Teaching computing via visualization

The following is not a particularly controversial statement: "In order to create a data visualization using [software/language], you first need to learn that [software/language]." But if one of the more exciting things you can do with [software/language] is data visualization, why not start learning [software/language] by learning to build data visualizations? In this talk we present a data-centric approach to teaching and learning R through creating data visualizations, as opposed to starting with fundamentals of R as a programming language. The talk will feature examples of in class activities, details of a curriculum that introduces students to data science through data visualization, and sample student work.

Transcript

1. Teaching computing via visualization mine-cetinkaya-rundel mine@stat.duke.edu @minebocek bit.ly/teach-viz-comp
2. In order to create a data visualization using __________, you first need to learn __________. [software/language] [software/language] bit.ly/teach-viz-comp · @minebocek
3. One way to learn __________, is to create data visualizations using __________. [software/language] [software/language] bit.ly/teach-viz-comp · @minebocek
4. [software/language] bit.ly/teach-viz-comp · @minebocek
5. In which of the following two scenarios do you feel like you have a better handle on the final product? Q bit.ly/teach-viz-comp · @minebocek
8. (a) (b)
9. start with cake 1
10. Which of the following two examples is more likely to be interesting for a wide range of students? Q bit.ly/teach-viz-comp · @minebocek
11. # Declare variables # of different types x !<- 8 y !<- "monkey" z !<- FALSE # Check class of x class(x) #> [1] "numeric" # Check class of y class(y) #> [1] "character" # Check class of z class(z) #> [1] "logical" Declare the following variables Then, determine the class of each variable (a) bit.ly/teach-viz-comp · @minebocek
12. (b) Open today’s example project Knit the document and discuss the results with your neighbor Then, change Turkey to a different country, and plot again bit.ly/teach-viz-comp · @minebocek
13. # Declare variables # of different types x !<- 8 y !<- "monkey" z !<- FALSE # Check class of x class(x) #> [1] "numeric" # Check class of y class(y) #> [1] "character" # Check class of z class(z) #> [1] "logical" (a) (b) bit.ly/teach-viz-comp · @minebocek
14. with great examples, comes a great amount of code… bit.ly/teach-viz-comp · @minebocek
15. but let’s focus on the task at hand… Open today’s example project Knit the document and discuss the results with your neighbor Then, change Turkey to a different country, and plot again bit.ly/teach-viz-comp · @minebocek
16. bit.ly/teach-viz-comp · @minebocek un_votes %>% filter(country %in% c("United States of America", "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") ) %>% filter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() + 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" )
17. bit.ly/teach-viz-comp · @minebocek un_votes %>% filter(country %in% c("United States of America", "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") ) %>% filter(votes > 5) %>% # only use records where there are more than 5 votes ggplot(mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() + 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" )
18. bit.ly/teach-viz-comp · @minebocek
19. Source: edx.org/course/introduction-r-data-science-1 bit.ly/teach-viz-comp · @minebocek
20. Source: www.coursera.org/specializations/jhu-data-science#courses bit.ly/teach-viz-comp · @minebocek
21. Source: datasciencebox.org/hello/topics Making rigorous conclusions Looking forward Fundamentals of data & data viz, revision exercises, confounding variables and Simpson’s paradox Rmd + Git + GitHub Tidy data, data frames vs. summary tables, recoding and transforming variables, web scraping and iteration Building and selecting models, visualizing interactions, prediction and model validity, inference via simulation & discussion of CLT Interactive visualization and reporting, Bayesian inference, text analysis, … Exploring data Visualize Wrangle bit.ly/teach-viz-comp · @minebocek
22. skip baby steps 2
23. Which of the following two visualizations is more likely to be motivating for a wide range of students? Q bit.ly/teach-viz-comp · @minebocek
24. ggplot(data = un_roll_calls, mapping = aes(x = amend)) + geom_bar() (a) bit.ly/teach-viz-comp · @minebocek
25. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() + 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" ) (b) bit.ly/teach-viz-comp · @minebocek
26. (a) (b) bit.ly/teach-viz-comp · @minebocek
27. non-trivial examples can be motivating, but need to avoid ! bit.ly/teach-viz-comp · @minebocek
28. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes)) bit.ly/teach-viz-comp · @minebocek
29. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes)) bit.ly/teach-viz-comp · @minebocek function( arguments ) often a verb what to apply that Verb to
30. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes)) bit.ly/teach-viz-comp · @minebocek rows = observations columns = variables “tidy” data frame
31. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes)) + geom_point() bit.ly/teach-viz-comp · @minebocek
32. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() bit.ly/teach-viz-comp · @minebocek
33. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() + geom_smooth(method = "loess", se = FALSE) bit.ly/teach-viz-comp · @minebocek
34. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() + geom_smooth(method = "loess", se = FALSE) + facet_wrap(~ issue) bit.ly/teach-viz-comp · @minebocek
35. ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() + 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" ) bit.ly/teach-viz-comp · @minebocek
36. cherish day one 3
37. Which of the following two tasks is more likely to be welcoming for a wide range of students? Q bit.ly/teach-viz-comp · @minebocek
38. (a) Install R Install RStudio Install the following packages: tidyverse rmarkdown … Load these packages Install git (b) Go to vm- manage.oit.duke.edu or rstudio.cloud (or some other server based solution) Log in with your ID & pass > hello R! bit.ly/teach-viz-comp · @minebocek
39. method of delivery, and medium of interaction matters bit.ly/teach-viz-comp · @minebocek
40. → → → → bit.ly/teach-viz-comp · @minebocek
41. tl;dr bit.ly/teach-viz-comp · @minebocek
42. 1 2 3 start with cake skip baby steps cherish day one
43. Why = ? Q bit.ly/teach-viz-comp · @minebocek
44. Let them eat cake (first)! You can tell them all about the ingredients later… mine-cetinkaya-rundel mine@stat.duke.edu @minebocek bit.ly/teach-viz-comp