RStudio on Amazon SageMaker is the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. In conjunction with tools like RStudio on SageMaker, users are analyzing, transforming, and preparing large amounts of data as part of the data science and ML workflow. Data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for large-scale data processing. Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing.

We are going to learn how to implement a Multiple Linear Regression model in R. This is a bit more complex than Simple Linear Regression but it's going to be so practical and fun. Multiple Linear Regression is a data science technique that uses several explanatory variables to predict the outcome of a response variable. A Multiple linear regression model attempts to model the relationship between two or more explanatory variables (independent variables) and a response variable (dependent variable), by fitting a linear equation to observed data. Every value of the independent variable x is associated with a value of the dependent variable y.

To work with any dataset, we have to import it to our IDE. In this article, I'm going to show you how to Import the Dataset in R, so we can start preparing it to make Machine Learning Models. I have prepared the dataset that we are going to use in this tutorial. It's just a simple dataset that we will use for the whole data preprocessing phase. You can download the dataset using the link below.Alternatively, to get the dataset that we are going to use in this tutorial just click here.

Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Many of the RStudio on SageMaker users are also users of Amazon Redshift, a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. The use of RStudio on SageMaker and Amazon Redshift can be helpful for efficiently performing analysis on large data sets in the cloud.

In a previous post, I described how I was captivated by the virtual landscape imagined by the RStudio education team while looking for resources on the RStudio website. In this post, I'll take a look at Cheatsheets another amazing resource hiding in plain sight. Apparently, some time ago when I wasn't paying much attention, cheat sheets evolved from the home made study notes of students with highly refined visual cognitive skills, but a relatively poor grasp of algebra or history or whatever to an essential software learning tool. I don't know how this happened in general, but master cheat sheet artist Garrett Grolemund has passed along some of the lore of the cheat sheet at RStudio. One day I put two and two together and realized that our Winston Chang, who I had known for a couple of years, was the same "W Chang" that made the LaTex cheatsheet that I'd used throughout grad school.

The R programming language is one of the most commonly used languages in the scientific space, being one of the most commonly used languages for machine learning (probably second following python) and arguably the most popular language amongst mathematicians and statisticians. It is easy to get started with, free to use, with support for many scientific and visualisation libraries. While R can help you analyse your data, the more data you have the more compute power you require and the more impactful your analysis is, the more repeatability and reproducibility is required. Analysts and Data Scientists need to find ways to fulfil such requirements. In this post we briefly describe the main ways of running your R workloads on the cloud, making use of Amazon SageMaker, the end-to-end Machine Learning cloud offering of AWS.

R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also started developing packages on R and offering enterprise version of R. R is a free language and environment for statistical computing and graphics. You can perform a variety of tasks using R language.

We'll be hosting a few different workshops in a variety of cities across the US and UK. See below for more details on each workshop and how to register. Chief Data Scientist Hadley Wickham is hosting his popular "Building Tidy Tools" workshop in Atlanta, Georgia this October. You should take this workshop if you have experience programming in R and want to learn how to tackle larger scale problems. You'll get the most from it if you're already familiar with functions and are comfortable with R's basic data structures (vectors, matrices, arrays, lists, and data frames).

As more organizations migrate their data science work to the cloud, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. While RStudio provides many different ways to support an organization's cloud strategyOpens a new window, we've heard from many customers who also use Amazon SageMaker. They wanted an easier way to combine RStudio's professional products with SageMaker's rich machine learning and deep learning capabilities, and to incorporate RStudio into their data science infrastructure on SageMaker. Based on this feedback, we are excited to announce RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team. Amazon SageMakerOpens a new window helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.

As more organizations migrate their data science work to the cloud, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. While RStudio provides many different ways to support an organization's cloud strategyOpens a new window, we've heard from many customers who also use Amazon SageMaker. They wanted an easier way to combine RStudio's professional products with SageMaker's rich machine learning and deep learning capabilities, and to incorporate RStudio into their data science infrastructure on SageMaker. Based on this feedback, we are excited to announce RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team. Amazon SageMakerOpens a new window helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.