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 new world of data-driven financial institutions. In this first post of what we hope will be a regular series about Finance, R and RStudio, we present a classic use case: import stock data, build a portfolio, and calculate the Sharpe Ratio.

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Here we offer ephemera, a little light reading and some more challenging material. We hope that least some of it will become the “talk of the town”. Worth Reading Highlights from the Moore/Sloan Data Science Environments Summit How Artificial Intelligence Will Kill Some Jobs But Create Others: A sober but optimistic view on the impact of AI Worth a Look Causality for medical statistics by David Cox: A 83-minute lecture by the master A visual history of population growth in the US from Flowing Data R Resources A Free Tutorial for using Quandl with R A SAS to R Tutorial Mastering Software Development in R, by Peng and Kross: A solid introduction to R, well worth purchasing Some vtreat Design Principles: A look at the ideas behind statistically sound data preparation The R Graph Gallery: a great resource and place to go for inspiration Using R packages and education to scale Data Science at Airbnb: a case study

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I found my way into data science and machine learning relatively late in my career. When I began reading papers on supervised learning I was delighted to find that good old logistic regression was considered a “go to” classifier. This was like learning that an old friend was admired for an achievement I didn’t know anything about. After a couple of comfortable experiences like this, I thought I would fit in quite nicely with this new (to me) tribe of data analysts studying pattern recognition and natural language processing.

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Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie is a brilliant read. If you are only ever going to buy one statistics book, or if you are thinking of updating your library and retiring a dozen or so dusty stats texts, this book would be an excellent choice. In 475 carefully crafted pages, Efron and Hastie examine the last 100 years or so of statistical thinking from multiple viewpoints.

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September was a bit of a slow month for new R packages. Only 96 new packages showed up on CRAN. Nevertheless, I have picked out 23 for special mention which I have listed in 5 categories. I used the same selection criteria as I described in the post for August picks. Data and Interfaces darksky v1.0.0: Provides an interface to the Dark Sky API which allows you to look up weather anywhere on the globe.

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141 new packages landed on CRAN in August. The following are my picks for the most interesting packages in four categories. My selection criteria were brutally simple: to make the list, a package had to have enough documentation for me to have some idea about what it does, and also, in my judgment, provide some functionality that is likely to appeal to a broad class of users. I am sure that through my ignorance and biases I have overlooked some really good work; for this, I apologize.

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Sometimes great ideas come from trying to solve simple problems. This seems to be especially true for software developers who are willing to put in an unreasonable amount of effort to solve a simple problem to their satisfaction. So the story goes that Torstein Hønsi, the founder and Chief Product Officer of Highcharts.

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Welcome to “R Views”, the new R Community blog from RStudio. For this first post, I sat down with J.J. Allaire, RStudio’s founder and CEO, to discuss RStudio’s history, its mission and JJ’s vision for its future. In a short time, we touched on a wide range of subjects including RStudio’s business, the growth of the R language, the importance of the R Consortium to the R Community and J.J.’s advice to anyone coming to R for the first time.

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