データ分析入門 / tokupon-ds2022

2022年7月9日に行われたとくぽんAI塾「データ分析入門」のスライドです。

テキスト: http://uribo.github.io/tokupon_ds/
リポジトリ: https://github.com/uribo/tokupon_ds

Transcript

1. ӝੜਅ໵
   ͱ͘ΆΜ"*क़
   σʔλ෼ੳೖ໳
   ಙౡେֶσβΠϯܕ"*ڭҭݚڀηϯλʔ
   

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2. ຊ୊ʹೖΔલʹʜ
   IUUQTVSJCPHJUIVCJPUPLVQPO@ET
   8FC্ʹڭࡐɺԋश؀ڥΛ༻ҙ͍ͯ͠·͢
   🚨
   ະ׬੒Ͱ͢
   🙇
   2
   8FCϒϥ΢βͰɹɹΛࢼͤ·͢
   ىಈʹ͕͔͔࣌ؒΔ͜ͱ͕͋Γ·͢
   IUUQTCJUMZO.,Y
   

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3. ࠓ೔ͷ಺༰
   σʔλ෼ੳͱ͸Կ͔
   σʔλͷछྨͱදݱํ๏
   σʔλͷಛ௃Λଊ͑Δ
   ม਺ͷؔ܎Λௐ΂Δ
   άϥϑͷ࡞੒
   ·ͱΊ
   

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4. ࠓ೔ͷ಺༰
   σʔλ෼ੳͱ͸Կ͔
   σʔλͷछྨͱදݱํ๏
   σʔλͷಛ௃Λଊ͑Δ
   ม਺ͷؔ܎Λௐ΂Δ
   άϥϑͷ࡞੒
   ·ͱΊ
   

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5. σʔλɺάϥϑʹ᱐͞Εͳ͍Ͱ
   ͜ͷάϥϑΛݟͯԿΛࢥ͔ͬͨͳʁ
   ಙౡݝ͸શࠃͰͲͷ͘Β͍ʹͳΔ͔ͳʁ
   5
   

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6. σʔλɺάϥϑʹ᱐͞Εͳ͍Ͱ
   ಉ͡σʔλΛ࢖͍ͬͯͯ΋
   ਎௕͕DN͔Β࢝·͍ͬͯͳ͍
   Կഒ΋͕ࠩ͋ΔΑ͏ʹݟ͑Δ
   άϥϑͷ࡞ΓํɺݟͤํͰ
   ༩͑Δҹ৅͕มΘΔ
   ԣ࣠ͷ஋Λ͔Β࢝ΊΔ
   ౎ಓ෎ݝؒͰͷ
   6
   ฏۉ਎௕ʹେ͖ͳࠩ͸ͳ͍
   

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7. ਎ͷճΓͷσʔλɺάϥϑʹ໨Λ޲͚Α͏
   ໌೔ ೔
   ͷબڍಛ൪Λબڍ݁Ռͱ߹ΘͤͯݟͯΈΑ͏
   ͲΜͳάϥϑ͕࢖ΘΕ͍ͯΔ͔ σʔλͷݟͤํʹಛผͳҙਤؚ͕·Ε͍ͯͳ͍͔
   ϝσΟΞ͕ൃ৴͢Δ৘ใ͕ਅ࣮ͱ͸ݶΒͳ͍
   ΢αΪͱΞώϧͷࡨ֮
   ࡞ऀෆ໌ύϒϦοΫυϝΠϯ8JLJNFEJB$PNNPOTΑΓ
   IUUQTDPNNPOTXJLJNFEJBPSHXJLJ'JMF,BOJODIFO@VOE@&OUFTWH
   7
   

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8. σʔλΛਓ͕ؒར༻Ͱ͖Δܗʹม׵ɾॲཧΛߦ͏͜ͱͰɺର৅ʹ͍ͭͯͷཧղ΍༧ଌΛ໨ࢦ͢खଓ͖
   σʔλ෼ੳͱ͸Կ͔
   σʔλ
   data
    ൑அ΍ཱ࿦ͷ΋ͱʹͳΔࢿྉɾ৘ใɾࣄ࣮Šʰεʔύʔେࣙྛʱ
   ࣮ࡏ͔Β৘ใΛநग़͠ɺූ߸Խ͢Δ
   ؍ଌ
   ࣮ݧ
   ௐࠪ
   σʔλԽ ཧղɾ༧ଌ
   8
   

