Selecting and Doing with Tidy Eval

Transcript

1. Selecting and Doing
   with Tidy Eval
   

   View Slide

2. • Goal: Create functions around tidyverse pipelines
   • Tidy eval, the easy way
   • Focus on two flavours of tidyverse functions
   Selecting and Doing
   

   View Slide

3. Why Tidy Eval?
   

   View Slide

4. Why Tidy Eval
   starwars[starwars$height < 200 &
   starwars$gender == "male", ]
   starwars %>%
   filter(
   height < 200,
   gender == "male"
   )
   Change the context of computation
   

   View Slide

5. Why Tidy Eval
   starwars %>%
   filter(
   height < 200,
   gender == "male"
   )
   
   SELECT *
   FROM `starwars`
   WHERE ((`height` < 200.0) AND
   (`gender` = 'male'))
   Change the context of computation
   

   View Slide

6. Why Tidy Eval
   Need to delay computations
   list(
   height < 200,
   gender == "male"
   )
   Error: object 'height' not found
   starwars %>%
   filter(
   height < 200,
   gender == "male"
   )
   

   View Slide

7. Why Tidy Eval
   How it works
   • Delay computations by quoting
   • Change the context and resume computation
   starwars %>%
   filter(
   height < 200,
   gender == "male"
   )
   

   View Slide

8. Quoted code is like a blueprint
   Flip side
   • Programming requires modifying the blueprint
   • !! is like a surgery operator for the blueprint
   

   View Slide

9. Two flavours of 

   tidy evaluation
   

   View Slide

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!
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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]
    

    View Slide

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
    

    View Slide

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
    

    View Slide

16. Sometimes they appear to work the same way...
    starwars %>% select(height)
    # A tibble: 87 x 1
    height
    
    1 172
    2 167
    3 96
    # … with 84 more rows
    starwars %>% transmute(height)
    # A tibble: 87 x 1
    height
    
    1 172
    2 167
    3 96
    # … with 84 more rows
    

    View Slide

17. starwars %>% select(1)
    # A tibble: 87 x 1
    name
    
    1 Luke Skywalker
    2 C-3PO
    3 R2-D2
    # … with 84 more rows
    starwars %>% transmute(1)
    # A tibble: 87 x 1
    `1`
    
    1 1
    2 1
    3 1
    # … with 84 more rows
    Sometimes they appear to work the same way...
    

    View Slide

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
    
    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
    

    View Slide

19. starwars %>% group_by(ends_with("color"))
    Error: No tidyselect variables were registered
    What about group_by()? It takes actions!
    

    View Slide

20. What about group_by()? It takes actions!
    starwars %>%
    group_by(height > 170) %>%
    summarise(n())
    # A tibble: 3 x 2
    `height > 170` `n()`
    
    1 FALSE 27
    2 TRUE 54
    3 NA 6
    

    View Slide

21. Tip: Use the _at dplyr variants to pass selections!
    starwars %>% group_by_at(vars(ends_with("color")))
    

    View Slide

22. Creating tidy eval
    functions
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide

26. Passing the dots
    

    View Slide

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(...)
    }
    

    View Slide

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 ... !
    

    View Slide

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()
    

    View Slide

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
    
    1 174. 97.3
    

    View Slide

31. starwars %>%
    summarise_at(
    vars(height:mass),
    summary_functions
    )
    # A tibble: 1 x 4
    height_mean mass_mean height_sd mass_sd
    
    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
    

    View Slide

32. summarise_sels <- function(.data, ...) {
    summarise_at(.data, vars(...), summary_functions)
    }
    Pass the dots ... !
    

    View Slide

33. starwars %>%
    summarise_sels(height:mass)
    # A tibble: 1 x 4
    height_mean mass_mean height_sd mass_sd
    
    1 174. 97.3 34.8 169.
    

    View Slide

34. starwars %>%
    group_by(gender) %>%
    summarise_sels(height, mass)
    # A tibble: 5 x 5
    gender height_mean mass_mean height_sd mass_sd
    
    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!
    

    View Slide

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
    

    View Slide

36. summarise_acts <- function(.data, ...) {
    .data %>%
    transmute(...) %>%
    summarise_all(summary_functions)
    }
    summarise_sels <- function(.data, ...) {
    .data %>% summarise_at(vars(...), summary_functions)
    }
    Pass the dots ... !
    

    View Slide

37. starwars %>%
    summarise_acts(
    heightm = height / 100,
    bmi = mass / heightm^2
    )
    # A tibble: 1 x 4
    heightm_mean bmi_mean heightm_sd bmi_sd
    
    1 1.74 32.0 0.348 54.9
    

    View Slide

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
    
    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
    

    View Slide

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
    

    View Slide

40. gather_summarise_acts <- function(.data, ...) {
    .data %>%
    transmute(...) %>%
    gather("Variable", "Value", everything()) %>%
    group_by(Variable) %>%
    summarise_at(vars(Value), summary_functions)
    }
    Pass the dots ... !
    

    View Slide

41. starwars %>%
    gather_summarise_acts(
    heightm = height / 100,
    bmi = mass / heightm^2
    )
    # A tibble: 2 x 3
    Variable mean sd
    
    1 bmi 32.0 54.9
    2 heightm 1.74 0.348
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    
    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
    

    View Slide

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()
    

    View Slide

46. View Slide