High-variety data can cause a slew of problems for data scientists. In this course, learn what these problems are and how to use the unique capabilities of R to solve them.
- [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.
- Name the three types of big data. List three considerations used to determine the appropriate R package for Excel. Determine the best package used to import entire Excel workbooks. Explain how to import standard text files using base R and tidyverse. Define the purpose of the foreign language package for R. Recognize restrictions when working on SAS files in the foreign language package. Identify the problems involved with extracting data from a PDF in R.
Skill Level Intermediate
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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