Japan.R 2015: {purrr} による非テーブルデータの処理

Transcript

1. {purrr} ʹΑΔ
   
   ඇςʔϒϧσʔλͷॲཧ
   

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2. ࣗݾ঺հ
   • @sinhrks
   
   • ۀ຿: σʔλ෼ੳ (ϝʔΧʔ)
   
   • ར༻ݴޠ: Python, Rͱ͔
   
   • झຯ: OSS ׆ಈ
   
   • pandas ※ ίϛολ (Python ύοέʔδ)
   
   ※ R ͷ data.frame + dplyr + tidyr + readr + haven + lubridate + stringr
   + ggplot2… (ҎԼུ ͷΑ͏ͳ΋ͷ
   

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3. ຊ೔͸
   Θ͚Ͱ͸͋Γ·ͤΜ
   Λ dis Γʹདྷͨ
   

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4. ͳͥ Japan.R ʹ?
   • Rͷύοέʔδ࡞ͬͨ
   (JU)VC"XBSET
   ݱࡏ
   • ͜ͷR։ൃऀ͕͍͢͝ (2015) by @u_ribo બग़
   
   • http://uribo.hatenablog.com/entry/2015/12/02/180004
   

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5. ͳͥ Λʁ
   

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6. ͳͥRΛ?
   • ϓϩάϥϛϯά͕ઐ໳Ͱ͸ͳ͍͕ɺઐ໳෼໺Ͱ
   ౷ܭ/ػցֶश͕ඞཁͩͬͨͷͰ
   
   • ΤϯδχΞ͕ͩɺ౷ܭ/ػցֶश͕ඞཁʹͳͬͨ
   ͷͰ
   

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7. ૝ఆ͢ΔϨϕϧͱΰʔϧ
   • લఏ: dplyr, ggplot2 ͸গ͠࢖ͬͨ͜ͱ͕͋Δ
   
   • ΰʔϧ: {purrr} ͷجຊతͳ࢖͍ํΛ஌Δ
   
   • ͜ΜͳίʔυΛॻ͔ͳͯ͘΋Α͘ͳΔ
   glm.fit1 glm.fit2 …
   predict(glm.fit1, newdata = newdata)
   predict(glm.fit2, newdata = newdata)
   …
   

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8. ຊ೔ͷ಺༰
   
   {purrr} Ͱσʔλॲཧ
   

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9. σʔλॲཧͷྲྀΕ
   σʔλͷ४උ ϞσϦϯά ධՁ
   

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10. RʹΑΔσʔλॲཧ
    EBUBGSBNF
    ϦετϕΫτϧ
    Ϟσϧ
    HHQMPU
    UJEZS
    EQMZS
    SFBES
    SWFTU
    SMJTU
    QVSSS
    ֤छ౷ܭػցֶश
    ύοέʔδ
    CSPPN
    DBSFU
    HHGPSUJGZ
    

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11. data.frameͷॲཧ
    

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12. data.frameͷॲཧ
    • σʔλͷॲཧʹ͸ύλʔϯ͕͋Δ
    
    • ྫ: data.frame Λ ͋Δྻͷ஋ͰάϧʔϓΘ͚͠ɺ
    ྻ͝ͱʹฏۉ஋Λܭࢉ͠ɺ݁ՌΛ data.frame ʹ
    ͍ͨ͠ɻ
    ෼ׂ ద༻ ݁߹
    

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13. plyr
    • ૊ΈࠐΈ:
    library(plyr)
    plyr::ddply(iris, .(Species), plyr::colwise(mean))
    ## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
    ## 1 setosa 5.006 3.428 1.462 0.246
    ## 2 versicolor 5.936 2.770 4.260 1.326
    ## 3 virginica 6.588 2.974 5.552 2.026
    iris_group res as.data.frame(t(res))
    ## Sepal.Length Sepal.Width Petal.Length Petal.Width
    ## setosa 5.006 3.428 1.462 0.246
    ## versicolor 5.936 2.770 4.260 1.326
    ## virginica 6.588 2.974 5.552 2.026
    • plyr: ෼ׂ - ద༻ - ݁߹ΛҰͭͷؔ਺Ͱ
    

