# R Programming

# Introduction to Data Science

## by Hansjörg Neth

This book provides a gentle introduction to data science for students of any discipline with little or no background in data analysis or computer programming. Based on notions of representation, measurement, and modeling, we examine key data types (e.g., logicals, numbers, text) and learn to clean, summarize, transform, and visualize (rectangular) data. By reflecting on the relations between representations, tasks, and tools, the course promotes data literacy and cultivates reproducible research practices that precede and enable practical uses of programming or statistics. This book is still being written and revised. It currently serves as a scaffold for a curriculum that will be filled with content as we go along. Read more →

# An Introduction to R for Research

## by Ramzi W. Nahhas

This text was written to provide Wright State University MPH students an introduction to the R programming language for use in research. […] This online book was written to provide Wright State University MPH students an introduction to the R programming language for use in research. The datasets used in this text are not being made publicly available. If you are using this material as part of a Wright State University course or tutorial, datasets will be provided to you by the instructor or in your learning management system. If you have any comments or suggestions, feel free to contact … Read more →

# Introduction to Computer Programming

## by คณาจารย์ประจำสาขาวิชาการจัดการเทคโนโลยีสารสนเทศ

คณาจารย์ประจำสาขาวิชาการจัดการเทคโนโลยีสารสนเทศ เอกสารประกอบการสอนนี้เรียบเรียงขึ้นเพื่อใช้ประกอบการเรียนในรายวิชา 946-141 การเขียนโปรแกรมคอมพิวเตอร์เบื้องต้น (Introduction to Computer Programming) ประกอบไปด้วยเนื้อหา ได้แก่ หลักการเกี่ยวกับการเขียนโปรแกรม การแก้ไขปัญหาด้วยขั้นตอนวิธี รหัสเทียม ผังงาน องค์ประกอบของภาษาโปรแกรม ชนิดของข้อมูลแบบต่าง ๆ โครงสร้างข้อมูลแบบอาร์เรย์ โครงสร้างโปรแกรมแบบตามลำดับ เลือกทำ และการวนซ้ำ การเรียกซับรูทีน การส่งผ่านค่าพารามิเตอร์ โดยใช้การเขียนโปรแกรมด้วยภาษาจาวาและการเขียนโปรแกรมแบบมีโครงสร้าง ในรายวิชานี้จึงเน้นรูปแบบการเขียนโปรแกรมเชิงกระบวนการ (procedural programming) เป็นสำคัญ โดยไม่ได้มุ่งเน้นการเขียนโปรแกรมเชิงวัตถุ (object-oriented programming: OOP) … Read more →

# R Bootcamp

## by Yun Dai

This is the ebook version of the four-week online R Bootcamp. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] Hello! This is the ebook version of the four-week online R Bootcamp for NYU Shanghai - NYU Stern MS programs in business. The website offers more recent updates than the video tutorials, and is designed to help readers find the information more quickly with its embedded links and navigation through the table of contents. The Bootcamp is designed for newcomers to R or programming in general. Our learning objectives for this program ... Read more →

# Data Visualization with R Programming

## by สมศักดิ์ จันทร์เอม

สมศักดิ์ จันทร์เอม ภาพนิทัศน์มีความสำคัญอย่างมากในการทำความเข้าใจข้อมูล และเพื่อประสิทธิภาพในการตัดสินใจ เครื่องมือที่ช่วยในการใช้สร้างภาพนิทัศน์ของข้อมูลในปัจจุบัน มีหลายตัว ในหนังสือเล่มจะใช้ภาษาอาร์ในการเขียนโปรแกรมเพื่อสร้างภาพนิทัศน์ และใช้โปรแกรม RStudio เพื่อช่วยการใช้เขียนโปรแกรมภาษาอาร์ให้มีความสะดวกสบายมากยิ่งขึ้นด้วยเครื่องมือช่วยที่หลากหลาย ในหนังสือเล่มไม่ได้สนใจในประเด็นตัวแบบสถิติ (statistics model) เศรษฐมิติ (econometrics) หรือการเรียนรู้ของเครื่องจักร (machine learning) ด้วยภาษาอาร์ แต่ถ้าผู้อ่านได้ศึกษาและทำความเข้าใจในหนังสือเล่มนี้แล้ว ผู้อ่านจะได้เรียนรู้พื้นฐานการเขียนโปรแกรมภาษาอาร์ที่จำเป็นอย่างมีหลักการ เช่นชนิดของโครงสร้างข้อมูลที่สำคัญคือวัตถุแบบเวคเตอร์ (vector) และกรอบข้อมูล (data frame) … Read more →

