TokyoR#103_DataProcessing

第103回Tokyo.Rでしゃべった際の資料です。

Transcript

1. #103
   @kilometer00
   2023.01.21
   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: ペーパークラフト
   

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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 After
   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. #103
   @kilometer00
   2023.01.21
   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. import Tidy Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    preprocessing
    Data processing
    Data science
    Data Science
    

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12. import Tidy Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    preprocessing
    Data science
    Data
    Observa?on Hypothesis feedback
    Data processing
    Data Science
    

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13. import Tidy Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    preprocessing
    Data science
    Data
    Observation Hypothesis feedback
    Data processing
    Narra/ve of data
    

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14. import Tidy Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    preprocessing
    Data science
    Data
    Observa?on Hypothesis
    Narra/ve of data
    feedback
    Data processing
    

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15. import Tidy Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    Data processing
    Data Science
    

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16. 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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17. data.frame
    tibble
    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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18. data.frame
    

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19. vector
    in Excel
    

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20. vector
    in R
    in Excel
    pre post > pre
    [1] 1 2 3 4 5
    > post
    [1] 5 10 15 20 25
    

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21. vector
    vec1 vec2 vec3 > 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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22. vector
    vec1 vec2 > vec1
    [1] 1 2 3 4 5
    > vec2
    [1] 1 2 3 4 5
    

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23. > ?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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24. vector
    vec1 vec2 vec3 > 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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25. vector
    vec1 > 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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26. list
    list1 > 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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27. list
    list1 > 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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28. named list
    list2 > 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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29. > 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

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30. list1 > class(list1)
    [1] "list"
    > names(list1)
    NULL
    list2 > class(list2)
    [1] "list"
    > names(list2)
    [1] "A" "B" "C"
    named list
    list
    

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31. list3 > class(list3)
    [1] "list"
    > names(list3)
    [1] "A" "B"
    df1 > class(df1)
    [1] "data.frame"
    > names(df1)
    [1] "A" "B"
    named list & data.frame
    

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32. > 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 df1 named list & data.frame
    

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33. > 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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34. 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 > 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

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35. data.frame
    variables
    observa*on
    

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36. data.frame
    tibble
    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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37. > 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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38. > 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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39. 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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40. 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 = name
    )
    

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41. data.frame / tibble
    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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42. 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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43. vignette("dplyr")
    

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44. 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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45. 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)
    Introduction to dplyr
    https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
    WFSCT {dplyr}
    ※ かなり意訳
    

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

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47. 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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48. 1JQFBMHFCSB
    X %>% f
    X %>% f(y)
    X %>% f %>% g
    X %>% f(y, .)
    f(X)
    f(X, y)
    g(f(X))
    f(y, X)
    %>% {magri8r}
    「dplyr再⼊⾨（基本編）」yutanihilaCon
    h"ps://speakerdeck.com/yutannihila6on/dplyrzai-ru-men-ji-ben-bian
    

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49. ①
    ②
    ③
    ④
    lift
    take
    pour
    put
    Bring milk from the kitchen!
    

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50. ①
    lift
    Bring milk from the kitchen!
    lift(Robot, glass, table) -> Robot'
    take
    ②
    take(Robot', fridge, milk) -> Robot''
    

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51. Bring milk from the kitchen!
    Robot' Robot'' Robot''' result result %
    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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52. The Pdyverse 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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53. Bring milk from the kitchen!
    Robot' Robot'' Robot''' result result %
    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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54. Robot' Robot'' Robot''' result result %
    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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55. Programing
    Write
    Run
    Read
    Think
    Write
    Run
    Read
    Think
    Communicate
    Share
    

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56. 1JQFBMHFCSB
    X %>% f
    X %>% f(y)
    X %>% f %>% g
    X %>% f(y, .)
    f(X)
    f(X, y)
    g(f(X))
    f(y, X)
    %>% {magri8r}
    「dplyr再⼊⾨（基本編）」yutanihilation
    https://speakerdeck.com/yutannihilation/dplyrzai-ru-men-ji-ben-bian
    

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

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

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

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61. 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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62. "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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63. George Boole
    1815 - 1864
    A Class-Room Introduc;on to Logic
    h"ps://niyamaklogic.wordpress.com/c
    ategory/laws-of-thoughts/
    Mathematician
    Philosopher
    &
    

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

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

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

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

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68. 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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69. (SBNNBSPGEBUBNBOJQVMBUJPO
    By constraining your options,
    it helps you think about your data
    manipulation challenges.
    Introduc6on to dplyr
    hEps://cran.r-project.org/web/packages/dplyr/vigneEes/dplyr.html
    

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70. 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
    hEps://cran.r-project.org/web/packages/dplyr/vigneEes/dplyr.html
    ※ まさに意訳
    (SBNNBSPGEBUBNBOJQVMBUJPO
    

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71. 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
    И8горь 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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72. import Tidy Transform
    Visualize
    Model
    Communicate
    Modified from “R for Data Science”, H. Wickham, 2017
    Data Science
    ①
    ②
    

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

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

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75. Text
    Image
    First, A. Next, B.
    Then C. Finally D.
    >me
    Intention
    encode
    "Frozen" structure
    A B C D Nme
    value
    α
    β
    

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76. Encode
    Apple
    （Real）
    Apple
    （Information）
    Decode
    

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77. Divergence
    Real
    Info.
    Data Apple = 1
    Encoding
    

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78. Apple
    Encode
    Fruit
    Red
    1
    (image)
    Real Information
    

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79. Apple
    Encode
    Fruit
    Red
    1
    (image)
    Real Information
    channel
    

