Connecting to database

• Working on Data-Warehouse (SQL) with R – dplyr
• Database Queries With R – dplyr
• Getting started with PostgreSQL in R – RPostgreSQL
• Connect to Database in R
• Setting up a database connection
• Introduction to MySQL with R
• SQL for LinkedIn
• Handling large data sets with Google BigQuery and R
• Introduction to dplyr for Databases
• A Comprehensive Introduction to Working with Databases using R
• Using SQL in RStudio
• Introduction to Working with Databases using R

Data Wrangling

• Data Manipulation with dpylyr – dplyr
• Manipulation de facteurs avec forcats – forcats
• dplyr tutorial – dplyr
• dplyr In a Nutshell – dplyr
• Introduction to apply
• Data Processing with dplyr & tidyr – dplyr
• How to explore and manipulate a dataset from the fivethirtyeight package in R
• Repository for dplyr tutorials by Suzan Beart – dplyr
• Data Analysis exampple with ggplot and dplyr
• Joining Data in R with dplyr – dplyr
• How do I make my own dplyr-style functions? – dplyr
• Simplify Your Code with %>%
• Five steps for missing data with Finalfit
• Pipelines in R and Data Analysis

Data Preparation

• Feature Engineering using R
• Tutorial on 5 Powerful R Packages used for imputing missing values
• Data Cleaning with R and the Tidyverse: Detecting Missing Values

Explorative Data Analysis

• Six Sigma DMAIC Series in R – Part 1 – DMAIC
• Beautiful and Powerful Correlation Tables in R
• Explore Your Dataset in R
• D3 Charts for Non_D3 Users
• A Brief Introduction to Various Correlation Coefficients and When to Use Which
• Beginners Cookbook for Interactive Visualization in R with highcharter – highcharter
• Correlogram in R: how to highlight the most correlated variables in a dataset
• Recreating data viz in ggplot2

Visualization

• Effective Visualizations for Data Driven Decisions
• A Compendium of Clean Graphs in R
• R graphics – ggplot2
• high quality resources for doing data journalism with R
• Top 50 ggplot2 Visualizations – ggplot2
• from Data to Viz
• Introduction to data visualization with R – ggplot2
• Visualising spatial data: from R to shiny - workshop
• Highlighting with ggplot2
• Labeling time series in ggplot2
• Happy collaboration with Rmd to docx
• Laying out multiple plots on a page – ggplot2
• Chapter 1. Data Visualization with ggplot2 – ggplot2
• ggplot2
• Tutorial 5.2 - The Grammar of Graphics in R (ggplot2)
• ggplot2 extentions
• Plotly in R: How to make ggplot2 charts interactive with ggplotly – ggplot2, plotly
• Comprehensive list of color palettes available in r
• 12 Extensions to ggplot2 for More Powerful R Visualizations
• Mapping ggplot geoms and aesthetic parameters
• How to Turn Your ggplot2 Visualization into an Interactive Tweet
• Themes and Labels – ggplot2
• A Detailed Guide to ggplot colors
• INTRODUCING 3D GGPLOTS WITH RAYSHADER – rayshader
• Highcharts for R users

Animation

• Animating Your Data Visualizations Like a Boss Using R
• Factfulness
• Using ggplot2 for functional time series
• gganimate: Animate YouR Security Analysis
• Create Trending Animated Bar Charts using R

Clustering

• A package for visualizing dendrograms – dendextend
• Difference Between K-Means And Hierarchical – kmeans
• R Clustering – kmeans
• Hierarchical Cluster Analysis
• Hierarchical Clustering in R
• Where Kmeans clustering fails Can DBSCAN be a solution?
• Determining The Optimal Number Of Clusters: 3 Must Know Methods
• Hierarchical Cluster Analysis
• K-means Clustering in R with Example
• Measuring feature importance in k-means clustering and variants thereof

PCA

• Multivariate Outlier Detection in High-Dimensional Spectral Data
• Principal Component Methods in R – factoextra
• Principal Component Analysis in R
• Principal component analysis : the basics you should read - R software and data mining
• Introduzione al trattamento Statistico dei Dati
• PRINCIPAL COMPONENT ANALYSIS (PCA)
• Understanding PCA using stack overflow data
• Dimension reduction with R
• Exploring Instacart Dataset with PCA
• Ten quick tips for effective dimensionality reduction