# Reproducible Miracle

## by Gökmen Altay

This book demonstrates 19 based coding system available in the Full Text of Quran text, which suggests Quran is intact and unchanged. […] This book presents 19 based system with reproducible coding evidences that I discovered, tested and witnessed in a book fully written 1387 years ago (632) than the publication date (2019) of this book. Most of the codings could only be practically realized by the invention of computers. You will not only witness some of the miraculous examples of the 19 based codings but also have the ability to easily test them yourself by running the R programming codes … Read more →

# An Introduction to Political and Social Data Analysis Using R

## by Thomas M. Holbrook

This book has two purposes: Provide students with a comprehensive, accessible overview of important issues related to political and social data analysis, and, at the same time, provide a gentle introduction to using the R programming environment to address those issues. […] … Read more →

# Introduction to Econometrics with R

## by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer

Beginners with little background in statistics and econometrics often have a hard time understanding the benefits of having programming skills for learning and applying Econometrics. ‘Introduction to Econometrics with R’ is an interactive companion to the well-received textbook ‘Introduction to Econometrics’ by James H. Stock and Mark W. Watson (2015). It gives a gentle introduction to the essentials of R programming and guides students in implementing the empirical applications presented throughout the textbook using the newly aquired skills. This is supported by interactive programming exercises generated with DataCamp Light and integration of interactive visualizations of central concepts which are based on the flexible JavaScript library D3.js. Read more →

# Unlocking the power of data visualization with R - Unlocking the Power of Data Visualization with R

## by fede_gazzelloni

A full list of Data Visualizations with code made with the R programming language. Welcome to Unlocking the Power of Data Visualization with R, where I proudly showcase my contributions to the #R4DS community through the #TidyTuesday, #30DayChartChallenge, and #30DayMapChallenge competitions, for 2021, 2022, and 2023. This platform is your gateway to data exploration, featuring a diverse collection of data visualizations created using the R programming language. Take a deep dive into the digital gallery, click on the image to discover insights, find inspiration, and learn from detailed … Read more →

# R Programming for Psychometrics

## by Susu Zhang

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] This ebook serves as a companion to the PSYC490 lab sessions. Chapters will be added and updated throughout the semester. Contributors: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International … Read more →

# An Introduction to R Analytics

## by GT CY

This is a blueprint of an introduction to R. […] Welcome to the world of data analysis! “Introduction to R in Data Analytics” is your friendly guide to understanding how to use the R programming language for playing with data. If you’re new to this, don’t worry - we’ve got you covered. This book takes you step by step, teaching you how to make sense of data using R. We’ll show you how to organize information, create cool charts and graphs, and even predict trends from data. You’ll learn all about the powerful tools that R offers for understanding numbers and patterns in data. But we won’t … Read more →

# Behavior Analysis with Machine Learning Using R

## by Enrique Garcia Ceja

Behavior Analysis with Machine Learning Using R teaches you how to train machine learning models in the R programming language to make sense of behavioral data collected with sensors and stored in electronic records. This book introduces machine learning concepts and algorithms applied to a diverse set of behavior analysis problems by focusing on practical aspects. Some of the topics include how to: Build supervised models to predict indoor locations based on Wi-Fi signals, recognize physical activities from smartphone sensors, use unsupervised learning to discover criminal behavioral patterns, build deep learning models to analyze electromyography signals, CNNs to detect smiles in images and much more. Read more →