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14. dplyr
    • plyr: ෼ׂ - ద༻ - ݁߹ΛҰͭͷؔ਺Ͱ
    library(plyr)
    plyr::ddply(iris, .(Species), plyr::colwise(mean))
    ## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
    ## 1 setosa 5.006 3.428 1.462 0.246
    ## 2 versicolor 5.936 2.770 4.260 1.326
    ## 3 virginica 6.588 2.974 5.552 2.026
    library(dplyr)
    dplyr::summarise_each(dplyr::group_by(iris, Species),
    dplyr::funs(mean))
    ## Source: local data frame [3 x 5]
    ##
    ## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
    ## (fctr) (dbl) (dbl) (dbl) (dbl)
    ## 1 setosa 5.006 3.428 1.462 0.246
    ## 2 versicolor 5.936 2.770 4.260 1.326
    ## 3 virginica 6.588 2.974 5.552 2.026
    • dplyr: ॲཧ͝ͱʹؔ਺Λద༻
    

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15. ύΠϓԋࢉࢠ %>%
    • magrittr
    
    iris %>%
    dplyr::group_by(Species) %>%
    dplyr::summarise_each(dplyr::funs(mean))
    ## Source: local data frame [3 x 5]
    ##
    ## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
    ## (fctr) (dbl) (dbl) (dbl) (dbl)
    ## 1 setosa 5.006 3.428 1.462 0.246
    ## 2 versicolor 5.936 2.770 4.260 1.326
    ## 3 virginica 6.588 2.974 5.552 2.026
    x %>% f(…) == f(x, …)
    

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16. data.frameͷॲཧ
    • ߟ͑ํ
    
    • ॲཧΛγϯϓϧͳؔ਺ͷ૊Έ߹ΘͤͰهड़
    
    • ؔ਺͸ύΠϓԋࢉࢠ %>% Ͱ઀ଓ
    
    • ಉ͜͡ͱΛ data.frame Ҏ֎Ͱ΋΍Γ͍ͨ
    

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17. data.frame Ҏ֎ͷॲཧ
    

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18. ͜Μͳ͜ͱ͋Γ·ͤΜ͔ʁ
    • ͋ΔॲཧΛɺ
    
    • ܁Γฦ͍ͨ͠
    
    • ෳ਺ͷର৅ʹߦ͍͍ͨ
    
    • ͦͷ··Ͱ͸ data.frame ʹͰ͖ͳ͍σʔλ
    (JSON / XMLͳͲ) Λѻ͍͍ͨ
    

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19. ྫ͑͹
    σʔλΛ8FC"1*͔Β+40/Ͱऔಘͯ͠੔ܗ
    ͋Δ৚݅ʹ߹க͠ͳ͍σʔλ͸আ֎
    EBUBGSBNFΛ࡞੒
    ෳ਺ͷϞσϧΛ࡞੒ൺֱϓϩοτ
    

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20. ૊ΈࠐΈؔ਺Ͱ΍Δͱ…
    • Apply ؔ਺܈
    
    • ߴ֊ؔ਺ ※ ܈
    BQQMZ
    lapply(list(1, 2, 3), function (x) { x + 1 })
    Map(function (x) { x + 1 }, list(1, 2, 3))
    MBQQMZ TBQQMZ UBQQMZ NBQQMZ
    3FEVDF 'JMUFS 'JOE .BQ /FHBUF 1PTJUJPO
    ແ໊ؔ਺ͷఆ͕ٛΊΜͲ͏
    ؔ਺ͷద༻Ҏ֎ͷॲཧ͸Ͱ͖ͳ͍
    ؔ਺͕ୈҰҾ਺ͷͨΊύΠϓԋࢉࢠ͕࢖͍ʹ͍͘
    ֮͑ΒΕͳ͍
    ※ ؔ਺ΛҾ਺΍ฦΓ஋ʹ͢Δؔ਺
    

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21. $SFBUJWF$PNNPOT$$GSPNUIF1JYBCBZ
    ͳΜͱ͔ͳΒͳ͍͔ʜ
    

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22. purrr !!!
    1IPUPCZԶ
    

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23. {purrr} ͱ͸
    

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24. {purrr} ͱ͸
    • RStudio BlogΑΓ
    
    • ؔ਺ܕϓϩάϥϛϯάͷͨΊͷπʔϧ
    
    • ॲཧΛγϯϓϧͳؔ਺ͷ૊Έ߹ΘͤͰهड़
    
    • ؔ਺͸ύΠϓԋࢉࢠͰ઀ଓ
    Purrr is a new package that fills in the missing
    pieces in R’s functional programming tools: it’s
    designed to make your pure functions purrr. Like
    many of my recent packages, it works with magrittr
    to allow you to express complex operations by
    combining simple pieces in a standard way.
    

