Hadley Wickham 
 @hadleywickham
 Chief Scientist, RStudio Should all statistics students
 be programmers? July 2018 No!

Hadley Wickham 
 @hadleywickham
 Chief Scientist, RStudio July 2018 Should all statistics students
 program? Yes!

What should a statistics student be able to do? Model

What should a statistics student be able to do? Tidy Surprises, but doesn't scale Create new variables & new summaries Visualise Transform Model Communicate Scales, but doesn't (fundamentally) surprise Automate Store data consistently Import Understand

Two primary computer interfaces: point & click GUI

Or: program with a CLI

1. Code is text 2. Code is read-able 3. Code is shareable 4. Code is open Why is programming preferable for statistics?

Code is text And this provides for two 
 extremely powerful techniques

⌘C ⌘V

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By Jenny Bryan ⌘⇧R

⌘⇧K

Code is read-able

Much of my work is developing tools

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

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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

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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")

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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)

library(tidyverse) library(magick) dir(pattern = ".png") %>% map(image_read) %>% image_join() %>% image_animate(fps = 1, loop = 25) %>% image_write("my_animation.gif") And hence you can read unfamiliar code https://twitter.com/ricardokriebel/status/849626401611411458 What does this code do?

https://twitter.com/ricardokriebel/status/849626401611411458

I live in fear of clicking the wrong thing

Code is shareable

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Why is sharing so important? Learn from others Open science Easily critique

Code is open

All modern programming languages are open source Free Students can use same tools as practitioners.
 Anyone can use best tools regardless of wealth.
 Anyone can re-run your analysis You can fix problems
 You can build your own tools Fluid

Conclusion

1. Code is text 2. Code is read-able 3. Code is shareable 4. Code is open Why is programming preferable for statistics?