Machine Learning

• modelDown: a website generator for your predictive models – modelDown
• 100 Days of ML Coding
• Bias-Variance Tradeoff in Machine Learning

Feauture Engineering

• My secret sauce to be in top 2% of a kaggle competition
• Feature Engineering for Machine Learning: A Comprehensive Overview

Regression

• Principal component regression in R – pls
• machine learning with CARET package in R – caret
• Going Deeper into Regression Analysis
• Simple Guide to Logistic Regression in R
• 7 Types of Regression Techniques you should know!
• Beginners Guide to Regression Analysis and Plot Interpretations
• Understanding Regression Error Metrics
• Residual Analysis in Linear Regression
• Using Linear Regression to Predict Energy Output of a Power Plant
• Elegant regression results tables and plots in R: the finalfit package
• Caret: an R package for machine learning

Classification

• Top 15 Evaluation Metrics for Classification Models
• Tuning xgboost in R – xgboost
• Predicting Binary Outcomes with R, gbm, glmnet – caret
• Survival Prediction Models in R – survival
• How to handle Imbalanced Classification
• Caret Package – A Practical – caret
• Visualizing ML Models with LIME – lime|
• Playing with dimensions – Rtsne
• Top 10 Algorithms for Newbies
• Automated Feature Selection – bounceR
• K NEAREST NEIGHBOR – `caret``
• Logistic Regression in R Tutorial
• Code for Workshop: Introduction to Machine Learning with R
• Metric Functions in a Classification Dataset – caret
• caret machine learning repository – caret
• Unbox the Black Box: Part #1 – lime
• Confusion Matrices – Evaluating your classification models
• Using the tidyverse for machine learning – caret

Tidymodels

• Tidymodels: tidy machine learning in R
• A Gentle Introduction to tidymodels
• Modelling with Tidymodels and Parsnip
• Modeling with parsnip and tidymodels
• How to use recipes package from tidymodels for one hot encoding
• How to do feature engineering in R with the recipes package

H2O

• Plot H2O Decision Trees in R
• H2O GBM Tuning Tutorial for R
  
• The one function call you need to know as a data scientist: h2o.automl – `h2o

Outlier & Anomaly detection

• A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)
• Techniques of Outlier Detection and Treatment
• Using R and H2O to identify product anomalies during the manufacturing process
• Anomaly Detection Resources

Sparklyr

• Predicting food preferences with sparklyr
• Machine learning and k-fold cross validation with sparklyr
• R and Spark: How to Analyze Data Using RStudio’s Sparklyr and H2O’s Rsparkling Packages
• Manipulating Data with dplyr
• taxiDemoH2O.Rmd
• Importing 30GB of data into R with sparklyr
• Comparison of ML Classifiers Using Sparklyr
• Analyzing US flight map with sparklyr
• Data Science Toolchain with Spark and R
• Data Science with Spark and R
• Sparklyr Resources

Deep Learning

• Introducing Keras in R
• Deep Learning in R
• Deep Learning with Keras and Shiny – keras
• I_LOVE_IKEA_SHINY_APP – keras
• R Markdown Notebooks for “Deep Learning with R” – keras
• Image classification with keras –keras
• Explaining Keras image classification models with lime – keras
• Kaggle #1 Winning Approach for Image Classification Challenge
• Advanced GaRdening with Convolutional NNs – Kaggle kernel – mxnet
• Deep Neural Networks
• Deep Learning With Keras To Predict Customer Churn – keras
• How to create a sequential model in Keras for R – keras
• Code for case study - Customer Churn with Keras/TensorFlow and H2O
• Neural Network Models in R

Image classification

• Computer Vision with R & Keras
• Applied Deep Learning - Part 4: Convolutional Neural Networks
• Concepts in object detection
• Example code Keras in R - VGG16 Base - Kaggle Kernel – keras
• Image Classification with Keras – keras
• Pre-trained model on R – MicrosoftML
• Using Pre-Trained Models
• Predicting Irish electricity consumption with neural networks
• Satellite imagery generation with Generative Adversarial Networks – keras
• Transfer Learning with Keras in R – keras
• R vs Python: Image Classification with Keras – keras
• Image Classification with Keras in R & Python

