Tokyo.R #97 Data Visualization

第97回Tokyo.Rの初心者セッションでトークした際のスライドです。

Transcript

1. #97 @kilometer00 2022.03.19 BeginneR Session -- Data Visualization --
2. Who！？ 誰だ？
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: むし社
4. 宣伝!!（書籍の翻訳に参加しました。）
5. BeginneR Session
6. BeginneR
7. Beginne R Advance d Hoxo_m If I have seen further it is by standing on the shoulders of Giants. -- Sir Isaac Newton, 1676
8. Before After BeginneR Session BeginneR BeginneR
9. "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
10. George Boole 1815 - 1864 A Class-Room Introduc2on to Logic h7ps://niyamaklogic.wordpress.com/c ategory/laws-of-thoughts/ Mathema;cian Philosopher &
11. 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
12. Programing
13. Programing
14. Programing Write Run Read Think Write Run Read Think Communicate Share
15. Text Image Information Intention Data decode encode Data analysis feedback ≠
16. Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D 8me value α β
17. 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(International Electrotechnical Commission, IEC)による定義
18. 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
19. σʔλ ৘ใͷ͏ͪҙࢥ఻ୡɾղऍɾॲཧʹ దͨ͠࠶ར༻Մೳͳ΋ͷ ৘ใ ࣮ଘΛූ߸Խͨ͠ද৅ ࣮ଘ ؍࡯ͷ༗ແʹΑΒͣଘࡏ͍ͯ͠Δ ΋ͷͦͷ΋ͷ ࣸ૾ʢූ߸Խʣ
20. ࣸ૾ Ϧϯΰ ʢ࣮ଘʣ Ϧϯΰ ʢ৘ใʣ mapping
21. ࣸ૾ (mapping) 𝑓: 𝑋 → 𝑌 𝑋 𝑌 ͋Δ৘ใͷू߹ͷཁૉΛɺผͷ৘ใͷू߹ͷ ͨͩͭͷཁૉʹରԠ͚ͮΔϓϩηε
22. ৘ใྔ ࣮ଘ ৘ใ σʔλ Ϧϯΰ 
     ූ߸Խ
23. ৘ใྔ ࣮ଘ ৘ใ σʔλ Ϧϯΰ 
     ූ߸Խ ৘ใྔͷଛࣦ
24. Ϧϯΰ ࣸ૾ ϑϧʔπ ੺৭  ը૾ ࣮ଘ ৘ใ νϟωϧ mapping channel
25. 𝑋 𝑌 𝑦! 𝑥! 𝑦" 𝑥" 𝑋 𝑌 𝑥! 𝑥" 𝑦! 𝑦" σʔλՄࢹԽ ࣸ૾ mapping
26. 𝑋 𝑌 𝑦! 𝑥! 𝑦" 𝑥" 𝑋 𝑌 𝑥! 𝑥" 𝑦! 𝑦" σʔλՄࢹԽ ࣸ૾ mapping x axis, y axis, color, fill, shape, linetype, alpha… aesthetic channels ৹ඒతνϟωϧ
27. 𝑋 𝑌 𝑦! 𝑥! 𝑦" 𝑥" 𝑋 𝑌 𝑥! 𝑥" 𝑦! 𝑦" 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
28. ࣮ଘ ࣸ૾ʢ؍࡯ʣ σʔλ ࣸ૾ʢσʔλՄࢹԽʣ άϥϑ 𝑋 𝑌 𝑦! 𝑥! 𝑦" 𝑥" 𝑋 𝑌 𝑥! 𝑥" 𝑦! 𝑦" EBUB mapping aesthetic channels ৹ඒతνϟωϧ σʔλՄࢹԽ
29. 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))
30. ॳΊͯͷHHQMPU
31. 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࢖༻
32. 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
33. ॳΊ͔ͯΒ൪໨ͷHHQMPU
34. 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
35. 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
36. 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
37. 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)
38. 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
39. 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
40. HFNP@ ؔ਺܈ DG
    IUUQTXXXSTUVEJPDPNSFTPVSDFTDIFBUTIFFUT
41. ෳ਺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
42. HHQMPUʹΑΔσʔλՄࢹԽ ࣮ଘ ࣸ૾ʢ؍࡯ʣ σʔλ ࣸ૾ʢσʔλՄࢹԽʣ άϥϑ 𝑋 𝑌 𝑦! 𝑥! 𝑦" 𝑥" SBX
    EBUB 写像 aesthetic channels ৹ඒతνϟωϧ ՄࢹԽʹదͨ͠EBUBܗࣜ 変形 ਤͷͭͷ৹ඒతνϟωϧ͕ σʔλͷͭͷม਺ʹରԠ͍ͯ͠Δ
43. > 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
44. > 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
45. 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
46. Wide Long Nested input output pivot_longer pivot_wider group_nest unnest ggplot visualization map output ggsave
47. Enjoy!! KMT©