TokyoR#104_DataProcessing

第104回Tokyo.Rでしゃべった際の資料です。

Transcript

1. #104 @kilometer00 2023.03.04 BeginneR Session Data processing & visualization
2. Who！？ Who?
3. Who！？ 名前： 三村 @kilometer 職業： ポスドク (こうがくはくし) 専⾨： ⾏動神経科学(霊⻑類) 脳イメージング 医療システム⼯学 R歴： ~ 10年ぐらい 流⾏: 椅⼦を新調
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. #104 @kilometer00 2023.03.03 BeginneR Session Data processing & visualization
10. import Tidy Transform Visualize Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 Data Science ① ②
11. σʔλ 情報のうち意思伝達・解釈・処理に 適した再利⽤可能なもの 国際電気標準会議(Interna@onal Electrotechnical Commission, IEC)による定義
12. σʔλ 情報のうち意思伝達・解釈・処理に 適した再利⽤可能なもの ৘ใ 実存を符号化した表象
13. σʔλ ৘ใͷ͏ͪҙࢥ఻ୡɾղऍɾॲཧʹ దͨ͠࠶ར༻Մೳͳ΋ͷ ৘ใ ࣮ଘΛූ߸Խͨ͠ද৅ ࣮ଘ ؍࡯ͷ༗ແʹΑΒͣଘࡏ͍ͯ͠Δ ΋ͷͦͷ΋ͷ ࣸ૾ʢූ߸Խʣ
14. 集合! 集合" 要素# 要素$ 写像 %: ! → "もしくは%: # ⟼ $ （始集合・定義域） （終集合・終域） 【写像】 ある集合の要素を他の集合のただ1つの要素に 対応づける規則
15. 地図空間 ⽣物種名空間 名空間 ⾦銭価値空間 (円) ⾦銭価値空間 (ドル) コーヒー ¥290 $2.53 [緯度, 経度] Homo sapiens 実存 写像 写像 写像 写像 写像 写像 情報 【写像】 ある集合の要素を他の集合のただ1つの要素に対応づける規則
16. ࣸ૾ Ϧϯΰ ʢ࣮ଘʣ Ϧϯΰ ʢ৘ใʣ mapping
17. ৘ใྔ ࣮ଘ ৘ใ σʔλ Ϧϯΰ 
     ූ߸Խ
18. ৘ใྔ ࣮ଘ ৘ใ σʔλ Ϧϯΰ 
     ූ߸Խ ৘ใྔͷଛࣦ
19. Experiment hypothesis observa=on principle phenotype model data Truth Knowledge f X (unknown)
20. raed_csv() write_csv() Table Data Wide form Long form pivot_longer() Nested form pivot_wider() Plot group_nest() unnest() {ggplot2} {patchwork} Image Files ggsave() Data Processing
21. data.frame *bble raed_csv() write_csv() Table Data Wide form Long form pivot_longer() Nested form pivot_wider() Plot group_nest() unnest() {ggplot2} {patchwork} Image Files ggsave() Data Processing
22. data.frame
23. vector in Excel
24. 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
25. 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
26. 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
27. > ?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, ...)