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9. σʔλΛཁ໿͢Δ͜ͱ
   σʔλ෼ੳͷ໨త
   σʔλͷҙຯɺσʔλؒͷؔ܎Λઆ໌͢Δ͜ͱ
   ৽ͨʹಘΒΕΔσʔλʹର͢Δ༧ଌΛߦ͏͜ͱ
   Α͏΍͘
   9
   

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10. σʔλ෼ੳͷ໨తσʔλͷཁ໿
    σʔλ෼ੳͰѻ͏σʔλ͸๲େʢ਺ඦʙ਺ेສ݅ʣ
    ͜ΕΒͷσʔλͷ಺༰Λ੔ཧ͠ɺ؆ܿʹ఻͑Δ͜ͱ͕ٻΊΒΕΔ
    ୅ද஋ʹΑΔσʔλͷू໿
    σʔλՄࢹԽ
    ͹Β͖ͭͷࢦඪͷܭࢉʹΑΔ෼෍ͷਪఆ
    ώετάϥϜ
    ശώήਤ
    ฏۉ஋
    ࠷খ஋ɾ࠷େ஋
    ඪ४ภࠩ
    ෼ࢄ
    ਓ͕ؒॲཧͰ͖Δ਺஋ͷ਺ʹ͸ݶΓ͕͋Δ
    ࣸਅͷڕͷମ௕͸ʁ
    σʔλ෼ੳͷख๏
    10
    

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11. σʔλ෼ੳͷ໨తσʔλͷઆ໌
    σʔλ͕΋ͭҙຯɺͦͷഎܠΛ୳Δ
    ෳ਺ͷσʔλΛൺֱ͠ɺͦͷؔ܎ੑΛ໌Β͔ʹ͢Δ
    ؔ܎ͷ਺஋Խ
    άϥϑɺදʹΑΔදݱ
    Ϋϩεूܭද
    ࢄ෍ਤ
    ૬ؔ܎਺
    ڞ෼ࢄ
    σʔλ෼ੳͷख๏
    11
    

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12. σʔλ෼ੳͷ໨తະ஌ͷσʔλ΁ͷ༧ଌ
    طଘͷσʔλͱσʔλͷؔ܎ੑΛઆ໌͢ΔϞσϧʹΑΓɺະ஌ͷσʔλ͕ಘΒΕͨ৔߹ͷ༧ଌΛߦ͏
    ճؼϞσϧ
    ෼ྨϞσϧ
    σʔλ෼ੳͷख๏
    12
    ମͷ෦Ґ͔Βछ໊Λਪఆ
    ମͷҰ෦෦Ґ͔Βଞͷ෦ҐͷαΠζΛਪఆ
    

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13. σʔλ෼ੳͷͨΊͷϓϩάϥϛϯάݴޠ3
    13
    ୭΋͕ࣗ༝ʹѻ͑ΔΦʔϓϯιϑτ΢ΣΞ
    ౷ܭղੳάϥϑΟοΫεΞϓϦέʔγϣϯ։ൃจܳతϓϩάϥϛϯάػցֶशɹͳͲ༻్͸͞·͟·
    ౷߹։ൃ؀ڥͰ͋Δ34UVEJPͷػೳ͕๛෋
    1ZUIPOͱฒͼɺੈքతʹ΋޿͘࢖ΘΕΔϓϩάϥϛϯάݴޠ
    IUUQTCJUMZO.,Y
    ԋश؀ڥ͸ͪ͜Β͔Β
    ιʔείʔυ͕ϑΝΠϧʹ࢒Γɺ෼ੳ݁Ռͷ࠶ݱɺ࢖͍ճ͕͠؆୯
    

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14. ಡΈࠐΈ ੔ܗ Ճ޻
    ՄࢹԽ
    Ϟσϧ
    ఻ୡ
    (BSSFUUBOE)BEMFZ
    Λݩʹ࡞੒
    σʔλ෼ੳͷखॱ
    14
    

