TokyoR#104_DataProcessing

第104回Tokyo.Rでしゃべった際の資料です。

Transcript

1. #104
   @kilometer00
   2023.03.04
   BeginneR Session
   Data processing &
   visualization
   

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2. Who！？
   Who?
   

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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: 椅⼦を新調
   

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4. 宣伝!!（書籍の翻訳に参加しました。）
   絶賛販売中！
   

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5. BeginneR Session
   

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6. BeginneR
   

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7. Before A'er
   BeginneR Session
   BeginneR BeginneR
   

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8. 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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9. #104
   @kilometer00
   2023.03.03
   BeginneR Session
   Data processing &
   visualization
   

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10. import Tidy
    Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    Data Science
    ①
    ②
    

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11. σʔλ
    情報のうち意思伝達・解釈・処理に
    適した再利⽤可能なもの
    国際電気標準会議(Interna@onal Electrotechnical Commission, IEC)による定義
    

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12. σʔλ
    情報のうち意思伝達・解釈・処理に
    適した再利⽤可能なもの
    ৘ใ 実存を符号化した表象
    

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13. σʔλ
    ৘ใͷ͏ͪҙࢥ఻ୡɾղऍɾॲཧʹ
    దͨ͠࠶ར༻Մೳͳ΋ͷ
    ৘ใ ࣮ଘΛූ߸Խͨ͠ද৅
    ࣮ଘ
    ؍࡯ͷ༗ແʹΑΒͣଘࡏ͍ͯ͠Δ
    ΋ͷͦͷ΋ͷ
    ࣸ૾ʢූ߸Խʣ
    

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14. 集合! 集合"
    要素# 要素$
    写像 %: ! → "もしくは%: # ⟼ $
    （始集合・定義域） （終集合・終域）
    【写像】
    ある集合の要素を他の集合のただ1つの要素に
    対応づける規則
    

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15. 地図空間
    ⽣物種名空間
    名空間
    ⾦銭価値空間
    (円)
    ⾦銭価値空間
    (ドル)
    コーヒー
    ¥290
    $2.53
    [緯度, 経度]
    Homo sapiens
    実存
    写像
    写像
    写像
    写像
    写像
    写像
    情報
    【写像】
    ある集合の要素を他の集合のただ1つの要素に対応づける規則
    

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16. ࣸ૾
    Ϧϯΰ
    ʢ࣮ଘʣ
    Ϧϯΰ
    ʢ৘ใʣ
    mapping
    

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17. ৘ใྔ
    ࣮ଘ
    ৘ใ
    σʔλ Ϧϯΰ
    ූ߸Խ
    

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18. ৘ใྔ
    ࣮ଘ
    ৘ใ
    σʔλ Ϧϯΰ
    ූ߸Խ
    ৘ใྔͷଛࣦ
    

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19. Experiment
    hypothesis observa=on
    principle phenotype
    model data
    Truth
    Knowledge f X
    (unknown)
    

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20. raed_csv()
    write_csv()
    Table Data
    Wide form Long form
    pivot_longer()
    Nested form
    pivot_wider()
    Plot
    group_nest() unnest()
    {ggplot2}
    {patchwork}
    Image Files
    ggsave()
    Data Processing
    

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21. data.frame
    *bble
    raed_csv()
    write_csv()
    Table Data
    Wide form Long form
    pivot_longer()
    Nested form
    pivot_wider()
    Plot
    group_nest() unnest()
    {ggplot2}
    {patchwork}
    Image Files
    ggsave()
    Data Processing
    

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22. data.frame
    

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23. vector
    in Excel
    

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24. 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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25. vector
    vec1 <- c(1, 2, 3, 4, 5)
    vec2 <- 1:5
    vec3 <- seq(from = 1, to = 5, by = 1)
    > vec1
    [1] 1 2 3 4 5
    > vec2
    [1] 1 2 3 4 5
    > vec3
    [1] 1 2 3 4 5
    

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26. vector
    vec1 <- seq(from = 1, to = 5, by = 1)
    vec2 <- seq(1, 5, 1)
    > vec1
    [1] 1 2 3 4 5
    > vec2
    [1] 1 2 3 4 5
    

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27. > ?seq
    vector
    seq{base}
    Sequence Generation
    Description
    Generate regular sequences. seq is a standard
    generic with a default method. …
    Usage
    seq(...)
    ## Default S3 method:
    seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),
    length.out = NULL, along.with = NULL, ...)
    

