Pipelines for data analysis in R

Transcript

1. Hadley Wickham 
 @hadleywickham Chief Scientist, RStudio Pipelines for 
 data analysis in R September 2015
2. Data analysis is the process by which data becomes understanding, knowledge and insight Data analysis is the process by which data becomes understanding, knowledge and insight
3. Data analysis is the process by which data becomes understanding, knowledge and insight Data analysis is the process by which data becomes understanding, knowledge and insight
4. Transform Visualise Model Tidy Import Surprises, but doesn't scale Scales, but doesn't (fundamentally) surprise Create new variables & new summaries Consistent way of storing data
5. Transform Visualise Model tidyr dplyr Tidy Import readr readxl haven DBI httr broom ggplot2 ggvis
6. Pipelines
7. Think it Do it Describe it Cognitive Computational (precisely)
8. Cognition time ≫ Computation time http://www.flickr.com/photos/mutsmuts/4695658106
9. %>% Inspirations: unix, F#, haskell, clojure, method chaining magrittr::
10. foo_foo <- little_bunny() bop_on( scoop_up( hop_through(foo_foo, forest), field_mouse ), head ) # vs foo_foo %>% hop_through(forest) %>% scoop_up(field_mouse) %>% bop_on(head)
11. x %>% f(y) # f(x, y) x %>% f(z, .) # f(z, x) x %>% f(y) %>% g(z) # g(f(x, y), z) # Turns function composition (hard to read) # into sequence (easy to read)
12. # Any function can use it. Only needs a simple # property: the type of the first argument # needs to be the same as the type of the result. # tidyr: pipelines for messy -> tidy data # dplyr: pipelines for data manipulation # ggvis: pipelines for visualisations # rvest: pipelines for html # purrr: pipelines for lists # xml2: pipelines for xml # stringr: pipelines for strings
13. Tidy
14. Transform Visualise Model tidyr dplyr Tidy Import readr readxl haven DBI httr ggplot2 ggvis broom
15. Storage Meaning Table / File Data set Rows Observations Columns Variables Tidy data = data that makes data analysis easy
16. Source: local data frame [5,769 x 22] iso2 year m04 m514 m014 m1524 m2534 m3544 m4554 m5564 m65 mu f04 f514 (chr) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) 1 AD 1989 NA NA NA NA NA NA NA NA NA NA NA NA 2 AD 1990 NA NA NA NA NA NA NA NA NA NA NA NA 3 AD 1991 NA NA NA NA NA NA NA NA NA NA NA NA 4 AD 1992 NA NA NA NA NA NA NA NA NA NA NA NA 5 AD 1993 NA NA NA NA NA NA NA NA NA NA NA NA 6 AD 1994 NA NA NA NA NA NA NA NA NA NA NA NA 7 AD 1996 NA NA 0 0 0 4 1 0 0 NA NA NA 8 AD 1997 NA NA 0 0 1 2 2 1 6 NA NA NA 9 AD 1998 NA NA 0 0 0 1 0 0 0 NA NA NA 10 AD 1999 NA NA 0 0 0 1 1 0 0 NA NA NA 11 AD 2000 NA NA 0 0 1 0 0 0 0 NA NA NA 12 AD 2001 NA NA 0 NA NA 2 1 NA NA NA NA NA 13 AD 2002 NA NA 0 0 0 1 0 0 0 NA NA NA 14 AD 2003 NA NA 0 0 0 1 2 0 0 NA NA NA 15 AD 2004 NA NA 0 0 0 1 1 0 0 NA NA NA 16 AD 2005 0 0 0 0 1 1 0 0 0 0 0 0 .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... Variables not shown: f014 (int), f1524 (int), f2534 (int), f3544 (int), f4554 (int), f5564 (int), f65 (int), fu (int) What are the variables in this dataset? (Hint: f = female, 
 u = unknown, 1524 = 15-24)
17. # To convert this messy data into tidy data # we need two verbs. First we need to gather # together all the columns that aren't variables tb2 <- tb %>% gather(demo, n, -iso2, -year, na.rm = TRUE) tb2
18. # Then separate the demographic variable into # sex and age tb3 <- tb2 %>% separate(demo, c("sex", "age"), 1) tb3 # Many tidyr verbs come in pairs: # spread vs. gather # extract/separate vs. unite # nest vs. unnest
