![[Review] R for Excel Users: An Introduction to R for Excel Analysts (John L Taveras) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2684994/c1a-085k3-v66p5n6whnn9-nmtskz.jpg)
Show Notes
R for Excel Users: An Introduction to R for Excel Analysts (John L Taveras)
- Amazon USA Store: https://www.amazon.com/dp/1500566357?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/R-for-Excel-Users%3A-An-Introduction-to-R-for-Excel-Analysts-John-L-Taveras.html
- Apple Books: https://books.apple.com/us/audiobook/practical-portfolio-performance-measurement-and/id1690835042?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=R+for+Excel+Users+An+Introduction+to+R+for+Excel+Analysts+John+L+Taveras+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1500566357/
#ExceltoRtransition #Rdatamanipulation #Vectorsandfunctions #Datasetreshaping #Reproducibledataworkflows #RforExcelUsers
R for Excel Users: An Introduction to R for Excel Analysts is a beginner oriented technical guide by John L. Taveras, first published in 2016. It addresses a particular transition: the move from familiar spreadsheet work to programming with R. Rather than presenting R as a replacement for Excel or attempting to cover every part of the language, the book concentrates on data management. Its scope includes importing data, creating and modifying variables, filtering records, producing summaries, and reshaping datasets. Taveras frames these tasks through comparisons with spreadsheet ideas wherever that comparison is useful, while introducing the R building blocks of vectors and functions. This makes the book less a general manual on statistics or graphics than a practical on ramp for analysts whose existing habits were formed in worksheets. The stated purpose is to reduce the initial burden of learning R by separating data manipulation from the additional challenges of statistical modeling and visualization. Its approach reflects a business analysis context in which analysts may continue using Excel while applying R when repeatability, scale, or more systematic transformations are needed.
- Amazon USA Store: https://www.amazon.com/dp/1500566357?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/R-for-Excel-Users%3A-An-Introduction-to-R-for-Excel-Analysts-John-L-Taveras.html
- Apple Books: https://books.apple.com/us/audiobook/practical-portfolio-performance-measurement-and/id1690835042?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=R+for+Excel+Users+An+Introduction+to+R+for+Excel+Analysts+John+L+Taveras+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1500566357/
#ExceltoRtransition #Rdatamanipulation #Vectorsandfunctions #Datasetreshaping #Reproducibledataworkflows #RforExcelUsers
R for Excel Users: An Introduction to R for Excel Analysts is a beginner oriented technical guide by John L. Taveras, first published in 2016. It addresses a particular transition: the move from familiar spreadsheet work to programming with R. Rather than presenting R as a replacement for Excel or attempting to cover every part of the language, the book concentrates on data management. Its scope includes importing data, creating and modifying variables, filtering records, producing summaries, and reshaping datasets. Taveras frames these tasks through comparisons with spreadsheet ideas wherever that comparison is useful, while introducing the R building blocks of vectors and functions. This makes the book less a general manual on statistics or graphics than a practical on ramp for analysts whose existing habits were formed in worksheets. The stated purpose is to reduce the initial burden of learning R by separating data manipulation from the additional challenges of statistical modeling and visualization. Its approach reflects a business analysis context in which analysts may continue using Excel while applying R when repeatability, scale, or more systematic transformations are needed.
