R-Ladies Dublin December meetup

This is a past event

39 people went

Details

R-Ladies Dublin next meetup is kindly hosted by Aon's Centre for Innovation and Analytics. Join us for an evening of networking and two exciting talks. We will hear from Chiara Cotroneo on data visualisation with R and Marian Keane on R code efficiencies.

When & Where

Tuesday, 4th December
Aon's Centre for Innovation and Analytics, Metropolitan Building, James Joyce Street, Dublin 1

The Agenda

6:00-6:30 Light refreshments & networking
6:30-7:15 Chiara Cotroneo, Replace Venn diagrams with UpSetR
7:15-8:00 Marian Keane, How to write efficient R code and reduce run times

The Speakers & Talks

Chiara Cotroneo, PhD Student, Computational Biology at University College Dublin
Chiara is working on the development of statistical methods for the analysis of bacterial genomes. Chiara had previously worked at Instituto dei Tumori di Milano, one of the main Italian institutions for cancer research and therapy. She was in charge of interpreting genomic and clinical patient data. Chiara has been using R for data analysis since 2010. In recent years she has mainly focused on R as a tool to produce beautiful (and informative!) data visualisations.

Replace Venn diagrams with UpSetR
One of the common issues in data analysis is to efficiently represent overlap between various classes of objects. The most widespread tool for the task, Venn diagrams, often have poor informative value or are hard to interpret. In this talk, Chiara will introduce UpSetR, originally developed for the analysis of genomic datasets, that provides a great alternative to Venn diagrams.

Marian Keane, Senior Consultant Analyst at Presidion, Version 1 Company
Marian works with public and commercial organisations to leverage the power of data in order to realise business goals. Marian had previously worked as an independent consultant, data scientist, and actuary.

How to write efficient R code and reduce run times
In this talk, Marian will look at techniques to make R code more efficient and reduce run times. We will try out different algorithms on the same data set to see the difference in run times.

Bring your laptop if you want to try out yourself what the speakers will talk about.