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Sendai.R #1 @kilometer BeginneR Session 2019.05.18 at DATACOM CORP.

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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: 出張

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BeginneR Session

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2018.04.21 Tokyo.R #69 BeginneR Session – Data import / Export 2018.06.09 Tokyo.R #70 BeginneR Session – Bayesian Modeling 2018.07.15 Tokyo.R #71 Landscape with R – the Japanese R community 2018.10.20 Tokyo.R #73 BeginneR Session – Visualization & Plot 2019.01.19 Tokyo.R #75 BeginneR Session – Data pipeline 2019.03.02 Tokyo.R #76 BeginneR Session – Data pipeline 2019.04.13 Tokyo.R #77 BeginneR Session – Data analysis

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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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What is Data?

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Input Output Data Pipeline

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packages you

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Input Output packages Data Pipeline

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Output Input Input Data Pipeline

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Output Input Input Data Pipeline

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Output Input Input Data Pipeline

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Data Pipeline

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Data Pipeline readable coding

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Programing Write Run Read Think

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Run!!! https://www.amazon.co.jp/dp/B00Y0UI990/

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Programing Write Run Read Think

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Programing Write Run Read Think coding style

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The tidyverse style guide https://style.tidyverse.org/ "Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread." Google's R Style Guide https://style.tidyverse.org/ "The goal of the R Programming Style Guide is to make our R code easier to read, share, and verify." R coding style guides

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The tidyverse style guide https://style.tidyverse.org/ "Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread." Google's R Style Guide https://style.tidyverse.org/ "The goal of the R Programming Style Guide is to make our R code easier to read, share, and verify." R coding style guides

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Programing Write Run Read Think Write Run Read Think Share

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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.co m/category/laws-of-thoughts/ Mathematician Philosopher &

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Programing

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Programing

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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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① ② ③ ④ lift take pour put Bring milk from the kitchen!

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① lift Bring milk from the kitchen! lift(Robot, glass, table) -> Robot' take ② take(Robot', fridge, milk) -> Robot''

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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

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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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Text Figure Information Intention Data decode encode feedback Programing

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Ageanda 0. Introduction 1. Data manipulation 2. Visualization in

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data.frame (復習)

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vector in Excel

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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

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It (dplyr) provides simple “verbs” to help you translate your thoughts into code. functions that correspond to the most common data manipulation tasks Introduction to dplyr https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html WFSCT {dplyr}

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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) Introduction to dplyr https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html WFSCT {dplyr} ※ かなり意訳

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mutate select filter arrange summaries # add column # select column # select row # arrange row # summary of vars {dplyr} WFSCT WFSCGVODUJPOT

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WFSCT {dplyr} mutate # カラムの追加 + mutate(dat, C = fun(A, B))

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WFSCT {dplyr} mutate # カラムの追加 + dat %>% mutate(C = fun(A, B))

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> df1 A B 1 1 11 2 2 12 3 3 13 df1 <- data.frame(A = 1:3, B = 11:13) WFSCT {dplyr} mutate # カラムの追加 > df2 A B C 1 1 11 11 2 2 12 24 3 3 13 39 df2 <- df1 %>% mutate(C = A * B)

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WFSCT {dplyr} select # カラムの選択 dat %>% select(tag, B)

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library(dplyr) iris %>% select(Sepal.Length, Sepal.Width) 'data.frame': 150 obs. of 6 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 ... WFSCT {dplyr}

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library(dplyr) iris %>% select(contains(“Width”)) 'data.frame': 150 obs. of 6 variables: $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 ... WFSCT {dplyr} Select help functions

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WFSCT {dplyr} # Select help functions starts_with("s") ends_with("s") contains("se") matches("^.e") one_of(c("Sepal.Length", "Species")) everything() https://kazutan.github.io/blog/2017/04/dplyr-select-memo/ 「dplyr::selectの活⽤例メモ」kazutan

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WFSCT {dplyr} filter # ⾏の絞り込み dat %>% filter(tag %in% c(1, 3, 5))

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library(dplyr) iris %>% filter(Species == "versicolor") WFSCT {dplyr} 'data.frame': 50 obs. of 5 variables: $ Sepal.Length: num 7 6.4 6.9 5.5 6.5 5.7 6.3 ... $ Sepal.Width : num 3.2 3.2 3.1 2.3 2.8 2.8 ... $ Petal.Length: num 4.7 4.5 4.9 4 4.6 4.5 4.7 ... $ Petal.Width : num 1.4 1.5 1.5 1.3 1.5 1.3 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...

