributes

Lecture 18 Selecting columns in a data frame

Section 4: Introduction to Factors in R

Lecture 19 Creating factors in R

Lecture 20 Factors with factor levels

Lecture 21 grep and gsub functions

Section 5: Importing data into R with tidyverse package

Lecture 22 Importing a csv file in r

Lecture 23 Importing an excel file in r with tidyverse package

Section 6: Data Analysis, Transformation and Manipulation

Lecture 24 Introduction to Data Manipulation

Lecture 25 sorting datasets with sort() function

Lecture 26 Appending

Lecture 27 Duplicated Values

Section 7: Merging with merge() function

Lecture 28 Understanding Merging

Lecture 29 Merging data frames with merge function

Lecture 30 left, right & outer merging using merge function

Section 8: reshape package: melting and casting

Lecture 31 what is melting and casting?

Lecture 32 melting with melt function

Lecture 33 casting with cast function

Section 9: tidyr package: gather and spread function

Lecture 34 introduction to gather and spread function

Lecture 35 gather function

Lecture 36 spread function

Section 10: dplyr package

Lecture 37 Introduction to dplyr package for data analysis

Lecture 38 create a table_df object from a data frame

Lecture 39 dplyr sort descending and ascending with arrange function

Lecture 40 subscripting with filter and select function

Lecture 41 add a new column with mutate function

Lecture 42 inner join function

Lecture 43 dplyr merging functions

Lecture 44 what is a pipe operator?

Lecture 45 pipe operator example

Section 11: Graphics in R: Beginner Level

Lecture 46 Introduction to Graphics

Lecture 47 Graphic device: create pdf file

Lecture 48 Graphic device: create image device

Lecture 49 Introduction to the plot function

Lecture 50 Plot function in R

Lecture 51 Plot Types

Lecture 52 Line Graph with base R

Lecture 53 Introduction to low level graphics functions in R

Lecture 54 Adding points and lines

Lecture 55 Adding text

Lecture 56 Adding Legend

Lecture 57 Multiple displays with par() function

Lecture 58 Coding Exercise Instructions

Lecture 59 Coding Exercise Solution

Section 12: Data Visualization in R: Beginner Level

Lecture 60 Introduction to Data Visualizations

Lecture 61 Barplots & Pie Charts –> The understanding

Lecture 62 Barplots in R: Favorite EPL team mock survey dataset

Lecture 63 Controlling width and space of the bars

Lecture 64 Adding Titles to barplot

Lecture 65 Adding legend and creating a horizontal bar plot

Lecture 66 Stacked and Grouped Bar plots

Lecture 67 Pie Chart in R

Lecture 68 Pie Chart with percentages with R

Lecture 69 Histogram –> The understanding

Lecture 70 Histogram with R

Lecture 71 Histogram with value marker: (Histogram with mean and labels)

Lecture 72 Histogram with Kernel density (KDE) in r

Lecture 73 Multiple Histograms

Lecture 74 Boxplot –> The understanding

Lecture 75 Boxplot in R

Lecture 76 Adding means to a boxplot

Lecture 77 Scatterplots –> The understanding

Lecture 78 Scatterplot revisited

Section 13: Beginner Project: Financial Budget Analysis

Lecture 79 Project Outline

Lecture 80 Project Solution

Lecture 81 Percentage Distributions of the Funds

Section 14: Beginner Project: Billionaires Analysis

Lecture 82 Project Outline

Lecture 83 Analyzing Billionaires by their Net Worth using R programming

Section 15: Intermediate Data Visualization & Graphics with GGPLOT 2

Lecture 84 Understanding ggplot2 package

Section 16: ggplot2 package: qplot function in action!

Lecture 85 Understanding qplot function

Lecture 86 Visualization with qplot function in R

Lecture 87 qplot function: adding geometric layers

Lecture 88 devices with ggplot2: ggsave() fucntion

Lecture 89 Available geometric layers in ggplot2: regular expression

Lecture 90 Creating scatterplots and line graphs with geometric layers with qplot function

Lecture 91 smooth with qplot function

Lecture 92 grouped scatterplots with qplot

Lecture 93 qplot: adding text to a scatterplot

Lecture 94 Boxplot and violin plot with qplot function

Lecture 95 Histogram with qplot function

Lecture 96 Creating density plot with qplot function

Section 17: ggplot2: ggplot() function in action!

Lecture 97 What are Aesthetics?

