R is often used to estimate complex statistical models that require significant computing power and time (hours or even days) to construct. By moving workflows to AWS, R users can overcome these barriers. Use cases for R on AWS Big data processingĪs data analysis trends towards larger datasets, R users-who may be used to running analyses locally on a laptop-often will hit barriers as the result of computing, memory, and cost constraints.
The company behind the IDE, also named RStudio, sponsors a lot of R development (including the Tidyverse), holds conferences, and provides both free and paid versions of R server software, including software for hosting R applications as web apps. In addition to providing the editor and debugger, RStudio lets users view in-memory data structures with a built-in data viewer that allows us to sort and filter like a spreadsheet, view any graphs we make, see our database connections, and so on. The Tidyverse is a good fit for a lot of data analysis tasks, and it plays a big role in the continuing popularity of R.Īnother important part of the R ecosystem is the development environment RStudio. These are designed to allow users to ingest data in whatever form in which it is received (e.g., from a CSV, an API, etc.) and easily transform it into the shape needed to analyze it, using a declarative “grammar” of data manipulation. One of the most popular sets of packages in the R ecosystem is the Tidyverse, a collection of libraries for transforming and using “tidy” data. There are also high-quality free online books and other documentation explaining how to use R effectively. Its large user community has developed thousands of freely available packages, including packages for data manipulation, data visualization, specialized statistical estimation procedures, machine learning, accessing public data APIs like US Census data or Spotify, easily making data-based web apps, and many, many other areas. R is a programming language popular with statisticians, scientists, and data analysts.
In part two, “Using R with Amazon Web Services for document analysis”, we’ll take a deeper dive into building an end-to-end document processing application with AWS services. In this two-part series, part one will cover the basics of R and common workload pairings for R on AWS.
This article is a guest post from David Kretch, Lead Data Scientist at Summit Consulting.Īs R workloads grow and become increasingly resource intensive, the ability to move from a local compute environment to scaleable, fully managed cloud services on Amazon Web Services (AWS) becomes extremely valuable for cost, speed, and resiliency reasons.
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