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Intermediate Data Visualization with ggplot2

4.2+
27 reviews
Intermediate

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

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Course Description

This ggplot2 course builds on your knowledge from the introductory course to produce meaningful explanatory plots. Statistics will be calculated on the fly and you’ll see how Coordinates and Facets aid in communication. You’ll also explore details of data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.
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In the following Tracks

Certification Available

Associate Data Scientist in R

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Data Visualization in R

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  1. 1

    Statistics

    Free

    A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

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    Stats with geoms
    50 xp
    Smoothing
    100 xp
    Grouping variables
    100 xp
    Modifying stat_smooth
    100 xp
    Modifying stat_smooth (2)
    100 xp
    Stats: sum and quantile
    50 xp
    Quantiles
    100 xp
    Using stat_sum
    100 xp
    Stats outside geoms
    50 xp
    Preparations
    100 xp
    Using position objects
    100 xp
    Plotting variations
    100 xp
  2. 2

    Coordinates

    The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

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  3. 4

    Best Practices

    Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

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For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Data Visualization in R

Go To Track

collaborators

Collaborator's avatar
Richie Cotton

prerequisites

Introduction to Data Visualization with ggplot2
Rick Scavetta HeadshotRick Scavetta

Rick Scavetta is a co-founder of Scavetta Academy.

Rick Scavetta is a biologist, workshop trainer, freelance data scientist and co-founder of Scavetta Academy, a company dedicated to helping scientists better understand and visualize their data. Rick's practical, hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.
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Don’t just take our word for it

*4.2
from 27 reviews
56%
26%
7%
7%
4%
  • Daniel N.
    13 days

    Ggplot 2 is displaying statistical graphs and physics graphs big time Quantum circuit remains me of ggplot 2 graphs display.

  • Kenny O.
    4 months

    I highly recommend the course. It's challenging but useful.

  • Thashalak A.
    5 months

    Amazing and engage Learner

  • Weixin W.
    about 1 year

    I like the course and learned a lot from the course. I like the design, format, and pace of the course. I really enjoyed learning on the datacamp platform.

  • Angad K.
    about 1 year

    Amazing course

"Ggplot 2 is displaying statistical graphs and physics graphs big time Quantum circuit remains me of ggplot 2 graphs display."

Daniel N.

"I highly recommend the course. It's challenging but useful."

Kenny O.

"Amazing and engage Learner"

Thashalak A.

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