Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
SappoRo.R_roundrobin
Search
kilometer
March 18, 2023
Programming
0
100
SappoRo.R_roundrobin
第10回Sapporo.Rで喋った際のスライドです。
kilometer
March 18, 2023
Tweet
Share
More Decks by kilometer
See All by kilometer
TokyoR#111_ANOVA
kilometer
1
750
TokyoR109.pdf
kilometer
1
370
TokyoR#108_NestedDataHandling
kilometer
0
670
TokyoR#107_R_GeoData
kilometer
0
340
TokyoR#104_DataProcessing
kilometer
1
580
TokyoR#103_DataProcessing
kilometer
0
780
TokyoR#102_RMarkdown
kilometer
1
560
TokyoR#101_RegressionAnalysis
kilometer
0
320
TokyoR#99_Divergence
kilometer
1
280
Other Decks in Programming
See All in Programming
1BRC--Nerd Sniping the Java Community
gunnarmorling
0
340
Anthropic Cookbook のおすすめレシピ
schroneko
7
990
Ruby GitHub Packages
bkuhlmann
0
630
if constexpr文はテンプレート世界のラムダ式である
faithandbrave
3
650
MicrosoftのPlatform Engineeringガイドを読んで実際になにかやってみた
ymd65536
1
340
単体テストを書かない技術 #phpcon_odawara
o0h
PRO
27
8.3k
ゆるい個人開発のススメ
kuroppe1819
10
990
Java 22 Overview
kishida
1
180
try! Swift Tokyo 初参加報告LT
hinakko2
0
220
Let's learn code review
riofujimon
2
430
Netty Chicago Java User Group 2024-04-17
sullis
0
180
[技育CAMPアカデミア]アイディアを形に!【超入門】スマホアプリ開発〜リリースまでの流れをご紹介
teamlab
PRO
0
380
Featured
See All Featured
A better future with KSS
kneath
231
16k
How GitHub Uses GitHub to Build GitHub
holman
468
290k
The Illustrated Children's Guide to Kubernetes
chrisshort
31
46k
Reflections from 52 weeks, 52 projects
jeffersonlam
345
19k
How to name files
jennybc
65
93k
GraphQLとの向き合い方2022年版
quramy
32
12k
jQuery: Nuts, Bolts and Bling
dougneiner
59
7.1k
Put a Button on it: Removing Barriers to Going Fast.
kastner
58
3.1k
Java REST API Framework Comparison - PWX 2021
mraible
PRO
18
6.9k
How To Stay Up To Date on Web Technology
chriscoyier
782
250k
The Art of Programming - Codeland 2020
erikaheidi
42
12k
10 Git Anti Patterns You Should be Aware of
lemiorhan
648
58k
Transcript
SappoRo.R #10 @kilometer00 2023.03.18 らくらく総当たり組み合わせ
Who!? Who?
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: アンキロサウルス
宣伝!!(書籍の翻訳に参加しました。) 絶賛販売中!
宣伝2!! R⾔語の地域コミュニティ@東京です。 定期的にR⾔語に関する勉強会を開催しています。 次回は4⽉22⽇!! 初⼼者特集回です!!
