R Programming in Data Science: High Variety Data

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Mark Niemann-Ross

1:27:59

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01 - Introduction

01 - Jumping over the high-variety hurdle.mp4

00:57

02 - Perspectives on high-variety data.mp4

04:19

02 - 1. Use R with Excel

01 - Excel packages compared.mp4

03:29

02 - Read a workbook from Excel.mp4

02:53

03 - Write a workbook to Excel.mp4

02:16

04 - Read ranges from Excel.mp4

03:10

05 - Write ranges to Excel.mp4

04:52

06 - Read rows and columns from Excel.mp4

02:57

07 - Write rows and columns to Excel.mp4

05:12

08 - Read individual cells from Excel.mp4

02:55

09 - Write individual cells to Excel.mp4

03:50

03 - 2. Importing Text Files

01 - Text files in R.mp4

05:30

02 - CSV files in R.mp4

03:39

03 - Tab-delimited files in R.mp4

03:32

04 - Fixed-width files in R.mp4

03:58

04 - 3. Understanding the Foreign Package

01 - What is the R foreign package.mp4

00:48

02 - Read form and write to DBF.mp4

02:51

03 - Read from and write to SPSS.mp4

03:19

04 - Read from and write to Stata.mp4

02:47

05 - Read from and write to SAS.mp4

02:59

05 - 4. Use R with Popular Data Formats

01 - XML in R.mp4

03:35

02 - JSON in R.mp4

02:37

03 - ODS files in R.mp4

02:30

04 - HTML files in R.mp4

03:02

05 - Extracting data from a PDF in R.mp4

02:38

06 - Google Docs with R.mp4

03:15

07 - Working with images in R.mp4

03:37

06 - Conclusion

01 - Next steps.mp4

00:32

Description

In a perfect world, every dataset would be stored as XML text with context for every piece of information. Numbers would never be stored as strings. Decimal values would never be stored as scientific notation. Strings would never be longer than 500 characters. But obviously, we don't live in a perfect world of data. And big data only makes this issue, well, bigger. This is the problem of variety; data arriving in multiple formats. Data scientists spend an inordinate amount of time with this problem, using brain power that would be better spent on valuable analysis tasks. In this course, Mark Niemann-Ross introduces the problem of data variety and demonstrates how to use the unique capabilities of R to solve them. Learn how to import a wide variety of data, from Excel to ODS files.

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Data Science

R Programming

Mark Niemann-Ross

Instructor's Courses

I’m friends with technology. But I’m not blindly in love with it. I ask it questions. I explain it. I prioritize people over technology. Bigger, better, and faster features make shinier products, but that isn’t what people want. People don’t want widgets, they want elegance and relevance. I’m an educator, manager, evangelist, marketer, technologist, futurist, coder. I point to the future. I explain the present. I learn from the past. People speak different languages: marketing, sales, engineering, management, strategy. I’m fluent in all of those. Sometimes I’m abrupt. Sometimes I apologize. I’m passionate, but willing to believe I’m wrong. I try to inform myself. “Why” is my favorite question, followed by “When.” I write science fiction. Sometimes it’s about spaceships, sometimes it’s about products. The goal is the same; explain where we want to be, point out hazards, celebrate arrival. I’m engaging. Informed. Opinionated. Respectful. Your thoughts?

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