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Data Analysis and Visualization in R for Ecologists
Unrestricted Use
CC BY
Rating
0.0 stars

Data Carpentry lesson from Ecology curriculum to learn how to analyse and visualise ecological data in R. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.

Subject:
Applied Science
Computer Science
Ecology
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ankenbrand, Markus
Arindam Basu
Ashander, Jaime
Bahlai, Christie
Bailey, Alistair
Becker, Erin Alison
Bledsoe, Ellen
Boehm, Fred
Bolker, Ben
Bouquin, Daina
Burge, Olivia Rata
Burle, Marie-Helene
Carchedi, Nick
Chatzidimitriou, Kyriakos
Chiapello, Marco
Conrado, Ana Costa
Cortijo, Sandra
Cranston, Karen
Cuesta, Sergio Martínez
Culshaw-Maurer, Michael
Czapanskiy, Max
Daijiang Li
Dashnow, Harriet
Daskalova, Gergana
Deer, Lachlan
Direk, Kenan
Dunic, Jillian
Elahi, Robin
Fishman, Dmytro
Fouilloux, Anne
Fournier, Auriel
Gan, Emilia
Goswami, Shubhang
Guillou, Stéphane
Hancock, Stacey
Hardenberg, Achaz Von
Harrison, Paul
Hart, Ted
Herr, Joshua R.
Hertweck, Kate
Hodges, Toby
Hulshof, Catherine
Humburg, Peter
Jean, Martin
Johnson, Carolina
Johnson, Kayla
Johnston, Myfanwy
Jordan, Kari L
K. A. S. Mislan
Kaupp, Jake
Keane, Jonathan
Kerchner, Dan
Klinges, David
Koontz, Michael
Leinweber, Katrin
Lepore, Mauro Luciano
Li, Ye
Lijnzaad, Philip
Lotterhos, Katie
Mannheimer, Sara
Marwick, Ben
Michonneau, François
Millar, Justin
Moreno, Melissa
Najko Jahn
Obeng, Adam
Odom, Gabriel J.
Pauloo, Richard
Pawlik, Aleksandra Natalia
Pearse, Will
Peck, Kayla
Pederson, Steve
Peek, Ryan
Pletzer, Alex
Quinn, Danielle
Rajeg, Gede Primahadi Wijaya
Reiter, Taylor
Rodriguez-Sanchez, Francisco
Sandmann, Thomas
Seok, Brian
Sfn_brt
Shiklomanov, Alexey
Shivshankar Umashankar
Stachelek, Joseph
Strauss, Eli
Sumedh
Switzer, Callin
Tarkowski, Leszek
Tavares, Hugo
Teal, Tracy
Theobold, Allison
Tirok, Katrin
Tylén, Kristian
Vanichkina, Darya
Voter, Carolyn
Webster, Tara
Weisner, Michael
White, Ethan P
Wilson, Earle
Woo, Kara
Wright, April
Yanco, Scott
Ye, Hao
Date Added:
03/20/2017
R for Reproducible Scientific Analysis
Unrestricted Use
CC BY
Rating
0.0 stars

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam H. Sparks
Ahsan Ali Khoja
Amy Lee
Ana Costa Conrado
Andrew Boughton
Andrew Lonsdale
Andrew MacDonald
Andris Jankevics
Andy Teucher
Antonio Berlanga-Taylor
Ashwin Srinath
Ben Bolker
Bill Mills
Bret Beheim
Clare Sloggett
Daniel
Dave Bridges
David J. Harris
David Mawdsley
Dean Attali
Diego Rabatone Oliveira
Drew Tyre
Elise Morrison
Erin Alison Becker
Fernando Mayer
François Michonneau
Giulio Valentino Dalla Riva
Gordon McDonald
Greg Wilson
Harriet Dashnow
Ido Bar
Jaime Ashander
James Balamuta
James Mickley
Jamie McDevitt-Irwin
Jeffrey Arnold
Jeffrey Oliver
John Blischak
Jonah Duckles
Josh Quan
Julia Piaskowski
Kara Woo
Kate Hertweck
Katherine Koziar
Katrin Leinweber
Kellie Ottoboni
Kevin Weitemier
Kiana Ashley West
Kieran Samuk
Kunal Marwaha
Kyriakos Chatzidimitriou
Lachlan Deer
Lex Nederbragt
Liz Ing-Simmons
Lucy Chang
Luke W Johnston
Luke Zappia
Marc Sze
Marie-Helene Burle
Marieke Frassl
Mark Dunning
Martin John Hadley
Mary Donovan
Matt Clark
Melissa Kardish
Mike Jackson
Murray Cadzow
Narayanan Raghupathy
Naupaka Zimmerman
Nelly Sélem
Nicholas Lesniak
Nicholas Potter
Nima Hejazi
Nora Mitchell
Olivia Rata Burge
Paula Andrea Martinez
Pete Bachant
Phil Bouchet
Philipp Boersch-Supan
Piotr Banaszkiewicz
Raniere Silva
Rayna Michelle Harris
Remi Daigle
Research Bazaar
Richard Barnes
Robert Bagchi
Rémi Emonet
Sam Penrose
Sandra Brosda
Sarah Munro
Sasha Lavrentovich
Scott Allen Funkhouser
Scott Ritchie
Sebastien Renaut
Thea Van Rossum
Timothy Eoin Moore
Timothy Rice
Tobin Magle
Trevor Bekolay
Tyler Crawford Kelly
Vicken Hillis
Yuka Takemon
bippuspm
butterflyskip
waiteb5
Date Added:
03/20/2017