28. 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
29. 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
30. 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"
31. 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
32. 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 $C [1] "a" "b" "c"
33. > list2$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"))
34. 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
35. 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
36. > 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
37. > 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
38. data.frame vs. 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), nrow = 3, ncol = 2)
39. data.frame variables observa*on
40. data.frame *bble raed_csv() write_csv() Table Data Wide form Long form pivot_longer() Nested form pivot_wider() Plot group_nest() unnest() {ggplot2} {patchwork} Image Files ggsave() Data Processing
41. > anscombe x1 x2 x3 x4 y1 y2 y3 y4 1 10 10 10 8 8.04 9.14 7.46 6.58 2 8 8 8 8 6.95 8.14 6.77 5.76 3 13 13 13 8 7.58 8.74 12.74 7.71 4 9 9 9 8 8.81 8.77 7.11 8.84 5 11 11 11 8 8.33 9.26 7.81 8.47 6 14 14 14 8 9.96 8.10 8.84 7.04 7 6 6 6 8 7.24 6.13 6.08 5.25 8 4 4 4 19 4.26 3.10 5.39 12.50 9 12 12 12 8 10.84 9.13 8.15 5.56 10 7 7 7 8 4.82 7.26 6.42 7.91 11 5 5 5 8 5.68 4.74 5.73 6.89 Wide form data
42. > df tag x1 x2 x3 x4 y1 y2 y3 y4 1 1 10 10 10 8 8.04 9.14 7.46 6.58 2 2 8 8 8 8 6.95 8.14 6.77 5.76 3 3 13 13 13 8 7.58 8.74 12.74 7.71 4 4 9 9 9 8 8.81 8.77 7.11 8.84 5 5 11 11 11 8 8.33 9.26 7.81 8.47 6 6 14 14 14 8 9.96 8.10 8.84 7.04 Wide form data df <- rownames_to_column( anscombe, var = "tag" )
43. Wide form → Long form data df_long_1 <- pivot_longer( data = df, cols = !tag ) df_long_2 <- pivot_longer( data = df, cols = !tag, names_to = c(".value", "key"), names_pattern = c("(.)(.)") )
44. Long form → Wide form data pivot_wider( data = df_long_1, values_from = value, names_from = name ) pivot_wider( data = df_long_2, values_from = c(x, y), names_from = tag )
45. data.frame / *bble raed_csv() write_csv() Table Data Wide form Long form pivot_longer() pivot_wider() Plot {ggplot2} Image Files ggsave() Data Processing
46. raed_csv() write_csv() Table Data Wide form Long form pivot_longer() pivot_wider() Plot {ggplot2} Image Files ggsave() Data Processing Long form Long form Long form Long form Long form Long form Long form Long form data.frame / *bble
47. vignette("dplyr")
48. 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 Introduc6on to dplyr h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html WFSCT
    {dplyr}
49. 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) Introduc6on to dplyr h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html WFSCT {dplyr} ※ かなり意訳
50. (SBNNBS
    PG
    EBUB
    NBOJQVMBUJPO By constraining your op@ons, it helps you think about your data manipula@on challenges. Introduc6on to dplyr hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
51. 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） Introduc6on to dplyr hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html ※ まさに意訳 (SBNNBS
