020-book_list.Rmd

# Career & Community

## Build Your Career in Data Science
Not R-specific but a great read! 

https://www.manning.com/books/build-a-career-in-data-science


##  Twitter for R Programmers
[Oscar Baruffa](https://twitter.com/OscarBaruffa), [Veerle van Son](https://twitter.com/veerlevanson)

A guide to help R programmers join the very active and friendly community on Twitter.

https://www.t4rstats.com


##  Twitter for Scientists
https://t4scientists.com/


## Conversations On Data Science
Roger Peng and Hilary Parker

This book collects many of their discussions from the podcast 
[__Not So Standard Deviations__](https://soundcloud.com/nssd-podcast)
and distills them into a readable format.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/conversationsondatascience


## Executive Data Science
Brian Caffo, [Roger D. Peng](https://twitter.com/rdpeng), and [Jeffrey Leek](https://twitter.com/jtleek)

A Guide to Training and Managing the Best Data Scientists. Learn what you need 
to know to begin assembling and leading a data science enterprise.

Pay what you want for the PDF, minimum $0.00

https://leanpub.com/eds

## Essays on Data Analysis
Roger Peng

This book draws a complete picture of the data analysis process, filling out 
many details that are missing from previous presentations. It presents a new 
perspective on what makes for a successful data analysis and how the quality 
of data analyses can be judged.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/dataanalysisessays



# Blogdown
## blogdown: Creating Websites with R Markdown
https://bookdown.org/yihui/blogdown/

# Bookdown
##  bookdown: Authoring Books and Technical Documents with R Markdown
https://bookdown.org/yihui/bookdown/

##  A Minimal Book Example
https://benmarwick.github.io/bookdown-ort/



# Data Science
## R for Data Science 
Whickham and Grolemund

A really good place to start

https://r4ds.had.co.nz/

## R for Data Science Solutions
Solutions for the hadley and Grolemund R4Ds book

https://jrnold.github.io/r4ds-exercise-solutions/

## Introduction to Data Science 
Rafael A Irizarry 

https://rafalab.github.io/dsbook/

Pay what you want for PDF, minimum $0.00

https://leanpub.com/datasciencebook


## R Programming for Data Science
Roger Peng

https://bookdown.org/rdpeng/rprogdatascience/

## Exploratory Data Analysis… by Roger D. Peng 
Pay what you want, minimum $0.00

https://leanpub.com/exdata

## edav.info/
https://edav.info/

## APS 135: Introduction to Exploratory Data Analysis with R
https://dzchilds.github.io/eda-for-bio/

## The Art of Data Science
[Roger D. Peng](https://twitter.com/rdpeng) and Elizabeth Matsui

A Guide for Anyone Who Works with Data

This book describes the process of analyzing data. The authors have extensive 
experience both managing data analysts and conducting their own data analyses, 
and this book is a distillation of their experience in a format that is applicable 
to both practitioners and managers in data science. Printed copies are available through [Lulu](https://www.lulu.com/content/paperback-book/the-art-of-data-science/18733039).

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/artofdatascience

## Report Writing for Data Science in R
[Roger D. Peng](https://twitter.com/rdpeng) 

This book teaches the fundamental concepts and tools behind reporting modern 
data analyses in a reproducible manner. As data analyses become increasingly 
complex, the need for clear and reproducible report writing is greater than ever.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/reportwriting

## The Elements of Data Analytic Style
[Jeffrey Leek](https://twitter.com/jtleek)

Data analysis is at least as much art as it is science. This book is focused 
on the details of data analysis that sometimes fall through the cracks in 
traditional statistics classes and textbooks. It is based in part on the authors 
blog posts, lecture materials, and tutorials.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/datastyle



# Data Visualization

##  ggplot2: Elegant Graphics for Data Analysis
https://ggplot2-book.org/

## ggplot2 in 2 
Lucy D'Agostino McGowan

Pay what you want, minimum $4.99

Really good overview of ggplot2. Oscar Baruffa made a sped-up [screencast](https://youtu.be/_G7_J8M9588) while working through it. 