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25. ؔ਺ܕϓϩάϥϛϯάͱ͸?
    • (Ұҙͷఆ͕ٛ͋ΔΘ͚Ͱ͸ͳ͍͕) ෳ਺ͷؔ਺ͷ
    ૊Έ߹ΘͤʹΑͬͯ࠷ऴతʹ΍Γ͍ͨॲཧΛه
    ड़͍ͯ͘͠ελΠϧɻ
    
    • ؔ਺ܕϓϩάϥϛϯάΛ૝ఆͨ͠ݴޠ͸ؔ਺
    ܕݴޠͱݺ͹ΕΔ
    

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26. R ͸ؔ਺ܕݴޠ͔?
    • Yes (by Hadley Wickham in “Advanced R”) .
    
    • ৄ͘͠͸ “Rݴޠపఈղઆ” (๜༁͕12/23ൃച)
    

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27. {purrr} ͷಛ௃
    • ϥϜμࣜ
    
    • ύΠϓԋࢉࢠͰ઀ଓͰ͖Δؔ਺܈
    
    • ஫ҙ఺
    
    • όʔδϣϯ͸0.1ɻࠓޙ ഁյతͳมߋ͕͋ΔՄೳੑ͕͋Δɻ
    
    • ຊ೔͸ओཁͳؔ਺ͷΈ͝঺հ (͜ΕͰ΄ͱΜͲͷ͜ͱ͸Ͱ͖Δ)ɻ
    

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28. ϥϜμࣜ
    • ໊લΛ΋ͨͳ͍ؔ਺ (ແ໊ؔ਺) Λγϯϓϧʹهड़
    
    • R ඪ४
    
    • {purrr} ͷϥϜμࣜ
    
    • {purrr} ͷؔ਺ͷதͰ࢖͏ (ߴ֊ؔ਺)
    function(x) { x + 1 }
    ~ . + 1
    map(x, ~ . + 1)
    υοτ
    ͕Ҿ਺ʹରԠ
    

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29. {purrr} ͷؔ਺܈ (Ұ෦)
    ඪ४ QVSSS
    BQQMZܥ NBQ
    'JMUFS LFFQ
    3FEVDF SFEVDF
    TQMJU TQMJU@CZ
    TPSU TPSU@CZ
    

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30. map
    • ؔ਺Λ֤ཁૉʹద༻
    map(c(1, 2, 3), ~ . + 1)
    ## x = list 3 (216 bytes)
    ## . [[1]] = double 1= 2
    ## . [[2]] = double 1= 3
    ## . [[3]] = double 1= 4
    map(list(a = 1, b = 2, c = 3), ~ . + 1)
    ## x = list 3 (544 bytes)
    ## . a = double 1= 2
    ## . b = double 1= 3
    ## . c = double 1= 4
    map(c(1, 2, 3), ~ . + 1) == list(1 + 1, 2 + 1, 3 + 1)
    

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31. map2
    • 2ͭͷҾ਺ʹରͯؔ͠਺ద༻
    map2(c(1, 2, 3), c(4, 5, 6), ~ .x * .y)
    ## x = list 3 (216 bytes)
    ## . [[1]] = double 1= 4
    ## . [[2]] = double 1= 10
    ## . [[3]] = double 1= 18
    map2(c(1, 2, 3), c(4, 5, 6), ~ .x * .y)
    == list(1 * 4, 2 * 5, 3 * 6)
    ͻͱͭΊ͔ΒͷཁૉΛY
    ;ͨͭΊ͔ΒͷཁૉΛZͰࢀর
    

