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
Updated on Apr, 2024
Language - English
Data Science and AI ML,Data Visualization,R Programming,Data & Analytics,Data Analysis,
Lectures -160
Resources -13
Duration -13 hours
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Course Description
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.
After completing the course you will receive the electronic certificate that you can add to your resume or CV and LinkedIn profile from Tutorialspoint.
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 studio
I am certain with all the material covered in this course you will be able to advance your Data visualization and Data Analysis skills!
See you in the first lecture!
Goals
What will you learn in this course:
In this data visualization course you will learn the following:
- R for beginners: Vectors, Matrices, Arrays, Data frames and Lists
- Factors in R: Create factors, understand factor levels
- regular expressions in r: grep and gsub functions
- reshape package for data analysis: melt and casting functions
- tidyr package for data analysis: gather and spread functions
- dplyr package for data analysis: merge functions, filter, select, sort, arrange, pipe operator etc
After 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 Level
Graphic Devices & Colors
The Plot Function
Low Level Functions
Data Visualization in R: Beginner Level
Barplots & Pie Charts
Histograms in r
Box and Whisker Plots
Scatterplots
Intermediate Data Visualization & Graphics in R
What is ggplot2?
qplot() function
ggplot() function
Data Visualization with Lattice Package
Lattice Graphics
High Level Functions in lattice package
Lattice Package panel functions
Going further with data visualization
How to Handle and switch between graphics
Controlling layout with layout function
ggplot2 scales and guides:
scale_x_continous, scale_y_continous, scale_color_manual,scale_fill_manual
scale_shape_manual,scale_shape_manual,scale_alpha_continous
guide_legend, gudei_colorbar
ggplot2 faceting: facet_wrap() vs facet_grid()
ggplot2 themes
ggvis package:
scatterplot with layers, interactive plots with input_slider(), add_legend(), add_axis etc
Prerequisites
What are the prerequisites for this course?
- 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!
Curriculum
Check out the detailed breakdown of what’s inside the course
Course Introduction
1 Lectures
-
Introduction to the Course
08:09
08:09
R and R Studio set up
2 Lectures
Download all the r scripts used!
1 Lectures
R for Beginners: Data structures crash
15 Lectures
Introduction to factors in R
3 Lectures
Importing data into r with tidyverse package
2 Lectures
Data analysis, transformation and manipulation
4 Lectures
Merging with merge() function
3 Lectures
Reshape package in r: melting and casting
3 Lectures
Tidyr package: gather and spread function
3 Lectures
Dplyr package
9 Lectures
Graphics in R: Beginner Level
14 Lectures
Data visualization in r: Beginner level
19 Lectures
Beginner Project: Financial Budget Analysis
2 Lectures
Beginner Project: Billionaires Analysis
1 Lectures
Intermediate Data Visualization & Graphics with GGPLOT 2
1 Lectures
Ggplot2 package: qplot function in action!
12 Lectures
Ggplot2: ggplot() function in action!
12 Lectures
Ggplot2 project: Billionaires Analysis with ggplot2 package
1 Lectures
Lattice Package
15 Lectures
Lattice Package project: Home Loan Approvals Visualization project
1 Lectures
Going Further with Data Visualizations
4 Lectures
Ggplot2 scales & guides
10 Lectures
Faceting with ggplot2
3 Lectures
Ggplot2 themes
3 Lectures
Credit Card Approvals Visualization Project
2 Lectures
Interactive r plots ggvis package from shiny
8 Lectures
Supermarket Sales Visualization Project
2 Lectures
3d scatter plots in r
4 Lectures
Instructor Details
Nkosingimele Ngcobo
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