Part 2 of 3: 300+ milestone for Big Book of R

This is part 2 of a 3 part series highlighting a selection of 35 new entries into Big Book of R.

Read part 1 and part 3.

The site now has well over 300 free R programming titles.

Onto the second batch of new books!

Handling Strings With R

by Gaston Sanchez

Handling character strings in R? Wait a second… you exclaim, R is not a scripting language like Perl, Python, or Ruby. Why would you want to use R for handling and processing text? Well, because sooner or later (I would say sooner than later) you will have to deal with some kind of string manipulation for your data analysis. So it’s better to be prepared for such tasks and know how to perform them inside the R environment.

Data Science: Theories, Models, Algorithms, and Analytics

by Sanjiv Ranjan Das

I developed these class notes for my Machine Learning with R course. It traces my evolution as a data scientist into redundancy, I expect I will be replaced by a machine soon!

Pack YouR Code

by Gaston Sanchez

The ultimate goal of this book is to teach you how to create a relatively simple R package based on the so-called S3 classes.

rlist Tutorial

by Kun Ren

rlist is a set of tools for working with list objects. Its goal is to make it easier to work with lists by providing a wide range of functions on non-tabular data stored in them. This package supports filtering, mapping, grouping, sorting, updating, searching and many other functions. It is pipe-friendly and strongly recommends functional programming style in list operations. This tutorial serves as complete guide to using rlist functionality to work with non-tabular data.

Doing Bayesian Data Analysis in brms and the tidyverse

by A Solomon Kurz

Kruschke began his text with “This book explains how to actually do Bayesian data analysis, by real people (like you), for realistic data (like yours).” In the same way, this project is designed to help those real people do Bayesian data analysis.

CSSS 508 Introduction to R for Social Scientists

by Charles Lanfear, Rebecca Ferrell

Course material with Youtube Video

Principles of Econometrics with R

by Constantin Colonescu

R supplementary resource for the “Principles of Econometrics” textbook by Carter Hill, William Griffiths and Guay Lim, 4-th edition

Using R for Introductory Econometrics

by Florian Heiss

An R book supplement to the Wooldridge’s “Introductory Econometrics” textbook

Introduction to R for Econometrics

by Kieran Marray

This is a short introduction to R to go with the first year econometrics courses at the Tinbergen Institute. It is aimed at people who are relatively new to R, or programming in general.

The goal is to give you enough of knowledge of the fundamentals of R to write and adapt code to fit econometric models to data, and to simulate your own data, working alone or with others. You will be able to: read data from csv files, plot it, manipulate it into the form you want, use sets of functions others have built (packages), write your own functions to compute estimators, simulate data to test the performance of estimators, and present the results in a nice format.

Most importantly, when things inevitably go wrong, you will be able to begin to interpret error messages and adapt others’ solutions to fit your needs.

Introduction to Econometrics with R

by Florian Oswald, Vincent Viers, Jean-Marc Robin, Pierre Villedieu, Gustave Kenedi

Welcome to Introductory Econometrics for 2nd year undergraduates at ScPo! On this page we outline the course and present the Syllabus. 2018/2019 was the first time that we taught this course in this format, so we are in year 3 now.

Newsletter subscribers get a free copy of Project Management Fundamentals for Data Analysts worth $12.

Once you’ve subscribed, you’ll get a follow up email with a link to your free copy.

Back to Top