Tokyo.R#73 BeginneRSession3

Tokyo.R#73の初心者セッション3「Plot and Visualization 」のスライドです。

Transcript

1. #73 @kilometer00 2018.10.20 at eureka BeginneR Session 3 -- Visualization & Plot --
2. 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: イタ飯
4. BeginneR Session
5. BeginneR
6. BeginneR Advanced Hoxo_m If I have seen further it is by standing on the shoulders of Giants. -- Sir Isaac Newton, 1676
7. Before After BeginneR Session BeginneR BeginneR
8. ブール演算⼦ Boolean Algebra A == B A != B A | B A & B A %in% B # equal to # not equal to # or # and # is A in B?
9. George Boole 1815 - 1864 A Class-Room Introduction to Logic https://niyamaklogic.wordpress.com/c ategory/laws-of-thoughts/ Mathematician Philosopher &
10. 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
11. {magrittr} # こう書きますか？ 536&
    */ dat1 <- f1(dat0, var1-1, var1-2) dat2 <- f2(dat1, var2) dat3 <- f3(dat2, var3) dat4 <- f4(var4-1, dat3, var4-2) dat5 <- f5(dat4, var5) dat6 <- f6(dat5, var6) 536&
    065 5IJOLJOH 3FBEJOH 1JQF
    BMHFCSB %>%
12. {magrittr} # こうやって書く事もできます。 dat <- dat0 %>% f1(var1-1, var1-2) %>% f2(var2) %>% f3(var3) %>% f4(var4-1, ., var4-2) %>% f5(var5) %>% f6(var6) */ 065 1JQF
    BMHFCSB %>%
14. #73 @kilometer00 2018.10.20 at eureka BeginneR Session 3 -- Visualization & Plot --
15. Ageanda 1. Vision 2. Visualization in
16. Visual image - in a way of Intention coding
17. Face-to-face communication output input/feedback decode encode internal status A B estimated B status A status Emotion, Intention, ... = Language, Gesture, Prosody, Face expression...
18. Text Image Information Intention Data decode encode Public writing no feedback
19. Text Image Information Intention Data decode encode Private (exploratory) writing Feedback
20. plot(iris)
21. I visualized iris data on R!! Please explain the figure in text only. you you WHAT!!? There are totally 18 scattered plots arrayed in 5 by 5 matrix. Each plot represent 2 of 5 parameters on x and y axis. Only one parameter is discrete value named Species, and the others are Width and Length of Sepal and Petal. And....
22. Text Image ≠
23. Text Image Information Intention Data decode encode Public writing no feedback ≠
24. Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β
25. How we "read" visual images?
26. Human brain
27. VC Mishkin 1983 TINS, Saur 2008 PNAS Visual pathway of the brain primary visual cortex
28. Mishkin 1983 TINS Visual pathway of the brain VC
29. Mishkin 1983 TINS Visual pathway of the brain VC
30. What Yamis 2014 PNAS Visual pathway of the brain Convolutional Feature map VC
31. Saur 2008 PNAS Auditory pathway of the brain VC AC
32. Saur 2008 PNAS Auditory pathway of the brain VC AC (repetition) (comprehension)
33. AC VC Mishkin 1983 TINS, Saur 2008 PNAS Visual & auditory pathway of the brain
34. AC VC Language Mishkin 1983 TINS, Saur 2008 PNAS Visual & auditory pathway of the brain
35. Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β
36. How we "read" visual images?
37. Hou 2007 IEEE
38. Hou 2007 IEEE
39. VC SC FEF White 2017 PNAS, Veale 2017 PhilosTransB Visual saliency & priority hypothesis SC: superior colliculus, FEF: frontal eye field
40. VC SC FEF White 2017 PNAS, Veale 2017 PhilosTransB Visual saliency & priority hypothesis SC: superior colliculus, FEF: frontal eye field
41. VC SC FEF White 2017 PNAS, Veale 2017 PhilosTransB Visual saliency & priority hypothesis SC: superior colliculus, FEF: frontal eye field
42. Original Saliency map Visual saliency map Hou 2007 IEEE
43. ・What & Where pathway ・ Image ⇔ Language(multimodality) ・ Spatial saliency How we "read" visual images?
44. Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β
45. Text Image Information Intention Data decode encode Public writing no feedback ≠
46. References
47. Ageanda 1. Vision 2. Visualization in
48. Sample data iris %>% str pipe algebra row column 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 ... $ Species : Factor w/ 3 levels "setosa", ... class names mode value
49. Basic plotting functions in {graphics} ・High-level: create a new plot ・Low-level: add more information ・Interactive: interactively add info.
