Lionel Henry
January 18, 2019
2k

# Selecting and Doing with Tidy Eval

January 18, 2019

## 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"
)

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"
)
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

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

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...

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

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()`

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

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

1 174. 97.3 34.8 169.
summary_functions <- list(
~ mean(., na.rm = TRUE),
~ sd(., na.rm = TRUE)
)
• Supports multiple functions

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

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

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

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

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

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

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