# MLFE R labs (2023 ed.)

## by Prof. Michela Cameletti & Tutor Rasoul Samei

Notes for the R labs of the MLFE course @ Unibg […] You are reading the lecture notes of the R labs for the Machine learning for Economics (MLFE) course at University of Bergamo (academic year 2022/23). The MLFE course is the second module of the Coding for Data Science course. The MLFE R labs are designed for students who already have some experience with R programming thanks to the first module of the Coding and Machine Learning course. Click here and here to access the R lab notes of the first module regarding introduction to R language and the tidyverse package. Enjoy the journey! … Read more →

# AI and Machine Learning For Finance 2022/23

## by Michela Cameletti

Notes for the R labs of the AIMLFF course @Unibg […] You are reading the lecture notes of the R lectures for the AI and Machine Learning for Finance (AIMLFF) course at University of Bergamo (academic year 2022/23). See here for more details. In this notes R programming language for data science will be introduced (with respect to data manipulation, data visualization and communication and implementation of machine learning methods). For this part I suggest the following on-line book: Enjoy the journey! In the following lecture notes, this font (with grey background) represents R code. The … Read more →

# Quantitative Methods Using R

## by Subash Parajuli

This book covers practical worked out examples which you can easily apply to your data set and also includes a discussion on how the example is working. We will cover descriptive and basic inferential statistics, including graphs, frequency distributions, central tendency, dispersion, probability, hypothesis testing, tests of mean differences, correlation, simple regression, and chi-square tests. This book is designed to facilitate graduate students of Educational Psychology to develop their knowledge and understanding of various statistical concepts and procedures in R programming as a … Read more →

# R for Non-Programmers: A Guide for Social Scientists

## by Daniel Dauber

This book is a springboard into the world of R without having to become a full-fledged programmer or possess abundant knowledge in other programming languages. This book guides you through the most common challenges in empirical research in the Social Sciences and offers practical and efficient solutions. Each chapter is dedicated to a common task we have to achieve to answer our research questions. In addition, it provides plenty of exercises and in-depth case studies based on actual data. Read more →

# Data Analysis in Medicine and Health using R

## by Kamarul Imran, Wan Nor Arifin, Tengku Muhammad Hanis Tengku Mokhtar

Data Analysis in Medicine and Health using R […] We wrote this book to help new R programming users with limited programming and statistical background. We understand the struggles they are going through to move from point-and-click statistical software such as SPSS or MS Excel to more code-centric software such as R and Python. From our experiences, frustration sets in early in learning this code-centric software. It often demotivates new users to the extent that they ditch them and return to using point-and-click statistical software. This book will minimise these struggles and gently … Read more →

# A primer for biostatistics in R

## by cjlortie

A brief introduction to statistical thinking in biostatistics supported by the R programming language. […] Welcome to a primer for biostatistics in R. Mathematical! Adventure time! Well, the mathematical part is up to you, but this is an adventure. This set of learning materials is a guide developed to support you in better developing critical thinking using statistics. Critical thinking very generally is a mode of thinking that is self-directed and evidence based (Facionie 2017). Statistical thinking is thus an ideal opportunity and partner in honing literacy adventure skills in this … Read more →

# An Introduction to ggplot2

## by Ozancan Ozdemir

A ggplot2 Tutorial […] Hi! Data Visualization is one of the important steps of the data analysis process. It is actually not only part of the data analysis, but also can be considered as an art. R Programming language provides a powerful visualization package to us, ggplot2. This book aims to show how you can make a well-known statistical plots by using ggplot2, and also how you can improve or customize them. The book is created by the lab notes of statistical computing (STAT 291-STAT 292) of Ozancan Ozdemir. For your opinions and suggestions, please send me an e-mail to … Read more →

# Introduction to R for Data Science: A LISA 2020 Guidebook

## by Jacob D. Holster

Introduction to R for Data Science: A LISA 2020 Guidebook […] Data science is emerging as a vital skill for researchers, analysts, librarians, and others who deal with data in their personal and professional work. In essence, data science is the application of the scientific method to data for the purpose of understanding the world we live in. More specifically, data science tasks emerge from an interdisciplinary amalgam of statistical analysis, computer science, and social science research conventions. Although other programming languages such as python exceed R in general popularity, R … Read more →