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28. vector
    vec1 <- rep(1:3, times = 2)
    vec2 <- rep(1:3, each = 2)
    vec3 <- rep(1:3, times = 2, each = 2)
    > vec1
    [1] 1 2 3 1 2 3
    > vec2
    [1] 1 1 2 2 3 3
    > vec3
    [1] 1 1 2 2 3 3 1 1 2 2 3 3
    

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29. vector
    vec1 <- 11:15
    > vec1
    [1] 11 12 13 14 15
    > vec1[1]
    [1] 11
    > vec1[3:5]
    [1] 13 14 15
    > vec1[c(1:2, 5)]
    [1] 11 12 15
    

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30. list
    list1 <- list(1:6, 11:15, c("a", "b", "c"))
    > list1
    [[1]]
    [1] 1 2 3 4 5 6
    [[2]]
    [1] 11 12 13 14 15
    [[3]]
    [1] "a" "b" "c"
    

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31. list
    list1 <- list(1:6, 11:15, c("a", "b", "c"))
    > list1[[1]]
    [1] 1 2 3 4 5 6
    > list1[[3]][2:3]
    [1] "b" "c"
    > list1[[2]] * 3
    [1] 33 36 39 42 45
    

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32. named list
    list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
    > list2
    $A
    [1] 1 2 3 4 5 6
    $B
    [1] 11 12 13 14 15
    $C
    [1] "a" "b" "c"
    

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33. > list2$A
    [1] 1 2 3 4 5 6
    > list2$C[2:3]
    [1] "b" "c"
    > list2$B * 3
    [1] 33 36 39 42 45
    named list
    list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
    

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34. list1 <- list(1:6, 11:15, c("a", "b", "c"))
    > class(list1)
    [1] "list"
    > names(list1)
    NULL
    list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
    > class(list2)
    [1] "list"
    > names(list2)
    [1] "A" "B" "C"
    named list
    list
    

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35. list3 <- list(A = 1:3, B = 11:13)
    > class(list3)
    [1] "list"
    > names(list3)
    [1] "A" "B"
    df1 <- data.frame(A = 1:3, B = 11:13)
    > class(df1)
    [1] "data.frame"
    > names(df1)
    [1] "A" "B"
    named list & data.frame
    

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36. > str(list3)
    List of 2
    $ A: int [1:3] 1 2 3
    $ B: int [1:3] 11 12 13
    > str(df1)
    'data.frame': 3 obs. of 2 variables:
    $ A: int 1 2 3
    $ B: int 11 12 13
    list3 <- list(A = 1:3, B = 11:13)
    df1 <- data.frame(A = 1:3, B = 11:13)
    named list & data.frame
    

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37. > list3
    $A
    [1] 1 2 3
    $B
    [1] 11 12 13
    > df1
    A B
    1 1 11
    2 2 12
    3 3 13
    named list & data.frame
    

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38. data.frame vs. matrix
    A B
    1 1 11
    2 2 12
    3 3 13
    [,1] [,2]
    [1,] 1 11
    [2,] 2 12
    [3,] 3 13
    df1 <- data.frame(A = 1:3, B = 11:13)
    > str(mat1)
    int [1:3, 1:2] 1 2 3 11 12 13
    > str(df1)
    'data.frame': 3 obs. of 2 vars.:
    $ A: int 1 2 3
    $ B: int 11 12 13
    mat1 <- matrix(c(1:3, 11:13), nrow = 3, ncol = 2)
    

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39. data.frame
    variables
    observa*on
    

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40. data.frame
    *bble
    raed_csv()
    write_csv()
    Table Data
    Wide form Long form
    pivot_longer()
    Nested form
    pivot_wider()
    Plot
    group_nest() unnest()
    {ggplot2}
    {patchwork}
    Image Files
    ggsave()
    Data Processing
    