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32. map_xxx
    • ݁ՌΛϕΫτϧͰऔಘɺܕͷࢦఆ͕ඞཁ
    map_int(list(a = 1L, b = 2L, c = 3L), ~ . + 1L)
    ## a b c
    ## 2 3 4
    NBQ@YYY ϕΫτϧͷܕ
    NBQ@MHM MPHJDBM
    NBQ@DIS DIBSBDUFS
    NBQ@JOU JOUFHFS
    NBQ@ECM OVNSJD
    

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33. keep
    • ؔ਺ͷ৚݅ʹ͋ͯ͸·ΔཁૉΛநग़
    keep(c(1, 2, 3), ~ . >= 2)
    ## [1] 2 3
    keep(list(a = 1, b = 2, c = 3), ~ . >= 2)
    ## x = list 2 (416 bytes)
    ## . b = double 1= 2
    ## . c = double 1= 3
    

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34. reduce
    • ͨͨΈࠐΈ
    reduce(c(1, 2, 3), `+`)
    ## [1] 6
    reduce(list(a = 1, b = 2, c = 3), `+`)
    ## [1] 6
    reduce(c(1, 2, 3), `+`) == ((1 + 2) + 3)
    

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35. split_by
    • base::split + ϥϜμࣜ
    split_by(c(1, 2, 3), ~ . %% 2)
    ## x = list 2 (424 bytes)
    ## . 0 = double 1= 2
    ## . 1 = double 2= 1 3
    split_by(list(a = 1, b = 2, c = 3), ~ . %% 2)
    ## x = list 2 (1040 bytes)
    ## . 0 = list 1
    ## . . b = double 1= 2
    ## . 1 = list 2
    ## . . a = double 1= 1
    ## . . c = double 1= 3
    

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36. sort_by
    • ιʔτ
    sort_by(c(2, -3, 1), ~ abs(.))
    ## [1] 1 2 -3
    sort_by(list(a = 2, b = - 3, c = 1), ~ abs(.))
    ## x = list 3 (544 bytes)
    ## . c = double 1= 1
    ## . a = double 1= 2
    ## . b = double 1= -3
    

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37. 2. αϯϓϧ
    

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38. {purrr}
    • 1. μϛʔσʔλͷ࡞੒
    
    • 2. Ϧετ͔Βͷσʔλબ୒
    
    • 3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱ
    
    • 4. ϓϩοτ
    

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39. 1. μϛʔσʔλͷ࡞੒
    • ద౰ͳμϛʔσʔλΛ࡞Γ͍ͨ
    
    • ҎԼͷଐੑΛ΋ͭϦετͷϦετ
    
    • name: ໊લ
    
    • age: ೥ྸ
    
    • likes: ޷͖ͳਓ
    

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40. 1. μϛʔσʔλͷ࡞੒
    ndata dummies age = sample(25:35, size = 1),
    likes = sample(ndata, size = 2)))
    dummies
    ## x = list 5 (3632 bytes)
    ## . [[1]] = list 3
    ## . . name = character 1= hoxo-eros
    ## . . age = integer 1= 29
    ## . . likes = character 2= hoxo-um hoxo-uri
    ## . [[2]] = list 3
    ## . . name = character 1= hoxo-m
    ## . . age = integer 1= 34
    ## . . likes = character 2= hoxo-uri hoxo-eros
    ## . [[3]] = list 3
    ## . . name = character 1= hoxo-um
    ## . . age = integer 1= 25
    ## . . likes = character 2= hoxo-m hoxo-uri
    ## . [[4]] = list 3
    ## . . name = character 1= hoxo-um
    ## . . age = integer 1= 29
    ## . . likes = character 2= hoxo-uri hoxo-eros
    ## . [[5]] = list 3
    ## . . name = character 1= hoxo-um
    ## . . age = integer 1= 35
    ## . . likes = character 2= hoxo-eros hoxo-um
    ϦετΛฦ͢ϥϜμࣜ
    

