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Intro to R and RStudio for Genomics
Unrestricted Use
CC BY
Rating
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Welcome to R! Working with a programming language (especially if it’s your first time) often feels intimidating, but the rewards outweigh any frustrations. An important secret of coding is that even experienced programmers find it difficult and frustrating at times – so if even the best feel that way, why let intimidation stop you? Given time and practice* you will soon find it easier and easier to accomplish what you want. Why learn to code? Bioinformatics – like biology – is messy. Different organisms, different systems, different conditions, all behave differently. Experiments at the bench require a variety of approaches – from tested protocols to trial-and-error. Bioinformatics is also an experimental science, otherwise we could use the same software and same parameters for every genome assembly. Learning to code opens up the full possibilities of computing, especially given that most bioinformatics tools exist only at the command line. Think of it this way: if you could only do molecular biology using a kit, you could probably accomplish a fair amount. However, if you don’t understand the biochemistry of the kit, how would you troubleshoot? How would you do experiments for which there are no kits? R is one of the most widely-used and powerful programming languages in bioinformatics. R especially shines where a variety of statistical tools are required (e.g. RNA-Seq, population genomics, etc.) and in the generation of publication-quality graphs and figures. Rather than get into an R vs. Python debate (both are useful), keep in mind that many of the concepts you will learn apply to Python and other programming languages. Finally, we won’t lie; R is not the easiest-to-learn programming language ever created. So, don’t get discouraged! The truth is that even with the modest amount of R we will cover today, you can start using some sophisticated R software packages, and have a general sense of how to interpret an R script. Get through these lessons, and you are on your way to being an accomplished R user! * We very intentionally used the word practice. One of the other “secrets” of programming is that you can only learn so much by reading about it. Do the exercises in class, re-do them on your own, and then work on your own problems.

Subject:
Applied Science
Biology
Computer Science
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ahmed Moustafa
Alexia Cardona
Andrea Ortiz
Jason Williams
Krzysztof Poterlowicz
Naupaka Zimmerman
Yuka Takemon
Date Added:
08/07/2020
Plotting and Programming in Python
Unrestricted Use
CC BY
Rating
0.0 stars

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Steer
Allen Lee
Andreas Hilboll
Ashley Champagne
Benjamin
Benjamin Roberts
CanWood
Carlos Henrique Brandt
Carlos M Ortiz Marrero
Cephalopd
Cian Wilson
Dan Mønster
Daniel W Kerchner
Daria Orlowska
Dave Lampert
David Matten
Erin Alison Becker
Florian Goth
Francisco J. Martínez
Greg Wilson
Jacob Deppen
Jarno Rantaharju
Jeremy Zucker
Jonah Duckles
Kees den Heijer
Keith Gilbertson
Kyle E Niemeyer
Lex Nederbragt
Logan Cox
Louis Vernon
Lucy Dorothy Whalley
Madeleine Bonsma-Fisher
Mark Phillips
Mark Slater
Maxim Belkin
Michael Beyeler
Mike Henry
Narayanan Raghupathy
Nigel Bosch
Olav Vahtras
Pablo Hernandez-Cerdan
Paul Anzel
Phil Tooley
Raniere Silva
Robert Woodward
Ryan Avery
Ryan Gregory James
SBolo
Sarah M Brown
Shyam Dwaraknath
Sourav Singh
Steven Koenig
Stéphane Guillou
Taylor Smith
Thor Wikfeldt
Timothy Warren
Tyler Martin
Vasu Venkateshwaran
Vikas Pejaver
ian
mzc9
Date Added:
08/07/2020