Graphics in R: Data Visualization and Data Analysis with R

Graphics in R: Data Visualization and Data Analysis with R, available at $69.99, has an average rating of 4.92, with 166 lectures, based on 6 reviews, and has 55 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners in R programmers who are not in a rush to master everything at once or Beginner R programmers who want to learn data visualization or Absolute beginners in Programming or University or college students wanting to learn data visualizations using R or Post graduates students who are keen on using R for exploration and data analysis It is particularly useful for Beginners in R programmers who are not in a rush to master everything at once or Beginner R programmers who want to learn data visualization or Absolute beginners in Programming or University or college students wanting to learn data visualizations using R or Post graduates students who are keen on using R for exploration and data analysis.

Enroll now: Graphics in R: Data Visualization and Data Analysis with R

Summary

Title: Graphics in R: Data Visualization and Data Analysis with R

Price: $69.99

Average Rating: 4.92

Number of Lectures: 166

Number of Published Lectures: 166

Number of Curriculum Items: 166

Number of Published Curriculum Objects: 166

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 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 summaryoverview 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

After completing the course you will receive the electronic certificate that you can add to 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 studio

I 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the Course

Chapter 2: R and R Studio set up

Lecture 1: R 4.2.2 and R Studio download and installation

Lecture 2: R studio walkthrough

Chapter 3: Download all the R scripts

Lecture 1: Course R Scripts

Chapter 4: R for Beginners: Data Structures Crash

Lecture 1: Creating vectors with c function

Lecture 2: Creating named vectors with names() function

Lecture 3: Vectors: Attributes

Lecture 4: Matrices: Creating matrices with rbind and cbind

Lecture 5: Matrices: Creating matrices with matrix function

Lecture 6: Matrices: creating matrices with names

Lecture 7: Arrays: Creating Arrays in r

Lecture 8: Arrays Attributes & subsets

Lecture 9: Creating lists

Lecture 10: subscripting lists: subsets of a list

Lecture 11: Referencing elements in a list

Lecture 12: Appending elements in a list

Lecture 13: Creating dataframe

Lecture 14: Querying data frames attributes

Lecture 15: Selecting columns in a data frame

Chapter 5: Introduction to Factors in R

Lecture 1: Creating factors in R

Lecture 2: Factors with factor levels

Lecture 3: grep and gsub functions

Chapter 6: Importing data into R with tidyverse package

Lecture 1: Importing a csv file in r

Lecture 2: Importing an excel file in r with tidyverse package

Chapter 7: Data Analysis, Transformation and Manipulation

Lecture 1: Introduction to Data Manipulation

Lecture 2: sorting datasets with sort() function

Lecture 3: Appending

Lecture 4: Duplicated Values

Chapter 8: Merging with merge() function

Lecture 1: Understanding Merging

Lecture 2: Merging data frames with merge function

Lecture 3: left, right & outer merging using merge function

Chapter 9: reshape package: melting and casting

Lecture 1: what is melting and casting?

Lecture 2: melting with melt function

Lecture 3: casting with cast function

Chapter 10: tidyr package: gather and spread function

Lecture 1: introduction to gather and spread function

Lecture 2: gather function in r

Lecture 3: spread function in r

Chapter 11: dplyr package

Lecture 1: Introduction to r dplyr package for data analysis

Lecture 2: create a table_df object from a data frame

Lecture 3: dplyr sort descending and ascending with arrange function

Lecture 4: dplyr filter & select function

Lecture 5: add a new column with mutate function

Lecture 6: inner join function

Lecture 7: dplyr merging functions: left_join(), right_join(), inner_join(), full_join()

Lecture 8: what is a pipe operator?

Lecture 9: pipe operator example

Chapter 12: Graphics in R: Beginner Level

Lecture 1: Introduction to Graphics

Lecture 2: Graphic device: create pdf file

Lecture 3: Graphic device: create image device

Lecture 4: Introduction to r plot function

Lecture 5: Plot function in R

Lecture 6: Plot Types

Lecture 7: Line Graph with base R

Lecture 8: Introduction to low level graphics functions in R

Lecture 9: Adding points and lines

Lecture 10: Adding text to a graphic in r

Lecture 11: Adding Legend to the r graphic

Lecture 12: Multiple displays with par() function

Lecture 13: Coding Exercise Instructions

Lecture 14: Coding Exercise Solution

Chapter 13: Data Visualization in R: Beginner Level

Lecture 1: Introduction to Data Visualizations

Lecture 2: Barplots & Pie Charts –> The understanding

Lecture 3: Barplots in R: Favorite EPL team mock survey dataset

Lecture 4: Controlling width and space of the bars

Lecture 5: Adding Titles to barplot

Lecture 6: Adding legend and creating a horizontal bar plot

Lecture 7: Stacked and Grouped Bar plots

Lecture 8: Pie Chart in R

Lecture 9: Pie Chart with percentages with R

Lecture 10: Histogram –> The understanding

Lecture 11: Histogram with R

Lecture 12: Histogram with value marker: (Histogram with mean and labels)

Lecture 13: Histogram with Kernel density (KDE) in r

Lecture 14: Multiple Histograms

Lecture 15: Boxplot –> The understanding

Lecture 16: Boxplot in R

Lecture 17: Adding means to a boxplot

Lecture 18: Scatterplots –> The understanding

Lecture 19: Scatterplot revisited

Chapter 14: Beginner Project: Financial Budget Analysis

Lecture 1: Project Outline

Lecture 2: Project Solution

Lecture 3: Percentage Distributions of the Funds

Chapter 15: Beginner Project: Billionaires Analysis

Lecture 1: Project Outline

Lecture 2: Analyzing Billionaires by their Net Worth using R programming

Chapter 16: Intermediate Data Visualization & Graphics with GGPLOT 2

Instructors

hi- mathstats
Quantitative Analysis , Data Science

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 5 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu