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Data Organization in Spreadsheets for Social Scientists
Unrestricted Use
CC BY
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Lesson on spreadsheets for social scientists. Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. Typically we organize data in spreadsheets in ways that we as humans want to work with the data. However computers require data to be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
David Mawdsley
Erin Becker
François Michonneau
Karen Word
Lachlan Deer
Peter Smyth
Date Added:
08/07/2020
Introduction to Cloud Computing for Genomics
Unrestricted Use
CC BY
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Data Carpentry lesson to learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources. The cloud is a fancy name for the huge network of computers that host your favorite websites, stream movies, and shop online, but you can also harness all of that computing power for running analyses that would take days, weeks or even years on your local computer. In this lesson, you’ll learn about renting cloud services that fit your analytic needs, and how to interact with one of those services (AWS) via the command line.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Abigail Cabunoc Mayes
Adina Howe
Amanda Charbonneau
Bob Freeman
Brittany N. Lasseigne, PhD
Bérénice Batut
Caryn Johansen
Chris Fields
Darya Vanichkina
David Mawdsley
Erin Becker
François Michonneau
Greg Wilson
Jason Williams
Joseph Stachelek
Kari L. Jordan, PhD
Katrin Leinweber
Maxim Belkin
Michael R. Crusoe
Piotr Banaszkiewicz
Raniere Silva
Renato Alves
Rémi Emonet
Stephen Turner
Taylor Reiter
Thomas Morrell
Tracy Teal
William L. Close
ammatsun
vuw-ecs-kevin
Date Added:
03/28/2017
Introduction to R for Geospatial Data
Unrestricted Use
CC BY
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The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Anne Fouilloux
Chris Prener
Claudia Engel
David Mawdsley
Erin Becker
François Michonneau
Ido Bar
Jeffrey Oliver
Juan Fung
Katrin Leinweber
Kevin Weitemier
Kok Ben Toh
Lachlan Deer
Marieke Frassl
Matt Clark
Miles McBain
Naupaka Zimmerman
Paula Andrea Martinez
Preethy Nair
Raniere Silva
Rayna Harris
Richard McCosh
Vicken Hillis
butterflyskip
Date Added:
08/07/2020
R for Reproducible Scientific Analysis
Unrestricted Use
CC BY
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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
R para Análisis Científicos Reproducibles
Unrestricted Use
CC BY
Rating
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Una introducción a R utilizando los datos de Gapminder. El objetivo de esta lección es enseñar a las programadoras principiantes a escribir códigos modulares y adoptar buenas prácticas en el uso de R para el análisis de datos. R nos provee un conjunto de paquetes desarrollados por terceros que se usan comúnmente en diversas disciplinas científicas para el análisis estadístico. Encontramos que muchos científicos que asisten a los talleres de Software Carpentry utilizan R y quieren aprender más. Nuestros materiales son relevantes ya que proporcionan a los asistentes una base sólida en los fundamentos de R y enseñan las mejores prácticas del cómputo científico: desglose del análisis en módulos, automatización tareas y encapsulamiento. Ten en cuenta que este taller se enfoca en los fundamentos del lenguaje de programación R y no en el análisis estadístico. A lo largo de este taller se utilizan una variedad de paquetes desarrolados por terceros, los cuales no son necesariamente los mejores ni se encuentran explicadas todas sus funcionalidades, pero son paquetes que consideramos útiles y han sido elegidos principalmente por su facilidad de uso.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
0xgc
A. s
Alejandra Gonzalez-Beltran
Ana Beatriz Villaseñor Altamirano
Antonio
AntonioJBT
Belinda Weaver
Claudia Engel
Cynthia Monastirsky
Daniel Beiter
David Mawdsley
David Pérez-Suárez
Erin Becker
EuniceML
François Michonneau
Gordon McDonald
Guillermina Actis
Guillermo Movia
Hely Salgado
Ido Bar
Ivan Ogasawara
Ivonne Lujano
James J Balamuta
Jamie McDevitt-Irwin
Jeff Oliver
Jonah Duckles
Juan M. Barrios
Katrin Leinweber
Kevin Alquicira
Kevin Martínez-Folgar
Laura Angelone
Laura-Gomez
Leticia Vega
Marcela Alfaro Córdoba
Marceline Abadeer
Maria Florencia D'Andrea
Marie-Helene Burle
Marieke Frassl
Matias Andina
Murray Cadzow
Narayanan Raghupathy
Naupaka Zimmerman
Paola Prieto
Paula Andrea Martinez
Raniere Silva
Rayna M Harris
Richard Barnes
Richard McCosh
Romualdo Zayas-Lagunas
Sandra Brosda
Sasha Lavrentovich
Shirley Alquicira Hernandez
Silvana Pereyra
Tobin Magle
Veronica Jimenez
juli arancio
raynamharris
saynomoregrl
Date Added:
08/07/2020