# R Programming for Data Science

## by Roger D. Peng

The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. Read more →

# MLFE R labs (2022 ed.)

## by Prof. Michela Cameletti & Tutor Marco Villa

Notes for the R labs of the MLFE course @ Unibg […] You are reading the lecture notes of the R labs for the Machine learning for Economics (MLFE) course at University of Bergamo (academic year 2021/22). The MLFE course is the second module of the Coding for Data Science course. The MLFE R labs are designed for students who already have some experience with R programming thanks to the first module of the Coding and Machine Learning course. Click here and here to access the R lab notes of the first module regarding introduction to R language and the tidyverse package. Enjoy the journey! … Read more →

# Fundamentals of bioacoustics using smartphones and R

## by Dena J. Clink, Isabel A. Comella & Maryam Zafar

Fundamentals of bioacoustics using smartphones and R […] The purpose of this lab exercise is to introduce students to bioacoustics, or the study of animal sounds and their habitats. For the field component, students use their smartphones to collect focal recordings of target animals as well as collect acoustic data that will be used to investigate variation in soundscapes at different times (e.g. dawn and dusk) and/or different locations (e.g. urban versus rural). The computer lab component utilizes the R programming environment to import sound files and visualize differences in acoustic … Read more →

# AI and Machine Learning For Finance 2021/22

## by Michela Cameletti

Notes for the R labs of the AIMLFF course @ Unibg […] You are reading the lecture notes of the R lectures for the AI and Machine Learning for Finance (AIMLFF) course at University of Bergamo (academic year 2021/22). See here for more details. In this notes R programming language for data science will be introduced (with respect to data manipulation, data visualization and communication and implementation of machine learning methods). For this part I suggest the following on-line book: Enjoy the journey! In the following lecture notes, this font (with grey background) represents R code. The … Read more →

# Practical Data Skills

## by Introduction To Data Science

Practical Data Skills […] The purpose of this book is to provide practical data science skills to managers and business analysts. The focus is helping the reader develop pragmatic skills they can apply within their organizations to extract value from data. This book will not provide a complete and rigorous overview of data science, statistics, or computer programming, but it will help the reader quickly learn how to process and analyze data in the R programming language. The book assumes nothing more than a high school level background in mathematics - it requires no prior knowledge of … Read more →

# Coding for Data Science 2021/22 - R part

## by Michela Cameletti

Notes for the R labs of the C4DS course @ Unibg […] You are reading the lecture notes of the R lectures for the Coding for Data Science (C4DS) course at University of Bergamo (academic year 2021/22). C4DS is the first module of the course named Coding and Machine Learning (see here for more details). The C4DS R lectures are designed for students who already have a programming background thanks to the first part of the C4DS course dedicated to Python. In this part of the module we will introduce R programming language for data science (including data manipulation, data visualization and … Read more →

# Introduction to R for Econometrics

## by Kieran Marray (Tinbergen Institute)

Introduction to R for Econometrics […] 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.1 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 … Read more →

# PSY317L & PSY120R Guidebook

## by James P. Curley & Tyler M. Milewski

PSY317L & PSY120R Guidebook […] This book is written to help students enrolled in the University of Texas at Austin Introduction to Statistics for the Behavioral Sciences (PSY317L) course or R Programming for Behavioral Sciences (PSY120R) led by Professor James Curley. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any suggestions for improvements, please get in touch with Professor Curley. This is in between a textbook and a study guide. We’re trying to build materials that will enable students to quickly find what they’re looking for … Read more →

# Machine Learning for Economics 2020/21: R labs

## by Michela Cameletti

Notes for the R labs of the MLFE course @ Unibg […] You are reading the lecture notes of the R labs for the Machine learning for Economics (MLFE) course at University of Bergamo (academic year 2020/21). The MLFE course is the second module of the Coding and Machine Learning course. The MLFE R labs are designed for students who already have some experience with R programming thanks to the first module of the Coding and Machine Learning course. Click here to access the R lab notes of the first module regarding introduction to R language and the tidyverse package. Enjoy the journey! … Read more →

