8 Introduction to Statistical Modelling
R provides tools for everything from simple linear regression to complex machine learning algorithms.
Below are some key resources that will help you develop expertise in statistical modeling with R.
Data Visualisation Geometries Encyclopedia: Geoms in the Grammar of Graphics: All Types of Plots by Thiyanga Talagala
This encyclopedia is a curated collection of geom available in different R programming software packages. This book can also be considered as an “Encyclopedia of Plots”.
R for Data Science by by Hadley Wickham and Garrett Grolemund
This is a great data science book for beginners interested in learning data science with R.
Advanced R by Hadley Wickham
This book aimed at intermediate and advanced R users.
ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
This book gives details on the basics of ggplot2 and Grammar of Graphics that ggplot2 uses.
An Introduction to Statistical Learning with Applications in R by Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani
This book covers topics, ranging from basic concepts to advanced machine learning techniques.
Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
This textbook provides a comprehensive introduction to time series analysis and forecasting method.
Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
This book is an empirical companion to Introduction to Econometrics by Stock and Watson (2020).
-
The CRAN Task View offers a comprehensive list of R packages specifically for econometrics, providing useful tools for economic data analysis and modeling
Geocomputation with R by Robin Lovelace, Jakub Nowosad and Jannes Muenchow
This book covers geographic data analysis, visualization, and modeling techniques
The
ceylonR package by Thiyanga Talagala
The ceylon R package enables easy map visualization of Sri Lanka.