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41. > anscombe
    x1 x2 x3 x4 y1 y2 y3 y4
    1 10 10 10 8 8.04 9.14 7.46 6.58
    2 8 8 8 8 6.95 8.14 6.77 5.76
    3 13 13 13 8 7.58 8.74 12.74 7.71
    4 9 9 9 8 8.81 8.77 7.11 8.84
    5 11 11 11 8 8.33 9.26 7.81 8.47
    6 14 14 14 8 9.96 8.10 8.84 7.04
    7 6 6 6 8 7.24 6.13 6.08 5.25
    8 4 4 4 19 4.26 3.10 5.39 12.50
    9 12 12 12 8 10.84 9.13 8.15 5.56
    10 7 7 7 8 4.82 7.26 6.42 7.91
    11 5 5 5 8 5.68 4.74 5.73 6.89
    Wide form data
    

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42. > df
    tag x1 x2 x3 x4 y1 y2 y3 y4
    1 1 10 10 10 8 8.04 9.14 7.46 6.58
    2 2 8 8 8 8 6.95 8.14 6.77 5.76
    3 3 13 13 13 8 7.58 8.74 12.74 7.71
    4 4 9 9 9 8 8.81 8.77 7.11 8.84
    5 5 11 11 11 8 8.33 9.26 7.81 8.47
    6 6 14 14 14 8 9.96 8.10 8.84 7.04
    Wide form data
    df <-
    rownames_to_column(
    anscombe,
    var = "tag"
    )
    

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43. Wide form → Long form data
    df_long_1 <-
    pivot_longer(
    data = df,
    cols = !tag
    )
    df_long_2 <-
    pivot_longer(
    data = df,
    cols = !tag,
    names_to = c(".value", "key"),
    names_pattern = c("(.)(.)")
    )
    

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44. Long form → Wide form data
    pivot_wider(
    data = df_long_1,
    values_from = value,
    names_from = name
    )
    pivot_wider(
    data = df_long_2,
    values_from = c(x, y),
    names_from = tag
    )
    

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45. data.frame / *bble
    raed_csv()
    write_csv()
    Table Data
    Wide form Long form
    pivot_longer()
    pivot_wider()
    Plot
    {ggplot2}
    Image Files
    ggsave()
    Data Processing
    

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46. raed_csv()
    write_csv()
    Table Data
    Wide form Long form
    pivot_longer()
    pivot_wider()
    Plot
    {ggplot2}
    Image Files
    ggsave()
    Data Processing
    Long form
    Long form
    Long form
    Long form
    Long form
    Long form
    Long form
    Long form
    data.frame / *bble
    

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47. vignette("dplyr")
    

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48. It (dplyr) provides simple “verbs” to help
    you translate your thoughts into code.
    func?ons that correspond to the most
    common data manipula?on tasks
    Introduc6on to dplyr
    h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html
    WFSCT {dplyr}
    

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49. 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)
    Introduc6on to dplyr
    h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html
    WFSCT {dplyr}
    ※ かなり意訳
    

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50. (SBNNBSPGEBUBNBOJQVMBUJPO
    By constraining your op@ons,
    it helps you think about your data
    manipula@on challenges.
    Introduc6on to dplyr
    hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
    

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51. 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）
    Introduc6on to dplyr
    hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
    ※ まさに意訳
    (SBNNBSPGEBUBNBOJQVMBUJPO
    

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52. 1. mutate()
    2. filter()
    3. select()
    4. group_by()
    5. summarize()
    6. left_join()
    7. arrange()
    Data.frame manipula@on
    

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53. 1. mutate()
    2. filter()
    3. select()
    4. group_by()
    5. summarize()
    6. left_join()
    7. arrange()
    Data.frame manipula@on
    0. %>%
    

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54. 1JQFBMHFCSB
    X %>% f
    X %>% f(y)
    X %>% f %>% g
    X %>% f(y, .)
    f(X)
    f(X, y)
    g(f(X))
    f(y, X)
    %>% {magri7r}
    「dplyr再⼊⾨（基本編）」yutanihila@on
    h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
    

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55. ①
    ②
    ③
    ④
    lift
    take
    pour
    put
    Bring milk from the kitchen!
    

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56. ①
    lift
    Bring milk from the kitchen!
    lift(Robot, glass, table) -> Robot'
    take
    ②
    take(Robot', fridge, milk) -> Robot''
    

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57. 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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58. The =dyverse style guides
    h"ps://style.;dyverse.org/syntax.html#object-names
    "There are only two hard things in Computer Science:
    cache invalida:on and naming things"
    

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59. 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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60. 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
    Thinking Reading
    Bring milk from the kitchen!
    

