- Challenges and characteristics of high-variety data
- Using R with Excel
- Exporting an R data structure to an Excel workbook
- Importing text, CSV, and tab-delimited files
- Working with the R foreign package
- Using R with XML files, HTML files, and Google Docs
- Working with images in R
Skill Level Intermediate
- [Mark] You work in a world full of data. Yeah, you, and everyone knows you're pretty good at working with R. So you get requests. "Hey, I have a project and some data. "Can you help me?" And you think, sure, why not? Then you look at the data. It's a mess of text files and Excel files and CSVs and PDFs. If you could bring it all into R, you could find an answer, but that depends on your ability to import that data, and that's what this course is about.
I'm Mark Niemann-Ross, and I'm going to help you import a variety of data, and by variety, I mean CSV and TDF and DBF and PDF and Excel and ODS and so many others, so let's get you the skills you need to get over the data import hurdle and back to the valuable analysis tasks you're really good at.
R Programming in Data Science: High Volume Datawith Mark Niemann-Ross1h 25m Intermediate
1. Use R with Excel
2. Importing Text Files
3. Understanding the Foreign Package
4. Use R with Popular Data Formats
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