19. Google for “tidyr” & “tidy data”
20. Transform
21. Transform Visualise Model tidyr dplyr Tidy ggplot2 ggvis broom Import readr readxl haven DBI httr
22. Think it Do it Describe it Cognitive Computational (precisely)
23. One table verbs • select: subset variables by name • filter: subset observations by value • mutate: add new variables • summarise: reduce to a single obs • arrange: re-order the observations + group by
24. Demo
25. right_join() full_join() inner_join() left_join() Mutating semi_join() anti_join() Filtering Set intersect() setdiff() union()
26. dplyr sources • Local data frame (C++) • Local data table • Local data cube (experimental) • RDMS: Postgres, MySQL, SQLite, Oracle, MS SQL, JDBC, Impala • MonetDB, BigQuery
27. Google for “dplyr”
28. Visualise
29. Transform Visualise Model tidyr dplyr Tidy ggplot2 ggvis broom Import readr readxl haven DBI httr
30. What is ggvis? •A grammar of graphics 
 (like ggplot2) •Reactive (interactive & dynamic) 
 (like shiny) •A pipeline (a la dplyr) •Of the web (drawn with vega)
31. Demo 4-ggvis.R 4-ggvis.Rmd
32. Google for “ggvis”
33. Model with broom, by David Robinson
34. Transform Visualise Model tidyr dplyr Tidy ggplot2 ggvis broom Import readr readxl haven DBI httr
35. 2.5 5.0 7.5 1990 1995 2000 2005 2010 2015 date log(sales) 46 TX cities, ~25 years of data What makes it hard to see the long term trend?
36. # Models are useful as tool for removing # known patterns tx <- tx %>% group_by(city) %>% mutate( resid = lm( log(sales) ~ factor(month), na.action = na.exclude ) %>% resid() )
37. −2 −1 0 1 1990 1995 2000 2005 2010 2015 date resid
38. # Models are also useful in their own right models <- tx %>% group_by(city) %>% do(mod = lm( log(sales) ~ factor(month), data = ., na.action = na.exclude) )
39. Model summaries • Model level: one row per model • Coefficient level: one row per coefficient (per model) • Observation level: one row per observation (per model)
40. Demo 5-broom.R
41. Google for “broom r”
42. Big data and R
43. Big Can’t fit in memory on one computer: >5 TB Medium Fits in memory on a server: 10 GB-5 TB Small Fits in memory on a laptop: <10 GB R is great at this!
44. R • R provides an excellent environment for rapid interactive exploration of small data. • There is no technical reason why it can’t also work well with medium size data. (But the work mostly hasn’t been done) • What about big data?
45. 1. Can be reduced to a small data problem with subsetting/sampling/ summarising (90%) 2. Can be reduced to a very large number of small data problems (9%) 3. Is irreducibly big (1%)
46. The right small data • Rapid iteration essential • dplyr supports this activity by avoiding cognitive costs of switching between languages.
47. Lots of small problems • Embarrassingly parallel (e.g. Hadoop) • R wrappers like foreach, rhipe, rhadoop • Challenging is matching architecture of computing to data storage
48. Irreducibly big • Computation must be performed by specialised system. • Typically C/C++, Fortran, Scala. • R needs to be able to talk to those systems.
49. Future work
50. End game Provide a fluent interface where you spent your mental energy on the specific data problem, not general data analysis process. The best tools become invisible with time! Still a lot of work to do, especially on the connection between modelling and visualisation.
51. Transform Visualise Model tidyr dplyr Tidy Import readr readxl haven DBI httr broom ggplot2 ggvis