Time series analysis

• Time Series of the World, Unite! – tsbox
• Deep Learning for Time Series Forecasting – keras
• Introduction to Time Series Analysis
• Forecasting time series using R
• Using R for Time Series Analysis
• Exploring & Visualizing Time Series
• Plot Time Series Data Using GGPlot
• Preparing Datetime Data for Analysis with padr and dplyr
• Moving Averages
• Introduction to Forecasting with ARIMA in R
• Time Series Forecasting with Machine Learning: An example from Kaggle
• How to add Trend Lines in R Using Plotly
• EDA+Prophet+ MLP Neural Network Forecasting – prophet
• Generate Quick and Accurate Time Series Forecasts using Facebook’s Prophet – prophet
• Time series animation
• Tidy forecasting in R
• Time Series Analysis for Business Forecasting with Artificial Neural Networks
• Aautomate your kpi forecasts with only 1 line of r code using autots

Maps & Spatial Analysis

• Drawing beautiful maps programmatically with R, sf and ggplot2
• Introduction to Mapping and Spatial Analysis with R
• Visualising italian wine production

Web Scraping

• Practical Introduction to Web Scraping using R
• Web Scraping in R
• Webscraping: (1) Server-side and CSS
• Web Scraping in R
• Scraping and Harvesting Web Data in R
• How to scrape Zomato Restaurants Data in R

Reporting

• Getting Started with R Markdown – rmarkdown
• Introduction to R Markdown – rmarkdown
• How to brand and automate your work with Rmarkdown – rmarkdown
• bookdown cheatsheet – bookdown
• A clean R Markdown template for your reports – rmarkdown
• Markdown Cheatsheet – rmarkdown
• Automatically knit R Markdown documents, build them with Jekyll, and serve the website with servr locally – blogdown
• Deploying to Github pages with Travis
• Getting used to R, RStudio, and R Markdown – rmarkdown
• Mastering Markdown
• How to self-publish a book
• Writing your thesis with bookdown
• How to self-publish a book: Customizing Bookdown
• R Markdown
• Introduction to R Markdown
• Render reports directly from R scripts
• Pimp my RMD: a few tips for R Markdown
• Comparing huxtable with other R packages for creating tables
• Introducing vitae
• Style knitr Code Output Appearance in HTML with Custom CSS Classes
• Efficient R programming
• Using R Markdown for Class Reports
• What is R Markdown?
• Emoji unicode
• Rmarkdown HTML Output – embedded video –
• Flipbooks

Creating Blog

• Build a Jekyll blog in minutes
• Setting up a Distill Blog with Netlify
• How to build a website with Blogdown in R
• Up and running with blogdown – blogdown
• Website Tutorial
• How TF does this blog thing work?

Shiny

• RinteRface Shiny Server
• An awesome R-shiny list! – shiny
• A Shiny application in a package R – shiny
• User authentication in R Shiny – shiny
• Create dashboards with semantic.dashboard – semantic.dashboard
• Shiny, R and HTML – shiny
• Shiny Database App (CRUD)
• DOCKERIZED SHINY APP DEVELOPMENT
• Creating Tools for Winning Teams – shiny
• Building interface for you and your data
• Build your first web app dashboard using Shiny and R *
• Shiny tips & tricks for improving your apps and solving common problems
• Adding Firebase Authentication to Shiny
• Deep Learning Image Classification with Keras and Shiny
• Shiny application (with modules) - Saving and Restoring from RDS
• Forget about Excel, Use these R Shiny Packages Instead

Shiny Code

• Shiny Dashboard example
• Building a Shiny web application to visualize spatial and spatio-temporal data
• Basic Insurer Dashboard
• Example Shiny Apps
• Shinyapps Gallery
• shiny image classification – github

Flexdashboard

• Automated Dashboard with various correlation visualizations in R
• Printing From Flex Dashboard
• How to host a flexdashboard on GitHub Pages
• Coranovirus dashboard

Programming

• Learning Functional Programming & purrr
• R Markdown website for learning how to use purrr
• R Coding Style Guide
• Building R Packages

