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R Resources Suggested by Students

• You can find lots of tutorials for those who wants to use R programming with Excel together.
• This one is a free book titled “R for SAS and SPSS users”
• How to make 3D Scatter Plots in R
• This document is about how to forecast time series using R
• Data sets, workshops, etc.
• Why you should learn R first for data science
• Why The R Programming Language Is Good For Business. This story contains interviews.
• The magick package: Advanced Image-Processing in R
• Types of Clustering Methods: Overview and Quick Start R Code
• You will complete your first machine learning project using R with this post.
• There are a lot of good analysis(all explained) in this page.
• This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.
• How to set up your own R blog with Github pages and Jekyll Bootstrap
• They are news and articles about data science and R in this site.
• Thera are a lot of R lessons to support BDA-503 class.
• You can see good examples of what you can do with RMarkdown. This site have examples including Documents, Interactive Documents, Dashboards and also websites
• This is a brief tutorial on using Spark Streaming to analyze social media data in real time.
• This document tells about how we import data from several platforms.
• Article help us to understand the basic of memory management in R. It told basic concepts and also gives exercise for all topics to use R much efective
• Simple but efective search engine, it is just concentrate on R.
• psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders
• This example is about the leading real estate marketplace Zillow
• This post really helps to understand the data better.
• It is a text mining and very thorough documentation
• CRAFTING A POWERPOINT PRESENTATION WITH R
• A classical analysis (Radio Swiss classic program).It is a well explained study.
• This example covers basic graphics by using ggplot2. Quick plots, bar plots, dot charts, histograms, strip plots, scatter&line plots and box plots.
• This is an example for creating a chart called “consultants’ chart” on ggplot2.
• This page shows an example on text mining of Twitter data with R packages twitteR, tm and wordcloud.
• It is a well defined example to make social media analysis, using the packages TwitteR, tm, topicmodels, sentiment140 and igraph.
• It is about where the last movie of Star Wars stand between the 25 movies by worldwide grosses and the other Star Wars movies in the series.
• An Introduction to XGBoost R package
• This article will tell one of the ways to do so using animated GIF images using gganimate in R.
• This example is about building a tool called BallR, using R Shiny framework, to explore NBA shot data at the player-level.
• This post is about Caret package for solve almost any supervised machine learning problem.
• Its a helpful document to understand data.table package that provides working with large data sets, also can behave just like a data frame.
• This pages gives detail information about “R” ggplot2 library which provides to create elegant graphics.
• This article can be described as “R for beginners” that gives clear answers for the new users as understanding its concept.
• A “R” master has shared his favorite R packages that can be used in Visualization, statistical analysis, Data Wrangling, Bioinformatics and sharing.
• This articale gives useful links for handful of sources for data to work with
• An example of statistical data analysis using the R environment for statistical computing
• Marketing Multi-Channel Attribution model with R (part 1: Markov chains concept)
• EDA of last weeks shooting in a concert in USA. It summarizes past mass shootings with some visualization. (Using Python)
• The work is basically summarizes competition data using EDA and visualization.(Zillow)
• ML tutorial for beginners. The work tries to predict whether a tumor is malignant or benign.
• Simple EDA for football players and radar chart using R and visualizations.
• FBI and Justice Department Homicide datasets are analyzed.
• Hillary Clinton’s 7,945 emails in response to a FOIA request are analyzed.
• This is a good EDA example. The script compares US-wide housing costs as percent of household income, and examines some potential relationships between housing budget and other factors.
• Mapping and Visualizing Violent Crime in San Francisco
• The case is about a company which wants to understand the reasons behind employee churn. In addition the company wants to forecast future employee resignations
• The kernel is about empirically checking if an US citizen/resident should pursue his/her education to higher levels, whether it is worth doing it. And also it looks for a correlation or pattern between higher income and higher degree level.
• The playlist examines R programming and its execution in finance.
• The quantmod package: Quantitative Financial Modelling & Trading Framework for R
• This CRAN Task View contains a list of packages useful for empirical work in Finance, grouped by topic.
• An introduction to R packages based on 11 of the most frequently asked user questions
• Top 50 ggplot2 Visualizations - The Master List (With Full R Code)
• Some R Packages for data visualization and manupulation
• Here is examples of data manipulation with 7 R Packages for each with different data set.
• Some data cleaning tips particularly useful for Excel users.
• It shows basic tools for handling and analyzing stock market data with R
• Data analysis and visualization project using survey data to examine gender pay gap.
• In this example the author analyzes UK’s elections of the last thirty years by using the data obtained from Guardian data blog.