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61. Programing
    Write
    Run
    Read
    Think
    Write
    Run
    Read
    Think
    Communicate
    Share
    

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62. 1JQFBMHFCSB
    X %>% f
    X %>% f(y)
    X %>% f %>% g
    X %>% f(y, .)
    f(X)
    f(X, y)
    g(f(X))
    f(y, X)
    %>% {magri7r}
    「dplyr再⼊⾨（基本編）」yutanihila@on
    h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
    

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63. 1. mutate()
    2. filter()
    3. select()
    4. group_by()
    5. summarize()
    6. left_join()
    7. arrange()
    Data.frame manipula@on
    0. %>%
    ✔
    

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

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

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

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67. ブール演算⼦ Boolean Algebra
    A == B A != B
    George Boole
    1815 - 1864
    A | B A & B
    A %in% B
    # equal to # not equal to
    # or # and
    # is A in B?
    wikipedia
    

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68. "a" != "b"
    # is A in B?
    ブール演算⼦ Boolean Algebra
    [1] TRUE
    1 %in% 10:100
    # is A in B?
    [1] FALSE
    

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69. George Boole
    1815 - 1864
    A Class-Room Introduc;on to Logic
    h"ps://niyamaklogic.wordpress.com/c
    ategory/laws-of-thoughts/
    Mathema=cian
    Philosopher
    &
    

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

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

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

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73. WFSCT {dplyr}
    # Select help func?ons
    starts_with("s") ends_with("s")
    contains("se") matches("^.e")
    one_of(c(”tag", ”B"))
    everything()
    hFps://kazutan.github.io/blog/2017/04/dplyr-select-memo/
    「dplyr::selectの活⽤例メモ」kazutan
    

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74. 1. mutate()
    2. filter()
    3. select()
    4. group_by()
    5. summarize()
    6. left_join()
    7. arrange()
    Data.frame manipula@on
    0. %>%
    ✔
    ✔
    ✔
    ✔
    

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75. 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）
    Introduc6on to dplyr
    hFps://cran.r-project.org/web/packages/dplyr/vigneFes/dplyr.html
    ※ まさに意訳
    (SBNNBSPGEBUBNBOJQVMBUJPO
    

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76. 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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77. import Tidy
    Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    Data Science
    ①
    ②
    

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78. Programing
    Write
    Run
    Read
    Think
    Write
    Run
    Read
    Think
    Communicate
    Share
    

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79. Text Image
    Information
    Intention
    Data
    decode
    encode
    Data analysis
    feedback
    ≠
    

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80. Text
    Image
    First, A. Next, B.
    Then C. Finally D.
    ?me
    Intention
    encode
    "Frozen" structure
    A B C D Xme
    value
    α
    β
    

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81. ࣸ૾
    Ϧϯΰ
    ʢ࣮ଘʣ
    Ϧϯΰ
    ʢ৘ใʣ
    mapping
    

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82. Ϧϯΰ
    ࣸ૾
    ϑϧʔπ
    ੺৭
    
    ը૾
    
    ࣮ଘ ৘ใ
    νϟωϧ
    mapping
    channel
    

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83. #
    $
    %!
    &!
    %"
    &"
    # $
    &!
    &"
    %!
    %"
    σʔλՄࢹԽ
    ࣸ૾
    mapping
    

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84. #
    $
    %!
    &!
    %"
    &"
    # $
    &!
    &"
    %!
    %"
    σʔλՄࢹԽ
    ࣸ૾
    mapping
    x axis, y axis, color, fill,
    shape, linetype, alpha…
    aesthetic channels
    ৹ඒతνϟωϧ
    

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85. #
    $
    %!
    &!
    %"
    &"
    # $
    &!
    &"
    %!
    %"
    σʔλՄࢹԽ
    ࣸ૾
    mapping
    x axis, y axis, color, fill,
    shape, linetype, alpha…
    aesthetic channels
    ৹ඒతνϟωϧ
    ggplot(data = my_data) +
    aes(x = X, y = Y)) +
    goem_point()
    HHQMPUʹΑΔ࡞ਤ
    