Making packages

• Useful links by Suzan Baert: making packages
• A home for your functions: creating an R package
• How to Create and Distribute an R Package

Deploy

• Building a Package that Lasts – package
• A Scalable Predictive API with PFA – aurelius
• Using R with Amazon
• R can API and So Can You!
• Docker for R user
• Enterprise Web Services with Neural Networks Using R and TensorFlow
• Beta release of SparkR job types in Cloud Dataproc
• How to Deploy Externally Generated R Models to Azure Machine Learning Studio Web Services
• An Introduction to Docker for R Users
• Are you leaking h2o? Call plumber!
• Create a Docker Image and deploy it on Google Cloud

Docker

• RStudio in Docker – now share your R code effortlessly!
• Create your R learning environment with Docker and RStudio Server in minutes

AWS Tutorial

• Using sparklyr with an Apache Spark cluster
• Analytics at Scale: h2o, Apache Spark and R on AWS EMR
• H2O & RStudio Server on Amazon Web Services (AWS), the Easy Way!
• Scaling H2O analytics with AWS and p(f)urrr (Part 1)

API & IDE

• Google Analytics R Tutorial – googleAnalyticsR
• Big Data Platforms
• Powered By R
• Services and tools for building intelligent R applications in the cloud
• Working with APIs from R
• API Development with R Part-I – plumber , rApache
• Iterative testing with plumber

RSTUDIO TIPS

• Code Snippets
• 4 ways to be more efficient using RStudio’s Code Snippets
• Discover and install useful RStudio addins
• RStudio IDE Easy Tricks You Might’ve Missed
• Motivate yourself with an .Rprofile
• Making hex and twittercard with bunny and magick

Teaching

• Teaching resources
• Use Flipbooks to Explain Your Code and Thought Process

RStudio Cloud

• teach-r-online

DataScience

• Machine Learning cheatsheets for Stanford’s CS 229
• Data Science Cheatsheets
• Visual Introduction to probability and statistics
• Some datasets for teaching data science
• Awesome Data Science repository
• Financial Times Visual Vocabulary
• The Data Visualisation Catalogue
• Teaching Data Science: A Short Course for Instructors
• Carbon: Create and share beautiful images of your source code
• Everything you did and didn’t know about PCA
• Data Science Interview Questions & Detailed Answers
• 6 Common Probability Distributions every data science professional should know
• Visual Vocabulary
• ML concepts explained Visually
• How to I interpret odds ratios in logistic regression
• Free SQL Download to practice queries
• Getting Started: The What, Where, Why & How of Data Visualisation
• Machine Learning Crash Course
• In-depth introduction to machine learning in 15 hours of expert videos

---

• End-to-End Machine Learning
• Student Classification Shiny App
• Introduction to Machine Learning for Coders!

R

• Top 20 R Programming Books To Teach Yourself From Scratch
• Learn R Programming
• Beyond Basic R - Introduction and Best Practices
• Getting used to R, RStudio, and R Markdown
• Structuring R projects
• R for Data Science Solutions
• Getting Started in R
• LeaRning Path on R
• RTutor: Interactive R Problem Sets
• R PRACTICE EXERCISES: LEVEL 1
• Programming Exercises for R
• reproducible workflows workshop - github repository –tidyverse
• Data Science en production avec R
• 50 ways to show your data - r script
• Rpubs collection by William Surles
• A review of functions to get started simulating data in R
• Where do things live in R? R for Excel Users
• The Ultimate Opinionated Guide to Base R Date Format Functions
• R Best practices
• Getting Started with Git and GitHub for R Users
• R: Best Resources to Learn R programming for Data Science
• The Automated R Instructor

Tidyverse

• Best Practices When Writing Functions That Call dplyr Verbs
• #TidyTuesday hotel bookings and recipes
• Transitioning into the tidyverse (part 2)
• The tidyverse: dplyr, ggplot2, and friends
• Tidyverse for beginners
• Animations of tidyverse verbs using R, the tidyverse, and gganimate
• Introducing the Grammar of Graphics Plotting Concept
• teachthat - gather,spread, pipe
• Data Manipulation with dplyr
• Intro to the Tidyverse Workshops
• tidyverse - Davis R-Users’ Group
• How to Filter in R: A Detailed Introduction to the dplyr Filter Function
• R-Ladies Sydney!
• A Beginner’s Guide to Tidyverse – The Most Powerful Collection of R Packages for Data Science
• Data Wrangling in R
• A Gentle Introduction to Tidy Statistics in R
• Bang Bang – How to program with dplyr

