Book release - Analyzing Financial and Economic Data with R (2º edition)

2021-02-28 – The book was revised in march 2021. See this blog post for details.

After a couple of unexpected delays, I am very pleased to announce the publication of the second edition of my book, Analyzing Financial and Economic Data with R. You can find it in Amazon as an ebook or paperback. An online version is available here. More details, including supplementary material, are available in the book webpage.

The first edition was released back in 2017 and it was a great journey working once again in this material. Many sections and chapters have been improved, along with new content. Here are the main changes:

Alignment with the tidyverse
  • Some base function are presented but priority is for readr, ggplot2, dplyr, stringr, purrr and so on.
  • 100+ pages of new content (a 25% overall increase from previous edition).
Teaching Material
  • Static end of chapter exercises, with solutions publicly available in the internet;
  • Rmarkdown slides for each chapter;
  • Dynamic 90+ exercises with the exams package. This means you can create and grade randomized unique tests for your students (see this post for details);
Book package afedR
  • This package makes it easier to import book datasets, slides, functions and solutions for end-of-chapter exercises. Available in GitHub only. See this blog post for instructions on installation.
Three new chapters
  • Cleaning and Structuring Financial and Economic Data – How to clean financial and economic data by dealing with long/wide dataframes, outlier detection/removal and desinflating prices and indexes.
  • Reporting Results – Using xtable and texreg to report tables and models. Includes a special section about the RMarkdown technology.
  • Optimizing Code – Profiling code for bottlenecks and using vectorization, rcpp and memoise to speed up R computations.
Two new packages in CRAN
  • simfinR – grabs corporate datasets from the SimFin project;
  • GetQuandlData– uses Quandl json api and caching for easier and faster data importation;

If you liked the material, please consider purchasing it and leaving your feedback at Amazon. Your oppinion is very important for promoting the book and help others learn more about R and RStudio. As an author, I certainly appreciate the gesture and will take it as a motivating factor for future editions of the book.

Marcelo S. Perlin
Marcelo S. Perlin
Senior Data Scientist & Associate Professor

My research interests include data analysis, finance and cientometrics.

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