Lecture 98 Understanding ggplot2 with ggplot function

Lecture 99 Visualization with ggplot function

Lecture 100 Aesthetics in R

Lecture 101 Creating scatterplots with ggplot function

Lecture 102 Example: Visualizing gdp growth with ggplot function

Lecture 103 Grouped line chart with ggplot function

Lecture 104 E-commerce website visits with ggplot function: Barplots with ggplot2

Lecture 105 Visualizing stock returns with ggplot function: Boxplots with ggplot2

Lecture 106 Stock returns with ggplot: Aesthetics in boxplots

Lecture 107 E-commerce website visits with ggplot function: Create histogram with ggplot2

Lecture 108 E-commerce website visits with ggplot function: Histogram mapping with ggplot2

Section 18: ggplot2 project: Billionaires Analysis with ggplot2 package

Lecture 109 Project Outline: Material

Lecture 110 Analyzing Billionares using ggplot2: Solution walkthrough

Section 19: Lattice Package

Lecture 111 Introduction to Lattice Package

Lecture 112 Creating scatterplots with Lattice Package

Lecture 113 Grouped Scatterplots with lattice package

Lecture 114 Grouping scatterplots with panels

Lecture 115 Creating Bargraphs with lattice package

Lecture 116 Grouped bar graphs with lattice package

Lecture 117 Grouping bar charts with panels using lattice package

Lecture 118 Creating Boxplots with lattice package

Lecture 119 Controlling layout with lattice package

Lecture 120 Creating dot plot and strip plot with lattice package

Lecture 121 Creating Histogram with lattice package

Lecture 122 Creating density plot with lattice package

Lecture 123 Understanding lattice panel functions

Lecture 124 Lattice package panel functions in R

Lecture 125 Creating a panel function with Lattice Package

Section 20: Lattice Package project: Home Loan Approvals Visualization project

Lecture 126 Home Loans Approval dataset

Lecture 127 Home loan approval analysis with lattice package: Solution

Section 21: Going Further with Data Visualizations

Lecture 128 Handling devices in R: Switching between devices in r

Lecture 129 Closing devices in r

Lecture 130 Layout function in r

Lecture 131 showing layout with

Section 22: ggplot2 scales and guides

Lecture 132 What is scaling in ggplot2?

Lecture 133 scale_x_continous()

Lecture 134 scale_y_continous()

Lecture 135 scale_color_manual

Lecture 136 scale_fill_manual

Lecture 137 scale_shape_manual

Lecture 138 scale_size_manual

Lecture 139 scale_alpha_continous

Lecture 140 ggplot2 with guide: guide = guide_legend()

Lecture 141 ggplot2 with guide argument: guide_colorbar()

Section 23: Faceting with gglot2

Lecture 142 Faceting ( a.k.a paneling)

Lecture 143 ggplot facet_wrap

Lecture 144 ggplot facet_grid

Section 24: ggplot2 themes

Lecture 145 ggplot2 themes examples

Lecture 146 ggplot2 legend themes

Lecture 147 ggplot2 global themes

Section 25: Credit Card Approvals Visualization Project

Lecture 148 Credit Card Approval dataset description

Lecture 149 Credit Card Approvals project solution with ggplot2

Lecture 150 Credit Card Approvals project solution with ggplot2 continue

Section 26: Interactive r plots ggvis package from shiny

Lecture 151 ggvis package explained

Lecture 152 Scatterplot with ggvis package

Lecture 153 ggvis interactive scatter plot: ggvis input slider()

Lecture 154 ggvis add_axis: labels, title

Lecture 155 ggvis add_legend

Lecture 156 ggvis add regression line , confidence intervals

Lecture 157 ggvis barplot or bar graph, line graph with points

Lecture 158 ggvis boxplot and interactive histogram example

Section 27: Supermarket Sales Visualization Project

Lecture 159 Supermarket Sales data and outline

Lecture 160 Supermarket Sales Analysis solution walkthrough

Lecture 161 Supermarket Sales Analysis solution walkthrough part 2

Section 28: 3d scatter plots in r

Lecture 162 3D Scatterplot example in r

Lecture 163 3D Scatterplot in r group by shapes

Lecture 164 3D Scatterplot in r group by color

Lecture 165 3D Scatterplot in r group by shapes and color

Beginners in R programmers who are not in a rush to master everything at once,Beginner R programmers who want to learn data visualization,Absolute beginners in Programming,University or college students wanting to learn data visualizations using R,Post graduates students who are keen on using R for exploration and data analysis

Course Information:

Udemy | English | 13h 16m | 5.49 GB
Created by: Nkosingimele Ngcobo, hi-mathstats

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