• It investigates the how GRE, GPA scores and prestige of home university of a student affect its admission chance to graduate school by logit regression.
• In this study, scholar Erik Meyersson compares last three elections and the referandum of Turkey on the basis of change in the voter tendencies by region.
• Introducing R language to experienced programmers.
• This a comprehensive for using R language for Spatial Data Analysis.Spatial data identifies the geographic location of features nad boundaries on Earth
• This is an hands-on practice on analysis of Yelp data using R and ggplot2 by Max Woolf.
• This an introductry article for DPLYR package in R.
• 10 Questions R Users always ask while using ggplot2 package
• The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data.
• You can watch the video explaining time series methods and shows practices for forecasting.
• Cleaning Data in R
• 40 questions to test your skill on R for Data Science
• Analyses about music choices from Insights Spotify & the ad-hoc project about music history analysis.
• Analyses about music choices from Insights Spotify & the ad-hoc project about music history analysis.
• This site gives some interesting examples about visualizing data with r using lattice package, ggplot2 package and challanges of them.
• SQL and R. In the article, it is mentioned that by using RSQLite, SQLDF and RJDBC packages, leveraging SQL skills whether accessing external data or simply cleaning, filtering and modifying data already loaded is possible.
• Why R is Hard to Learn
• There is a good starting problem about simple probability problems which will be solved by using R
• There is a good explanation on creating world map showing the points of Beijing and Shanghai using maps, mapdata and ggplot2 libraries.
• It is an R Notebook on Titanic data set. The notebook also covers feature engineering, missing data imputation and modeling.
• Using R for psychological research
• Using R to forecast Senate elections
• This blog is related Twitter analitics.This site show that how R connect to Twitter.
• This includes heat map analysis and We learn how to use a map with R.
• Example R code / analysis for housing data. This example includes regression with multiple variables and residual plots for normal assumptions
• Trying to improve rerstaurant CRM’s by Yelp reviews analysis and automation
• Walmart Predicting store sales using historical markdown data
• How Lloyd’s of London uses R for Insurance
• Trying to predict where will a new guest book their first travel experience on Airbnb?
• Word cloud generator in R : One killer function to do everything you need
• What is the difference between Artificial Intelligence, Machine Learning, Statistics, and Data Mining
• 21 Reasons You Should Learn R, Python, and Hadoop
• How to write the first for loop in R
• R, Python or SAS: Which one should you learn first?
• 100 FREE TUTORIALS FOR LEARNING R
• Here is the list of cheat related with data science. 24 Data Science, R, Python, Excel, and Machine Learning Cheat Sheet
• This article show that how data can be used for prevent churn in enterprise company
• How AirBnB solve real world problems with analyzing data
• This article is showing how to analyze tweets (hash tags)
• Analyzing 1.1 Billion NYC Taxi and Uber Trips, with a Vengeance
• Analyzing the NYC Bike Share System.This article shows that how bikes are change way of transportation comparing with Taxi or Uber
• R Tutorial. There is a lot of documentation and examples which you can try online on website
• R For Dummies is a book for beginners on R programming
• R-Bloggers is a platform to learn more and gain different visions about R. R-Bloggers is about empowering bloggers to empower other R users.
• It is a special collection of different datasets for R programming language
• Google’s R Style Guide.The guideline includes fundamental information such as simple tricks to write readable code and what should we avoid to use.
• Python vs R: Which programming language is better for data science?
• R’s remarkable growth
• A curated list of awesome R packages and tools.
• 38 Seminal Articles Every Data Scientist Should Read
• Visualizations with R and Databases
• Flexdashboard: Easy interactive dashboards for R
• This page is a website of an online community for showcasing R & Python tutorials.
• This R notebook is a good example for how to explore the data to check for missing values/erroneous entries and also comment on redundant features and add additional ones.
• This R notebook covers analysis and approach through different process flows in the data science pipeline, which includes statistical inference and exploratory data analysis.The main goal is to understand the reasoning behind employee turnover and to come up with a model to classify an employee’s risk of attrition.
• Data Science with R
• Example of Deep Learning in R
• Example showing the results of Cox from the Proportional Hazard Models in SimPH and R
• An example showing how we can use various cluster analysis techniques to explore the relationship with the distance matrix
• An article describing Bayesian Classification by Gauss
• When Are Citi Bikes Faster Than Taxis in New York City?
• This is an early Exploratory Data Analysis for the Personalized Medicine: Redefining Cancer Treatment challenge. Using ggplot2 and the tidyverse tools to study and visualise the structures in the data.
• The Art of Data Science. This book describes the process of analyzing data.
• Understanding Support Vector Machine algorithm from examples (along with code)