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15. σʔλ෼ੳͷྺ࢙ίϨϥͷྲྀߦʹର͢Δδϣϯɾεϊ΢ͷ׆༂
    ੈلϩϯυϯͰະ஌ͷӸපͱͯ͠ίϨϥ͕ྲྀߦ
    ೥ʹδϣϯɾεϊ΢͕࡞੒ͨ͠ΰʔϧσϯɾεΫΤΞͷϒϩʔυɾ
    ετϦʔτपลʹ͓͚Δࢮ๢ऀͷঢ়ଶΛࣔ͢஍ਤύϒϦοΫυϝΠϯ
    IUUQTDPNNPOTXJLJNFEJBPSHXJLJ'JMF4OPXDIPMFSBNBQKQH
    15
    δϣϯɾεϊ΢͸ɺ஍ݩॅຽΒ΁ͷฉ͖ࠐΈௐࠪ౳Λߦ͍ɺ
    ࠷ऴతʹίϨϥͷൃੜݯ͕ɺਫಓϙϯϓͰ͋Δͱಛఆ
    ױऀ͕࢖༻͍ͯͨ͠ҪށਫͷҐஔͱɹɹɹɹ
    ҪށਫΛڙڅ͢Δਫಓձࣾʹ͍ͭͯ෼ੳ
    ໰୊ͱͳΔҪށΛಛఆͨ͠ΓɺਫಓձࣾͷൺֱΛ࣮ࢪ
    ਫͷར༻Λఀࢭͤ͞Δ͜ͱͰҰ෦ͷ஍ҬͰ
    ίϨϥΛ཈͑Δ͜ͱʹ੒ޭ
    ίϨϥͷྲྀߦ͸੔උ͞ΕͨԼਫಓʹΑΓ
    

    ޮ཰తʹ޿͕ͬͯൃੜ͍ͯͨ͠ʢͷͪʹ൑໌ʣ
    

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16. σʔλ෼ੳͷྺ࢙φΠνϯήʔϧͷ౷ܭʹجͮ͘ҩྍӴੜվֵ
    φΠνϯήʔϧ͸೥͔Β೥ͷؒʹൃੜ͍ͯͨ͠ΫϦϛΞઓ૪ʹ͓͍ͯɺ
    16
    ϑϩʔϨϯεɾφΠνϯήʔϧʹΑΔ͘͞ͼܗάϥϑɻ
    IUUQTDPNNPOTXJLJNFEJBPSHXJLJ'JMF/JHIUJOHBMFNPSUBMJUZKQH
    ύϒϦοΫυϝΠϯ
    ͘͞ͼͷҰͭҰ͕݄ͭΛද͢ɻ͘͞ͼͷதʹࢮҼʹ͍ͭͯͷͭͷঢ়ଶΛදݱ
    ࢮҼͰଟ͍ͷ͸ෛইͰ͸ͳ࣬͘පʹΑΔ΋ͷɻ
    ઓ৔Ͱෛইͨ͠ฌ࢜ͷ؃ޢͱӴੜ໘ͷվળʹऔΓ૊Ή
    ઓ૪ऴྃޙɺઓ૪ࢮऀͷݪҼΛ෼ੳதʹɺɹɹ
    ઓಆͰෛͬͨই͕ݪҼͰ๢͘ͳΔฌ࢜ΑΓ΋ɺ
    ෛইޙʹԿΒ͔ͷەʹײછͨ͠ӨڹͰපؾͱɹ
    ͳΓࢮ๢͢Δฌ࢜ͷ΄͏͕ѹ౗తʹଟ͍͜ͱΛ
    ໌Β͔ʹͨ͠
    

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17. σʔλ෼ੳͷྺ࢙ΤΠϒϥϋϜɾ΢ΥʔϧυͷੜଘऀόΠΞε
    17
    ੺ؙ͍͕ଛইՕॴ
    .BSUJO(SBOEKFBO WFDUPS
    .D(FEEPO QJDUVSF
    $BNFSPO.PMM DPODFQU
    
    $$#:4"
    8JLJNFEJB$PNNPOTΑΓ
    IUUQTDSFBUJWFDPNNPOTPSHMJDFOTFTCZTB
    ୈೋ࣍ੈքେઓதɺ೚຿͔Β໭ͬͨػମ͕ड͚ͨ
    ଛইՕॴΛ෼ੳ
    Ͳ͜Λิڧ͢Δͷ͕ద੾ͩΖ͏͔
    

