Selecting and Doing with Tidy Eval

Transcript

1. Selecting and Doing with Tidy Eval
2. • Goal: Create functions around tidyverse pipelines • Tidy eval, the easy way • Focus on two flavours of tidyverse functions Selecting and Doing
3. Why Tidy Eval?
4. Why Tidy Eval starwars[starwars$height < 200 & starwars$gender == "male", ] starwars %>% filter( height < 200, gender == "male" ) Change the context of computation
5. Why Tidy Eval starwars %>% filter( height < 200, gender == "male" ) <SQL> SELECT * FROM `starwars` WHERE ((`height` < 200.0) AND (`gender` = 'male')) Change the context of computation
6. Why Tidy Eval Need to delay computations list( height < 200, gender == "male" ) Error: object 'height' not found starwars %>% filter( height < 200, gender == "male" )
7. Why Tidy Eval How it works • Delay computations by quoting • Change the context and resume computation starwars %>% filter( height < 200, gender == "male" )
8. Quoted code is like a blueprint Flip side • Programming requires modifying the blueprint • !! is like a surgery operator for the blueprint
9. Two flavours of 
 tidy evaluation
10. Two flavours starwars %>% mutate(birth_year - 100) starwars %>% group_by(birth_year) starwars %>% select(birth_year) starwars %>% filter(birth_year < 50) One of these things is not like the other things!
11. Two flavours starwars %>% mutate(birth_year - 100) starwars %>% group_by(birth_year) starwars %>% select(birth_year) starwars %>% filter(birth_year < 50) One of these things is not like the other things! Action Selection
12. tmp <- starwars$birth_year - 100 starwars$`birth_year - 100` <- tmp starwars %>% mutate(birth_year - 100) Most verbs take actions 1. New vectors are created 2. The data frame is modified
13. Some verbs take selections 1. The position of columns is looked up 2. The data frame is reorganised starwars %>% select(birth_year) tmp <- match("birth_year", colnames(starwars)) starwars[, tmp]
14. starwars %>% select(c(1, height)) starwars %>% select(1:height) starwars %>% select(-1, -height) Selections have special properties 1. c(), `-` and `:` understand positions and names 2. Selection helpers know about current variables
15. starwars %>% select(ends_with("color")) starwars %>% select(matches("^[nm]a") starwars %>% select(10, everything()) 1. c(), `-` and `:` understand positions and names 2. Selection helpers know about current variables Selections have special properties
16. Sometimes they appear to work the same way... starwars %>% select(height) # A tibble: 87 x 1 height <int> 1 172 2 167 3 96 # … with 84 more rows starwars %>% transmute(height) # A tibble: 87 x 1 height <int> 1 172 2 167 3 96 # … with 84 more rows
17. starwars %>% select(1) # A tibble: 87 x 1 name <chr> 1 Luke Skywalker 2 C-3PO 3 R2-D2 # … with 84 more rows starwars %>% transmute(1) # A tibble: 87 x 1 `1` <dbl> 1 1 2 1 3 1 # … with 84 more rows Sometimes they appear to work the same way...
18. What about group_by()? starwars %>% group_by(gender) # A tibble: 87 x 13 # Groups: gender [5] name height mass hair_color skin_color eye_color <chr> <int> <dbl> <chr> <chr> <chr> 1 Luke… 172 77 blond fair blue 2 C-3PO 167 75 NA gold yellow 3 R2-D2 96 32 NA white, bl… red # … with 84 more rows, and 7 more variables
19. starwars %>% group_by(ends_with("color")) Error: No tidyselect variables were registered What about group_by()? It takes actions!
20. What about group_by()? It takes actions! starwars %>% group_by(height > 170) %>% summarise(n()) # A tibble: 3 x 2 `height > 170` `n()` <lgl> <int> 1 FALSE 27 2 TRUE 54 3 NA 6
21. Tip: Use the _at dplyr variants to pass selections! starwars %>% group_by_at(vars(ends_with("color")))
22. Creating tidy eval functions
23. Three challenges 1. Taking user selections or actions like a tidy eval function 2. Modifying these selections or actions 3. Passing selections or actions to other tidy eval functions
24. Three challenges 1. Taking user selections or actions like a tidy eval function 2. Modifying these selections or actions 3. Passing selections or actions to other tidy eval functions
25. Three challenges 1. Taking user selections or actions like a tidy eval function 2. Modifying these selections or actions 3. Passing selections or actions to other tidy eval functions
26. Passing the dots
27. • Tidy eval, the easy way • Make use of existing components • Solves challenges 1 and 3 • Limited but useful! my_component <- function(.data, ...) { .data %>% summarise(...) }
28. Three examples 1. Create a selection verb with dplyr 2. Transform that verb to take actions instead 3. Add tidyr step to the pipeline Pass the dots ... !
