Jonathan Seidman and Ramesh Venkataramaiah present how they run R on Hadoop in order to perform distributed analysis on large data sets, including some alternatives to their solution.

In today's data-driven world, statistical analysis plays a critical role in uncovering insights, validating hypotheses, and driving decision-making across industries. R, a powerful programming ...

The Data Science Lab Revealing Secrets with R and Factor Analysis Let's use this classical statistics technique -- and some R, of course -- to get to some of the latent variables hiding in your data.

The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis — plus a few miscellaneous tasks tossed in. The package names in the table are clickable ...

Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions. The R system has three components: a scripting language, an interactive ...

This workshop is hands-on and intended for beginners; no previous knowledge of data analysis and/or R is required. This session will cover the following topics for R: data preparation, descriptive ...

Reading data for competing risk analysis in R As an example of competing risk analysis in R, we analyze data from 35 patients with acute leukaemia who underwent HSCT.

Integrative analysis of multiple data types is perhaps the least standardizable task in genomic data analysis, where the need for a flexible working environment in a high-level language such as R ...

DataCamp’s data science bootcamps aim to provide practical, hands-on experience in areas such as statistics, programming, machine learning, data analysis and data visualization.

Intro to Data Analysis with R This workshop is hands-on and intended for beginners; no previous knowledge of data analysis and/or R is required. This session will cover the following topics for R: ...