You can't do data science in a GUI

Transcript

1. Hadley Wickham 
 @hadleywickham
 Chief Scientist, RStudio You can’t do data science in a GUI March 2018
2. Data science is the process by which data becomes understanding, knowledge and insight Data science is the process by which data becomes understanding, knowledge and insight
3. Data science is the process by which data becomes understanding, knowledge and insight Data science is the process by which data becomes understanding, knowledge and insight
4. Tidy Import Store data consistently
5. Tidy Import Understand Store data consistently
6. Tidy Import Surprises, but doesn't scale Create new variables & new summaries Visualise Transform Model Scales, but doesn't (fundamentally) surprise Store data consistently
7. Tidy Import Surprises, but doesn't scale Create new variables & new summaries Visualise Transform Model Communicate Scales, but doesn't (fundamentally) surprise Automate Store data consistently
8. Tidy Import Visualise Transform Model Program tibble tidyr purrr magrittr dplyr forcats hms ggplot2 broom modelr readr readxl haven xml2 lubridate stringr tidyverse.org r4ds.had.co.nz
9. Why program?
10. Cognitive Computational Think it Describe it (precisely) Do it
13. table %>% rename(player = X1, team = X2, position = X3) %>% filter(player != 'PLAYER') %>% mutate( college = ifelse(player == position, player, NA) ) %>% fill(college) %>% filter(player != college) Programming languages are languages
14. It’s just text! And this gives you access to two extremely powerful techniques
15. ⌘C ⌘V
17. And provides provenance Reproducible Readable Diffable Open
26. I live in fear of clicking the wrong thing
27. Why program 
 in R?
28. x <- sample(100, 10) x > 50 #> [1] TRUE FALSE FALSE TRUE TRUE #> [6] TRUE TRUE FALSE FALSE TRUE sum(x > 50) #> [1] 6 # (There are no scalars! ) R is a vector language
29. y <- sample(c(1:5, NA)) y #> [1] 1 NA 2 3 5 4 y > 2 #> [1] FALSE NA FALSE TRUE TRUE TRUE y == NA #> [1] NA NA NA NA NA NA Missing values are baked in
30. john_age <- NA mary_age <- NA john_age == mary_age #> [1] NA An example makes this clearer
31. y <- sample(c(1:5, NA)) y #> [1] 1 NA 2 3 5 4 y > 2 #> [1] FALSE NA FALSE TRUE TRUE TRUE is.na(y) #> [1] FALSE TRUE FALSE FALSE FALSE FALSE Missing values are baked in
32. data.frame( x = 1:4, y = sample(letters[1:4]), z = runif(4) ) #> x y z #> 1 1 c 0.1189635 #> 2 2 a 0.0518956 #> 3 3 b 0.4471441 #> 4 4 d 0.0818547 So are relational tables (aka data frames/tibbles)
33. # It's well suited to data science but I # can't (yet) articulate why # Something about having a standard # container for 80% of problems, and # needing to do something to each element # of that container # Whole object thinking? Functional programming
34. x <- seq(0, 2 * pi, length = 100) plot(x, sin(x), type = "l") Metaprogramming 0 1 2 3 4 5 6 −1.0 −0.5 0.0 0.5 1.0 x sin(x)
36. Which makes it a great place to write DSLs
37. Why program in R with the ?
38. Solve complex https://unsplash.com/photos/tjX_sniNzgQ simple pieces combining problems by
39. library(tidycensus) geo <- get_acs( geography = "metropolitan statistical area...", variables = "DP03_0021PE", summary_var = "B01003_001", survey = "acs1", endyear = 2016 ) # Thanks to Kyle Walker (@kyle_e_walker) # For package and example A small example
41. big_metro <- geo %>% filter(summary_est > 2e6) %>% select(-variable) %>% mutate( NAME = gsub(" Metro Area", "", NAME) ) %>% separate(NAME, c("city", "state"), ", ") %>% mutate( city = str_extract(city, "^[A-Za-z ]+"), state = str_extract(state, "^[A-Za-z ]+"), name = paste0(city, ", ", state), summary_moe = na_if(summary_moe, -555555555) ) Followed by data munging
43. big_metro %>% ggplot(aes( x = estimate, y = reorder(name, estimate)) ) + geom_errorbarh( aes( xmin = estimate - moe, xmax = estimate + moe ), width = 0.1 ) + geom_point(color = "navy")
44. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Indianapolis, IN Kansas City, MO Riverside, CA Charlotte, NC Dallas, TX Tampa, FL Detroit, MI Columbus, OH Phoenix, AZ Cincinnati, OH Houston, TX Orlando, FL Sacramento, CA Austin, TX San Antonio, TX San Juan, PR St, MO San Diego, CA Atlanta, GA Cleveland, OH Las Vegas, NV Miami, FL Denver, CO Minneapolis, MN Los Angeles, CA Pittsburgh, PA Baltimore, MD Portland, OR Philadelphia, PA Seattle, WA Chicago, IL Boston, MA Washington, DC San Francisco, CA New York, NY 0 10 20 30 estimate reorder(name, estimate)
45. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Indianapolis, IN Kansas City, MO Riverside, CA Charlotte, NC Dallas, TX Tampa, FL Detroit, MI Columbus, OH Phoenix, AZ Cincinnati, OH Houston, TX Orlando, FL Sacramento, CA Austin, TX San Antonio, TX San Juan, PR St, MO San Diego, CA Atlanta, GA Cleveland, OH Las Vegas, NV Miami, FL Denver, CO Minneapolis, MN Los Angeles, CA Pittsburgh, PA Baltimore, MD Portland, OR Philadelphia, PA Seattle, WA Chicago, IL Boston, MA Washington, DC San Francisco, CA New York, NY 0% 10% 20% 30% 2016 1−year ACS estimates Residents who take public transportation to work Source: ACS Data Profile variable DP03_0021P / tidycensus
46. No matter how complex and polished the individual operations are, it is often the quality of the glue that most directly determines the power of the system. — Hal Abelson
47. My goal is to make a pit of success http://blog.codinghorror.com/falling-into-the-pit-of-success/
48. But
49. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 20 30 40 2 3 4 5 6 7 displ hwy
50. df %>% select( date = `Date Created`, name = Name, plays = `Total Plays`, loads = `Total Loads`, apv = `Average Percent Viewed` ) But this is painful!
52. df %>% filter(n > 1e6) %>% mutate(x = f(y))) %>% ??? # How predictable is next step from # previous steps? What next?
53. Can we do more with autocomplete? Where do dialogs and autocomplete intersect?
54. Learning from examples http://vis.stanford.edu/papers/wrangler
55. What about deep learning? https://twitter.com/carroll_jono/status/914254139873361920
56. Conclusion
57. I believe that: 1. Huge advantages to code 2. R provides great environment 3. DSLs help express your thoughts 4. Code should be primary artefact (but might be generated other than typing)