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41. 1. μϛʔσʔλͷ࡞੒
    dummies ~ list(name = .,
    age = sample(30:35, size = 1),
    likes = sample(ndata[ndata != .],
    size = sample(1:(length(ndata) - 1),
    size = 1))))
    dummies
    ## x = list 4 (2896 bytes)
    ## . [[1]] = list 3
    ## . . name = character 1= hoxo-m
    ## . . age = integer 1= 31
    ## . . likes = character 2= hoxo-um hoxo-uri
    ## . [[2]] = list 3
    ## . . name = character 1= hoxo-eros
    ## . . age = integer 1= 31
    ## . . likes = character 3= hoxo-uri hoxo-um ...
    ## . [[3]] = list 3
    ## . . name = character 1= hoxo-um
    ## . . age = integer 1= 30
    ## . . likes = character 1= hoxo-m
    ## . [[4]] = list 3
    ## . . name = character 1= hoxo-uri
    ## . . age = integer 1= 34
    ## . . likes = character 2= hoxo-um hoxo-m
    

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42. 1. μϛʔσʔλͷ࡞੒
    gen age = sample(30:35, size = 1)
    size = sample(1:(length(ndata) - 1), size = 1)
    likes = sample(ndata[ndata != name], size = size)
    return (list(name = name, age = age, likes = likes))
    }
    dummies dummies
    ## x = list 4 (2896 bytes)
    ## . [[1]] = list 3
    ## . . name = character 1= hoxo-m
    ## . . age = integer 1= 31
    ## . . likes = character 2= hoxo-um hoxo-uri
    ## . [[2]] = list 3
    ## . . name = character 1= hoxo-eros
    ## . . age = integer 1= 31
    ## . . likes = character 3= hoxo-uri hoxo-um ...
    ## . [[3]] = list 3
    ## . . name = character 1= hoxo-um
    ## . . age = integer 1= 30
    ## . . likes = character 1= hoxo-m
    ## . [[4]] = list 3
    ## . . name = character 1= hoxo-uri
    ## . . age = integer 1= 34
    ## . . likes = character 2= hoxo-um hoxo-m
    ී௨ͷؔ਺Λ౉ͯ͠΋Α͍
    

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43. 1. μϛʔσʔλͷ࡞੒
    ndata %>%
    map(~ list(name = ., age = sample(30:35, size = 1))) %>%
    dplyr::bind_rows()
    EBUBGSBNFʹ͚ͨ͠Ε͹
    EQMZSCJOE@SPXT
    
    ## Source: local data frame [4 x 2]
    ##
    ## name age
    ## (chr) (int)
    ## 1 hoxo-m 34
    ## 2 hoxo-eros 35
    ## 3 hoxo-um 32
    ## 4 hoxo-uri 34
    

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44. 2. Ϧετ͔Βͷσʔλબ୒
    • ؆୯ͳૢ࡞͸ {purrr} Ͱ΋Ͱ͖Δ
    
    • Ϩίʔυͷબ୒
    
    • ଐੑͷબ୒
    
    • ࢀߟ: {rlist} ͱͷൺֱ
    
    • {purrr} ͰϦετσʔλΛૢ࡞͢Δ <1>
    
    • {purrr} ͰϦετσʔλΛૢ࡞͢Δ <2>
    

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45. 2. Ϧετ͔Βͷσʔλબ୒
    keep(dummies, ~ .$name == 'hoxo-m')
    ## x = list 1 (752 bytes)
    ## . [[1]] = list 3
    ## . . name = character 1= hoxo-m
    ## . . age = integer 1= 31
    ## . . likes = character 2= hoxo-um hoxo-uri
    OBNFଐੑ͕bIPYPN`ͷ
    ϨίʔυΛબ୒
    

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46. 2. Ϧετ͔Βͷσʔλબ୒
    keep(dummies, ~ .$age > 30)
    ## x = list 3 (2256 bytes)
    ## . [[1]] = list 3
    ## . . name = character 1= hoxo-m
    ## . . age = integer 1= 31
    ## . . likes = character 2= hoxo-um hoxo-uri
    ## . [[2]] = list 3
    ## . . name = character 1= hoxo-eros
    ## . . age = integer 1= 31
    ## . . likes = character 3= hoxo-uri hoxo-um ...
    ## . [[3]] = list 3
    ## . . name = character 1= hoxo-uri
    ## . . age = integer 1= 34
    ## . . likes = character 2= hoxo-um hoxo-m
    BHFଐੑ͕ΑΓେ͖͍
    ͓͡͞ΜΛબ୒
    ˞࣮ࡍͷ೥ྸͱ͸ҟͳΔՄೳੑ͕͋Γ·͢
    

