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# バイオインフォ分野におけるtidyなデータ解析の最新動向

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# バイオインフォ分野におけるtidyなデータ解析の最新動向

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

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

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### バイオインフォ分野におけるtidyなデータ解析の最新動向

1. 1. 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)))
2. 2. 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 })
3. 3. 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)
4. 4. input %>% f() %>% g() %>% h() -> output tidyverse data.frame tibble
5. 5. input %>% ... %>% summary()  input %>% ... %>% ggplot()  input %>% ... %>% plot_ly()  input %>% ... %>% save() input %>% ... -> tmp_object for apply filter mutate
6. 6. tidyverse ggplot2
7. 7. ❌ ❌ ✅ ✅
8. 8. tidyr pivot_wider pivot_longer
9. 9. ✅ ✅ ✅ ❌
10. 10. ✅ ✅ ❌ ✅
11. 11. tidyverse readr read_delim tidyr pivot_wider pivot_longer magrittr %>% dplyr select, filter, group_by ungroup, mutate ggplot2 purrr tidyverse apply map/map2
12. 12. iris > iris
13. 13. > iris %>% select(Petal.Width, Species) select
14. 14. > iris %>% select(Petal.Width, Species) %>% filter(Species != "virginica") filter
15. 15. > iris %>% select(Petal.Width, Species) %>% filter(Species != "virginica") %>% group_by(Species) group_by
16. 16. > iris %>% select(Petal.Width, Species) %>% filter(Species != "virginica") %>% group_by(Species) %>% mutate(mean=mean(Petal.Width)) mutate
17. 17. > iris %>% select(Petal.Width, Species) %>% filter(Species != "virginica") %>% group_by(Species) %>% mutate(mean=mean(Petal.Width)) ungroup %>% map(., unique) ungroup map
18. 18. tibble tibble tibble tibble tibble tibble tibble tibble tibble
19. 19. tibble tibble tibble tidybulk tibble tidybulk keep_abundant identify_abundant reduce_dimensions tibble
20. 20. input %>% ... %>% %>% ...
21. 21. fit <- lm(y ~ x, data) data %>% ... %>% lm(y ~ x, .)
22. 22. list tidy ... lm(y ~ x, .) %>% ...
23. 23. iris %>% group_by(Species) %>% nest() %>% mutate(model_lm = map(data, ~lm(Petal.Length ~ Sepal.Width, data = .))) -> res.lm
24. 24. library(broom) iris %>% group_by(Species) %>% do(tidy(lm(Petal.Length ~ Sepal.Width, data = .))) -> res.lm
25. 25. tidy()
26. 26. %>%? %>% %>% %>% %>%?
27. 27. recipes broom