50. Basic plotting functions in {graphics} ・High-level: create a new plot ・Low-level: add more information ・Interactive: interactively add info.
51. ・High-level: create a new plot Basic plotting functions in {graphics} x <- c(1:10) y <- 0.5 * x + 3 plot(x, y) Scatterplot of two vectors
52. ・High-level: create a new plot Basic plotting functions in {graphics} x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "b") Scatterplot of two vectors
53. ・High-level: create a new plot Basic plotting functions in {graphics} x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o") Scatterplot of two vectors
54. ・High-level: create a new plot Basic plotting functions in {graphics} x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o", col = "red") Scatterplot of two vectors
55. ・High-level: create a new plot Basic plotting functions in {graphics} x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o", col = "red", pch = 2) Scatterplot of two vectors
56. ・High-level: create a new plot Basic plotting functions in {graphics} Scatterplot of two vectors R-Tips, http://cse.naro.affrc.go.jp/takezawa/r-tips/r/53.html
57. ・High-level: create a new plot Basic plotting functions in {graphics} x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o", col = "red", pch = 2, lwd = 5) Scatterplot of two vectors
58. Basic plotting functions in {graphics} How to plot multiple data series? + x <- 1:10 y <- 0.5 * x + 3 x <- 1:10 y2 <- 0.25 * x + 0.1 * x^2 + 1
59. Basic plotting functions in {graphics} How to plot multiple data series?
60. Basic plotting functions in {graphics} How to plot multiple data series? x <- c(1:10) y1 <- 0.5 * x + 3 y2 <- 0.25 * x + 0.1 * x^2 + 1 # create & set a new plot: high level plot(0, 0, type = "n", xlim = c(min(x), max(x)), ylim = c(min(y1, y2), max(y1, y2)), xlab = "x", ylab = "y") # add elements: low level funcs. lines(x, y1, col = "red") lines(x, y2, col = "blue") points(x, y1, col = "red") points(x, y2, col = "blue", pch = 2)
61. Basic plotting functions in {graphics} How to plot multiple data series? x <- c(1:10) y1 <- 0.5 * x + 3 y2 <- 0.25 * x + 0.1 * x^2 + 1 # create & set a new plot: high level plot(0, 0, type = "n", xlim = c(min(x), max(x)), ylim = c(min(y1, y2), max(y1, y2)), xlab = "x", ylab = "y") # add elements: low level funcs lines(x, y1, col = "red") lines(x, y2, col = "blue") points(x, y1, col = "red") points(x, y2, col = "blue", pch = 2)
62. Basic plotting functions in {graphics} How to custom graphic parameters?
63. # set graphic params: par par(mar = c(3, 3, 0.5, 0.5), tcl = -0.2, mgp = c(1.5, 0.3, 0), bty = "l") Basic plotting functions in {graphics} How to custom graphic parameters? cf. R-Tips
64. # set graphic params: par par(mar = c(3, 3, 0.5, 0.5), tcl = -0.2, mgp = c(1.5, 0.3, 0), bty = "l") # create & set a new plot: high level plot(0, 0, type = "n", xlab = "x", ylab = "y", xlim = c(min(x), max(x)), ylim = c(min(y1, y2), max(y1, y2))) # add elements: low level funcs. lines(x, y1, col = "red") lines(x, y2, col = "blue") points(x, y1, col = "red") points(x, y2, col = "blue", pch = 2) Basic plotting functions in {graphics}
65. # set graphic params: par par(mar = c(3, 3, 0.5, 0.5), tcl = -0.2, mgp = c(1.5, 0.3, 0), bty = "l") # create & set a new plot: high level plot(0, 0, type = "n", xlab = "x", ylab = "y", xlim = c(min(x), max(x)), ylim = c(min(y1, y2), max(y1, y2))) # add elements: low level funcs. lines(x, y1, col = "red") lines(x, y2, col = "blue") points(x, y1, col = "red") points(x, y2, col = "blue", pch = 2) Basic plotting functions in {graphics} TOO BAD default values TOO MANY series names TOO MANY series parameters
66. Basic plotting functions in {graphics} plot data.frame plot(iris)
67. Sample data iris %>% str row column 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 ... $ Species : Factor w/ 3 levels "setosa", ... class names mode value
68. Basic plotting functions in {graphics} plot(iris) Round robin 2D scatter plots plot data.frame
69. Basic plotting functions in {graphics} plot(iris) Round robin 2D scatter plots ONLY plot data.frame
70. "declarative" graphic package {ggplot2} Basic plotting functions in {graphics} ⇔ ふつ〜に思ったことを書けば描けるというニュアンス (直訳すると"平叙⽂の"ぐらいの意味)
