Abstract: The goal of this tutorial is to guide new learners into R.
We'll introduce how to program in R by cleaning and processing data as the use case.
This will help transition new learners who work with data in spreadsheets
but want to utilize the power of R in their work.
The only pre-requsites needed for the tutorial is having R installed with the `tidyverse`, `data.table` packages installed.
The basics of the R language will be taught from the perspective of data processing.
The course will be mainly focused around the ""tidyverse"",
however, where appropriate, we will draw similarities to base R and data.table for completeness.
The tutorial will be Carpentries-like where we will be live coding together as a class,
with breaks and exercises every 45-55 minutes.
The main goal of the tutorial is for learners be able to:
1. Load up their own dataset, whether it is a csv or Excel file,
2. Look at conditional slices of the data
3. Be able to identify the steps necessary to make it ""tidy""
4. Write functions that can be applied to dataframes
This will give newcomers the motivation to continue learning the language and library.
Some of the topics in the tutorial will showcase how R fits into the larger data science ecosystem (e.g., fitting models),
to help contextualize the skills that will be learned from this tutorial.
Please refer this repo for setup: https://github.com/chendaniely/odsc-east-2020-intro_r
Come learn and ask questions!
Bio: Daniel is a PhD candidate in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, currently working in the Social and Decision Analytics Laboratory in the Biocomplexity Institute at Virginia Tech. His current interests are in understanding how attitudes change and spread within social networks as well as performing analytics for precision medicine.
Daniel received his MPH in the Department of Epidemiology at Columbia University and hold a BA in Psychology - Behavioral Neuroscience with minors in Biology and Computer Science from the Macaulay Honors College at CUNY Hunter College.
Daniel enjoys teaching and volunteers some of his time to Software Carpentry by teaching, serving on the Mentoring subcommittee, and chair the Assessment subcommittee.
Data Science Consultant | Lander Analytics