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library(dplyr) iris %>% filter(Species == "versicolor") WFSCT {dplyr} NSE (Non-Standard Evaluation) 'data.frame': 50 obs. of 5 variables: $ Sepal.Length: num 7 6.4 6.9 5.5 6.5 5.7 6.3 ... $ Sepal.Width : num 3.2 3.2 3.1 2.3 2.8 2.8 ... $ Petal.Length: num 4.7 4.5 4.9 4 4.6 4.5 4.7 ... $ Petal.Width : num 1.4 1.5 1.5 1.3 1.5 1.3 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...

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mutate select filter arrange summaries # カラムの追加 # カラムの選択 # ⾏の絞り込み # ⾏の並び替え # 値の集約 {dplyr} WFSCT WFSCؔ਺܈

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(SBNNBSPGEBUBNBOJQVMBUJPO By constraining your options, it helps you think about your data manipulation challenges. Introduction to dplyr https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html

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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） Introduction to dplyr https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html ※ まさに意訳 (SBNNBSPGEBUBNBOJQVMBUJPO

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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

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Ageanda 0. Introduction 1. Data manipulation 2. Visualization in

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Text Image Information Intention Data decode encode Public writing 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 do we "read" figures?

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Human brain

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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 (repetition) (comprehension)

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

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Real world Figure Language Entropy "Ramen" How do we "read" figures?

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Real world Figure Language Entropy "Ramen" Loss of Information How do we "read" figures?

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Real world Figure Language Entropy "Ramen" Loss of Information Model selection How do we "read" figures?

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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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demo..

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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 x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "b") Scatterplot of two vectors Basic plotting functions in {graphics}

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‧High-level: create a new plot x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o") Scatterplot of two vectors Basic plotting functions in {graphics}

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‧High-level: create a new plot x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o", col = "red") Scatterplot of two vectors Basic plotting functions in {graphics}

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‧High-level: create a new plot x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o", col = "red", pch = 2) Scatterplot of two vectors Basic plotting functions in {graphics}

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‧High-level: create a new plot Scatterplot of two vectors R-Tips, http://cse.naro.affrc.go.jp/takezawa/r-tips/r/53.html Basic plotting functions in {graphics}

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‧High-level: create a new plot x <- c(1:10) y <- 0.5 * x + 3 plot(x, y, type = "o", col = "red", pch = 2, lwd = 5) Scatterplot of two vectors Basic plotting functions in {graphics}

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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 Basic plotting functions in {graphics}

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

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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) Basic plotting functions in {graphics}

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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) Basic plotting functions in {graphics}

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

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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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"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_point()+ scale_... theme_... in {graphics} in {ggplot2}

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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_point()+ scale_...()+ theme_...() in {graphics} in {ggplot2} ONLY data.frame

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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 ① data ② encode ③ style (optional)

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Text Image Information Intention Data decode encode Public writing feedback ≠ ① ②

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demo..

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

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

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

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

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

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ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_point(aes(shape=Species), size = 4, alpha = 0.5) "declarative" graphic package {ggplot2}

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ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species))+ geom_smooth()+ geom_point()+ scale_color_manual(values = c("Blue", "Red", "Black")) "declarative" graphic package {ggplot2}

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

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

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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 ① data ② encode ③ style (optional)

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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} # open graphic device png("img.png", ...) # colse graphic device dev.off() ggsave("img.png", width = 5, height = 4)

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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} # 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

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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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Summary...

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Real world Figure Language Entropy "Ramen" Loss of Information Model selection How do we "read" figures?

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Text Image Information Intention Data decode encode Public writing feedback ≠

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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

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Enjoy!! KMT©