May 2019
Beginner to intermediate
266 pages
5h 57m
English
Content preview from Hands-On Exploratory Data Analysis with R
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What to learn next
In the previous chapters, we experienced a variety of implementations with various datasets with the main focus on performing various statistical approaches with the R tool. The R programming language can be tricky for data scientists with limited programming experience. Therefore, it is considered important to create a learning path for R.
Creating this learning path for R was a continuous trade-off between being pragmatic and exhaustive. Therefore, we decided on creating the outline for the learning path.
The outline for the learning path is as follows:
- Why R?
- Environmental setup.
- Gist to R syntax and its pattern.
- Primary packages.
- Understanding the help system in R.
- The data analysis workflow:
- Data import
- Manipulating ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.