https://leanpub.com/ggplot2in2


##  Data Visualization - A practical introduction
https://socviz.co/


## Data Processing & Visualization
https://m-clark.github.io/data-processing-and-visualization/

## Data Visualization in R
Brooke Anderson

https://geanders.github.io/navy_public_health/index.html#prerequisites

## Data Visualization with R
Rob Kabakoff

https://rkabacoff.github.io/datavis/

## R Graphics Cookbook, 2nd edition
https://r-graphics.org/



## plotly Interactive web-based data visualization with R, plotly, and shiny
https://plotly-r.com/

## BBC Visual and Data Journalism cookbook for R graphics
https://bbc.github.io/rcookbook/


## Fundamentals of Data Visualization
https://clauswilke.com/dataviz/

# Distributed computing
## Mastering Spark with R
https://therinspark.com/


# Getting, cleaning and wrangling data
##  A Beginner's Guide to Clean Data - beginners-guide-to-clean-data
https://b-greve.gitbook.io/beginners-guide-to-clean-data/

## 21 Recipes for Mining Twitter Data with rtweet
https://rud.is/books/21-recipes/

## Text Mining with R
https://www.tidytextmining.com/

## Spreadsheet Munging Strategies
Great for dealing with messy spreadsheets

https://nacnudus.github.io/spreadsheet-munging-strategies/




# Geospatial

## Geocomputation with R
https://geocompr.robinlovelace.net/

## Spatial Data Science
https://keen-swartz-3146c4.netlify.app/

## Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
https://www.paulamoraga.com/book-geospatial/

## Introduction to R - R Spatial
https://rspatial.org/intr/index.html


# Machine Learning

##  Hands-On Machine Learning with R
https://bradleyboehmke.github.io/HOML/

## Feature Engineering and Selection: A Practical Approach for Predictive Models
http://www.feat.engineering/index.html

## Interpretable Machine Learning
[Christoph Molnar](https://twitter.com/ChristophMolnar)

A Guide for Making Black Box Models Explainable

[Online book](https://christophm.github.io/interpretable-ml-book/)

Pay what you want for the ebook, minimum $0.00

[Leanpub PDF](https://leanpub.com/interpretable-machine-learning)


## Supervised Machine Learning for Text Analysis in R
[Emil Hvitfeldt](https://twitter.com/Emil_Hvitfeldt), [Julia Silge](https://twitter.com/juliasilge)

Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.

https://smltar.com/


## Machine Learning for Factor Investing

[Guillaume Coqueret](https://twitter.com/g_coqueret) and [Tony Guida](https://twitter.com/TonyGUIDA_Quant)

This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics. 

http://www.mlfactor.com/




# Network analysis
##  Network Analysis in R Cookbook
http://sachaepskamp.com/files/Cookbook.html

# Text analysis

## Text Mining with R
https://www.tidytextmining.com/

## Supervised Machine Learning for Text Analysis in R
[Emil Hvitfeldt](https://twitter.com/Emil_Hvitfeldt), [Julia Silge](https://twitter.com/juliasilge)

Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.

https://smltar.com/

# R Markdown

## Getting used to R, RStudio, and R Markdown
https://bookdown.org/chesterismay/rbasics/

## Introduction to R Markdown
https://m-clark.github.io/Introduction-to-Rmarkdown/

## RMarkdown for Scientists
https://rmd4sci.njtierney.com/

## Pimp my RMD: a few tips for R Markdown
https://holtzy.github.io/Pimp-my-rmd/


# R package development

## R packages
http://r-pkgs.had.co.nz/

## rOpenSci Packages: Development, Maintenance, and Peer Review

This book is a package development guide for authors, maintainers, reviewers and editors of rOpenSci.

https://devguide.ropensci.org/index.html


# R programming
##  Modern R with the tidyverse
https://b-rodrigues.github.io/modern_R/

##  What They Forgot to Teach You About R
https://rstats.wtf/

## Field Guide to the R Ecosystem
https://fg2re.sellorm.com/

## YaRrr! The Pirate’s Guide to R
https://bookdown.org/ndphillips/YaRrr/

## Advanced R.
http://adv-r.had.co.nz/


## Efficient R programming
https://csgillespie.github.io/efficientR/

## The Tidyverse Cookbook
https://rstudio-education.github.io/tidyverse-cookbook/

## The tidyverse style guide
[Hadley Whickham](https://twitter.com/hadleywickham)

Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. This site describes the style used throughout the tidyverse. It was derived from Google’s original R Style Guide - but Google’s current guide is derived from the tidyverse style guide.

