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[ONLINE] Introduction to R syntax: part 1: operators, expressions, variables, built-in functions, arguments, data types, vectors (Online)

[ONLINE] Introduction to R syntax: part 1: operators, expressions, variables, built-in functions, arguments, data types, vectors (Online) Online

In these tutorials you will learn to read and write R scripts to do computations, data manipulation, and simple analysis. You will get hands-on coding experience via a web application, no software installation is required to attend. The goal is to explain the rules of the R syntax in general terms so you can make more sense of an introductory book or of the recipes you will find when searching for solutions on the internet. This tutorial is full of short exercises and code examples and it is directed to undergraduate or graduate students interested in learning R to apply it in assignments, projects, or theses but with no programming experience.

Part 1 (Aug 27 @ 4) covers using operators, expressions, variables, and built-in functions in R. Understanding assignment and how arguments work when calling functions. The data types and structures R offers to model data. Introduction to vectors. Part 2 (Aug 28 @ 4) covers the remaining data structures to model and manipulate data: factors, matrices, arrays, lists, and finally, the workhorse of data analysis, the data frame. We will study the basics of subsetting and ordering for data structures. Finally, we will have a look at user functions and basic control flow.

Tuesday, August 27, 2024
4:00pm - 5:30pm
Time Zone:
Mountain Time - US & Canada (change)
This is an online event. Event URL will be sent via registration email.
  Lab NEXT     Programming     Research Skills     Technology training  

Registration is required. There are 55 seats available.

This workshop is taught by Pablo Adames.  Pablo is a professional engineer in the province of Alberta with Masters in chemical and software engineering and 15 years of experience in software development. Currently working at Intelliview Technologies in Calgary, developing computer vision solutions through edge computing for 24/7 automated detection, location, and quantification of leaks from hydrocarbon and mining fluids in facilities and production sites.  Some of Pablo's R projects include the software to do the data analysis for a paper on the selection of mathematical models for flow simulation through pipelines using data-driven classification techniques and the conception and deployment of an R- Shiny app for system performance metrics gathering during in-house soaking system tests.

All of the material for the course was developed using R and Shiny.io cloud infrastructure and it is free to use and can be accessed at: