Tokyo.R#93 Data processing

第93回Tokyo.Rでトークした際の資料です。

Transcript

1. #93 @kilometer00 2021.07.03 BeginneR Session -- Data processing --
2. Who！？ Who?
3. Who！？ ・ @kilometer ・Postdoc Researcher (Ph.D. Eng.) ・Neuroscience ・Computational Behavior ・Functional brain imaging ・R： ~ 10 years
4. 宣伝!!（書籍の翻訳に参加しました。） 絶賛販売中！
5. BeginneR Session
6. BeginneR
7. Before A'er BeginneR Session BeginneR BeginneR
8. BeginneR Advanced Hoxo_m If I have seen further it is by standing on the shoulders of Giants. -- Sir Isaac Newton, 1676
9. import Tidy Transform Visualise Model Communicate Modified from “R for Data Science”, H. Wickham, 2017
10. import Tidy Transform Visualise Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 preprocessing Data processing Data science
11. import Tidy Transform Visualise Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 preprocessing Data science Data Observa@on Hypothesis feedback Data processing Narra/ve of data
12. import Tidy Transform Visualise Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 preprocessing Data science Data Observa@on Hypothesis Narra/ve of data feedback Data processing
13. import Tidy Transform Visualise Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 Data processing
14. raed_csv() write_csv() Data Wide form Long form pivot_longer() Nested form pivot_wider() Figures group_nest() unnest() {ggplot2} {patchwork} Data processing
15. raed_csv() write_csv() Data Wide form Long form pivot_longer() Nested form pivot_wider() Figures group_nest() unnest() {ggplot2} {patchwork} data.frame tibble Data processing
16. data.frame
17. vector in Excel
18. vector in R in Excel pre <- c(1, 2, 3, 4, 5) post <- pre * 5 > pre [1] 1 2 3 4 5 > post [1] 5 10 15 20 25
19. vector vec1 <- c(1, 2, 3, 4, 5) vec2 <- 1:5 vec3 <- seq(from = 1, to = 5, by = 1) > vec1 [1] 1 2 3 4 5 > vec2 [1] 1 2 3 4 5 > vec3 [1] 1 2 3 4 5
20. vector vec1 <- seq(from = 1, to = 5, by = 1) vec2 <- seq(1, 5, 1) > vec1 [1] 1 2 3 4 5 > vec2 [1] 1 2 3 4 5
21. > ?seq vector seq{base} Sequence Generation Description Generate regular sequences. seq is a standard generic with a default method. … Usage seq(...) ## Default S3 method: seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)), length.out = NULL, along.with = NULL, ...)
22. vector vec1 <- rep(1:3, times = 2) vec2 <- rep(1:3, each = 2) vec3 <- rep(1:3, times = 2, each = 2) > vec1 [1] 1 2 3 1 2 3 > vec2 [1] 1 1 2 2 3 3 > vec3 [1] 1 1 2 2 3 3 1 1 2 2 3 3
23. vector vec1 <- 11:15 > vec1 [1] 11 12 13 14 15 > vec1[1] [1] 11 > vec1[3:5] [1] 13 14 15 > vec1[c(1:2, 5)] [1] 11 12 15
24. list list1 <- list(1:6, 11:15, c("a", "b", "c")) > list1 [[1]] [1] 1 2 3 4 5 6 [[2]] [1] 11 12 13 14 15 [[3]] [1] "a" "b" "c"
25. list list1 <- list(1:6, 11:15, c("a", "b", "c")) > list1[[1]] [1] 1 2 3 4 5 6 > list1[[3]][2:3] [1] "b" "c" > list1[[2]] * 3 [1] 33 36 39 42 45
26. named list list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c")) > list2 $A [1] 1 2 3 4 5 6 $B [1] 11 12 13 14 15 $C [1] "a" "b" "c"
27. > list2$A [1] 1 2 3 4 5 6 > list2$C[2:3] [1] "b" "c" > list2$B * 3 [1] 33 36 39 42 45 named list list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
28. list1 <- list(1:6, 11:15, c("a", "b", "c")) > class(list1) [1] "list" > names(list1) NULL list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c")) > class(list2) [1] "list" > names(list2) [1] "A" "B" "C" named list list
29. list3 <- list(A = 1:3, B = 11:13) > class(list3) [1] "list" > names(list3) [1] "A" "B" df1 <- data.frame(A = 1:3, B = 11:13) > class(df1) [1] "data.frame" > names(df1) [1] "A" "B" named list & data.frame
30. > str(list3) List of 2 $ A: int [1:3] 1 2 3 $ B: int [1:3] 11 12 13 > str(df1) 'data.frame': 3 obs. of 2 variables: $ A: int 1 2 3 $ B: int 11 12 13 list3 <- list(A = 1:3, B = 11:13) df1 <- data.frame(A = 1:3, B = 11:13) named list & data.frame
31. > list3 $A [1] 1 2 3 $B [1] 11 12 13 > df1 A B 1 1 11 2 2 12 3 3 13 named list & data.frame
32. > list3 $A [1] 1 2 3 $B [1] 11 12 13 > df1 A B 1 1 11 2 2 12 3 3 13 named list & data.frame observa9on variable
33. data.frame v.s. matrix A B 1 1 11 2 2 12 3 3 13 [,1] [,2] [1,] 1 11 [2,] 2 12 [3,] 3 13 df1 <- data.frame(A = 1:3, B = 11:13) > str(mat1) int [1:3, 1:2] 1 2 3 11 12 13 > str(df1) 'data.frame': 3 obs. of 2 vars.: $ A: int 1 2 3 $ B: int 11 12 13 mat1 <- matrix(c(1:3, 11:13), 3, 2)
34. raed_csv() write_csv() Data Wide form Long form pivot_longer() Nested form pivot_wider() Figures group_nest() unnest() {ggplot2} {patchwork} data.frame tibble Data processing
