Tokyo.R#82 Data visualization

第82回Tokyo.Rでトークしたスライドです。

Transcript

1. BeginneR Session - データの可視化 - #82 Tokyo.R 2019.10.25 @kilometer00
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: 時差ぼけ
4. 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
5. Before After BeginneR Session BeginneR 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. BeginneR Session - データの可視化 -
8. Cave art Hieroglyphs
9. Words Figures
10. Words Beyond words Figures =
11. Text Image Information Intention Data decode encode feedback
12. Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β
13. https://en.wikipedia.org/wiki/Frank_Anscombe
14. Francis Anscombe https://en.wikipedia.org/wiki/Frank_Anscombe
15. "Graphs in Statistical Analysis" Anscombe, F.J. American Statistician 27 (1), 1973
16. "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.
17. "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
20. x y ?
22. Wide Long
24. 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
25. Spark joy!!
27. 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)))
30. x y mapping g <- ggplot(data = dat, mapping = aes(x = x, y = y)) data
31. 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
32. 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)
33. Anscombe’s quartet
34. 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
35. "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
36. Summary...
37. Words Beyond words Figures =
38. Text Image Information Intention Data decode encode feedback
39. Text Image First, A. Next, B. Then C. Finally D. time Intention encode "Frozen" structure A B C D time value α β
40. x y ? mapping data
41. Wide Long
43. x y mapping g <- ggplot(data = dat, mapping = aes(x = x, y = y)) data
44. 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
45. 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)
46. Anscombe’s quartet
47. "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
48. "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 ... It's time that was changed.
49. Enjoy!!
50. bar dradra