総当たり組み合わせ Round-robin そう あ あ く
dat_nest <- palmerpenguins::penguins %>% dplyr::group_nest(species) データを畳み込む > dat_nest # A
tibble: 3 × 2 species data <fct> <list<tibble[,7]>> 1 Adelie [152 × 7] 2 Chinstrap [68 × 7] 3 Gentoo [124 × 7] (息を吐くように)
# A tibble: 9 × 4 species.x species.y data.x data.y
<fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Adelie [152 × 7] [152 × 7] 2 Chinstrap Adelie [68 × 7] [152 × 7] 3 Gentoo Adelie [124 × 7] [152 × 7] 4 Adelie Chinstrap [152 × 7] [68 × 7] 5 Chinstrap Chinstrap [68 × 7] [68 × 7] 6 Gentoo Chinstrap [124 × 7] [68 × 7] 7 Adelie Gentoo [152 × 7] [124 × 7] 8 Chinstrap Gentoo [68 × 7] [124 × 7] 9 Gentoo Gentoo [124 × 7] [124 × 7] 総当たり組み合わせ # A tibble: 3 × 4 species.x species.y data.x data.y <fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Chinstrap [152 × 7] [68 × 7] 2 Adelie Gentoo [152 × 7] [124 × 7] 3 Chinstrap Gentoo [68 × 7] [124 × 7] 組み合わせ(combination) (round-robin)
base::expand.grid()関数 > dat_nest # A tibble: 3 × 2 species
data <fct> <list<tibble[,7]>> 1 Adelie [152 × 7] 2 Chinstrap [68 × 7] 3 Gentoo [124 × 7] dat_nest$species
grid <- dat_nest$species %>% expand.grid(., .) base::expand.grid()関数 > grid Var1
Var2 1 Adelie Adelie 2 Chinstrap Adelie 3 Gentoo Adelie 4 Adelie Chinstrap 5 Chinstrap Chinstrap 6 Gentoo Chinstrap 7 Adelie Gentoo 8 Chinstrap Gentoo 9 Gentoo Gentoo
dplyr::left_join()関数 > grid Var1 Var2 1 Adelie Adelie 2 Chinstrap
Adelie 3 Gentoo Adelie 4 Adelie Chinstrap 5 Chinstrap Chinstrap 6 Gentoo Chinstrap 7 Adelie Gentoo 8 Chinstrap Gentoo 9 Gentoo Gentoo > dat_nest # A tibble: 3 × 2 species data <fct> <list<tibble[,7]>> 1 Adelie [152 × 7] 2 Chinstrap [68 × 7] 3 Gentoo [124 × 7] ①対応づけて結合 ②対応づけて結合
dplyr::left_join()関数 dat_rr <- grid %>% tibble::as_tibble() %>% dplyr::left_join( dat_nest %>%
dplyr::rename(Var1 = "species"), by = "Var1" ) %>% dplyr::left_join( dat_nest %>% dplyr::rename(Var2 = "species"), by = "Var2" )
dplyr::left_join()関数 dat_rr <- grid %>% tibble::as_tibble() %>% dplyr::left_join( dat_nest %>%
dplyr::rename(Var1 = "species"), by = "Var1" ) %>% dplyr::left_join( dat_nest %>% dplyr::rename(Var2 = "species"), by = "Var2" ) ① ②
> dat_rr # A tibble: 9 × 4 Var1 Var2
data.x data.y <fct> <fct> <list<tibble[,7]>> <list<tibble[,7]> 1 Adelie Adelie [152 × 7] [152 × 7] 2 Chinstrap Adelie [68 × 7] [152 × 7] 3 Gentoo Adelie [124 × 7] [152 × 7] 4 Adelie Chinstrap [152 × 7] [68 × 7] 5 Chinstrap Chinstrap [68 × 7] [68 × 7] 6 Gentoo Chinstrap [124 × 7] [68 × 7] 7 Adelie Gentoo [152 × 7] [124 × 7] 8 Chinstrap Gentoo [68 × 7] [124 × 7] 9 Gentoo Gentoo [124 × 7] [124 × 7]
dplyr::rename()関数 dat_rr_rename <- dat_rr %>% rename(species.x = Var1) %>% rename(species.y
= Var2) > dat_rr_rename # A tibble: 9 × 4 species.x species.y data.x data.y <fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Adelie [152 × 7] [152 × 7] 2 Chinstrap Adelie [68 × 7] [152 × 7] 3 Gentoo Adelie [124 × 7] [152 × 7] 4 Adelie Chinstrap [152 × 7] [68 × 7] 5 Chinstrap Chinstrap [68 × 7] [68 × 7] 6 Gentoo Chinstrap [124 × 7] [68 × 7] 7 Adelie Gentoo [152 × 7] [124 × 7] 8 Chinstrap Gentoo [68 × 7] [124 × 7] 9 Gentoo Gentoo [124 × 7] [124 × 7]
