Mishima.syk#16 https://connpass.com/event/164605/

6. for
tmp1 <- f(input)
tmp2 <- g(tmp1)
output <- h(tmp2)
for(i in 1:I){
# 1 tmp1
}
for(j in 1:J){
# 2 tmp2
}
for(k in 1:K){
# 3 output
}
output <- h(g(f(input)))

7. apply
sapply lapply
apply apply
tmp1 <- apply(input, 1, function(x){
# 1
})
tmp2 <- apply(tmp1, 1, function(x){
# 2
})
output <- apply(tmp2, 1, function(x){
# 3
})

8. output <- apply(input, 1, function(x){
# 1
apply(x, 1, function(y){
# 2
apply(y, 1, function(z){
# 3
})
})
})
tmp1 <- apply(input, 1, function(x, y){
# 1
}, y=input2)

9. input %>% f() %>% g() %>% h() -> output
tidyverse
data.frame tibble

10. input %>% ... %>% summary()
input %>% ... %>% ggplot()
input %>% ... %>% plot_ly()
input %>% ... %>% save()
input %>% ... -> tmp_object
for apply
filter mutate

11. tidyverse
ggplot2

13. ❌
❌
✅
✅

14. tidyr
pivot_wider pivot_longer

16. ✅
✅
✅
❌

18. ✅
✅
❌
✅

19. tidyverse
readr read_delim
tidyr pivot_wider pivot_longer
magrittr %>%
dplyr
select, filter, group_by ungroup, mutate
ggplot2
purrr tidyverse apply map/map2

20. iris
> iris

21. > iris %>%
select(Petal.Width, Species)
select

22. > iris %>%
select(Petal.Width, Species) %>%
filter(Species != "virginica")
filter

23. > iris %>%
select(Petal.Width, Species) %>%
filter(Species != "virginica") %>%
group_by(Species)
group_by

24. > iris %>%
select(Petal.Width, Species) %>%
filter(Species != "virginica") %>%
group_by(Species) %>%
mutate(mean=mean(Petal.Width))
mutate

25. > iris %>%
select(Petal.Width, Species) %>%
filter(Species != "virginica") %>%
group_by(Species) %>%
mutate(mean=mean(Petal.Width))
ungroup %>%
map(., unique)
ungroup
map

28. tibble
tibble
tibble
tibble
tibble
tibble
tibble
tibble
tibble

29. tibble
tibble
tibble tidybulk
tibble tidybulk
keep_abundant identify_abundant
reduce_dimensions
tibble

31. input %>% ... %>% %>% ...

32. fit <- lm(y ~ x, data)
data %>% ... %>% lm(y ~ x, .)

33. list
tidy
... lm(y ~ x, .) %>% ...

34. iris %>%
group_by(Species) %>%
nest() %>%
mutate(model_lm = map(data, ~lm(Petal.Length ~ Sepal.Width, data = .))) -> res.lm

35. library(broom)
iris %>%
group_by(Species) %>%
do(tidy(lm(Petal.Length ~ Sepal.Width, data = .))) -> res.lm

36. tidy()

37. %>%?
%>%
%>%
%>%
%>%?

39. recipes
broom