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86. ࣮ଘ
    ࣸ૾ʢ؍࡯ʣ
    σʔλ
    ࣸ૾ʢσʔλՄࢹԽʣ
    άϥϑ
    !
    "
    #!
    $!
    #"
    $"
    # $
    &!
    &"
    %!
    %"
    EBUB
    mapping
    aesthetic channels
    ৹ඒతνϟωϧ
    σʔλՄࢹԽ
    

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87. ॳΊͯͷHHQMPU
    library(tidyverse)
    dat <-
    data.frame(tag = rep(c("a", "b"), each = 2),
    X = c(1, 3, 5, 7),
    Y = c(3, 9, 4, 2))
    ggplot() +
    geom_point(data = dat,
    mapping = aes(x = X, y = Y))
    

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88. ॳΊͯͷHHQMPU
    

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89. ॳΊͯͷHHQMPU
    library(tidyverse)
    dat <-
    data.frame(tag = rep(c("a", "b"), each = 2),
    X = c(1, 3, 5, 7),
    Y = c(3, 9, 4, 2))
    ggplot() +
    geom_point(data = dat,
    mapping = aes(x = X, y = Y))
    EBUBGSBNFͷࢦఆ
    BFT
    ؔ਺ͷதͰ৹ඒతཁૉͱͯ͠ม਺ͱνϟωϧͷରԠΛࢦఆ
    ඳը։࢝Λએݴ ه߸Ͱͭͳ͙
    BFT
    ؔ਺ͷҾ਺໊
    EBUͷม਺໊
    άϥϑͷछྨʹ߹ΘͤͨHFPN@
    ؔ਺Λ࢖༻
    

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90. library(tidyverse)
    dat <-
    data.frame(tag = rep(c("a", "b"), each = 2),
    X = c(1, 3, 5, 7),
    Y = c(3, 9, 4, 2))
    ggplot() +
    geom_point(data = dat,
    mapping = aes(x = X, y = Y)) +
    geom_path(data = dat,
    mapping = aes(x = X, y = Y))
    ॳΊ͔ͯΒ൪໨ͷHHQMPU
    

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91. ॳΊ͔ͯΒ൪໨ͷHHQMPU
    

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92. HHQMPUίʔυͷॻ͖ํͷ৭ʑ
    ggplot() +
    geom_point(data = dat,
    mapping = aes(x = X, y = Y)) +
    geom_path(data = dat,
    mapping = aes(x = X, y = Y))
    ggplot(data = dat,
    mapping = aes(x = X, y = Y)) +
    geom_point() +
    geom_path()
    ggplot(data = dat) +
    aes(x = X, y = Y) +
    geom_point() +
    geom_path()
    ڞ௨ͷࢦఆΛHHQMPU
    ؔ਺ͷதͰߦ͍ɺҎԼলུ͢Δ͜ͱ͕Մೳ
    NBQQJOHͷ৘ใ͕ॻ͔ΕͨBFT
    ؔ਺ΛHHQMPU
    ؔ਺ͷ֎ʹஔ͘͜ͱ΋Ͱ͖Δ
    

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93. HHQMPUίʔυͷॻ͖ํͷ৭ʑ
    ggplot() +
    geom_point(data = dat,
    mapping = aes(x = X, y = Y, color = tag)) +
    geom_path(data = dat,
    mapping = aes(x = X, y = Y))
    ggplot(data = dat) +
    aes(x = X, y = Y) + # 括り出すのは共通するものだけ
    geom_point(mapping = aes(color = tag)) +
    geom_path()
    ϙΠϯτͷ৭ͷNBQQJOHΛࢦఆ
    

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94. HHQMPUίʔυͷॻ͖ํͷ৭ʑ
    ggplot(data = dat) +
    aes(x = X, y = Y) +
    geom_point(aes(color = tag)) +
    geom_path()
    ggplot(data = dat) +
    aes(x = X, y = Y) +
    geom_path() +
    geom_point(aes(color = tag))
    ͋ͱ͔ΒͰॏͶͨཁૉ͕લ໘ʹඳը͞ΕΔ
    