35. raed_csv() write_csv() Data Wide form Long form pivot_longer() Nested form pivot_wider() Figures group_nest() unnest() {ggplot2} {patchwork} data.frame ,bble Data processing Transform (verb func@ons) {dplyr}
36. vignette("dplyr")
37. It (dplyr) provides simple “verbs” to help you translate your thoughts into code. func?ons that correspond to the most common data manipula?on tasks Introduc>on to dplyr h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html WFSCT
    {dplyr}
38. dplyrは、あなたの考えをコードに翻訳 するための【動詞】を提供する。 データ操作における基本のキを、 シンプルに実⾏できる関数 (群) Introduc>on to dplyr h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html WFSCT {dplyr} ※ かなり意訳
39. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula9on
40. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula9on 0. %>%
41. 1JQF
    BMHFCSB X %>% f X %>% f(y) X %>% f %>% g X %>% f(y, .) f(X) f(X, y) g(f(X)) f(y, X) %>% {magri8r} 「dplyr再⼊⾨（基本編）」yutanihilaCon h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
42. ① ② ③ ④ lift take pour put Bring milk from the kitchen!
43. ① lift Bring milk from the kitchen! lift(Robot, glass, table) -> Robot' take ② take(Robot', fridge, milk) -> Robot''
44. Bring milk from the kitchen! Robot' <- lift(Robot, glass, table) Robot'' <- take(Robot', fridge, milk) Robot''' <- pour(Robot'', milk, glass) result <- put(Robot''', glass, table) result <- Robot %>% lift(glass, table) %>% take(fridge, milk) %>% pour(milk, glass) %>% put(glass, table) by using pipe, # ① # ② # ③ # ④ # ① # ② # ③ # ④
45. The @dyverse style guides h"ps://style.;dyverse.org/syntax.html#object-names "There are only two hard things in Computer Science: cache invalidation and naming things"
46. Bring milk from the kitchen! Robot' <- lift(Robot, glass, table) Robot'' <- take(Robot', fridge, milk) Robot''' <- pour(Robot'', milk, glass) result <- put(Robot''', glass, table) result <- Robot %>% lift(glass, table) %>% take(fridge, milk) %>% pour(milk, glass) %>% put(glass, table) by using pipe, # ① # ② # ③ # ④ # ① # ② # ③ # ④
47. Robot' <- lift(Robot, glass, table) Robot'' <- take(Robot', fridge, milk) Robot''' <- pour(Robot'', milk, glass) result <- put(Robot''', glass, table) result <- Robot %>% lift(glass, table) %>% take(fridge, milk) %>% pour(milk, glass) %>% put(glass, table) by using pipe, # ① # ② # ③ # ④ # ① # ② # ③ # ④ Thinking Reading Bring milk from the kitchen!
48. Programing Write Run Read Think Write Run Read Think Communicate Share
49. 1JQF
    BMHFCSB X %>% f X %>% f(y) X %>% f %>% g X %>% f(y, .) f(X) f(X, y) g(f(X)) f(y, X) %>% {magrittr} 「dplyr再⼊⾨（基本編）」yutanihilation https://speakerdeck.com/yutannihilation/dplyrzai-ru-men-ji-ben-bian
50. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula9on 0. %>% ✔
51. WFSCT {dplyr} mutate # カラムの追加 + mutate(dat, C = fun(A, B))
52. WFSCT {dplyr} mutate # カラムの追加 + dat %>% mutate(C = fun(A, B))
53. WFSCT {dplyr} filter # 行の絞り込み dat %>% filter(tag %in% c(1, 3, 5))
54. ブール演算⼦ Boolean Algebra A == B A != B George Boole 1815 - 1864 A | B A & B A %in% B # equal to # not equal to # or # and # is A in B? wikipedia
55. "a" != "b" # is A in B? ブール演算⼦ Boolean Algebra [1] TRUE 1 %in% 10:100 # is A in B? [1] FALSE
56. George Boole 1815 - 1864 A Class-Room Introduc;on to Logic h"ps://niyamaklogic.wordpress.com/c ategory/laws-of-thoughts/ Mathema=cian Philosopher &
57. WFSCT {dplyr} select # カラムの選択 dat %>% select(tag, B)
58. WFSCT {dplyr} select # カラムの選択 dat %>% select("tag", "B")
59. WFSCT {dplyr} select # カラムの選択 dat %>% select("tag", "B") dat %>% select(tag, B)
60. WFSCT {dplyr} # Select help func?ons starts_with("s") ends_with("s") contains("se") matches("^.e") one_of(c(”tag", ”B")) everything() https://kazutan.github.io/blog/2017/04/dplyr-select-memo/ 「dplyr::selectの活⽤例メモ」kazutan
61. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula9on 0. %>% ✔ ✔ ✔ ✔
62. (SBNNBS
    PG
    EBUB
    NBOJQVMBUJPO By constraining your op9ons, it helps you think about your data manipula9on challenges. Introduc>on to dplyr hLps://cran.r-project.org/web/packages/dplyr/vigneLes/dplyr.html
63. 選択肢を制限することで、 データ解析のステップを シンプルに考えられますヨ。 （めっちゃ意訳） Introduc>on to dplyr hLps://cran.r-project.org/web/packages/dplyr/vigneLes/dplyr.html ※ まさに意訳 (SBNNBS
    PG
    EBUB
    NBOJQVMBUJPO
64. より多くの制約を課す事で、 魂の⾜枷から、より⾃由になる。 Igor Stravinsky И8горь Ф Страви́нский The more constraints one imposes, the more one frees one's self of the chains that shackle the spirit. 1882 - 1971 ※ 割と意訳
65. Enjoy!!