# Simulation and Modelling to Understand Change

## by Manuele Leonelli

These are lecture notes for the module Simulation and Modelling to Understand Change given in the School of Human Sciences and Technology at IE University, Madrid, Spain. The module is given in the 2nd semester of the 1st year of the bachelor in Data and Business Analytics. Knowledge of basic elements of R programming as well as probability and statistics is assumed. […] These are lecture notes for the module Simulation and Modelling to Understand Change given in the School of Human Sciences and Technology at IE University, Madrid, Spain. The module is given in the 2nd semester of the 1st … Read more →

# JavaScript for R

## by John Coene

Invite JavaScript into your Data Science workflow. […] This is the online version of JavaScript for R, a book currently under development and intended for release as part of the R series by CRC Press. The R programming language has seen the integration of many languages; C, C++, Python, to name a few, can be seamlessly embedded into R so one can conveniently call code written in other languages from the R console. Little known to many, R works just as well with JavaScript—this book delves into the various ways both languages can work together. The ultimate aim of this work is to demonstrate … Read more →

# Efficient R programming

## by Colin Gillespie, Robin Lovelace

Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. […] This is the online version of the O’Reilly book: Efficient R programming. Pull requests and general comments are welcome. Get a hard copy from: Amazon (UK), Amazon (USA), O’Reilly Colin Gillespie is Senior Lecturer (Associate Professor) at Newcastle University, UK. He is an Executive Editor of the R Journal, with research interests including high performance statistical computing and Bayesian statistics. Colin founded the … Read more →

# R Gallery Book

## by Kyle W. Brown

This is the complete guide to the R Gallery. […] Welcome the R Gallery Book, a complete guide to the R Graph Gallery website. This information is taken directly from R graph gallery with careful detail in reproducing plots and completing ideas. This material created by Kyle W. Brown as way to have one single reading collection of updated R gallery plots and graphs. While this book was created to encapsulate the entire R Graph Gallery website into one readable source, another purpose is serving as introductory level into data visualization using R programming … Read more →

# Working with Data in R

## by Eric van Holm, PhD

This is a textbook written to introduce some basic steps of working with and preparing data for use in quantitative analysis. […] This book will provide readers a few basic steps to begin working with data in R. It is not meant as a comprehensive introduction to using R for all of the different functions that are possible. Rather, it is tailored to help an individual that has quantitative data they would like to work with, but has not worked in R previously. The presentation of material is meant to be accessible to students with little to no background in R or computer programming. In many … Read more →

# Review and Homework for Programming for Educational Research

## by Chaeeun Song

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] 이것은 2020학년도 2학기 교육용 프로그래밍의 이해 수업을 들으며 작성한 필기 노트와 과제 모음입니다. R markdown is a file format that can be converted into many different documents such as HTML, PDF, Microsoft Word, and other dynamic documents. That is, it’s a nice example of one source multi … Read more →

# Worked Solutions to Project Euler in R

## by See contributing.md

Worked Solutions to Project Euler in R […] Project Euler is a series of challenging mathematical/computer programming problems that will require more than just mathematical insights to solve. Although mathematics will help you arrive at elegant and efficient methods, the use of a computer and programming skills will be required to solve most problems. The motivation for starting Project Euler, and its continuation, is to provide a platform for the inquiring mind to delve into unfamiliar areas and learn new concepts in a fun and recreational context. The intended audience include students … Read more →

# R Programming - 1st Homework (202AIE42)

## by Hong hyejin

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] … Read more →

# DondeRs Group

## by Henrik Eckermann

This bookdown-project contains introductory material to learn the R programming language […] Instructor: My name is Henrik. I am a PhD-candidate in the Developmental Psychobiology lab group at the Donders Institute in Nijmegen. I find that the R programming language is an extremely useful tool for Scientists, especially (but not only) for data analysis and visualization. I can help you learning the basics of the R programming language and how to approach learning a programming language so you can advance in learning whatever is needed in your specific field. Target audience: Anyone at … Read more →