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95. library(tidyverse)
    dat <-
    data.frame(tag = rep(c("a", "b"), each = 2),
    X = c(1, 3, 5, 7),
    Y = c(3, 9, 4, 2))
    g <-
    ggplot(data = dat) +
    aes(x = X, y = Y) +
    geom_path() +
    geom_point(mapping = aes(color = tag))
    HHQMPUը૾ͷอଘ
    ggsave(filename = "fig/demo01.png",
    plot = g,
    width = 4, height = 3, dpi = 150)
    

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96. library(tidyverse)
    dat <-
    data.frame(tag = rep(c("a", "b"), each = 2),
    X = c(1, 3, 5, 7),
    Y = c(3, 9, 4, 2))
    g <-
    ggplot(data = dat) +
    aes(x = X, y = Y) +
    geom_path() +
    geom_point(mapping = aes(color = tag))
    HHQMPUը૾ͷอଘ
    ggsave(filename = "fig/demo01.png",
    plot = g,
    width = 4, height = 3, dpi = 150)
    αΠζ͸σϑΥϧτͰ͸Πϯν୯ҐͰࢦఆ
    

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97. library(tidyverse)
    dat <-
    data.frame(tag = rep(c("a", "b"), each = 2),
    X = c(1, 3, 5, 7),
    Y = c(3, 9, 4, 2))
    g <-
    ggplot(data = dat) +
    aes(x = X, y = Y) +
    geom_path() +
    geom_point(mapping = aes(color = tag))
    HHQMPUը૾ͷอଘ
    ggsave(filename = "fig/demo01.png",
    plot = g,
    width = 10, height = 7.5, dpi = 150,
    units = "cm") # "cm", "mm", "in"を指定可能
    

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98. HFNP@
    ؔ਺܈ DGIUUQTXXXSTUVEJPDPNSFTPVSDFTDIFBUTIFFUT
    

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99. ෳ਺ͷܥྻΛඳը͢Δ
    > head(anscombe)
    x1 x2 x3 x4 y1 y2 y3 y4
    1 10 10 10 8 8.04 9.14 7.46 6.58
    2 8 8 8 8 6.95 8.14 6.77 5.76
    3 13 13 13 8 7.58 8.74 12.74 7.71
    4 9 9 9 8 8.81 8.77 7.11 8.84
    5 11 11 11 8 8.33 9.26 7.81 8.47
    6 14 14 14 8 9.96 8.10 8.84 7.04
    ggplot(data = anscombe) +
    geom_point(aes(x = x1, y = y1)) +
    geom_point(aes(x = x2, y = y2), color = "Red") +
    geom_point(aes(x = x3, y = y3), color = "Blue") +
    geom_point(aes(x = x4, y = y4), color = "Green")
    ͜Ε·Ͱͷ஌ࣝͰؤுΔͱ͜͏ͳΔ
    

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100. HHQMPUʹΑΔσʔλՄࢹԽ
     ࣮ଘ
     ࣸ૾ʢ؍࡯ʣ
     σʔλ
     ࣸ૾ʢσʔλՄࢹԽʣ
     άϥϑ
     !
     "
     #!
     $!
     #"
     $"
     SBXEBUB
     写像
     aesthetic channels
     ৹ඒతνϟωϧ
     ՄࢹԽʹదͨ͠EBUBܗࣜ
     変形
     ਤͷͭͷ৹ඒతνϟωϧ͕
     σʔλͷͭͷม਺ʹରԠ͍ͯ͠Δ
     

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101. > head(anscombe)
     x1 x2 x3 x4 y1 y2 y3 y4
     1 10 10 10 8 8.04 9.14 7.46 6.58
     2 8 8 8 8 6.95 8.14 6.77 5.76
     3 13 13 13 8 7.58 8.74 12.74 7.71
     4 9 9 9 8 8.81 8.77 7.11 8.84
     5 11 11 11 8 8.33 9.26 7.81 8.47
     6 14 14 14 8 9.96 8.10 8.84 7.04
     > head(anscombe_long)
     key x y
     1 1 10 8.04
     2 2 10 9.14
     3 3 10 7.46
     4 4 8 6.58
     5 1 8 6.95
     6 2 8 8.14
     ggplot(data = anscombe_long) +
     aes(x = x, y = y, color = key) +
     geom_point()
     ৹ඒతνϟωϧ Y࣠ Z࣠ ৭
     ʹରԠ͢Δม਺ʹͳΔΑ͏มܗ
     ݟ௨͠ྑ͘γϯϓϧʹՄࢹԽͰ͖Δ
     

