Posts discussing how R is used for data science including big data applications, data acquisition and manipulation, machine learning and predictive analytics

Get Ready for RStudio::Conf

by Joseph Rickert The 2017 R Conference season will get off to an early start on January 13th and 14th with RStudio::Conf 2017 in Orlando, Florida. The schedule promises an intense but collegial experience with plenty of hands-on practice working with R and the RStudio tool chain of packages and products. To prepare for the [...]

R for Enterprise: How to Scale Your Analytics Using R

by Sean Lopp At RStudio, we work with many companies interested in scaling R. They typically want to know: How can R scale for big data or big computation? How can R scale for a growing team of data scientists? This post provides a framework for answering both questions. Scaling R for Big Data or [...]

Reproducible Finance with R: A Shiny ETF Map

by Jonathan Regenstein In a previous post, we built an R Notebook that laid the groundwork for a Shiny app that allows users to graph country ETFs by clicking on a world map. In today’s Fun Friday post, we’ll charge forth to build that app, again using a flexdashboard so that we can stay in [...]

Reproducible Finance with R: Pulling and Displaying ETF Data

by Jonathan Regenstein It’s the holiday season, and that can mean only one thing: time to build a leaflet map as an interface to country Exchange Traded Fund (ETF) data! In previous posts, we examined how to import stock data and then calculate and display the Sharpe Ratio of a portfolio. Today, we’re going to [...]

December ’16 RStudio Tips and Tricks

by Sean Lopp Here is this month’s collection of RStudio Tips and Tricks. Thank you to those who responded to last month’s post; many of your tips are included below! Be sure to subscribe to @rstudiotips on Twitter for more. This month’s tips fall into two categories: Keyboard Shortcuts and Easier R Markdown Keyboard Shortcuts [...]

Missing Values, Data Science and R

by Joseph Rickert One great advantages of working in R is the quantity and sophistication of the statistical functions and techniques available. For example, R’s quantile() function allows you to select one of the nine different methods for computing quantiles. Who would have thought there could be so many ways to do something that seems [...]

Reproducible Finance with R: A Sharpe Ratio Shiny App

by Jonathan Regenstein In this previous post, we used an R Notebook to grab the monthly return data on three stocks, build a portfolio, visualize portfolio performance, and calculate the Sharpe Ratio. The Notebook format emphasized reproducibility and reuse by other R coders. Today, we’ll convert that Notebook into a Shiny application that allows end [...]

Make R a Legitimate Part of Your Organization

by Nathan Stephens How R Enters Through the Back Door In many organizations, R enters through the back door when analysts download the free software and install it on their local workstations Jamie has been an avid R programmer since college. When she takes a new job at a large corporation, she finds that she [...]

RStudio IDE Easy Tricks You Might’ve Missed

by Sean Lopp The RStudio IDE reached version 1.0 this month. The IDE has come a long way since the initial release 5 and a half years ago. Many major features have been built: projects, package building tools, notebooks. During that same period, often hidden in the shadows, a growing list of smaller features has [...]

Reproducible Finance with R: The Sharpe Ratio

by Jonathan Regenstein Financial applications were an early driving force behind the adoption of the R language, but as data science becomes increasingly critical to banks, hedge funds, investment managers, data providers, exchanges, etc., R is becoming even more important to Finance. We are excited and inspired by what the future holds in the brave [...]

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