    PG
    EBUB
    NBOJQVMBUJPO
52. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula@on
53. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula@on 0. %>%
54. 1JQF
    BMHFCSB X %>% f X %>% f(y) X %>% f %>% g X %>% f(y, .) f(X) f(X, y) g(f(X)) f(y, X) %>% {magri7r} 「dplyr再⼊⾨（基本編）」yutanihila@on h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
55. ① ② ③ ④ lift take pour put Bring milk from the kitchen!
56. ① lift Bring milk from the kitchen! lift(Robot, glass, table) -> Robot' take ② take(Robot', fridge, milk) -> Robot''
57. 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
58. The =dyverse style guides h"ps://style.;dyverse.org/syntax.html#object-names "There are only two hard things in Computer Science: cache invalida:on and naming things"
59. 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
60. 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!
61. Programing Write Run Read Think Write Run Read Think Communicate Share
62. 1JQF
    BMHFCSB X %>% f X %>% f(y) X %>% f %>% g X %>% f(y, .) f(X) f(X, y) g(f(X)) f(y, X) %>% {magri7r} 「dplyr再⼊⾨（基本編）」yutanihila@on h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
63. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula@on 0. %>% ✔
64. WFSCT {dplyr} mutate # カラムの追加 + mutate(dat, C = fun(A, B))
65. WFSCT {dplyr} mutate # カラムの追加 + dat %>% mutate(C = fun(A, B))
66. WFSCT {dplyr} filter # 行の絞り込み dat %>% filter(tag %in% c(1, 3, 5))
67. 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
68. "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
69. George Boole 1815 - 1864 A Class-Room Introduc;on to Logic h"ps://niyamaklogic.wordpress.com/c ategory/laws-of-thoughts/ Mathema=cian Philosopher &
70. WFSCT {dplyr} select # カラムの選択 dat %>% select(tag, B)
71. WFSCT {dplyr} select # カラムの選択 dat %>% select("tag", "B")
72. WFSCT {dplyr} select # カラムの選択 dat %>% select("tag", "B") dat %>% select(tag, B)
73. WFSCT {dplyr} # Select help func?ons starts_with("s") ends_with("s") contains("se") matches("^.e") one_of(c(”tag", ”B")) everything() hFps://kazutan.github.io/blog/2017/04/dplyr-select-memo/ 「dplyr::selectの活⽤例メモ」kazutan
74. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula@on 0. %>% ✔ ✔ ✔ ✔
75. 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） Introduc6on to dplyr hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html ※ まさに意訳 (SBNNBS
    PG
    EBUB
    NBOJQVMBUJPO
76. 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 И@горь 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
77. import Tidy Transform Visualize Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 Data Science ① ②
78. Programing Write Run Read Think Write Run Read Think Communicate Share
79. Text Image Information Intention Data decode encode Data analysis feedback ≠
80. Text Image First, A. Next, B. Then C. Finally D. ?me Intention encode "Frozen" structure A B C D Xme value α β
81. ࣸ૾ Ϧϯΰ ʢ࣮ଘʣ Ϧϯΰ ʢ৘ใʣ mapping
82. Ϧϯΰ ࣸ૾ ϑϧʔπ ੺৭  ը૾ ࣮ଘ ৘ใ νϟωϧ mapping channel
83. # $ %! &! %" &" # $ &! &" %! %" σʔλՄࢹԽ ࣸ૾ mapping
84. # $ %! &! %" &" # $ &! &" %! %" σʔλՄࢹԽ ࣸ૾ mapping x axis, y axis, color, fill, shape, linetype, alpha… aesthetic channels ৹ඒతνϟωϧ
