In this video, Mark Niemann-Ross demonstrates several methods of working with comma-separated value files. Learn the pros and cons of different functions available for CSV file import and export.
[Instructor] In data science you are going to be importing…a lot of comma separated value files.…So let's go over how to do that with R.…If you look in the sample files folder…in the exercise files, I've provided a sample CSV file…called Extra-vehicular_Activity_EVA etc., etc.…Let's take a look at it real quick.…And what you'll immediately notice about this file…is that it uses commas to separate the fields.…
This is what is different about this…and maybe a tab delimited file…which is separated by tabs.…But the important thing to note is that this data…is all separated into fields using commas.…Note that some of these fields contain dates…and they're not always consistently represented.…Notice that this is zero six zero three 1965,…and this is a string marked as March 16-17, 1966.…There can be long text, there can be bouillons,…there can be all kinds of things mixed into the text,…and we'll see how that works differently in some cases.…
So let's go back to our code.…If you haven't installed readr…from the tidyverse package, do so,…
- 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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