R Programming in Data Science: High Variety Data

R Programming in Data Science: High Variety Data
R Programming in Data Science: High Variety Data

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 27m | 269 MB
eLearning | Skill level: Intermediate

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.

Topics include:

  • 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
+ Table of Contents

1 Jumping over the high-variety hurdle
2 Perspectives on high-variety data

Use R with Excel
3 Excel packages compared
4 Read a workbook from Excel
5 Write a workbook to Excel
6 Read ranges from Excel
7 Write ranges to Excel
8 Read rows and columns from Excel
9 Write rows and columns to Excel
10 Read individual cells from Excel
11 Write individual cells to Excel

Importing Text Files
12 Text files in R
13 CSV files in R
14 Tab-delimited files in R
15 Fixed-width files in R

Understanding the Foreign Package
16 What is the R foreign package
17 Read form and write to DBF
18 Read from and write to SPSS
19 Read from and write to Stata
20 Read from and write to SAS

Use R with Popular Data Formats
21 XML in R
22 JSON in R
23 ODS files in R
24 HTML files in R
25 Extracting data from a PDF in R
26 Google Docs with R
27 Working with images in R

28 Next steps

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