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47. 2. Ϧετ͔Βͷσʔλબ୒
    keep(dummies, ~ 'hoxo-m' %in% .$likes)
    ## x = list 3 (2192 bytes)
    ## . [[1]] = list 3
    ## . . name = character 1= hoxo-eros
    ## . . age = integer 1= 31
    ## . . likes = character 3= hoxo-uri hoxo-um ...
    ## . [[2]] = list 3
    ## . . name = character 1= hoxo-um
    ## . . age = integer 1= 30
    ## . . likes = character 1= hoxo-m
    ## . [[3]] = list 3
    ## . . name = character 1= hoxo-uri
    ## . . age = integer 1= 34
    ## . . likes = character 2= hoxo-um hoxo-m
    bIPYPN`͞Μͷ͜ͱ͕
    ޷͖ͳਓΛநग़
    ˞ࣄ࣮ͱ͸ҟͳΔՄೳੑ͕͋Γ·͢
    

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48. 2. Ϧετ͔Βͷσʔλબ୒
    keep(dummies, ~ 'hoxo-m' %in% .$likes) %>%
    map(~ .$name)
    ## x = list 3 (376 bytes)
    ## . [[1]] = character 1= hoxo-eros
    ## . [[2]] = character 1= hoxo-um
    ## . [[3]] = character 1= hoxo-uri
    ϑΟϧλ݁Ռ͔Β
    OBNFଐੑͷΈΛநग़
    keep(dummies, ~ 'hoxo-m' %in% .$likes) %>%
    map('name')
    ## x = list 3 (376 bytes)
    ## . [[1]] = character 1= hoxo-eros
    ## . [[2]] = character 1= hoxo-um
    ## . [[3]] = character 1= hoxo-uri
    NBQʹจࣈྻΛ
    ౉ͯ͠΋Α͍
    

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49. • σʔλղੳͷͨΊͷ౷ܭϞσϦϯάೖ໳
    
    • ୈ3ষΑΓ
    
    • y: छࢠ਺
    
    • x: ২෺ͷαΠζ
    
    • f: ࢪං͋Γ(T) / ͳ͠
    3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱ
    df head(df)
    ## y x f
    ## 1 6 8.31 C
    ## 2 6 9.44 C
    ## 3 6 9.50 C
    ## 4 12 9.07 C
    ## 5 10 10.16 C
    ## 6 4 8.32 C
    

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50. 3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱ
    formulas results ~ glm(formula = ., family = poisson, data =
    df))
    results[[1]]
    Call: glm(formula = .x, family = .y, data = df)
    Coefficients:
    (Intercept) x
    1.29172 0.07566
    Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
    Null Deviance: 89.51
    Residual Deviance: 84.99 AIC: 474.8
    GPSNVMBͷϦετΛ࡞੒
    ֤GPSNVMBͰ
    ϞσϧΛ࡞੒
    

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51. 3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱ
    map(results, logLik)
    ## x = list 3 (2128 bytes)
    ## . mod1 = double 1( logLik )= -235.39
    ## . A nobs = integer 1= 100
    ## . A df = integer 1= 2
    ## . mod2 = double 1( logLik )= -237.63
    ## . A nobs = integer 1= 100
    ## . A df = integer 1= 2
    ## . mod3 = double 1( logLik )= -235.29
    ## . A nobs = integer 1= 100
    ## . A df = integer 1= 3
    ݁Ռ͢΂ͯʹ
    MPH-JLؔ਺Λద༻
    

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52. 3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱ
    map(results, AIC)
    ## x = list 3 (544 bytes)
    ## . mod1 = double 1= 474.77
    ## . mod2 = double 1= 479.25
    ## . mod3 = double 1= 476.59
    sort_by(results, AIC) %>% names()
    ## [1] "mod1" "mod3" "mod2"
    ݁Ռ͢΂ͯʹ
    "*$ؔ਺Λద༻
    "*$ͷॱʹฒ΂ସ͑ɺ
    Ϟσϧ໊Λදࣔ
    

