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🔗 bit.ly/dsbox-dscwav mine-cetinkaya-rundel [email protected] @minebocek MINE ÇETINKAYA-RUNDEL DUKE UNIVERSITY + RSTUDIO

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🔗 bit.ly/dsbox-dscwav Three questions that keep me up at night… 1 What should my students learn? 2 How will my students learn best? 3 What tools will enhance my students’ learning?

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🔗 bit.ly/dsbox-dscwav 1 What should my students learn? 2 How will my students learn best? 3 What tools will enhance my students’ learning? Three questions that keep me up at night… Content Pedagogy Infrastructure

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🔗 bit.ly/dsbox-dscwav Infrastructure Pedagogy Content

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🔗 bit.ly/dsbox-dscwav Infrastructure Pedagogy Content

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🔗 bit.ly/dsbox-dscwav

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🔗 bit.ly/dsbox-dscwav 🔗 datasciencebox.org 🔗 rstudio-education/datascience-box

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🔗 bit.ly/dsbox-dscwav AUDIENCE I have been teaching with R for a while, but I want to update my teaching materials I’m new to teaching with R and need to build up my course materials This teaching slide deck I came across on Twitter is pretty cool, but I have no idea what type of course it belongs in

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🔗 bit.ly/dsbox-dscwav TOPICS Fundamentals of data & data viz, confounding variables, Simpson’s paradox + R / RStudio, R Markdown, simple Git Tidy data, data frames vs. summary tables, recoding & transforming, web scraping & iteration + collaboration on GitHub Building & selecting models, visualizing interactions, prediction & validation, inference via simulation Interactive viz & reporting, text analysis, Bayesian inference + communication & dissemination

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🔗 bit.ly/dsbox-dscwav CONTENTS 🖥 48 slide decks 🏄 10 application exercises 👩🔬 14 computing labs ✍ 10 homework assignments ✔ 2 take-home exams 📝 1 open-ended project website datasciencebox.org repository 🎥 48 videos 🤹 9 interactive tutorials package dsbox 🤹 9 interactive tutorials

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🔗 bit.ly/dsbox-dscwav DESIGN PRINCIPLES 🎉 cherish day one 👶 skip baby steps 🍰 start with cake 🌲 leverage the ecosystem 🥦 hide the veggies

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DESIGN PRINCIPLES Which kitchen would you rather bake a cake?

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DESIGN PRINCIPLES Which kitchen would you rather bake a cake?

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DESIGN PRINCIPLES 🎉 Cherish day one

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DESIGN PRINCIPLES How do you prefer your cake recipes? Words only, or words & pictures?

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DESIGN PRINCIPLES How do you prefer your cake recipes? Words only, or words & pictures?

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DESIGN PRINCIPLES 🍰 Start with cake ‣ Open today’s demo project ‣ Knit the document and discuss the results with your neighbor ‣ Then, change Turkey to a different country, and plot again

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DESIGN PRINCIPLES 🍰 Start with cake With great examples, comes a great amount of code… but let’s focus on the task at hand… ‣ Open today’s demo project ‣ Knit the document and discuss the results with your neighbor ‣ Then, change Turkey to a different country, and plot again

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un_votes %>% f i lter(country %in% c("UK & NI", “US”, "Turkey")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" ) DESIGN PRINCIPLES 🍰 Start with cake

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un_votes %>% f i lter(country %in% c("UK & NI", “US”, "Turkey")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" ) DESIGN PRINCIPLES 🍰 Start with cake

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DESIGN PRINCIPLES 🍰 Start with cake un_votes %>% f i lter(country %in% c("UK & NI", “US”, "Turkey")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" )

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DESIGN PRINCIPLES 🍰 Start with cake un_votes %>% f i lter(country %in% c("UK & NI", “US”, “France")) %>% inner_join(un_roll_calls, by = "rcid") %>% inner_join(un_roll_call_issues, by = "rcid") %>% group_by(country, year = year(date), issue) %>% summarize( votes = n(), percent_yes = mean(vote = = "yes") ) %>% f i lter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) + labs( title = "Percentage of Yes votes in the UN General Assembly", subtitle = "1946 to 2015", y = "% Yes", x = "Year", color = "Country" )

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DESIGN PRINCIPLES 🍰 Start with cake

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DESIGN PRINCIPLES Which motivates you more to learn how to cook: perfectly chopped onions or ratatouille?

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DESIGN PRINCIPLES Which motivates you more to learn how to cook: perfectly chopped onions or ratatouille?

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DESIGN PRINCIPLES 👶 Skip baby steps Re-insert

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DESIGN PRINCIPLES Which is more likely to appeal to someone who has never tried broccoli?

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DESIGN PRINCIPLES Which is more likely to appeal to someone who has never tried broccoli?

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DESIGN PRINCIPLES 🥦 Hide the veggies ‣ Today we go from this to that ‣ And do so in a way that is easy to replicate for another state →

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DESIGN PRINCIPLES Lesson: Web scraping essentials for turning a structured table into a data frame in R. 🥦 Hide the veggies

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DESIGN PRINCIPLES Lesson: Web scraping essentials for turning a structured table into a data frame in R. Ex 1: Scrape the table off the web and save as a data frame. 🥦 Hide the veggies

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DESIGN PRINCIPLES Lesson: Web scraping essentials for turning a structured table into a data frame in R. Ex 1: Scrape the table off the web and save as a data frame. Ex 2: What other information do we need represented as variables to make this figure? 🥦 Hide the veggies

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DESIGN PRINCIPLES Lesson: Web scraping essentials for turning a structured table into a data frame in R. Ex 1: Scrape the table off the web and save as a data frame. Ex 2: What other information do we need represented as variables to make this figure? Lesson: “Just enough” regex 🥦 Hide the veggies

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DESIGN PRINCIPLES If you are already taking a baking class, which will be easier to venture on to?

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DESIGN PRINCIPLES If you are already taking a baking class, which will be easier to venture on to?

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DESIGN PRINCIPLES 🌲 Leverage the ecosystem student + instructor instructor

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DESIGN PRINCIPLES 🌲 Leverage the ecosystem student + instructor instructor 💫 VERY NEAR 💫 FUTURE

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🔗 bit.ly/dsbox-dscwav USAGE in full to jumpstart / overhaul your teaching in bits & pieces to supplement your teaching

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🔗 bit.ly/dsbox-dscwav LICENSE

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mine-cetinkaya-rundel [email protected] @minebocek MINE ÇETINKAYA-RUNDEL DUKE UNIVERSITY + RSTUDIO 🗂 datasciencebox.org 📦 rstudio-education.github.io/dsbox 🖥 bit.ly/dsbox-dscwav