![]() While R is a great tool for tabular data, you may find using other approaches to analyse non-tabular sources (such as newspaper transcriptions) more useful. Once you understand how to write some of the basic functions and how to import your own data files, you can analyze and visualize the data quickly and efficiently. R is ideal for analyzing larger data sets that would take too long to compute manually. The lesson will also cover different input methods for R such as matrices and using CSV files. The tutorial will do so by going through a series of mini-lessons that will show the kinds of sources R works well with and examples of how to do calculations to find information that could be relevant to historical research. It will take you through the installation process, explain some of the tools that you can use in R, as well as explain how to work with data sets while doing research. It will go through some of the basic functions of R and serves as an introduction to the language. ![]() ![]() This tutorial presumes no prior knowledge of R. Because you can save your code, R lets you re-purpose or revise functions for future projects, making it a flexible part of your toolkit. Because you can repeatedly re-run the same code on the same sources, R lets you analyze data quickly and produces repeatable results. As such, it can be used to complete quantitative analysis on historical sources, including but not limited to statistical tests. R is a programming language with strengths in statistical analyses. As more and more historical records are digitized, having a way to quickly analyze large volumes of tabular data makes research faster and more effective.
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