Tokyo.R#82 Data visualization

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BeginneR Session - データの可視化 - #82 Tokyo.R 2019.10.25 @kilometer00

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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: 時差ぼけ

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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 2019.05.25 Tokyo.R #78 BeginneR Session – Data analysis 2019.06.29 Tokyo.R #79 BeginneR Session – 確率の基礎 2019.07.27 Tokyo.R #80 R Interface to Python 2019.09.29 Tokyo.R #81 IntRoduction & DemonstRation

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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 shoulders of Giants. -- Sir Isaac Newton, 1676

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BeginneR Session - データの可視化 -

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Cave art Hieroglyphs

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Words Figures

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Words Beyond words Figures =

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Text Image Information Intention Data decode encode 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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https://en.wikipedia.org/wiki/Frank_Anscombe

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Francis Anscombe https://en.wikipedia.org/wiki/Frank_Anscombe

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"Graphs in Statistical Analysis" Anscombe, F.J. American Statistician 27 (1), 1973

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"Graphs in Statistical Analysis" Anscombe, F.J. American Statistician 27 (1), 1973 Most text books on statistical methods, and most statistical computer programs, pay too little attention to graphs.

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"Graphs in Statistical Analysis" Anscombe, F.J. American Statistician 27 (1), 1973 Good statistical analysis ... should be sensitive both to peculiar features in given numbers and also whatever background information is available about the variables. 特異的な変数特徴量

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x y ?

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library(tidyverse) anscombe %>% rowid_to_column("obs") %>% gather(key, val, -obs) %>% separate(key, into = c("xy", "No"), sep = 1L) %>% spread(xy, val) %>% select(No, obs, x, y) %>% arrange(No) -> dat

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Spark joy!!

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dat %>% group_nest(No) %>% mutate(mean_x = map_dbl(data, ~mean(.$x)), mean_y = map_dbl(data, ~mean(.$y)), sd_x = map_dbl(data, ~sd(.$x)), sd_y = map_dbl(data, ~sd(.$y))) %>% mutate(model_lm = map(data, ~lm(y ~ x, data = .)), rsq = map_dbl(model_lm, ~summary(.) %>% .$r.sq), cor = map_dbl(data, ~cor(.$x, .$y)))

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x y mapping g <- ggplot(data = dat, mapping = aes(x = x, y = y)) data

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g <- g+ geom_smooth(method = "lm", se = F)+ geom_point() g <- ggplot(data = dat, aes(x = x, y = y)) 2. Add HFPN@MBZFST 1. Create HHQMPU object with NBQQJOH g <- g+ facet_wrap(facets = ~ No, ncol = 4) 3. Set options

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g <- ggplot(data = dat, aes(x = x, y = y))+ geom_smooth(method = "lm", se = F)+ geom_point()+ facet_wrap(facets = ~ No, ncol = 4)+ theme_bw() ggsave("fig.png", g)

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Anscombe’s quartet

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install.packages("datasauRus") https://github.com/lockedata/datasauRus Download the Datasaurus: Never trust summary statistics alone; always visualize your data http://www.thefunctionalart.com/2016/08/download-datasaurus-never-trust-summary.html

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Summary...

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Words Beyond words Figures =

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Text Image Information Intention Data decode encode 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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x y ? mapping data

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x y mapping g <- ggplot(data = dat, mapping = aes(x = x, y = y)) data

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g <- ggplot(data = dat, aes(x = x, y = y))+ geom_smooth(method = "lm", se = F)+ geom_point()+ facet_wrap(facets = ~ No, ncol = 4)+ theme_bw() ggsave("fig.png", g)

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Anscombe’s quartet

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"Graphs in Statistical Analysis" Anscombe, F.J. American Statistician 27 (1), 1973 Unfortunately, most persons who have resources to a computer for statistical analysis of data are not much interested either in computer programming or in statistical method