swcarpentry/r-novice-gapminder: Software Carpentry: R for Reproducible Scientific Analysis, June 2019

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

doi:10.5281/zenodo.3265164

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“…Furthermore, the contents of R for Data Analysis are centered around the idea of the "process of data analysis" broadly applied to any discipline. This differs from other high-quality resources, such as "R for Reproducible Scientific Analysis" (Zimmerman et al, 2019), which teaches similar topics in the context of the scientific process.…”

Section: Statement Of Needmentioning

confidence: 98%

R for Data Analysis: An open-source resource for teaching and learning analytics with R

French¹

2023

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R for Data Analysis is a sequential learning system designed to teach anyone how to use R to analyze data. The materials can be used in aggregate to learn how to analyze data programmatically or broken into modules and used to supplement an existing educational program. Each lesson contains a conceptual overview, practical examples, resources for further study, and exercises designed to reinforce understanding.The materials have been made publicly available at: https://trevorfrench.github. io/R-for-Data-Analysis/ and licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0) License.

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Section: Statement Of Needmentioning

confidence: 98%

R for Data Analysis: An open-source resource for teaching and learning analytics with R

French¹

2023

JOSE

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swcarpentry/r-novice-gapminder: Software Carpentry: R for Reproducible Scientific Analysis, June 2019

Cited by 1 publication

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R for Data Analysis: An open-source resource for teaching and learning analytics with R

R for Data Analysis: An open-source resource for teaching and learning analytics with R

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