85. # $ %! &! %" &" # $ &! &" %! %" σʔλՄࢹԽ ࣸ૾ mapping x axis, y axis, color, fill, shape, linetype, alpha… aesthetic channels ৹ඒతνϟωϧ ggplot(data = my_data) + aes(x = X, y = Y)) + goem_point() HHQMPUʹΑΔ࡞ਤ
86. ࣮ଘ ࣸ૾ʢ؍࡯ʣ σʔλ ࣸ૾ʢσʔλՄࢹԽʣ άϥϑ ! " #! $! #" $" # $ &! &" %! %" EBUB mapping aesthetic channels ৹ඒతνϟωϧ σʔλՄࢹԽ
87. ॳΊͯͷHHQMPU library(tidyverse) dat <- data.frame(tag = rep(c("a", "b"), each = 2), X = c(1, 3, 5, 7), Y = c(3, 9, 4, 2)) ggplot() + geom_point(data = dat, mapping = aes(x = X, y = Y))
88. ॳΊͯͷHHQMPU
89. ॳΊͯͷHHQMPU library(tidyverse) dat <- data.frame(tag = rep(c("a", "b"), each = 2), X = c(1, 3, 5, 7), Y = c(3, 9, 4, 2)) ggplot() + geom_point(data = dat, mapping = aes(x = X, y = Y)) EBUBGSBNFͷࢦఆ BFT ؔ਺ͷதͰ৹ඒతཁૉͱͯ͠ม਺ͱνϟωϧͷରԠΛࢦఆ ඳը։࢝Λએݴ ه߸Ͱͭͳ͙ BFT ؔ਺ͷҾ਺໊ EBUͷม਺໊ άϥϑͷछྨʹ߹ΘͤͨHFPN@ ؔ਺Λ࢖༻
90. library(tidyverse) dat <- data.frame(tag = rep(c("a", "b"), each = 2), X = c(1, 3, 5, 7), Y = c(3, 9, 4, 2)) ggplot() + geom_point(data = dat, mapping = aes(x = X, y = Y)) + geom_path(data = dat, mapping = aes(x = X, y = Y)) ॳΊ͔ͯΒ൪໨ͷHHQMPU
91. ॳΊ͔ͯΒ൪໨ͷHHQMPU
92. HHQMPUίʔυͷॻ͖ํͷ৭ʑ ggplot() + geom_point(data = dat, mapping = aes(x = X, y = Y)) + geom_path(data = dat, mapping = aes(x = X, y = Y)) ggplot(data = dat, mapping = aes(x = X, y = Y)) + geom_point() + geom_path() ggplot(data = dat) + aes(x = X, y = Y) + geom_point() + geom_path() ڞ௨ͷࢦఆΛHHQMPU ؔ਺ͷதͰߦ͍ɺҎԼলུ͢Δ͜ͱ͕Մೳ NBQQJOHͷ৘ใ͕ॻ͔ΕͨBFT ؔ਺ΛHHQMPU ؔ਺ͷ֎ʹஔ͘͜ͱ΋Ͱ͖Δ
93. HHQMPUίʔυͷॻ͖ํͷ৭ʑ ggplot() + geom_point(data = dat, mapping = aes(x = X, y = Y, color = tag)) + geom_path(data = dat, mapping = aes(x = X, y = Y)) ggplot(data = dat) + aes(x = X, y = Y) + # 括り出すのは共通するものだけ geom_point(mapping = aes(color = tag)) + geom_path() ϙΠϯτͷ৭ͷNBQQJOHΛࢦఆ
94. HHQMPUίʔυͷॻ͖ํͷ৭ʑ ggplot(data = dat) + aes(x = X, y = Y) + geom_point(aes(color = tag)) + geom_path() ggplot(data = dat) + aes(x = X, y = Y) + geom_path() + geom_point(aes(color = tag)) ͋ͱ͔Β ͰॏͶͨཁૉ͕લ໘ʹඳը͞ΕΔ
95. library(tidyverse) dat <- data.frame(tag = rep(c("a", "b"), each = 2), X = c(1, 3, 5, 7), Y = c(3, 9, 4, 2)) g <- ggplot(data = dat) + aes(x = X, y = Y) + geom_path() + geom_point(mapping = aes(color = tag)) HHQMPUը૾ͷอଘ ggsave(filename = "fig/demo01.png", plot = g, width = 4, height = 3, dpi = 150)
96. library(tidyverse) dat <- data.frame(tag = rep(c("a", "b"), each = 2), X = c(1, 3, 5, 7), Y = c(3, 9, 4, 2)) g <- ggplot(data = dat) + aes(x = X, y = Y) + geom_path() + geom_point(mapping = aes(color = tag)) HHQMPUը૾ͷอଘ ggsave(filename = "fig/demo01.png", plot = g, width = 4, height = 3, dpi = 150) αΠζ͸σϑΥϧτͰ͸Πϯν୯ҐͰࢦఆ
97. library(tidyverse) dat <- data.frame(tag = rep(c("a", "b"), each = 2), X = c(1, 3, 5, 7), Y = c(3, 9, 4, 2)) g <- ggplot(data = dat) + aes(x = X, y = Y) + geom_path() + geom_point(mapping = aes(color = tag)) HHQMPUը૾ͷอଘ ggsave(filename = "fig/demo01.png", plot = g, width = 10, height = 7.5, dpi = 150, units = "cm") # "cm", "mm", "in"を指定可能
98. HFNP@ ؔ਺܈ DG
    IUUQTXXXSTUVEJPDPNSFTPVSDFTDIFBUTIFFUT