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18. ౴͑߹Θͤ
    ΤΠϒϥϋϜɾ΢Υʔϧυ͸ܸ௢͞Εͨരܸ
    ػ͕෼ੳʹؚ·Ε͍ͯͳ͍͜ͱΛࢦఠ
    18
    ؼؐͨ͠ػମ͕ଛইΛड͚͍ͯͳ͍ՕॴΛ
    ิڧ͢ΔΑ͏ʹࢦࣔ
    ੺ؙ͍Ͱࣔ͢Օॴ͸ଛইΛड͚ͯ΋҆શʹ
    ؼؐͰ͖Δ৔ॴͱͯ͠ߟ͑ͨ΋ͷ
    

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19. ߟ͑ͯΈΑ͏
    ਓͷΫϥεͰߦΘΕͨςετʢ఺ຬ఺ʣͷฏۉ఺͕఺Ͱͨ͠ɻ
    ͜ͷͱ͖ɺ఺਺͕఺ͩͬͨਓ͸Ϋϥεͷ্Ґਓͷதʹؚ·ΕΔͰ͠ΐ͏͔ɻ
    ޙ΄Ͳ౴͑߹ΘͤΛ͠·͢
    19
    

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20. ࠓ೔ͷ಺༰
    σʔλ෼ੳͱ͸Կ͔
    σʔλͷछྨͱදݱํ๏
    σʔλͷಛ௃Λଊ͑Δ
    ม਺ͷؔ܎Λௐ΂Δ
    άϥϑͷ࡞੒
    ·ͱΊ
    

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21. σʔλͷछྨ
    ม਺ʜڞ௨ͷख๏ʹΑͬͯಘΒΕͨ஋ɻର৅ʹΑͬͯ਺஋͕มԽ͢Δ஋Λҙຯ͢Δ
    ྫ͑͹ɺ
    ΁Μ͢͏
    ಈ෺ͷମॏɺಈ෺ͷ෼ྨ܈ɺಈ෺ԂͷདྷԂऀ਺
    
    
    
    ৯೑ྨ
    ௗྨ
    ৯೑ྨ
    
    
    
    ྔతม਺ ࣭తม਺ ྔతม਺
    ࿈ଓม਺ ཭ࢄม਺
    σʔλΛه࿥͢Δਫ਼౓ʹΑͬͯখ਺఺ҎԼͷ஋͕มΘΔ ͱΓಘΔ஋͕ҰఆͷִؒʹΑΓόϥόϥ
    ྔతม਺͸଍ͨ͠ΓׂͬͨΓͱ͍͏ԋࢉ͕
    Ͱ͖Δ͚Ͳ࣭తม਺Ͱ͸ͦΕ͕Ͱ͖ͳ͍Α
    21
    

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22. σʔλϑϨʔϜσʔλΛදܗࣜͰ·ͱΊͯදݱͨ͠΋ͷ
    ಈ෺ʹ͍ͭͯͷ෼ྨ܈ͱ໊শʢछ໊ʣɺମ௕ͱମॏͷͭͷม਺Λه࿥
    ৯೑ྨ
    ྶ௕ྨ
    ྶ௕ྨ
    
    
    
    
    
    
    Ϩοαʔύϯμ
    νϯύϯδʔ
    Ϛϯτώώ
    ৯೑ྨ
    ௗྨ
    ϥΠΦϯ
    ϑϯϘϧτϖϯΪϯ
    
    
    
    
    σʔλ෼ੳͰ͸σʔλϑϨʔϜͷܗࣜͰσʔλΛѻ͏ͷ͕Ұൠత
    22
    

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23. σʔλϑϨʔϜͷಡΈํ
    ෼ྨ܈
    ৯೑ྨ
    ྶ௕ྨ
    ྶ௕ྨ
    
    
    
    
    
    
    Ϩοαʔύϯμ
    νϯύϯδʔ
    Ϛϯτώώ
    ৯೑ྨ
    ௗྨ
    ϥΠΦϯ
    ϑϯϘϧτϖϯΪϯ
    
    
    
