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RStudio

R-Lab #2: Shiny & maps + earthquakes | Milan, April 11th

Events, R blog By March 30, 2017 Tags: , , , No Comments

The R-Labs are monthly evening meetings where we co-work together on a real data science problem, hands on code. We are ready for the R-Lab#2, on April 11th!
With the help of EarthCloud, we will try to map the earthquakes that happened over a specific fault, to study the fault evolution over time and provide qualitative and quantitative insights about it. To achieve this goal, we will review and use Shiny, the R framework for interactive visualization, and R packeges for maps as ggmap and Leaflet

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R-Lab #1: hands on R code! | Milan, March 14th

Events, R blog By March 1, 2017 Tags: , , , No Comments

Great news for the MilanoR community: we are launching R-Lab, a monthly R project of co-working with R on real data science projects.
Either if you are an R expert, a beginner, or you just curious, you are welcome to join us! The first event will be on March 14th, in Mikamai, Milano. We will introduce the R-Lab project and then go to the theme of the day: "RStudio addins: a shortcut to your favourite functionalities"

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Visual debugging with RStudio

R blog By July 23, 2013 Tags: , , , , , 1 Comment

Introduction

From release 098.208 the last RStudio IDE comes with a visual debugger. Now debugging with R and RStudio becomes a simple and efficient task.

This short post does not want to be a crash course: “debugging with R” nor can be a full explanation of the RStudio visual debugging capabilities: we guess that all of these will be fully documented as soon as this tool will be officially released.

This post is a simple sharing of something we have learned about R and RStudio.

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Learning RStudio for R Statistical Computing

R blog By February 6, 2013 Tags: , 1 Comment

Book cover
"Learning RStudio for R Statistical Computing" will teach you how to quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics. R developers will learn about package development, coding principles, and version control with RStudio.

This book will help you to learn and understand RStudio features to effectively perform statistical analysis and reporting, code editing, and R development.

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