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Going from Beginner to Advanced in ggplot2

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Going from Beginner to Advanced in ggplot2, available at $69.99, has an average rating of 4.7, with 123 lectures, based on 52 reviews, and has 324 subscribers.

You will learn about Explain what aesthetic mappings are Explain the inheritance of aesthetic mappings Create any plot in ggplot2 on your own Solve common problems in creating plots in ggplot2 Dodge bar charts and be able to explain how it can be done Explain the aesthetics of geom_point Order bar charts Use scales to adjust the mapping between aesthetics and variables Use facets to create multiple plots at once Use summary statistics to do calculations on your data on they fly with ggplot2 (e.g. error bars, means, confidence intervals) Make your plot look beautiful with custom themes Use annotations to spice up your plots Add mathematical notations to your plot Combine multiple plots with patchwork Adding significance bars to barplots Adding regression lines to scatterplots Export plots to high quality Use various apps from ggplot2tor to work with scales, theme and aesthetics in ggplot2 This course is ideal for individuals who are Any person who wants to learn ggplot2 from the ground up or Data scientist interested in learning how to create visualizations in ggplot2 fast and effectively or Data journalists who want to create print-ready visualizations or Students interested in creating visualizations for their theses or Scientists and researchers whose daily bread is to plot data It is particularly useful for Any person who wants to learn ggplot2 from the ground up or Data scientist interested in learning how to create visualizations in ggplot2 fast and effectively or Data journalists who want to create print-ready visualizations or Students interested in creating visualizations for their theses or Scientists and researchers whose daily bread is to plot data.

Enroll now: Going from Beginner to Advanced in ggplot2

Summary

Title: Going from Beginner to Advanced in ggplot2

Price: $69.99

Average Rating: 4.7

Number of Lectures: 123

Number of Published Lectures: 123

Number of Curriculum Items: 123

Number of Published Curriculum Objects: 123

Original Price: €84.99

Quality Status: approved

Status: Live

What You Will Learn

  • Explain what aesthetic mappings are
  • Explain the inheritance of aesthetic mappings
  • Create any plot in ggplot2 on your own
  • Solve common problems in creating plots in ggplot2
  • Dodge bar charts and be able to explain how it can be done
  • Explain the aesthetics of geom_point
  • Order bar charts
  • Use scales to adjust the mapping between aesthetics and variables
  • Use facets to create multiple plots at once
  • Use summary statistics to do calculations on your data on they fly with ggplot2 (e.g. error bars, means, confidence intervals)
  • Make your plot look beautiful with custom themes
  • Use annotations to spice up your plots
  • Add mathematical notations to your plot
  • Combine multiple plots with patchwork
  • Adding significance bars to barplots
  • Adding regression lines to scatterplots
  • Export plots to high quality
  • Use various apps from ggplot2tor to work with scales, theme and aesthetics in ggplot2

Who Should Attend

  • Any person who wants to learn ggplot2 from the ground up
  • Data scientist interested in learning how to create visualizations in ggplot2 fast and effectively
  • Data journalists who want to create print-ready visualizations
  • Students interested in creating visualizations for their theses
  • Scientists and researchers whose daily bread is to plot data

Target Audiences

  • Any person who wants to learn ggplot2 from the ground up
  • Data scientist interested in learning how to create visualizations in ggplot2 fast and effectively
  • Data journalists who want to create print-ready visualizations
  • Students interested in creating visualizations for their theses
  • Scientists and researchers whose daily bread is to plot data

My goal with this course is for you to learn ggplot2 from the ground up. ggplot2 has a huge community and endless resources, but here’s why I think this course might be for you:

Creating data visualizations in ggplot2 is tough for beginners. You need to know about data types, geometric objects, aesthetics, aesthetic mappings, dozens of functions, faceting, scales, themes and much more. You’ll find many resources on the internet that teach you this content. Finding these resources takes time, and often they don’t teach the fundamentals you need to know to become an independent data visualization specialist in ggplot2. This course will get you up to speed with ggplot2. While creating this course I not only created the videos, but also a comprehensive package of educational materials. Here is what you will get from this course:

  • More than 12 hours of videos

  • 8 brand-new cheat sheetson the most fundamental concepts of ggplot2 which you won’t find anywhere else on the internet

  • 3 educational web appson three of the most fundamentals problems: findings aesthetics of geometric objects, finding scales, and designing your theme

  • A repository with all the R-code for the course

In this course we will start with the most important concept of ggplot2, aesthetic mappings. We will then learn how to create the most basic plots. Once you are able to create these plots, we will discuss common pitfalls that beginners to ggplot2 often run into. In the next modules, you will learn how to customize aesthetic mappings with scales, how to create multiple plots by faceting, how to calculate summary statistics, and how to change the themeof your plots. Finally, you will get to know tips and tricks that everyone learning ggplot2 should know. Along the way, we will also create four best practice visualizations that cover all of the fundamental concepts we learn in this course.