    
    ମॏ LN
    
    ମ௕ DN
    
    छ໊ ྻͷ໊લͱͯ͠ม਺໊͕ه࿥͞ΕΔ
    ߦ
    ྻ
    ৯೑ྨ
    Ϩοαʔύϯμ
    ෼ྨ܈
    ৯೑ྨ
    ྶ௕ྨ
    ྶ௕ྨ
    ৯೑ྨ
    ௗྨ
    ؍ଌର৅ʹ͍ͭͯͷ͢΂ͯͷม਺ͷ஋ΛؚΉ ม਺ͷதʹશσʔλͷ஋ΛؚΉ
    23
    

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24. ίʔεͰొ৔͢Δσʔλ
    ϖϯΪϯσʔλʜQFOHVJOT
    ಈ෺σʔλʜEG@[PP
    24
    ೆۃେ཮ʹੜҭ͢ΔϖϯΪϯͷେ͖͞ʹ͍ͭͯͷ؍ଌσʔλ
    ͱ͘͠·ಈ෺ԂͰࣂҭ͞ΕΔಈ෺ͷମͷେ͖͞ͱମॏ
    ೥݄೔࣌఺ͷ৘ใΛ΋ͱʹ࡞੒
    छͷಈ෺ʹ͍ͭͯͷ໊শͱ෼ྨ܈ɺ
    ମͷେ͖͞ʢମ௕DNʣͱମॏʢLHʣΛ8JLJQFEJBͷϖʔδ
    ͔Βඥ෇͚ͯ࡞੒
    

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25. ࠓ೔ͷ಺༰
    σʔλ෼ੳͱ͸Կ͔
    σʔλͷछྨͱදݱํ๏
    σʔλͷಛ௃Λଊ͑Δ
    ม਺ͷؔ܎Λௐ΂Δ
    άϥϑͷ࡞੒
    ·ͱΊ
    

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26. 㲔
    Ͳ͏΍ͬͯσʔλΛཁ໿͢Δ͔
    source("data-raw/zoo.R")
    df_zoo$body_length_cm
    #> [1] 63.5 100.0 64.0 110.0 85.0 66.0 80.0 168.0 134.0 250.0 130.0 175.0
    #> [13] 31.0 NA 1.2 250.0 35.0 69.0 NA NA 40.0 NA
    ܽଛ஋
    ԿΒ͔ͷཧ༝ʹΑΓσʔλ͔Βܽམͨ͠஋
    هड़౷ܭྔ σʔλՄࢹԽ ਤදΛ༻͍ͨཁ໿
    ਺஋ʹΑΔཁ໿
    σʔλʹؚ·ΕΔ਺஋͕Ґஔ͢Δͱ͜Ζʹ͍ͭͯେ·͔ʹ܏޲Λ೺Ѳ͢Δ
    ୅ද஋
    ͹Β͖ͭ
    σʔλʹؚ·ΕΔ਺஋શମ͕Ͳͷఔ౓όϥͭ͘ͷ͔Λ೺Ѳ͢Δ
    ώετάϥϜ
    ശώήਤ
    ౓਺෼෍ද
    26
    

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27. ୅ද஋ฏۉ஋
    σʔλʹؚ·ΕΔ஋Λ͢΂ͯ଍͠߹Θͤͯɺσʔλͷ਺Ͱׂͬͨ஋
    ⚠ฏۉ஋Λѻ͏ͱ͖ͷ஫ҙ🚨
    ฏۉ஋͸ඞͣ͠΋σʔλͷਅΜதΛࣔ͢஋Ͱ͸ͳ͍
    ฏۉ஋͸֎Ε஋ͷӨڹΛड͚΍͍͢
    ฏۉ஋
    27
    1 3 5 7 10
    x (1 + 10 + 5 + 3 + 7) / length(x)
    #> [1] 5.2
    # mean()ؔ਺Λ༻͍ͯฏۉ஋Λܭࢉ͠·͢ɻ
    mean(x)
    #> [1] 5.2
    