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80. 写像 (mapping)
    𝑓: 𝑋 → 𝑌
    𝑋 𝑌
    ある情報の集合の要素を、別の情報の集合の
    ただ1つの要素に対応づけるプロセス
    

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81. 写像 (mapping)
    𝑚𝑎𝑝𝑝𝑖𝑛𝑔
    𝐷𝑎𝑡𝑎 𝐼𝑚𝑎𝑔𝑒
    

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82. 𝑋
    𝑌
    𝑦!
    𝑥!
    𝑦"
    𝑥"
    𝑋 𝑌
    𝑥!
    𝑥"
    𝑦!
    𝑦"
    可視化 ⊂ 写像
    mapping
    

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83. 𝑋
    𝑌
    𝑦!
    𝑥!
    𝑦"
    𝑥"
    𝑋 𝑌
    𝑥!
    𝑥"
    𝑦!
    𝑦"
    可視化 ⊂ 写像
    mapping
    x axis, y axis, color, fill,
    shape, linetype, alpha…
    aesthetic channels
    

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84. Apple
    Encode
    Fruit
    Red
    1
    (image)
    Real Information
    channel
    

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85. 𝑋
    𝑌
    𝑦!
    𝑥!
    𝑦"
    𝑥"
    𝑋 𝑌
    𝑥!
    𝑥"
    𝑦!
    𝑦"
    mapping
    x axis, y axis, color, fill,
    shape, linetype, alpha…
    aesthetic channels
    data
    ggplot2 package
    

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86. ggplot2
    # install.packages("tidyverse")
    library(tidyverse)
    dat Attach package
    Simple example
    > dat
    a b
    1 1 8
    2 2 9
    3 3 10
    

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87. dat ggplot(data = dat) +
    geom_point(mapping = aes(x = a, y = b))
    ggplot2
    

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88. dat ggplot(data = dat) +
    geom_point(mapping = aes(x = a, y = b))
    𝑋
    𝑌
    𝑦!
    𝑥!
    𝑦"
    𝑥"
    𝑋 𝑌
    𝑥!
    𝑥"
    𝑦!
    𝑦"
    mapping
    x axis, y axis, color, fill,
    shape, linetype, alpha…
    aesthetic channels
    data
    ggplot2
    

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89. dat ggplot(data = dat) +
    geom_point(mapping = aes(x = a, y = b)) +
    geom_path(mapping = aes(x = a, y = b))
    

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90. dat ggplot(data = dat,
    mapping = aes(x = a, y = b)) +
    geom_point() +
    geom_path()
    inheritance
    

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91. dat ggplot(data = dat) +
    aes(x = a, y = b) +
    geom_point() +
    geom_path()
    

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92. dat ggplot(data = dat) +
    aes(x = a, y = b) +
    geom_point() +
    geom_path()
    

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93. dat g ggplot(data = dat) +
    aes(x = a, y = b)) +
    geom_point()
    g +
    geom_path()
    

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94. Anscombe's quartet
    > 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
    

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95. ggplot(data = anscombe) +
    aes(x = x1, y = y1) +
    geom_point()
    Anscombe's quartet
    

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96. > 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
    x
    y
    mapping
    Anscombe's quartet
    

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97. > 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
    a
    > anscombe_long
    # A tibble: 44 x 3
    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
    7 3 8 6.77
    8 4 8 5.76
    Wide form Long form
    

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98. > 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
    anscombe_long pivot_longer(data = anscombe,
    cols = everything(),
    names_pattern = "(.)(.)",
    names_to = c(".value", "key"))
    Wide -> Long form
    

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99. anscombe_long pivot_longer(data = anscombe,
    cols = everything(),
    names_pattern = "(.)(.)",
    names_to = c(".value", "key"))
    > anscombe_long
    # A tibble: 44 x 3
    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
    7 3 8 6.77
    8 4 8 5.76
    Anscombe's quartet
    

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100. anscombe_long pivot_longer(data = anscombe,
     cols = everything(),
     names_pattern = "(.)(.)",
     names_to = c(".value", "key"))
     > anscombe_long
     # A tibble: 44 x 3
     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
     7 3 8 6.77
     8 4 8 5.76
     Anscombe's quartet
     g_anscomb ggplot(data = anscombe_long) +
     aes(x = x, y = y, color = key) +
     geom_point()
     

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101. anscombe_long pivot_longer(data = anscombe,
     cols = everything(),
     names_pattern = "(.)(.)",
     names_to = c(".value", "key"))
     g_anscomb ggplot(data = anscombe_long)+
     aes(x = x, y = y, color = key)+
     geom_point()
     > anscombe_long
     # A tibble: 44 x 3
     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
     7 3 8 6.77
     8 4 8 5.76
     Anscombe's quartet
     

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102. g_anscomb +
     facet_wrap( ~ key)
     Anscombe's quartet
     

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103. g_anscomb +
     facet_wrap( ~ key) +
     theme(legend.position = "none")
     Anscombe's quartet
     

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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
     ObservaPon Hypothesis
     Narra/ve 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. 𝑋
     𝑌
     𝑦!
     𝑥!
     𝑦"
     𝑥"
     𝑋 𝑌
     𝑥!
     𝑥"
     𝑦!
     𝑦"
     mapping
     x axis, y axis, color, fill,
     shape, linetype, alpha…
     aesthetic channels
     data
     ggplot2 package
     

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113. dat ggplot(data = dat) +
     aes(x = a, y = b) +
     geom_point() +
     geom_path()
     

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114. Enjoy!!
     KMT©
     

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