I am confident that you won’t find similar material anywhere else on the internet and that you will truly understand ggplot2 from the ground up if you take this course.

Disclaimer: We will cover version 3.3.4 of ggplot2.

Course Curriculum

Chapter 1: Introduction and set-up

Lecture 1: Pre-requisites

Lecture 2: Getting the R-scripts, cheat sheets and best practice visualizations

Lecture 3: The World Bank dataset

Lecture 4: Best practice visualizations

Chapter 2: Aesthetic mappings

Lecture 1: What is a data visualization?

Lecture 2: Aesthetics of geometric objects

Lecture 3: Data types

Lecture 4: Aesthetic mappings

Lecture 5: Mapping and setting aesthetics

Lecture 6: Exercise: From plots to aesthetic mappings

Lecture 7: Exercise: From aesthetic mappings to plots

Lecture 8: Don't use the same aesthetic twice!

Lecture 9: Exercise: Mapped and set aesthetics

Lecture 10: Exercise: Why is this plot hard to comprehend?

Lecture 11: Summary

Chapter 3: Fundamental visualizations

Lecture 1: Introduction

Lecture 2: The ggplot function

Lecture 3: Set up your canvas

Lecture 4: How to create a line chart

Lecture 5: How to create an area chart

Lecture 6: How to create a scatterplot

Lecture 7: How to create a histogram

Lecture 8: How to create a boxplot

Lecture 9: How to create a bar chart

Lecture 10: Exercise: geom_text and geom_label

Lecture 11: Exercise: geom_jitter

Lecture 12: Exercise: geom_errorbar / geom_linerange / geom_pointrange

Lecture 13: Best Practice Viz: Internet usage

Lecture 14: Best Practice Viz: Renewable energies

Lecture 15: Best Practice Viz: Forest land

Lecture 16: Best Practice Viz: Life expectancy

Chapter 4: Level-up! One

Lecture 1: Introduction

Lecture 2: Layering of geometric objects

Lecture 3: Layering geom_point with geom_text

Lecture 4: Layering geom_col with geom_errorbar and geom_point

Lecture 5: Layering geom_col with geom_text

Lecture 6: Inheritance of geometric objects

Lecture 7: Typical problems: + vs. %>%

Lecture 8: Typical problems: Grouping of geom_line

Lecture 9: Typical problems: 01 – Dodging bar charts

Lecture 10: Typical problems: 02 – Dodging bar charts

Lecture 11: Typical problems: Ordering of discrete variables (e.g. bar charts)

Lecture 12: Typical problems: The aesthetics of geom_point and geom_jitter

Lecture 13: Exercise: Development of renewable energies

Lecture 14: Exercise: Birth rate in China

Lecture 15: Exercise: Obesity

Lecture 16: Best Practice Viz: Forest land

Lecture 17: ggplot2tor – Aesthetics app

Chapter 5: Scales

Lecture 1: Introduction to scales

Lecture 2: The five types of scales

Lecture 3: A complete guide to scales

Lecture 4: x/y scales – Limits

Lecture 5: x/y scales – Breaks and minor breaks

Lecture 6: x/y scales – Labels

Lecture 7: x/y scales – Scales package

Lecture 8: Color/fill scales – Part One

Lecture 9: Color/fill scales – Part Two

Lecture 10: Color/fill scales – Part Three

Lecture 11: Color/fill scales – Part Four

Lecture 12: Working with date, datetime, and time

Lecture 13: Exercise 1

Lecture 14: Exercise 2

Lecture 15: Exercise 3

Lecture 16: Exercise 4

Lecture 17: Exercise 5

Lecture 18: Exercise 6

Lecture 19: Best Practice Viz – Internet Usage

Lecture 20: Best Practice Viz – Renewable Energies

Lecture 21: Best Practice Viz – Forest Land

Lecture 22: Best Practice Viz – Life Expectancy

Lecture 23: BONUS: How to expand continuous axes

Lecture 24: BONUS: How to expand discrete axes

Lecture 25: BONUS: Change aesthetics after scaling with after_scale

Chapter 6: Faceting

Lecture 1: Introduction to faceting

Lecture 2: facet_wrap

Lecture 3: Free scales or not?

Lecture 4: facet_grid

Lecture 5: Ordering of panels

Lecture 6: Showing all geometric objects in the panels

Lecture 7: Dealing with continuous variables

Lecture 8: Best Practice Viz – Life Expectancy

Chapter 7: Summary statistics

Lecture 1: Introduction

Lecture 2: Hidden summary statistics in ggplot2

Lecture 3: The stat_summary function

Lecture 4: How to plot measures of center

Lecture 5: How to plot measures of spread – Part one

Lecture 6: How to plot measures of spread – Standard deviation, standard error, and …

Lecture 7: Combining stat_summary functions

Lecture 8: Best Practice Viz – Life Expectancy

Chapter 8: Theme

Lecture 1: Why theme?

Lecture 2: Pre-defined themes

Lecture 3: Theme categories

Instructors

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  • 5 stars: 39 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!

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