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53. 3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱ
    formulas families results2 ~ glm(formula = .x, family = .y, data = df))
    results2[[1]]
    Call: glm(formula = .x, family = .y, data = df)
    Coefficients:
    (Intercept) x
    1.29172 0.07566
    Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
    Null Deviance: 89.51
    Residual Deviance: 84.99 AIC: 474.8
    ෳ਺ύϥϝʔλͷ৔߹͸
    NBQ
    

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54. 4. ϓϩοτ
    plot(results[[1]], which = 2)
    map(results, ~ plot(., which = 2))
    ݁Ռͷ͏ͪͻͱͭΛ
    22ϓϩοτ
    ͢΂ͯΛ22ϓϩοτ
    ॱ൪ʹදࣔ͞ΕΔ
    αϒϓϩοτ͚ͨ͠Ε͹QBS
    
    

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55. ͓·͚
    

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56. {purrr} ͱ૊Έ߹Θͤͯ
    ಛʹ͏Ε͍͠ύοέʔδ
    

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57. {caret}
    • ػցֶशશ෦ೖΓ
    library(mlbench)
    library(caret)
    data(PimaIndiansDiabetes)
    control methods trained %
    purrr::map(~ train(diabetes ~ ., data = PimaIndiansDiabetes,
    method = ., trControl = control))
    resampled summary(resampled)
    ## Call:
    ## summary.resamples(object = resampled)
    ##
    ## Models: Model1, Model2, Model3
    ## Number of resamples: 30
    ##
    ## Accuracy
    ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
    ## Model1 0.6883 0.7435 0.7662 0.7682 0.7915 0.8442 0
    ## Model2 0.6711 0.7273 0.7451 0.7499 0.7785 0.8312 0
    ## Model3 0.6883 0.7403 0.7662 0.7678 0.7922 0.8312 0
    ##
    ## Kappa
    ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
    ## Model1 0.3037 0.4297 0.4763 0.4711 0.5268 0.6578 0
    ## Model2 0.2339 0.3657 0.4078 0.4245 0.4733 0.6128 0
    ## Model3 0.2518 0.4043 0.4610 0.4607 0.5138 0.6165 0
    ൺֱ͍ͨ͠ϞσϧΛϦετʹ͠ɺ
    ֤ϞσϧΛUSBJO
    

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58. {broom}
    • ϞσϧΛ data.frame ʹม׵͢Δ
    library(broom)
    glance(results[[1]])
    ## null.deviance df.null logLik AIC BIC deviance df.residual
    ## 1 89.50694 99 -235.3863 474.7725 479.9828 84.993 98
    ؔ਺ ֓ཁ
    UJEZ Ϟσϧͷཁ໿
    BVHNFOU Ϟσϧͷ৘ใΛݩͷEBUBGSBNFʹ෇༩͢Δ
    HMBODF Ұߦͷཁ໿
    • 3. ෳ਺Ϟσϧͷ࡞੒ͱൺֱͷ݁ՌΛྲྀ༻
    

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59. {broom}
    • ϞσϧΛ data.frame ʹม׵͢Δ
    map(results, broom::glance) %>%
    dplyr::bind_rows()
    ## Source: local data frame [3 x 7]
    ##
    ## null.deviance df.null logLik AIC BIC deviance df.residual
    ## (dbl) (int) (dbl) (dbl) (dbl) (dbl) (int)
    ## 1 89.50694 99 -235.3863 474.7725 479.9828 84.99300 98
    ## 2 89.50694 99 -237.6273 479.2545 484.4649 89.47501 98
    ## 3 89.50694 99 -235.2937 476.5874 484.4029 84.80793 97
    ෳ਺ͷϞσϧͷཁ໿Λ
    EBUBGSBNFʹ
    

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60. {ggfortify}
    • ϞσϧΛ ggplot2::autoplot Λར༻ͯ͠ඳը͢Δ
    
    • {purrr} ͰϞσϧͷϦετΛ࡞Δ -> autoplot
    library(ggfortify)
    autoplot(results, which = 2)
    

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61. ·ͱΊ
    • {purrr} ͸ؔ਺ܕϓϩάϥϛϯάͷͨΊͷπʔϧ
    
    • ॲཧΛγϯϓϧͳؔ਺ͷ૊Έ߹ΘͤͰهड़
    
    • ؔ਺͸ύΠϓԋࢉࢠͰ઀ଓ
    

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62. Enjoy!
    

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