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28. ୅ද஋தԝ஋
    σʔλʹؚ·ΕΔ਺ͷਅΜதͱͳΔ஋
    # xͷ਺஋͸େ͖͞ͷॱ൪ʹͳ͍ͬͯͳ͍ͷͰฒͼସ͑Δ
    sort(x)
    #> [1] 1 3 5 7 10
    sort(x)[3]
    #> [1] 5
    median(x)
    #> [1] 5
    # σʔλͷݸ਺͕ۮ਺ͷ৔߹ͷதԝ஋ͷٻΊํ
    x # ਅΜதͷ྆ྡͷ஋ͷฏۉ஋Λதԝ஋ͱ͢Δ
    median(x)
    #> [1] 3
    தԝ஋
    ۮ਺ͷ৔߹
    தԝ஋
    28
    1 3 5 7 10
    1 2 4 6
    

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29. quantile(penguins$flipper_length_mm, na.rm = TRUE)
    #> 0% 25% 50% 75% 100%
    #> 172 190 197 213 231
    தԝ஋Λ֦ுͨ͠ߟ͑ํʜ࢛෼Ґ఺
    σʔλΛ஋ͷখ͍͞ॱʹฒͼସ͑ͨͱ͖ɺσʔλશମΛۉ౳ͳ਺͔ΒͳΔͭͷάϧʔϓʹ෼͚Δ
    ͜ͷͱ͖ͷάϧʔϓΛ෼͚Δͭͷ఺ʢ஋ʣΛ࢛෼Ґ఺ͱ͍͏
    ୈ࢛෼Ґ఺ ୈ࢛෼Ґ఺ ୈ࢛෼Ґ఺
    தԝ஋
    σʔλͷؚ͕·ΕΔ
    σʔλͷؚ͕·ΕΔ
    σʔλͷؚ͕·ΕΔ
    29
    

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30. x # ࠷ස஋ΛٻΊ·͢
    names(which(table(x) == max(table(x))))
    #> [1] "5"
    ୅ද஋࠷ස஋
    σʔλʹؚ·ΕΔ஋ͷதͰ࠷΋ଟ͍஋
    ࠷ස஋
    30
    1 3
    3
    5
    5
    5
    7 10
    

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31. σʔλͷ͹Β͖ͭ
    ୅ද஋͚ͩͰ͸୅ද஋Ҏ֎ͷ஋ʹ͍ͭͯઆ໌Ͱ͖ͳ͍
    ࠷ස஋
    σʔλ͕ͲͷΑ͏ʹ෼෍͢Δ͔Λ͹Β͖ͭʹΑͬͯௐ΂Δ
    ಉ͡୅ද஋Ͱ͋ͬͯ΋σʔλͷ෼෍͸ҟͳΔ
    31
    

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32. σʔλͷ͹Β͖ͭൣғ
    ࠷ස஋
    ࠷খ஋ɾ࠷େ஋ͷൣғ
    x range(x)
    #> [1] 1 10
    min(x)
    #> [1] 1
    max(x)
    #> [1] 10
    32
    

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33. c(0, 0, 0, 0, 0) c(1, 2, 3, 2, 1) c(1, 100, 5, 8, 1) c(1, 6, 40, 56, 1)
    σʔλͷ͹Β͖ͭ෼ࢄWBSJBODF
    ֤஋͕ฏۉ஋Λத৺ͱͯ͠ͲͷΑ͏ʹࢄΒ͹͍ͬͯΔ͔Λࣔ͢
    ฏۉ஋
    ྫ
    ϖϯΪϯͷ֤ݸମͷମ௕ʹ͍ͭͯ
    શൠతʹۉҰͳ஋ʁ
    ಛఆͷݸମ͕ฏۉ஋ΑΓ΋ಛஈߴ͍ɾ௿͍ʁ
    ମ௕͕ߴ͍ݸମͱ௿͍͕όϥόϥʁ
    σʔλͷ෼෍ʹ͍ͭͯ۩ମతͳઆ໌͕Ͱ͖ΔΑ͏ʹ
    ॎ๮͸ฏۉ஋Λࣔ͢
    33
    

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34. ෼ࢄͷٻΊํ
    ภࠩΛ৐͢Δ
    ม਺ͷ֤஋ͱฏۉ஋ͷࠩΛٻΊΔʢภࠩʣ
    ม਺ͷฏۉ஋Λग़͢
    ͢΂ͯͷ஋ʹର͔ͯ͠ΒΛ܁Γฦ͠ɺ߹ܭ͢Δ
    ߹ܭͨ͠஋Λσʔλͷ਺ͰׂΔ
    34
    ΁Μ͞
    

