Teaching computing via visualization mine-cetinkaya-rundel [email protected] @minebocek bit.ly/teach-viz-comp 

In order to create a data visualization using __________, you first need to learn __________. [software/language] [software/language] bit.ly/teach-viz-comp · @minebocek 

One way to learn __________, is to create data visualizations using __________. [software/language] [software/language] bit.ly/teach-viz-comp · @minebocek 

[software/language] bit.ly/teach-viz-comp · @minebocek 

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 

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(a) (b) 

start with cake 1 

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 

# 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 

(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 

# 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 

with great examples, comes a great amount of code… bit.ly/teach-viz-comp · @minebocek 

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 

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" ) 

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" ) 

bit.ly/teach-viz-comp · @minebocek 

Source: edx.org/course/introduction-r-data-science-1 bit.ly/teach-viz-comp · @minebocek 

Source: www.coursera.org/specializations/jhu-data-science#courses bit.ly/teach-viz-comp · @minebocek 

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 

skip baby steps 2 

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 

ggplot(data = un_roll_calls, mapping = aes(x = amend)) + geom_bar() (a) bit.ly/teach-viz-comp · @minebocek 

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 

(a) (b) bit.ly/teach-viz-comp · @minebocek 

non-trivial examples can be motivating, but need to avoid ! bit.ly/teach-viz-comp · @minebocek 

ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes)) bit.ly/teach-viz-comp · @minebocek 

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 

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 

ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes)) + geom_point() bit.ly/teach-viz-comp · @minebocek 

ggplot(data = un_votes_joined, mapping = aes(x = year, y = percent_yes, color = country)) + geom_point() bit.ly/teach-viz-comp · @minebocek 

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 

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 

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 

cherish day one 3 

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 

(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 

method of delivery, and medium of interaction matters bit.ly/teach-viz-comp · @minebocek 

→ → → → bit.ly/teach-viz-comp · @minebocek 

tl;dr bit.ly/teach-viz-comp · @minebocek 

1 2 3 start with cake skip baby steps cherish day one 

Why = ? Q bit.ly/teach-viz-comp · @minebocek 

Let them eat cake (first)! You can tell them all about the ingredients later… mine-cetinkaya-rundel [email protected] @minebocek bit.ly/teach-viz-comp