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102. > head(anscombe)
     x1 x2 x3 x4 y1 y2 y3 y4
     1 10 10 10 8 8.04 9.14 7.46 6.58
     2 8 8 8 8 6.95 8.14 6.77 5.76
     3 13 13 13 8 7.58 8.74 12.74 7.71
     4 9 9 9 8 8.81 8.77 7.11 8.84
     5 11 11 11 8 8.33 9.26 7.81 8.47
     6 14 14 14 8 9.96 8.10 8.84 7.04
     > head(anscombe_long)
     key x y
     1 1 10 8.04
     2 2 10 9.14
     3 3 10 7.46
     4 4 8 6.58
     5 1 8 6.95
     6 2 8 8.14
     ৹ඒతνϟωϧ Y࣠ Z࣠ ৭
     ʹରԠ͢Δม਺ʹͳΔΑ͏มܗ
     anscombe_long <-
     pivot_longer(data = anscombe,
     cols = everything(),
     names_to = c(".value",
     "key"),
     names_pattern = "(.)(.)")
     ԣ௕σʔλ
     ॎ௕σʔλ
     

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103. ggplot(data = anscombe_long) +
     aes(x = x, y = y, color = key) +
     geom_point()
     ggplot(data = anscombe_long) +
     aes(x = x, y = y, color = key) +
     geom_point() +
     facet_wrap(facets = . ~ key, nrow = 1)
     ਫ४ͰਤΛ෼ׂ͢Δ
     

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104. まとめ
     

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105. import Tidy
     Transform
     Visualize
     Model
     Communicate
     Modified from “R for Data Science”, H. Wickham, 2017
     Data Science
     ①
     ②
     

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106. import Tidy
     Transform
     Visualize
     Model
     Communicate
     Modified from “R for Data Science”, H. Wickham, 2017
     preprocessing
     Data science
     Data
     Observa=on Hypothesis
     NarraFve of data
     feedback
     Data processing
     

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107. data.frame / *bble
     raed_csv()
     write_csv()
     Table Data
     Wide form Long form
     pivot_longer()
     pivot_wider()
     Plot
     {ggplot2}
     Image Files
     ggsave()
     Data Processing
     

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108. raed_csv()
     write_csv()
     Table Data
     Wide form Long form
     pivot_longer()
     pivot_wider()
     Plot
     {ggplot2}
     Image Files
     ggsave()
     Data Processing
     Long form
     Long form
     Long form
     Long form
     Long form
     Long form
     Long form
     Long form
     data.frame / *bble
     

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109. It (dplyr) provides simple “verbs” to help
     you translate your thoughts into code.
     func?ons that correspond to the most
     common data manipula?on tasks
     Introduc6on to dplyr
     h"ps://cran.r-project.org/web/packages/dplyr/vigne"es/dplyr.html
     WFSCT {dplyr}
     

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110. 1. mutate()
     2. filter()
     3. select()
     4. group_by()
     5. summarize()
     6. left_join()
     7. arrange()
     Data.frame manipula@on
     

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111. import Tidy
     Transform
     Visualize
     Model
     Communicate
     Modified from “R for Data Science”, H. Wickham, 2017
     Data Science
     ①
     ②
     

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112. #
     $
     %!
     &!
     %"
     &"
     # $
     &!
     &"
     %!
     %"
     mapping
     x axis, y axis, color, fill,
     shape, linetype, alpha…
     aesthetic channels
     data
     ggplot2 package
     

     View Slide

113. HHQMPUʹΑΔσʔλՄࢹԽ
     ࣮ଘ
     ࣸ૾ʢ؍࡯ʣ
     σʔλ
     ࣸ૾ʢσʔλՄࢹԽʣ
     άϥϑ
     !
     "
     #!
     $!
     #"
     $"
     SBXEBUB
     写像
     aesthetic channels
     ৹ඒతνϟωϧ
     ՄࢹԽʹదͨ͠EBUBܗࣜ
     変形
     ਤͷͭͷ৹ඒతνϟωϧ͕
     σʔλͷͭͷม਺ʹରԠ͍ͯ͠Δ
     

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114. Enjoy!!
     

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