dplyr::rename()関数 dat_rr_rename <- dat_rr %>% rename(species.x = Var1) %>% rename(species.y
= Var2) key <- "species" x <- stringr::str_c(key, ".x") y <- stringr::str_c(key, ".y") dat_rr_rename <- dat_rr %>% rename(!!x := Var1) %>% rename(!!y := Var2) 別解 {rlang}パッケージの演算⼦
# A tibble: 9 × 4 species.x species.y data.x data.y
<fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Adelie [152 × 7] [152 × 7] 2 Chinstrap Adelie [68 × 7] [152 × 7] 3 Gentoo Adelie [124 × 7] [152 × 7] 4 Adelie Chinstrap [152 × 7] [68 × 7] 5 Chinstrap Chinstrap [68 × 7] [68 × 7] 6 Gentoo Chinstrap [124 × 7] [68 × 7] 7 Adelie Gentoo [152 × 7] [124 × 7] 8 Chinstrap Gentoo [68 × 7] [124 × 7] 9 Gentoo Gentoo [124 × 7] [124 × 7] 総当たり組み合わせ # A tibble: 3 × 4 species.x species.y data.x data.y <fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Chinstrap [152 × 7] [68 × 7] 2 Adelie Gentoo [152 × 7] [124 × 7] 3 Chinstrap Gentoo [68 × 7] [124 × 7] 組み合わせ(combination) (round-robin)
grid <- dat_nest$species %>% expand.grid(., .) %>% subset(unclass(Var1) < unclass(Var2))
%>% tibble::as_tibble() base::subset()関数 > grid # A tibble: 3 × 2 Var1 Var2 <fct> <fct> 1 Adelie Chinstrap 2 Adelie Gentoo 3 Chinstrap Gentoo
という変換を パッケージにしました。 devtools::install_github( "kilometer0101/roundrobin" ) (4回⼿打ちしたら⾯倒臭くなったので)
roundrobin::roundrobin()関数 # A tibble: 9 × 4 species.x species.y data.x
data.y <fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Adelie [152 × 7] [152 × 7] 2 Chinstrap Adelie [68 × 7] [152 × 7] 3 Gentoo Adelie [124 × 7] [152 × 7] 4 Adelie Chinstrap [152 × 7] [68 × 7] 5 Chinstrap Chinstrap [68 × 7] [68 × 7] 6 Gentoo Chinstrap [124 × 7] [68 × 7] 7 Adelie Gentoo [152 × 7] [124 × 7] 8 Chinstrap Gentoo [68 × 7] [124 × 7] 9 Gentoo Gentoo [124 × 7] [124 × 7] library(roundrobin) palmerpenguins::penguins %>% roundrobin(key = "species")
library(roundrobin) palmerpenguins::penguins %>% roundrobin(key = "species", combination = TRUE) roundrobin::roundrobin()関数
# A tibble: 3 × 4 species.x species.y data.x data.y <fct> <fct> <list<tibble[,7]>> <list<tibble[,7]>> 1 Adelie Chinstrap [152 × 7] [68 × 7] 2 Adelie Gentoo [152 × 7] [124 × 7] 3 Chinstrap Gentoo [68 × 7] [124 × 7]
使ってみますか。
library(tidyverse) library(palmerpenguins) library(roundrobin) dat <- palmerpenguins::penguins %>% na.omit() %>% #
NA除去 mutate_at( vars(c(contains("mm"), contains("g"))), ~ (. - mean(.)) / sd(.) # 標準化 ) %>% select(species, contains("mm"), contains("g")) 前処理
> dat # A tibble: 333 × 5 species bill_length_mm
bill_depth_mm flipper_length_mm body_mass_g <fct> <dbl> <dbl> <dbl> <dbl> 1 Adelie -0.895 0.780 -1.42 -0.568 2 Adelie -0.822 0.119 -1.07 -0.506 3 Adelie -0.675 0.424 -0.426 -1.19 4 Adelie -1.33 1.08 -0.568 -0.940 5 Adelie -0.858 1.74 -0.782 -0.692 6 Adelie -0.931 0.323 -1.42 -0.723 7 Adelie -0.876 1.24 -0.426 0.581 8 Adelie -0.529 0.221 -1.35 -1.25 9 Adelie -0.986 2.05 -0.711 -0.506 10 Adelie -1.72 2.00 -0.212 0.240 # … with 323 more rows # i Use `print(n = ...)` to see more rows 前処理
dat_long <- dat %>% rowid_to_column("id") %>% pivot_longer( cols = !species,
