Going from Beginner to Advanced in ggplot2
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:
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More than 12 hours of videos
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8 brand-new cheat sheetson the most fundamental concepts of ggplot2 which you won’t find anywhere else on the internet
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3 educational web appson three of the most fundamentals problems: findings aesthetics of geometric objects, finding scales, and designing your theme
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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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Christian Burkhart
Senior Instructional Designer and Data Scientist
Rating Distribution
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- 4 stars: 13 votes
- 5 stars: 39 votes
Frequently Asked Questions
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