29. • Most dplyr verbs have variants suffixed with _at • They take selections within vars(...) • mutate_at() and summarise_at() apply a function on each of those vars 1. Selections with dplyr mutate_at()
 summarise_at()
 filter_at()
 rename_at()
 arrange_at()
30. starwars %>% summarise_at( vars(height, mass), ~ mean(., na.rm = TRUE) ) starwars %>% summarise( height = mean(height, na.rm = TRUE), mass = mean(mass, na.rm = TRUE) ) # A tibble: 1 x 2 height mass <dbl> <dbl> 1 174. 97.3
31. starwars %>% summarise_at( vars(height:mass), summary_functions ) # A tibble: 1 x 4 height_mean mass_mean height_sd mass_sd <dbl> <dbl> <dbl> <dbl> 1 174. 97.3 34.8 169. summary_functions <- list( ~ mean(., na.rm = TRUE), ~ sd(., na.rm = TRUE) ) • Supports multiple functions • Results spread across columns
32. summarise_sels <- function(.data, ...) { summarise_at(.data, vars(...), summary_functions) } Pass the dots ... !
33. starwars %>% summarise_sels(height:mass) # A tibble: 1 x 4 height_mean mass_mean height_sd mass_sd <dbl> <dbl> <dbl> <dbl> 1 174. 97.3 34.8 169.
34. starwars %>% group_by(gender) %>% summarise_sels(height, mass) # A tibble: 5 x 5 gender height_mean mass_mean height_sd mass_sd <chr> <dbl> <dbl> <dbl> <dbl> 1 female 165. 54.0 23.0 8.37 2 hermaphrodite 175 1358 NA NA 3 male 179. 81.0 35.4 28.2 4 none 200 140 NA NA 5 NA 120 46.3 40.7 24.8 Works with groups!
35. • How could we pass actions instead of selections? • In dplyr, transmute() is the fundamental action verb • Returns as many columns as supplied actions 2. Actions with dplyr
36. summarise_acts <- function(.data, ...) { .data %>% transmute(...) %>% summarise_all(summary_functions) } summarise_sels <- function(.data, ...) { .data %>% summarise_at(vars(...), summary_functions) } Pass the dots ... !
37. starwars %>% summarise_acts( heightm = height / 100, bmi = mass / heightm^2 ) # A tibble: 1 x 4 heightm_mean bmi_mean heightm_sd bmi_sd <dbl> <dbl> <dbl> <dbl> 1 1.74 32.0 0.348 54.9
38. starwars %>% group_by(gender) %>% summarise_acts( heightm = height / 100, bmi = mass / heightm^2 ) # A tibble: 5 x 5 gender heightm_mean bmi_mean heightm_sd bmi_sd <chr> <dbl> <dbl> <dbl> <dbl> 1 female 1.65 18.8 0.230 3.71 2 hermaphrodite 1.75 443. NA NA 3 male 1.79 25.7 0.354 6.49 4 none 2 35 NA NA 5 NA 1.2 31.9 0.407 4.33
39. • What if we'd like to gather results across rows? • Let's develop the pipeline with a tidyr step • Handling groups will be trickier 3. Gather with tidyr
40. gather_summarise_acts <- function(.data, ...) { .data %>% transmute(...) %>% gather("Variable", "Value", everything()) %>% group_by(Variable) %>% summarise_at(vars(Value), summary_functions) } Pass the dots ... !
41. starwars %>% gather_summarise_acts( heightm = height / 100, bmi = mass / heightm^2 ) # A tibble: 2 x 3 Variable mean sd <chr> <dbl> <dbl> 1 bmi 32.0 54.9 2 heightm 1.74 0.348
42. starwars %>% group_by(gender) %>% gather_summarise_acts( heightm = height / 100, bmi = mass / heightm^2 ) Warning messages: 1: In mean.default(Value, na.rm = TRUE) : argument is not numeric or logical: returning NA 2: In mean.default(Value, na.rm = TRUE) : argument is not numeric or logical: returning NA 3: In mean.default(Value, na.rm = TRUE) : argument is not numeric or logical: returning NA • gather() also gathers grouping variables • Summaries can't be applied on character
43. gather_summarise_acts <- function(.data, ...) { .data %>% transmute(...) %>% gather("Variable", "Value", -one_of(group_vars(.))) %>% group_by(Variable) %>% summarise_at(vars(Value), my_summarisers) } Solution: Remove the grouping variables from the gathering
44. starwars %>% group_by(gender) %>% gather_summarise_acts( heightm = height / 100, bmi = mass / heightm^2 ) # A tibble: 10 x 4 # Groups: gender [?] gender Variable mean sd <chr> <chr> <dbl> <dbl> 1 female bmi 18.8 3.71 2 female heightm 1.65 0.230 3 hermaphrodite bmi 443. NA 4 hermaphrodite heightm 1.75 NA 5 male bmi 25.7 6.49 6 male heightm 1.79 0.354 7 none bmi 35 NA 8 none heightm 2 NA 9 NA bmi 31.9 4.33 10 NA heightm 1.2 0.407
45. • Pass dots to create tidy eval functions easily • Do you need actions or selections? • The _at variants and transmute() are useful • Requires knowledge of tidyverse verbs — transferable • Think about grouped tibbles summary()