https://style.tidyverse.org/


## Tidyverse design guide
Tidyverse team

The goal of this book is to help you write better R code. It has four main components:

1. Design problems which lead to suboptimal outcomes.

1. Useful patterns that help solve common problems.

1. Key principles that help you balance conflicting patterns.

1. Selected case studies that help you see how all the pieces fit together with real code.

It is used by the tidyverse team to promote consistency across packages in the core tidyverse. 

https://design.tidyverse.org/

## Hands-On Programming with R
https://rstudio-education.github.io/hopr/

## The R Language
[R Core team](https://stat.ethz.ch/R-manual/R-patched/doc/AUTHORS)
https://stat.ethz.ch/R-manual/R-patched/doc/html/

## R language for programmers
[John D Cook](https://www.johndcook.com/blog/services-2/)
https://www.johndcook.com/blog/r_language_for_programmers/

## R Cookbook - 2nd edition
JD Long, Paul Teetor

Not to be confused with Cookbook for R
https://rc2e.com/index.html

## Cookbook for R
Winston Chang

Not to be confused with R Cookbook
http://www.cookbook-r.com/


## Tidy evaluation
https://tidyeval.tidyverse.org/

## Python to R: The Tidynomicon
http://tidynomicon.tech/

## The R Inferno
Patrick Burns

If R's behaviour has ever suprised you, then this book is a guide for many more surprises, written in the style of Dante. It's a concise report on number of common-errors and unexpected behaviours in R. This book would make more sense, if you have been programming and are familiar with such behaviours (not all though), as there is little time spent on explaining why part of behaviour. As mentioned, it's a concise book, 126 books only.

https://www.burns-stat.com/pages/Tutor/R_inferno.pdf

## A sufficient Introduction to R
Derek l. Sonderegger
https://dereksonderegger.github.io/570L/

## Mastering Software Development in R
[Roger D. Peng](https://twitter.com/rdpeng), [Sean Kross](https://twitter.com/seankross), 
and [Brooke Anderson](https://twitter.com/gbwanderson)

This book covers R software development for building data science tools. 
This book provides rigorous training in the R language and covers modern 
software development practices for building tools that are highly reusable, 
modular, and suitable for use in a team-based environment or a community of 
developers.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/msdr

# Shiny
## A gRadual intRoduction to Shiny
https://laderast.github.io/gradual_shiny/

##  Mastering Shiny
https://mastering-shiny.org/


##  Shiny Production with AWS Book
https://business-science.github.io/shiny-production-with-aws-book/

## Production Shiny App
https://engineering-shiny.org/

# Statistics

##  Common statistical tests are linear models: a work through
https://steverxd.github.io/Stat_tests/

## Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1)
https://learningstatisticswithr-bookdown.netlify.app/

## Answering questions with data
Matthew J. Crump

Looks like a comprehensive stats resource!

https://crumplab.github.io/statistics/

## Forecasting: Principles and Practice
https://otexts.com/fpp2/


## stats545
https://stat545.com/


## An Introduction to Statistical and Data Sciences via R
https://moderndive.com/


## Statistical Rethinking
A Bayesian Course with Examples in R and Stan
https://xcelab.net/rm/statistical-rethinking/

## OpenIntro Statistics
David Diez, [Mine Cetinkaya-Rundel](https://twitter.com/minebocek), 
Christopher Barr, and [OpenIntro](https://twitter.com/OpenIntroOrg).

A complete foundation for Statistics, also serving as a foundation for Data Science.

Leanpub revenue supports OpenIntro (US-based nonprofit) so we can provide free 
desk copies to teachers interested in using OpenIntro Statistics in the classroom 
and expand the project to support free textbooks in other subjects.

More resources: [openintro.org](https://www.openintro.org/).

