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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/11/r-excel-history.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>While spreadsheets are fine tools for collecting and sharing data, the temptation is often there to also use them for in-depth analysis better suited to reproducible systems like R. Historian Jesse Sadler recently published the useful guide <a rel="nofollow" href="https://www.jessesadler.com/post/excel-vs-r/" target="_blank">Excel vs R: A Brief Introduction to R</a>, which provides useful advice to data analysts currently using spreadsheets on how to transition to R:</p>
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<p>Quantitative research often begins with the humble process of counting. Historical documents are never as plentiful as a historian would wish, but counting words, material objects, court cases, etc. can lead to a better understanding of the sources and the subject under study. When beginning the process of counting, the first instinct is to open a spreadsheet. The end result might be the production of tables and charts created in the very same spreadsheet document. In this post, I want to show why this spreadsheet-centric workflow is problematic and recommend the use of a programming language such as <a rel="nofollow" href="https://www.r-project.org/" target="_blank">R</a> as an alternative for both analyzing and visualizing data.</p>
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<p>The post provides a good overview of the pros and cons of using spreadsheets for data analysis, and then provides a useful — aimed at spreadsheet users — to using R for the problematic parts. It includes:</p>
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<li>Basics of the R command line</li>
<li>An overview of the <a rel="nofollow" href="https://www.tidyverse.org/" target="_blank">Tidyverse</a>, a suite of R packages for data manipulation</li>
<li>Working with data in R: numbers, strings and dates</li>
<li>Manipulating data frames by linking operations together with the <a rel="nofollow" href="http://blog.revolutionanalytics.com/2014/07/magrittr-simplifying-r-code-with-pipes.html" target="_blank">pipe operator</a></li>
<li>Visualizing data with the ggplot2 package</li>
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<p><a rel="nofollow" href="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b8d2bbaa58970c-pi" target="_blank"><img border="0" alt="Correspondence" title="Correspondence" src="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b8d2bbaa58970c-800wi"></a></p>
<p>The guide is built around a worked analysis of an interesting historical data set: the correspondence network from 6,600 letters written to the 16th-century Dutch diplomat Daniel van der Meulen. You can find the complete guide, including a link to download the data for the examples, at the link below.</p>
<p>Jesse Sadler: Excel vs R: <a rel="nofollow" href="https://www.jessesadler.com/post/excel-vs-r/" target="_blank">A Brief Introduction to R</a></p>
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