names_to = "parameter", values_to = "value" ) %>% group_by(parameter) %>% ungroup() .y <- dat_long %>% ungroup() %>% group_by(species) %>% summarise( mean_id = mean(id), min_id = min(id) ) dat_long %>% ggplot() + aes(parameter, id) + geom_tile(aes(fill = value)) + geom_hline( yintercept = max(dat_long$id) ) + geom_hline(data = .y, aes(yintercept = min_id)) + scale_y_continuous( breaks = .y$mean_id, labels = .y$species, expand = c(0, 0)) + theme( axis.title = element_blank(), axis.text.x = element_text( angle = 30, hjust = 1 ) ) 可視化コード (ちょちょいのちょい)
可視化
> dat_rr # A tibble: 9 × 4 Var1 Var2
data.x data.y <fct> <fct> <list<tibble[,4]>> <list<tibble[,4]>> 1 Adelie Adelie [146 × 4] [146 × 4] 2 Chinstrap Adelie [68 × 4] [146 × 4] 3 Gentoo Adelie [119 × 4] [146 × 4] 4 Adelie Chinstrap [146 × 4] [68 × 4] 5 Chinstrap Chinstrap [68 × 4] [68 × 4] 6 Gentoo Chinstrap [119 × 4] [68 × 4] 7 Adelie Gentoo [146 × 4] [119 × 4] 8 Chinstrap Gentoo [68 × 4] [119 × 4] 9 Gentoo Gentoo [119 × 4] [119 × 4] 総当たり組み合わせ dat_rr <- dat %>% roundrobin(key = "species", rename = FALSE)
例えばマハラノビス距離 dat_rr_mahaD <- dat_rr %>% mutate(mahaD2 = map2( data.x, data.y,
# yに対するxの距離 ~ mahalanobis(.x, colMeans(.y), cov(.y)) )) %>% mutate(Var2 = str_c("vs. ", Var2)) > dat_rr_mahaD # A tibble: 9 × 5 Var1 Var2 data.x data.y mahaD2 <fct> <chr> <list<tibble[,4]>> <list<tibble[,4]>> <list> 1 Adelie vs. Adelie [146 × 4] [146 × 4] <dbl [146]> 2 Chinstrap vs. Adelie [68 × 4] [146 × 4] <dbl [68]> 3 Gentoo vs. Adelie [119 × 4] [146 × 4] <dbl [119]> 4 Adelie vs. Chinstrap [146 × 4] [68 × 4] <dbl [146]> 5 Chinstrap vs. Chinstrap [68 × 4] [68 × 4] <dbl [68]> 6 Gentoo vs. Chinstrap [119 × 4] [68 × 4] <dbl [119]> 7 Adelie vs. Gentoo [146 × 4] [119 × 4] <dbl [146]> 8 Chinstrap vs. Gentoo [68 × 4] [119 × 4] <dbl [68]> 9 Gentoo vs. Gentoo [119 × 4] [119 × 4] <dbl [119]>
例えばマハラノビス距離 dat_rr_mahaD <- dat_rr %>% mutate(mahaD2 = map2( data.x, data.y,
# yに対するxの距離 ~ mahalanobis(.x, colMeans(.y), cov(.y)) )) %>% mutate(Var2 = str_c("vs. ", Var2)) dat_mahaD <- dat_rr_mahaD %>% select(!data.y) %>% unnest(everything())
> dat_mahaD # A tibble: 999 × 7 Var1 Var2
bill_length_mm bill_…¹ flipp…² body_…³ mahaD2 <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Adelie vs. Adelie -0.895 0.780 -1.42 -0.568 2.84 2 Adelie vs. Adelie -0.822 0.119 -1.07 -0.506 1.95 3 Adelie vs. Adelie -0.675 0.424 -0.426 -1.19 4.26 4 Adelie vs. Adelie -1.33 1.08 -0.568 -0.940 3.32 5 Adelie vs. Adelie -0.858 1.74 -0.782 -0.692 5.57 6 Adelie vs. Adelie -0.931 0.323 -1.42 -0.723 2.47 7 Adelie vs. Adelie -0.876 1.24 -0.426 0.581 5.94 8 Adelie vs. Adelie -0.529 0.221 -1.35 -1.25 5.27 9 Adelie vs. Adelie -0.986 2.05 -0.711 -0.506 7.75 10 Adelie vs. Adelie -1.72 2.00 -0.212 0.240 15.2 # … with 989 more rows, and abbreviated variable names # ¹bill_depth_mm, ²flipper_length_mm, ³body_mass_g # ℹ Use `print(n = ...)` to see more rows 例えばマハラノビス距離
例えばマハラノビス距離 ggplot(dat_mahaD) + aes(mahaD2, color = Var1, fill = Var1)
+ geom_density(alpha = 0.5) + facet_wrap(~Var2)
総当たり組み合わせ Round-robin そう あ あ く devtools::install_github( "kilometer0101/roundrobin" )
Enjoy!