99. ෳ਺ͷܥྻΛඳը͢Δ > head(anscombe) x1 x2 x3 x4 y1 y2 y3 y4 1 10 10 10 8 8.04 9.14 7.46 6.58 2 8 8 8 8 6.95 8.14 6.77 5.76 3 13 13 13 8 7.58 8.74 12.74 7.71 4 9 9 9 8 8.81 8.77 7.11 8.84 5 11 11 11 8 8.33 9.26 7.81 8.47 6 14 14 14 8 9.96 8.10 8.84 7.04 ggplot(data = anscombe) + geom_point(aes(x = x1, y = y1)) + geom_point(aes(x = x2, y = y2), color = "Red") + geom_point(aes(x = x3, y = y3), color = "Blue") + geom_point(aes(x = x4, y = y4), color = "Green") ͜Ε·Ͱͷ஌ࣝͰؤுΔͱ͜͏ͳΔ
100. HHQMPUʹΑΔσʔλՄࢹԽ ࣮ଘ ࣸ૾ʢ؍࡯ʣ σʔλ ࣸ૾ʢσʔλՄࢹԽʣ άϥϑ ! " #! $! #" $" SBX
     EBUB 写像 aesthetic channels ৹ඒతνϟωϧ ՄࢹԽʹదͨ͠EBUBܗࣜ 変形 ਤͷͭͷ৹ඒతνϟωϧ͕ σʔλͷͭͷม਺ʹରԠ͍ͯ͠Δ
101. > head(anscombe) x1 x2 x3 x4 y1 y2 y3 y4 1 10 10 10 8 8.04 9.14 7.46 6.58 2 8 8 8 8 6.95 8.14 6.77 5.76 3 13 13 13 8 7.58 8.74 12.74 7.71 4 9 9 9 8 8.81 8.77 7.11 8.84 5 11 11 11 8 8.33 9.26 7.81 8.47 6 14 14 14 8 9.96 8.10 8.84 7.04 > head(anscombe_long) key x y 1 1 10 8.04 2 2 10 9.14 3 3 10 7.46 4 4 8 6.58 5 1 8 6.95 6 2 8 8.14 ggplot(data = anscombe_long) + aes(x = x, y = y, color = key) + geom_point() ৹ඒతνϟωϧ Y࣠ Z࣠ ৭ ʹରԠ͢Δม਺ʹͳΔΑ͏มܗ ݟ௨͠ྑ͘γϯϓϧʹՄࢹԽͰ͖Δ
102. > head(anscombe) x1 x2 x3 x4 y1 y2 y3 y4 1 10 10 10 8 8.04 9.14 7.46 6.58 2 8 8 8 8 6.95 8.14 6.77 5.76 3 13 13 13 8 7.58 8.74 12.74 7.71 4 9 9 9 8 8.81 8.77 7.11 8.84 5 11 11 11 8 8.33 9.26 7.81 8.47 6 14 14 14 8 9.96 8.10 8.84 7.04 > head(anscombe_long) key x y 1 1 10 8.04 2 2 10 9.14 3 3 10 7.46 4 4 8 6.58 5 1 8 6.95 6 2 8 8.14 ৹ඒతνϟωϧ Y࣠ Z࣠ ৭ ʹରԠ͢Δม਺ʹͳΔΑ͏มܗ anscombe_long <- pivot_longer(data = anscombe, cols = everything(), names_to = c(".value", "key"), names_pattern = "(.)(.)") ԣ௕σʔλ ॎ௕σʔλ
103. ggplot(data = anscombe_long) + aes(x = x, y = y, color = key) + geom_point() ggplot(data = anscombe_long) + aes(x = x, y = y, color = key) + geom_point() + facet_wrap(facets = . ~ key, nrow = 1) ਫ४ͰਤΛ෼ׂ͢Δ
104. まとめ
105. import Tidy Transform Visualize Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 Data Science ① ②
106. import Tidy Transform Visualize Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 preprocessing Data science Data Observa=on Hypothesis NarraFve of data feedback Data processing
107. data.frame / *bble raed_csv() write_csv() Table Data Wide form Long form pivot_longer() pivot_wider() Plot {ggplot2} Image Files ggsave() Data Processing
108. raed_csv() write_csv() Table Data Wide form Long form pivot_longer() pivot_wider() Plot {ggplot2} Image Files ggsave() Data Processing Long form Long form Long form Long form Long form Long form Long form Long form data.frame / *bble
109. 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 Introduc6on to dplyr h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html WFSCT
     {dplyr}
110. 1. mutate() 2. filter() 3. select() 4. group_by() 5. summarize() 6. left_join() 7. arrange() Data.frame manipula@on
111. import Tidy Transform Visualize Model Communicate Modified from “R for Data Science”, H. Wickham, 2017 Data Science ① ②
112. # $ %! &! %" &" # $ &! &" %! %" mapping x axis, y axis, color, fill, shape, linetype, alpha… aesthetic channels data ggplot2 package
113. HHQMPUʹΑΔσʔλՄࢹԽ ࣮ଘ ࣸ૾ʢ؍࡯ʣ σʔλ ࣸ૾ʢσʔλՄࢹԽʣ άϥϑ ! " #! $! #" $" SBX
     EBUB 写像 aesthetic channels ৹ඒతνϟωϧ ՄࢹԽʹదͨ͠EBUBܗࣜ 変形 ਤͷͭͷ৹ඒతνϟωϧ͕ σʔλͷͭͷม਺ʹରԠ͍ͯ͠Δ
114. Enjoy!!