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R Programming in Data Science: High Variety Data

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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.


01 - Introduction
  • 01 - Jumping over the high-variety hurdle
  • 02 - Perspectives on high-variety data

  • 02 - 1. Use R with Excel
  • 01 - Excel packages compared
  • 02 - Read a workbook from Excel
  • 03 - Write a workbook to Excel
  • 04 - Read ranges from Excel
  • 05 - Write ranges to Excel
  • 06 - Read rows and columns from Excel
  • 07 - Write rows and columns to Excel
  • 08 - Read individual cells from Excel
  • 09 - Write individual cells to Excel

  • 03 - 2. Importing Text Files
  • 01 - Text files in R
  • 02 - CSV files in R
  • 03 - Tab-delimited files in R
  • 04 - Fixed-width files in R

  • 04 - 3. Understanding the Foreign Package
  • 01 - What is the R foreign package
  • 02 - Read form and write to DBF
  • 03 - Read from and write to SPSS
  • 04 - Read from and write to Stata
  • 05 - Read from and write to SAS

  • 05 - 4. Use R with Popular Data Formats
  • 01 - XML in R
  • 02 - JSON in R
  • 03 - ODS files in R
  • 04 - HTML files in R
  • 05 - Extracting data from a PDF in R
  • 06 - Google Docs with R
  • 07 - Working with images in R

  • 06 - Conclusion
  • 01 - Next steps
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    مدت زمان: 88 دقیقه
    تاریخ انتشار: 27 دی 1401
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