Putting It All Together – R, the tidyverse Ecosystem, and APIs

Thomas W. MacFarland
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Thomas W. MacFarland: Nova Southeastern University, Office of Institutional Effectiveness and College of Computing and Engineering

Chapter Chapter 7 in Introduction to Data Science in Biostatistics, 2024, pp 433-523 from Springer

Abstract: Abstract The purpose of this lesson is to provide summary information on how R and more specifically R’s tidyverse ecosystem are both used in support of data science. A few key concepts about the tidyverse ecosystem are reinforced, such as: (1) use of an Application Programming Interface (API) in an effort to obtain data; (2) the need to put data into tidy format; (3) and use of the tidyverse ecosystem in support of statistical analyses and the creation of figures, maps, and other visuals. An introduction is also offered on how data scientists prepare reports and the way supporting software and processes for the same can be integrated into R. A few comments are made on the next steps for those who wish to continue in data science. Perhaps most importantly for those who are in the early days of career exploration and advancement, there is also a discussion on the soft skills needed by those who wish to become leaders in data science and in turn use data science to promote societal improvement.

Keywords: American Community Survey (ACS); Application Programming Interface (API); Bar plot; Base R; Beanplot; Beautiful graphics; Beeswarm plot; Boxplot; Centiles; Density plot; Dotplot; Federal Information Processing System (FIPS); GEOID; Histogram; Kurtosis; LaTeX; Line chart; MULTIPOLYGON; Markdown; Maximum; Mean; Median; Minimum; Mode; Mosaic plot; Percentiles; Pirate plot; Quantile-Quantile (QQ) plot; Quantiles; R; Range; Scatter plot; Scatter plot matrix; Skewness; Soft skills; Standard deviation; Standard error; Sum; tidyverse ecosystem; Typesetting; Variance; Violin plot; Waffle plot (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46383-9_7

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