R Tutorial: Introduction to Data Visualization with ggplot2
Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-data-visualization-with-ggplot2 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
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Hi and welcome the first course in DataCamp's data visualization with ggplot2 series!
My name is Rick Scavetta and I'll be the instructor for this series.
I've been training scientists on how to better understand and visualize their data since 2012. I'm very excited to bring my experience to DataCamp.
So what is data viz?
Data visualization is an essential skill for data scientists. It combines statistics and design in meaningful and appropriate ways.
On the one hand, data vis is a form of graphical data analysis, emphasizing accurate representation and interpretation of data.
On the other hand, data vis relies on good design choices, not only to make our plots attractive, but to also aid both the understanding and communication of results.
On top of that, there is an element of creativity, since at it's heart, data vis is a form of visual communication.
It's important to understand the distinction between exploratory and explanatory visualizations.
Exploratory visualizations are easily-generated, data-heavy and intended for a small specialist audience, for example yourself and your colleagues - their primary purpose is graphical data analysis.
Explanatory visualizations are labor-intensive, data-specific and intended for a broader audience, e.g. in publications or presentations - they are part of the communications process.
As a data scientist, it's essential that you can quickly explore data, but you'll also be tasked with explaining your results to stake-holders.
Good design begins with thinking about the audience - and sometimes that just means ourselves.
This data set contains the average brain and body weights of 62 land mammals. To understand the relationship here, the most obvious first step is to make a scat
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