# R Programming Tutorial

## by Thieu Nguyen

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language … Read more →

# Прикладна аналітика для активістів природоохоронного руху

## by Василенко Євген

This is a practical example of using the R programming language to environmental protection activism. All examples based on practical cases of Public Association «Ecological Council of Kryvorizhzhya» (Ukraine) […] Ця книга є наочним посібником для активістів природоохоронного руху. Тут містяться складні, проте дуже важливі на сьгоднішній момент практичні поради: яким чином довести до основної маси населення проблеми надзвичайного забруднення українського довкілля. Як показати людям те, що нічого в природі не виникає «просто так». Як показати людям вплив десятків невидимих неозброєним оком … Read more →

# 經濟資料視覺化處理

## by 林茂廷, 國立臺北大學經濟學系

經濟資料視覺化 […] This course is designed to develop the skill of efficient graphic language, where efficiency is defined as the data information delivery that is self-contained, concise, and non-distorting. The programming language is mainly based on R, with a little bit of Javascript toward the end. Though there is no computer programming knowledge required, basic R knowledge will help (the ebook, R for Data Science, would be a good start). By the end of the course, students who learn well should be able to design professional … Read more →

# 課程介紹

## by tpemartin

經濟資料視覺化 […] This course is designed to develop the skill of efficient graphic language, where efficiency is defined as the data information delivery that is self-contained, concise, and non-distorting. The programming language is mainly based on R, with a little bit of Javascript toward the end. Though there is no computer programming knowledge required, basic R knowledge will help (the ebook, R for Data Science, would be a good start). By the end of the course, students who learn well should be able to design professional … Read more →

# How to Build a Shiny Application from Scratch

## by Hadrien@rstudio.com

How to Build a Shiny Application from Scratch […] Shiny is a powerful R package which allows you to create interactive web applications using the R programming language. It is particularly useful for creating applications that run on data and include some sort of data analysis or visualization. In addition to leveraging the power of R and its thousands of packages, one of the big benefits of shiny is the ease of developing applications using R only. Although it is possible to incorporate more traditional web design languages such as custom CSS or Javascript into your shiny application, it … Read more →

# 課程大綱

## by tpemartin

經濟資料視覺化處理 […] This course is designed to develop the skill of efficient graphic language, where efficiency is defined as the data information delivery that is self-contained, concise, and non-distorting. The programming language is mainly based on R, with a little bit of Javascript toward the end. Though there is no computer programming knowledge required, basic R knowledge will help (the ebook, R for Data Science, would be a good start). By the end of the course, students who learn well should be able to … Read more →

# Predictive Soil Mapping with R

## by Tomislav Hengl and Robert A. MacMillan

Predictive Soil Mapping aims to produce the most accurate, most objective, and most usable maps of soil variables by using state-of-the-art Statistical and Machine Learning methods. This books explains how to implement common soil mapping procedures within the R programming language. […] This is the online version of the Open Access book: Predictive Soil Mapping with R. Pull requests and general comments are welcome. These materials are based on technical tutorials initially developed by the ISRIC’s Global Soil Information Facilities (GSIF) development team over the period 2014–2017. This book is … Read more →

# Predictive Soil Mapping with R

## by Tomislav Hengl and Robert A. MacMillan

Predictive Soil Mapping aims to produce the most accurate, most objective, and most usable maps of soil variables by using state-of-the-art Statistical and Machine Learning methods. This books explains how to implement common soil mapping procedures within the R programming language. […] This is the online version of the Open Access book: Predictive Soil Mapping with R. Pull requests and general comments are welcome. These materials are based on technical tutorials initially developed by the ISRIC’s Global Soil Information Facilities (GSIF) development team over the period 2014–2017. This book is … Read more →

