Refeshments - Talk/demo - Lunch

**Schedule**

11:30 - 12:00 Refreshments (in-person)
12:00 - 13:00 Talk/demo (hybrid)
13:00 - 14:00 Lunch (in-person)

**Abstract**

Reproducibility of scientific research enables others including your future self to validate, extend, and build upon analytical results. This in turn, helps build confidence in the results of scientific analyses. However, reproducibility is not a binary concept, rather, there is a scale from less reproducible to more reproducible, where various tools and practices can help enhance it.

The R package {targets}, developed and maintained by Will Landau, is a workflow management package designed to increase reproducibility in R based data analysis. The major features of {targets} include automation of workflows, caching of intermediate steps, batch creation of workflow steps, and parallelisation at the level of the workflow. These features not only help to reproduce scientific analysis, but also help you to tackle several other challenges in your research workflows. For example, it supports you to return to a project after working on something else and still be able to immediately pick up where you left off without confusion or trying to remember what you were doing. If you change the workflow, then you only have to re-run the parts that are affected by the change. It is also possible to scale up the workflow, to say, handle large datasets, without changing the underlying individual functions.

This session will provide an introduction to using {targets} for reproducible data analysis in R. Participants will learn how to structure their workflows to make their data analysis more reproducible in R.

Warwick R User Group (R Programming Language)

Heather Turner

XINRAN GAO

Carlos Cámara

(More) Reproducible Data Analysis in R using {targets} - Hybrid

MB0.07 Mathematical Sciences Building

### **Executive Summary**

The training workshop titled **"R-Syntax: An Alternative to Questionnaire Administration for Reporting and Research"** is designed to equip researchers, analysts, and professionals with practical skills in utilizing the R programming language for efficient data collection, analysis, and reporting. Traditional methods of questionnaire administration often face challenges such as time consumption, high costs, and manual errors in data handling. This workshop presents R-Syntax as a robust, efficient, and reproducible alternative that streamlines these processes.
Participants will learn to leverage R for designing virtual surveys, automating data entry, and conducting advanced statistical analyses. The workshop will cover key topics such as:

* Introduction to R and its applications in research.
* Writing R-Syntax for data collection and cleaning.
* Using R packages (e.g., `shiny`, `ggplot2`, and `dplyr`) to create interactive forms and visualizations.
* Automating reporting and exporting results for academic or professional use.

This hands-on training emphasizes practical implementation through interactive exercises, live coding sessions, and case studies. By the end of the workshop, attendees will have gained the confidence to integrate R-Syntax into their workflows, enabling faster, more accurate, and scalable research outputs without the reliance on traditional paper or online questionnaires.
This innovative approach not only reduces manual workload but also promotes reproducibility, transparency, and cost-efficiency in research and reporting, making it a valuable skill for academics, industry professionals, and policymakers.

Osun R User Group Meetup Group

Timothy A OGUNLEYE 

Dr Akinpelu Gabriel Akinyemi 

DOUMATEY FRANCK

R-Syntax: An Alternative to Questionnaire Administration 4 Reporting & Research

Online event

Gradient Boosting Machines (GBMs) in the Age of LLMs and ChatGPT

Szilard Pafka, PhD
Chief Scientist, Epoch

Gradient Boosting Machines (GBMs) have been considered (in the last decade) as the best machine learning algorithm (in terms of highest accuracy) for supervised learning/predictive analytics with structured/tabular data (widely encountered in business applications). Are they still relevant in the age of Large Language Models (LLMs) and ChatGPT? This talk will tackle this very question and will also present updates to the author's GBM-perf benchmark (available on GitHub) including the newest results of training XGBoost and LightGBM on monster CPU servers (192 cores on c7i.metal-48xl and c7a.metal-48xl) and powerful GPUs (A100, H100).

Bio:
Szilard studied Physics in the 90s and obtained a PhD by using statistical methods to analyze the risk of financial portfolios. He worked in finance, then in 2006 he moved to become the Chief Scientist of a tech company in Santa Monica, California doing everything data (analysis, modeling, data visualization, machine learning, data infrastructure etc). He was the founder/organizer of several meetups in the Los Angeles area (R, data science etc) and the data science community website datascience.la for more than a decade until he relocated to Texas in 2021. He is the author of a well-known machine learning benchmark on github (1000+ stars), a frequent speaker at conferences (keynote/invited at KDD, R-finance, Crunch, eRum and contributed at useR!, PAW, EARL, H2O World, Data Science Pop-up, Dataworks Summit etc.), and he has developed and taught graduate data science and machine learning courses as a visiting professor at two universities (UCLA in California and CEU in Europe).

LinkedIn: https://www.linkedin.com/in/szilard
Twitter: https://twitter.com/SzilardPafka/
Github: https://github.com/szilard/

R Meetup - Real Data Science USA (formerly LA R Users Group)

Szilard Pafka

Anil Gandhi

Gradient Boosting Machines (GBMs) in the Age of LLMs and ChatGPT

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