Introduction to dplyr, ggplot2 and Other tidyverse Friends: Modern Tools for Data Exploration and Visualization
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Abstract
In recent years, interest in the development of predictive models and the use of machine learning libraries has grown rapidly. As part of the efficient implementation of different models, a fundamental component of this process deals with data preparation and cleaning, followed by exploration, summaries, and visualizations. Mastering modern tools for data analysis can empower students and researchers in a wide variety of fields, to better explore and understand data generated by experiments, simulations, surveys, and others. This webinar provides an introduction to powerful tools from the tidyverse family of R packages, utilizing datasets from different STEM applications and case studies.
This tutorial uses the tidyverse Data Science Tools for STEM Applications and Datasets found on nanoHUB.
Bio
Reinaldo (Rei) Sanchez-Arias earned his Bachelor of Science degree in Mathematics from Universidad del Valle in Cali, Colombia, and a PhD in Computational Science from The University of Texas at El Paso. He completed a postdoctoral researcher appointment for the Army High Performance Computing Research Center (AHPCRC) working in reduced order models for underbody-blast simulations and data compression techniques. Since the Fall 2018 term, he is part of the Department of Data Science and Business Analytics at Florida Polytechnic University, where he teaches courses in data science, statistical learning, scientific computing, and data mining, while participating in research projects with undergraduate and graduate students. His general areas of interest include data mining and machine learning, computational linear algebra and optimization, and data science education. His work has been presented at international and national conference meetings including the Society for Industrial and Applied Mathematics meetings, the International Conference for High Performance Computing, the IEEE International Conference in Machine Learning and Applications, and the International Conference of the Engineering in Medicine and Biology Society.
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