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35. ෼ࢄΛࢉग़ͯ͠ΈΑ͏
    ϖϯΪϯσʔλͷ͏ͪɺΞσϦʔϖϯΪϯͷ಄ͷମॏ CPEZ@NBTT@H
    ʹ͍ͭͯߟ͑Δ
    library(palmerpenguins)
    library(dplyr)
    df penguins |>
    filter(species == "Adelie") |>
    select(body_mass_g) |>
    filter(!is.na(body_mass_g)) |>
    slice_head(n = 5)
    df
    #> # A tibble: 5 × 1
    #> body_mass_g
    #>
    #> 1 3750
    #> 2 3800
    #> 3 3250
    #> 4 3450
    #> 5 3650
    35
    

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36. ෼ࢄΛࢉग़ͯ͠ΈΑ͏
    36
    ภࠩΛ৐͢Δ
    ภࠩΛٻΊΔ
    ม਺ͷฏۉ஋Λग़͢
    ͢΂ͯͷ஋ʹର͔ͯ͠ΒΛ܁Γฦ͠ɺ߹ܭ͢Δ
    ߹ܭͨ͠஋Λσʔλͷ਺ͰׂΔ
    df df |>
    # ֤஋ʹ͍ͭͯภࠩ deviationʢฏۉΑΓ΋͍͘Βେ͖͍͔খ͍͔͞ʣΛٻΊΔ
    mutate(deviation = body_mass_g - mean(df$body_mass_g, na.rm = TRUE))
    df
    #> # A tibble: 5 × 2
    #> body_mass_g deviation
    #>
    #> 1 3750 170
    #> 2 3800 220
    #> 3 3250 -330
    #> 4 3450 -130
    #> 5 3650 70
    ਖ਼ͷ஋ͱෛͷ஋ͷ྆ํ͕ࠞ͟Δ
    ߹ܭ͢ΔͱʹͳΔ
    ภࠩͷಛ௃
    ෛͷ஋Ͱ΋৐͢Δͱਖ਼ͷ஋ʹͳΔ
    

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37. 37
    ภࠩΛ৐͢Δ
    ภࠩΛٻΊΔ
    ม਺ͷฏۉ஋Λग़͢
    ͢΂ͯͷ஋ʹର͔ͯ͠ΒΛ܁Γฦ͠ɺ߹ܭ͢Δ
    ߹ܭͨ͠஋Λσʔλͷ਺ͰׂΔ
    df df |>
    mutate(deviation2 = deviation^2)
    df
    #> # A tibble: 5 × 3
    #> body_mass_g deviation deviation2
    #>
    #> 1 3750 170 28900
    #> 2 3800 220 48400
    #> 3 3250 -330 108900
    #> 4 3450 -130 16900
    #> 5 3650 70 4900
    sum(df$deviation2) / nrow(df)
    #> [1] 41600
    ෼ࢄΛࢉग़ͯ͠ΈΑ͏
    var(df$body_mass_g)
    #> [1] 52000
    3ͷඪ४ؔ਺Ͱ෼ࢄΛٻΊΔ
    ˞σʔλͷ਺ͰׂΔෆภ෼ࢄ
    

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38. σʔλͷ͹Β͖ͭඪ४ภࠩTUBOEBSEEFWJBUJPO
    ඪ४ภࠩͷٻΊํʜ෼ࢄʹ͍ͭͯฏํࠜΛٻΊΔ
    ෼ࢄΛٻΊͨͱ͖ʹ৐ͨ͠΋ͷΛݩʹ໭ͨ͢Ί
    ฏํࠜΛར༻͢Δཧ༝
    ৐͢Δͱ୯Ґ͕มΘΔ΋ͷͷӨڹΛऔΓআ͘
    DNŠDN?
    TRSU DN?
    ŠDN
    38
    