Teaching Shiny

• Shiny Tutorials
• Learn R Shiny through studying the rise in ocean plastics - Part One
• Using Shiny in the Classroom
• Materials for the Intro to Shiny workshop with RLadies Nijmegen
• Shiny Apps: Development and Deployment

RPubs must read

Basic

• Learning R on iris
• Missing Value Treatment
• Cluster Analysis with R
• Classification with Decision Trees
• kNN Using caret R package
• Pie Chart Revisited
• Naive Bayes Classifier: theory and R example

Advanced

• Classifications in R
• Market basket analysis
• Fraud Detection with Machine Learning
• Machine Learning, Key to Your Formulation Challenges
• Data Analysis in Semiconductor Manufacturing

Kaggle kernel

• Happiness 2017 (Visualization + Prediction)
• Stack Overflow 2018 survey: age, gender, sexuality
• Breast Cancer or Not (with 15 ML)
• Exploring Survival on the Titanic
• Detailed Data Analysis & Ensemble Modeling – caret
• Exploratory Analysis - Instacart – ggplot2
• R/rmd kaggle Kernels ordered by votes
• Resources for R
• Woggler- The Women Kaggler

Case studies

• Which promotion should be used to increase sales? A fast-food chain’s marketing campaign case study

• How to model with gradient boosting machine in R

• Analyzing London Crimes Data in R

• Uber Data Analysis Project ## Sport Analytics

• An Introduction to Modelling Soccer Matches in R (part 1)

• An Introduction to Modelling Soccer Matches in R (part 2)

For Fun

• analysing your fitbit sleeping patterns

Conference

2019

• rstudio-conf 2019 Workshop materials
• Speaker slides and materials for SatRday Chicago 2019
• NEW YORK R CONFERENCE
• useR2019
• Keynote lectures at useR! 2019

2020

• CelebRation 2020

Github repository

• Data Analysis with R
• Presentations from H2O meetups & conferences by the H2O.ai team
• H2O for Internet of Things + Deep Water project
• hex-stickers
• BuzzFeedNews/everything
• Links to slides from rstudio::conf 2019

COVID19

• Statistical Modelling of COVID-19 Outbreak in Italy
• Just another plot on Coronavirus
• La lotta contro il Covid-19: ora tocca alla fase 4
• A summary dashboard of the covid19 cases in Italy
• Simulating COVID-19 interventions with R
• COVID-19 Spain forecast
• https://rpubs.com/VitoFanelli1990/covid-19-italy-21032020
• https://www.statsandr.com/blog/top-r-resources-on-covid-19-coronavirus/

R Programming

• Writing an R package from scratch
• R Code – Best practices
• usethis workflow for package development
• Code Smells and Feels
• Machine Learning Cheatsheet Data set
• R package primer
• Google’s R Style Guide

Football analysis

• Functions to aid football / soccer analysis

• Expected Goals - Overachieving or just lucky?

Datasets

• Awesome Public Datasets
• 8 Fun Machine Learning Projects for Beginners
• Datasets to Practice Your Data Mining
• Generate random data sets
• Data Science Projects
• Awesome Public Datasets
• 16 Strange Datasets for Machine Learning
• UKgrid
• Data & Code at FiveThirtyEightabc news
• Google dataset search
• Global spending on R&D
• Retrieve oil price data from eia.gov
• Awesome Public Datasets
• Formatted datasets for Machine Learning With R by Brett Lantz
• Applied Predictive Modeling
• R dataset
• Official repo for the #tidytuesday project
• Outlier Detection DataSets (ODDS)
• UCI Machine Learning Repository in R
• SMS Spam Collection
• tidytuesday.rocks
• Datasets for teaching data science – dslabs
• Retail Data: R Package – retail

Simulating dataset

• Create Fake Data in R for tutorials – fakir
• How to generate meaningful fake data for learning, experimentation and teaching – fakir