Tokyo.R#73 BeginneRSession3

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#73 @kilometer00 2018.10.20 at eureka BeginneR Session 3 -- Visualization & Plot --

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Who！？

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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

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BeginneR Session

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BeginneR

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BeginneR Advanced Hoxo_m If I have seen further it is by standing on the shoulders of Giants. -- Sir Isaac Newton, 1676

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Before After BeginneR Session BeginneR BeginneR

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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?

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George Boole 1815 - 1864 A Class-Room Introduction to Logic https://niyamaklogic.wordpress.com/c ategory/laws-of-thoughts/ Mathematician Philosopher &

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1JQFBMHFCSB 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

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{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 1JQFBMHFCSB %>%

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{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 1JQFBMHFCSB %>%

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No content

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#73 @kilometer00 2018.10.20 at eureka BeginneR Session 3 -- Visualization & Plot --

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Ageanda 1. Vision 2. Visualization in

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Visual image - in a way of Intention coding

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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...

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Text Image Information Intention Data decode encode Public writing no feedback

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Text Image Information Intention Data decode encode Private (exploratory) writing Feedback

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plot(iris)

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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....

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Text Image ≠

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Text Image Information Intention Data decode encode Public writing no feedback ≠

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Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β

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How we "read" visual images?

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Human brain

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VC Mishkin 1983 TINS, Saur 2008 PNAS Visual pathway of the brain primary visual cortex

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Mishkin 1983 TINS Visual pathway of the brain VC

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Mishkin 1983 TINS Visual pathway of the brain VC

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What Yamis 2014 PNAS Visual pathway of the brain Convolutional Feature map VC

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Saur 2008 PNAS Auditory pathway of the brain VC AC

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Saur 2008 PNAS Auditory pathway of the brain VC AC (repetition) (comprehension)

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AC VC Mishkin 1983 TINS, Saur 2008 PNAS Visual & auditory pathway of the brain

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AC VC Language Mishkin 1983 TINS, Saur 2008 PNAS Visual & auditory pathway of the brain

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Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β

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How we "read" visual images?

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Hou 2007 IEEE

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Hou 2007 IEEE

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VC SC FEF White 2017 PNAS, Veale 2017 PhilosTransB Visual saliency & priority hypothesis SC: superior colliculus, FEF: frontal eye field

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VC SC FEF White 2017 PNAS, Veale 2017 PhilosTransB Visual saliency & priority hypothesis SC: superior colliculus, FEF: frontal eye field

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VC SC FEF White 2017 PNAS, Veale 2017 PhilosTransB Visual saliency & priority hypothesis SC: superior colliculus, FEF: frontal eye field

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Original Saliency map Visual saliency map Hou 2007 IEEE

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・What & Where pathway ・ Image ⇔ Language(multimodality) ・ Spatial saliency How we "read" visual images?

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Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β

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Text Image Information Intention Data decode encode Public writing no feedback ≠

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References

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Ageanda 1. Vision 2. Visualization in

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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

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Basic plotting functions in {graphics} ・High-level: create a new plot ・Low-level: add more information ・Interactive: interactively add info.

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Basic plotting functions in {graphics} ・High-level: create a new plot ・Low-level: add more information ・Interactive: interactively add info.

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・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

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・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

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・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

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・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

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・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

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・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

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・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

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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

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Basic plotting functions in {graphics} How to plot multiple data series?

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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)

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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)

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Basic plotting functions in {graphics} How to custom graphic parameters?

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# 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

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# 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}

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Basic plotting functions in {graphics} plot data.frame plot(iris)

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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

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Basic plotting functions in {graphics} plot(iris) Round robin 2D scatter plots plot data.frame

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Basic plotting functions in {graphics} plot(iris) Round robin 2D scatter plots ONLY plot data.frame

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"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)

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"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

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# 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

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Text Image Information Intention Data decode encode Public writing no feedback ≠ ① ②

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_point()

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_point() mode: factor

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Petal.Length))+ geom_point() continuous volume

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_line()

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points()

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"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)

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"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"))

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"declarative" graphic package {ggplot2} ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_points()+ theme_classic()+

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"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")

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"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

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Text Image Information Intention Data decode encode Public writing no feedback ≠

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・What & Where pathway ・ Image ⇔ Language(multimodality) ・ Spatial saliency How we "read" visual images?

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Social communication output input/feedback decode encode internal status A B estimated B status A status Emotion, Intention, ... = Language, Gesture, Prosody, Face expression...

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Before After BeginneR Session BeginneR BeginneR

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BeginneR Advanced Hoxo_m If I have seen further it is by standing on the sholders of Giants. -- Sir Isaac Newton, 1676