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Reddit mentions of ggplot2: Elegant Graphics for Data Analysis (Use R!)
Sentiment score: 5
Reddit mentions: 6
We found 6 Reddit mentions of ggplot2: Elegant Graphics for Data Analysis (Use R!). Here are the top ones.
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Here's what I'd recommend.
GETTING STARTED WITH DATA SCIENCE
If you're interested in learning data science I'd suggest the following:
 
Tools
Learning these has several benefits: they streamline your workflow. They speed up your learning process, since they are very easy to use. And perhaps most importantly, they really teach you how to think about analyzing data. GGplot2 has a deep underlying structure to the syntax, based on the Grammar of Graphics theoretical framework. I won’t go into that too much, but suffice it to say, when you learn the ggplot2 syntax, you’re actually learning how to think about data visualization in a very deep way. You’ll eventually understand how to create complex visualizations without much effort.
 
Skill Areas
My recommendations are:
(But, again I recommend learning these in R’s ggplot2.) The reason I recommend these is
As with any discipline, you need to learn the foundations first; this will dramatically speed your progress in the intermediate to advanced stages.
I usually suggest learning these with dummy data (for simplicity) but if you have a simple .csv file, that should work to.
After you learn data visualization, I suggest that you “back into” data management. For this, you should find a dataset and learn to reshape it.
The core data management skills:
You can start learning these here. Again, I recommend learning these in R’s dplyr because dplyr makes these tasks very straight forward. It also teaches you how to think about data wrangling in terms of workflow: the “chaining operator” in dplyr helps you wire these commands together in a way that really matches the analytics workflow. dplyr makes it seamless.
ML is sort of like the “data science 301” course vs. the 102 and 103 levels of the data-vis and data manipulation stuff I outlined above.
Here, I’ll just give book recos:
This is a highly regarded introduction
After you get these foundations, then you can move on to specialize in a particular area.
 
OTHER RESOURCES:
Data Visualization
 
TL;DR
I'd recommend learning R for data science before Python. Learn data visualization first (with R's ggplot2), using simple data or dummy data. Then find a more complicated dataset. Learn data manipulation second (with R's dplyr), and practice data manipulation on your more complex data. Learn machine learning last.
Anything by Tufte and the Flowing Data book and blog are great starting places. Tufte is more theory driven, for lack of a better term, while the Flowing Data sources have more "worked" examples (with R, Python, etc).
It would be worth learning ggplot2 as well if you are interested in data visualization as that seems to be the current "standard" tool. Hadley Wickham's website and UseR book on ggplot2 are great places to start.
Relatedly, Wickham's PhD thesis is all about tools and strategies for data visualization and can be found for free on his website. There is also an hour long seminar and slides to go with the paper.
There are a few example based approaches in R. Hadley's book on ggplot2 is worth a look, as is the online documentation. Both the book and the docs are more instructions on how to use ggplot2 than general guides for visualization, but the core ideas behind the grammar of graphics and ggplot2 are good starting points. As a bonus, the book is cheap and all the code in the examples is available online. Data Analysis and Graphics Using R is a much longer and more general introduction to experiments, statistics and graphics. If you are looking for an example heavy text to help you work through both stats and data visualization I recommend it. However it is long and somewhat expensive.
Tufte is certainly worth your time. I doubt there is a definitive guide. Data visualization is a bit like UI/UX design. There are a bunch of canonical rules which you shouldn't break until you know exactly what you are doing--then breaking them can be extremely valuable.
there's always the book by the author of ggplot: ggplot2: Elegant Graphics for Data Analysis. i haven't actually read it, but i'd imagine it's pretty thorough. there's going to be a new edition coming out, coinciding with a newer version of ggplot, iirc. there's also the official documentation here (looks like they just revamped the site).
but i learned it just by figuring out what i wanted to plot, and then using google/stackoverflow
Hadley (ggplot2 author) also has a book on the package if you want to get a solid foundation: here