# Notes for ST463/ST683 Linear Models 1

## by Katarina Domijan, Catherine Hurley

These are the notes for ST463/ST683 Linear Models 1 course offered by the Mathematics and Statistics Department at Maynooth University. This module is offered at as a part of of MSc in Data Science and Data Analytics. It is an introductory course for students who have basic background in Statistics, Data analysis, R Programming and linear algebra (matrices). […] There are many good resources, e.g. Weisberg (2005), Fox (2005), Fox (2016), Ramsey and Schafer (2002), Draper and Smith (1966). We will use Minitab and R (R Core Team 2017). To create this document, I am using the bookdown package … Read more →

# Foundations of Statistics with R

## by Darrin Speegle

This book is written for the purposes of teaching STAT 3850 at Saint Louis University. […] This is a book on probability and statistics suitable for the sophomore or junior level at university. We assume knowledge of calculus at the level of Calculus II. We do not assume prior experience with statistics or programming, though students who have no experience with either statistics or programming before starting this class should expect to have to work hard. We will be using R as an integral part of the exposition — you should not read this book without first getting R Studio installed. We … Read more →

# Functional programming and unit testing for data munging with R

## by Bruno Rodrigues

This book is an introduction to functional programming and unit testing with the R programming language, for the purpose of data muning […] This book is still being written, some chapters are not finished yet, and there might be (there are) some typos. Don’t hesitate to write to me if you notice something weird. You can purchase a digital copy of this book at leanpub. The version on Leanpub will not always be up-to-date, I only update it when I made very big changes (new chapters, etc). But once this book will be finished, both version are going to be the same. This book serves to show how … Read more →

# ModernDive

## by Chester Ismay and Albert Y. Kim STARRING FRANK MCGRADE

An open-source and fully-reproducible electronic textbook bridging the gap between traditional introductory statistics and data science courses. […] Help! I’m new to R and RStudio and I need to learn about them! However, I’m completely new to coding! What do I do? If you’re asking yourself this question, then you’ve come to the right place! Start with our Introduction for Students. This is version 0.2.0 of ModernDive published on August 02, 2017. For previous versions of ModernDive, see Section 1.4. This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding … Read more →

# Advanced R

## by Hadley Wickham

This is the website for 2nd edition of “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as help you to understand why R works the way it does. If you’re looking for the 1st edition, you can find it at http://adv-r.had.co.nz/. This work, as a whole, is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The code contained in this book is simultaneously … Read more →

# Introduction to Data Science

## by Rafael A. Irizarry

This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with GitHub, and reproducible document preparation with R markdown. Read more →

# Tidy Modeling with R

## by Max Kuhn and Julia Silge

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This book provides a thorough introduction to how to use tidymodels, and an outline of good methodology and statistical practice for phases of the modeling process. […] Welcome to Tidy Modeling with R! This book is a guide to using a collection of software in the R programming language for model building called tidymodels, and it has two main goals: First and foremost, this book provides a practical introduction to how to use these specific R packages to create models. We … Read more →

# What They Forgot to Teach You About R

## by Jennifer Bryan, Jim Hester, Shannon Pileggi, E. David Aja

Jennifer Bryan Jim Hester Shannon Pileggi E. David Aja This book is a work in progress. This book focuses on content intrinsically related to the infrastructure surrounding data analysis in R, but does not delve into the data analysis itself. A holistic workflow provides guidance on project-oriented workflows that address common sources of friction in data analysis. Personal R administration empowers R users to confidently manage their R programming environment. All is Fail showcases functions, options, and RStudio capabilities for debugging code, facilitating more efficient resolution of … Read more →

# Yet another ‘R for Data Science’ study guide

## by Bryan Shalloway

Notes and solutions to Garrett Grolemund and Hadley Wickham’s ‘R for Data Science’ […] This book contains my solutions and notes to Garrett Grolemund and Hadley Wickham’s excellent book, R for Data Science (Grolemund and Wickham 2017). R for Data Science (R4DS) is my go-to recommendation for people getting started in R programming, data science, or the “tidyverse”. First and foremost, this book was set-up as a resource and refresher for myself1. If you are looking for a reliable solutions manual to check your answers as you work through R4DS, I would recommend using the solutions created and … Read more →