71. "declarative" graphic package {ggplot2} Basic plotting functions in {graphics} ⇔ ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. https://ggplot2.tidyverse.org/index.html ふつ〜に思ったことを書けば描ける
72. # set graphic params par(...) # set a new plot plot(0, 0, type = "n", ...) # add elements lines(x, y1,..) lines(x, y2, ...) points(x, y1, ...) points(x, y2, ...) ggplot(data, aes(x, y, ...))+ geom_path()+ geom_points()+ scale_... theme_... in {graphics} in {ggplot2}
73. # set graphic params par(...) # set a new plot plot(0, 0, type = "n", ...) # add elements lines(x, y1,..) lines(x, y2, ...) points(x, y1, ...) points(x, y2, ...) ggplot(data, aes(x, y, ...))+ geom_path()+ geom_points()+ scale_...()+ theme_...() in {graphics} in {ggplot2} ONLY data.frame
74. Text Image Information Intention Data decode encode Public writing no feedback ≠ ① ②
75. # set graphic params par(...) # set a new plot plot(0, 0, type = "n", ...) # add elements lines(x, y1,..) lines(x, y2, ...) points(x, y1, ...) points(x, y2, ...) ggplot(data, aes(x, y, ...))+ geom_path()+ geom_points()+ scale_...()+ theme_...() in {graphics} in {ggplot2} ONLY data.frame ① data ② encode optionally, ③ style
76. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_point()
77. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_point() mode: factor
78. Sample data iris %>% str row column 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 ... $ Species : Factor w/ 3 levels "setosa", ... class names mode value
79. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Petal.Length))+ geom_point() continuous volume
80. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_line()
81. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()
82. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points()
83. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points(aes(shape=Species), size = 4, alpha = 0.5)
84. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points()+ scale_color_manual(values = c("Blue", "Red", "Black"))
85. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points()+ theme_classic()+
86. "declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points()+ theme_classic()+ theme(legend.position = "bottom")
87. # set graphic params par(...) # set a new plot plot(0, 0, type = "n", ...) # add elements lines(x, y1,..) lines(x, y2, ...) points(x, y1, ...) points(x, y2, ...) ggplot(data, aes(x, y, ...))+ geom_path()+ geom_points()+ scale_...()+ theme_...() in {graphics} in {ggplot2} ONLY data.frame ① data ② encode optionally, ③ style
88. # set graphic params par(...) # set a new plot plot(0, 0, type = "n", ...) # add elements lines(x, y1,..) lines(x, y2, ...) points(x, y1, ...) points(x, y2, ...) ggplot(data, aes(x, y, ...))+ geom_path()+ geom_points()+ scale_...()+ theme_...() in {graphics} in {ggplot2} # open graphic device png("img.png", ...) # colse graphic device dev.off() ggsave("img.png", width = 5, height = 4)
89. # set graphic params par(...) # set a new plot plot(0, 0, type = "n", ...) # add elements lines(x, y1,..) lines(x, y2, ...) points(x, y1, ...) points(x, y2, ...) ggplot(data, aes(x, y, ...))+ geom_path()+ geom_points()+ scale_...()+ theme_...() in {graphics} in {ggplot2} # open graphic device png("img.png", ...) # colse graphic device dev.off() ggsave("img.png", width = 5, height = 4) 1 2 3 4 5 1 2 3 4 5
90. "declarative" graphic package {ggplot2} ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. https://ggplot2.tidyverse.org/index.html ふつ〜に思ったことを書けば描ける
91. Text Image Information Intention Data decode encode Public writing no feedback ≠
92. ・What & Where pathway ・ Image ⇔ Language(multimodality) ・ Spatial saliency How we "read" visual images?
93. Social communication output input/feedback decode encode internal status A B estimated B status A status Emotion, Intention, ... = Language, Gesture, Prosody, Face expression...
94. Before After BeginneR Session BeginneR BeginneR
95. BeginneR Advanced Hoxo_m If I have seen further it is by standing on the sholders of Giants. -- Sir Isaac Newton, 1676
96. Enjoy!! KMT©
97. Bar DraDra KMT©