In the past, I lost track of reality trying to track a gazillion links covering every data-science-friendly programming language under the sun. **shakes head** Bad idea. Since I program in R daily, I like to keep track of R and RStudio developments. I’m mostly going to share R resources and some other miscellaneous resources that I find useful for analytics, statistical programming, machine learning, data science workflows, and web app development. I’m enjoying the Julia programming language recently, so I’ll share resources that I find bookmark worthy as well.
R and Julia are open-source programming languages for statistical computing and graphics. R has an open and friendly community devoted to using R for data science and making business analytics easy to attain. One of the things that I like most about R is the thousands of packages available making almost everything in R a little easier from ETL, to method chaining, to developing interactive web apps. Julia is a newer programming language (newer to me) recommended by a coworker. Unlike interpreter languages such as R and Python, Julia has significant performance advantages for numerical computations. Julia is a multi-threaded programming language (native parallel processing and designed for distributed computing), it compiles (it doesn’t interpret lower-level languages like C or C++), and there is a growing body of scientists and developers finding unique ways to use the language. I certainly welcome any suggestions that you might have for the lists below!
R Classics
- R for Data Science: Phenomenal introduction to R, the RStudio IDE, and the
tidyverse collection of packages
- Advanced R: Covers R concepts, methods, and structures in detail
- Mastering Shiny: Designed to teach the foundations of Shiny for web development and more advanced concepts such as the introduction of modules to the Shiny framework
- R Packages: The definitive reference point for R package development “covering workflow and process, alongside the presentation of all the important moving parts that make up an R package”
R Applied Resources
- Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis
- Hands-On Machine Learning with R: I can’t recommend this resource enough and it’s one that I often have on my desk… Put best by the authors, “this book is meant to help R users learn to use the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, lime, and others to effectively model and gain insight from your data.”
- Deep Learning with R: In-depth introduction to artificial intelligence and deep learning applications with R using the Keras library
- Forecasting Principles and Practice: Said best by the author, “The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective.”
- Regression and Other Studies: Super applied textbook on advanced regression techniques, Bayesian inference, and causal inference
- Tidy Modeling with R: Over the last few months, I’ve learned a lot from this A to Z resource on predictive modeling workflows using the tidymodels framework
R Packages (my favorites)
- tidyverse: A collection of packages for data manipulation and functional programming (I use dplyr, stringr, and purrr on a daily basis)
- tidyverts: A collection of packages for time series analysis that comes out of Rob Hyndman’s lab
- tidymodels: Hands-down my preferred collection of packages for building reproducible machine learning recipes, workflows, model tuning, model stacking, and cross-validation
- knitr::kable and kableExtra: Learn how to build polished tables for use in reports, slides, and web apps
- shinydashboardPlus: This is my preferred UI framework for clean, enterprise-grade Shiny apps
- leaflet: R implementation of the popular Leaflet JavaScript library for developing interactive maps
- plotly: An extensive graphic library for creating interactive visualizations and 3D (WebGL) charts
Julia
- Official Manual: I’m in the process of reading through this documentation to better understand the Julia methods, operators, functions, function-building, and modules
- Learning Materials: Many resources are listed here on the official Julia Lang site
- Juno: An Atom-based flexible IDE for Julia
- Installation Instructions: Install Julia, Atom, and Juno
- Genie: This library is a full-stack web framework for modern web apps using Julia
- Medium articles on Julia that interested me:
- Installing Julia on a Raspberry Pi