Pay what you want for the ebook, minimum $0.00, however if you are able to, please 
consider the cause above. Thanks!

https://leanpub.com/openintro-statistics


## Statistical inference for data science
Brian Caffo

This book gives a brief, but rigorous, treatment of statistical inference 
intended for practicing Data Scientists.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/LittleInferenceBook

## Statistics (The Easier Way) With R, 3rd. Ed. (TIDYVERSION)
[Nicole Radziwill](https://radziwill.cc)

This introductory applied statistics handbook shows you how to run tests analytically,
and then how to run exactly the same steps using R. No steps are skipped, making this
particularly well suited for beginners or people who need a quick lookup. Used at 30+
universities around the globe.

https://amzn.to/3b9ha8s - varies between $37-43 & you can request free PDF after your order
https://www.e-junkie.com/ecom/gb.php?&c=single&cl=147256&i=1614407 - $25 for PDF only


## End-to-End Solved Problems With R: a catalog of 26 examples using statistical inference
[Nicole Radziwill](https://radziwill.cc)

Lots of worked problems, analytically and in R! Useful supplement for an introductory
applied stats class.

https://amzn.to/2EREAn2 - used for $4-18, new $19-20
https://www.e-junkie.com/ecom/gb.php?c=single&cl=147256&i=1548704 - $10 for PDF only


# Version control
## Happy Git and GitHub for the useR
https://happygitwithr.com/

# Workflow
##  How I Use R
https://howiuser.com/

## Agile Data Science with R
https://edwinth.github.io/ADSwR/



# Field specific
##  Analyzing Financial and Economic Data with R
https://www.msperlin.com/afedR/

##  Data Science in Education Using R
https://datascienceineducation.com/

## The Plain Person’s Guide to Plain Text Social Science
https://plain-text.co/index.html#introduction

## Technical Foundations of Informatics
https://info201.github.io/

## Practical R for Mass Communication and Journalism
http://www.machlis.com/R4Journalists/index.html

## An introduction to quantitative analysis of political data in R

[Erik Gahner Larsen](https://twitter.com/erikgahner) & [Zoltán Fazekas](https://twitter.com/fazol)

In this book, we aim to provide an easily accessible introduction to R for the collection, study and presentation of different types of political data. Specifically, the book will teach you how to get different types of political data into R and manipulate, analyze and visualize the output. In doing this, we will not only teach you how to get existing data into R, but also how to collect your own data.

http://qpolr.com/


## Orchestrating Single-Cell Analysis with Bioconductor

[Aaron Lun, Robert Amezquita, Stephanie Hicks, Raphael Gottardo](https://osca.bioconductor.org/contributors.html)

This is the website for “Orchestrating Single-Cell Analysis with Bioconductor”, a book that teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq).

https://osca.bioconductor.org/



## Machine Learning for Factor Investing

[Guillaume Coqueret](https://twitter.com/g_coqueret) and [Tony Guida](https://twitter.com/TonyGUIDA_Quant)

This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics. 

http://www.mlfactor.com/


## Computational Genomics with R
http://compgenomr.github.io/book/

## Introduction to Econometrics with R
https://www.econometrics-with-r.org/

## Data Analysis for the Life Sciences
Rafael A Irizarry and Michael I Love

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/dataanalysisforthelifesciences

[Accompanying website](http://genomicsclass.github.io/book/)

## How to be a modern scientist
[Jeffrey Leek](https://twitter.com/jtleek)

A book about how to be a scientist the modern, open-source way. 
The face of academia is changing. It is no longer sufficient to just publish or perish. 
We are now in an era where Twitter, Github, Figshare, and Alt Metrics are regular 
parts of the scientific workflow. Here I give high level advice about which tools 
to use, how to use them, and what to look out for. This book is appropriate for 
scientists at all levels who want to stay on top of the current technological 
developments affecting modern scientific careers.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/modernscientist




# Other compendiums
##  Data Science with R: A Resource Compendium
Another big book of resources!

https://bookdown.org/martin_monkman/DataScienceResources_book/



## R on the web

[Guillaume Coqueret](https://twitter.com/g_coqueret)

https://github.com/shokru/rstats/blob/master/material/R_links.md