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39. ෼෍Λࢹ֮Խ͢Δ౓਺෼෍ද
    ͋Δ஋͕σʔλʹؚ·ΕΔ਺ʜ౓਺·ͨ͸ස౓
    Ͳ͢͏ ͻΜͲ
    ౓਺ͷ෼෍Λදܗࣜʹ·ͱΊͨ΋ͷʜ౓਺෼෍ද
    ಈ෺σʔλͷ෼ྨ܈Λ౓਺Ͱදݱͯ͠ΈΑ͏
    df_zoo$taxon
    #> [1] "৯೑ྨ" "ௗྨ" "৯೑ྨ" "ௗྨ" "ྶ௕ྨ" "ྶ௕ྨ"
    #> [7] "ྶ௕ྨ" "৯೑ྨ" "ᴩࣃྨ" "৯೑ྨ" "ௗྨ" "ۮఙྨ"
    #> [13] "৯೑ྨ" "৯೑ྨ" "ௗྨ" "৯೑ྨ" "ྶ௕ྨ" "ௗྨ"
    #> [19] "ܵۮఙྨ" "حఙྨ" "ᴩࣃྨ" "ܵۮఙྨ"
    ͜ͷਤͰ͸ྶ௕ྨ͸
    39
    

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40. ෼෍Λࢹ֮Խ͢Δ౓਺෼෍ද
    df_zoo$taxon
    #> [1] "৯೑ྨ" "ௗྨ" "৯೑ྨ" "ௗྨ" "ྶ௕ྨ" "ྶ௕ྨ"
    #> [7] "ྶ௕ྨ" "৯೑ྨ" "ᴩࣃྨ" "৯೑ྨ" "ௗྨ" "ۮఙྨ"
    #> [13] "৯೑ྨ" "৯೑ྨ" "ௗྨ" "৯೑ྨ" "ྶ௕ྨ" "ௗྨ"
    #> [19] "ܵۮఙྨ" "حఙྨ" "ᴩࣃྨ" "ܵۮఙྨ"
    40
    

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41. ෼෍Λࢹ֮Խ͢Δ౓਺෼෍ද
    ྔతม਺ʹରͯ͠౓਺෼෍දΛ࡞੒͢Δͱ͖͸
    ม਺͕ͱΓಘΔ஋Λ͍͔ͭ͘ͷ۠ؒʹ෼ׂͨ͠֊ڃ DMBTT
    Λߟ͑Δ
    41
    ஋͕ݶఆతͳ཭ࢄม਺
    αΠίϩͷग़໨ͳͲ
    ஋Λ֊ڃͱͯ͠௚઀༻͍Δ
    ಈ෺ͷମॏͳͲ
    ֤౓਺ʹؚ·ΕΔ۠ؒͷ෯Λ֊ڃ෯ͱ͍͏
    ֊ڃ෯΍֊ڃ਺͸σʔλͷൣғΛݟܾͯΊΔ
    ࿈ଓม਺
    ద౰ͳൣғΛ֊ڃʹ༻͍Δ
    weight_freq table(cut(penguins$body_mass_g,
    breaks = seq(2000,
    7000,
    by = 1000),
    dig.lab = 4))
    tibble::tibble(
    class = names(weight_freq),
    frequency = weight_freq)
    

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42. penguins |>
    ggplot(aes(body_mass_g)) +
    # ώετάϥϜͰ͸பͷ֊ڃΛϏϯ bin ͱݺͼ·͢
    geom_histogram(bins = 5) +
    ylab("Frequency") +
    xlab("Body mass (g)") +
    labs(title = "ϖϯΪϯͷମॏͷώετάϥϜ")
    ෼෍Λࢹ֮Խ͢ΔώετάϥϜ
    ౓਺෼෍දΛ΋ͱʹάϥϑΛ࡞੒
    ֊ڃ͝ͱʹபΛઃ͚ɺபͷߴ͞Ͱ౓਺Λදݱ
    42
    பͱபͷؒʹܺؒΛ࡞Βͳ͍
    ʢ๮άϥϑͱ͸ҟͳΔ఺ʣ
    

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43. ෼෍ͷܗ͍Ζ͍Ζ
    ώετάϥϜͷ֊ڃ਺͕ҟͳΔͱ෼෍ͷܗ΋มԽ͢Δ͜ͱ͕͋Δ
    43
    

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44. ෼෍ͷܗ͍Ζ͍Ζ
    σʔλͷ͹Β͖ͭʹԠͯ͡σʔλͷ෼෍΋ҟͳΔ
    44
    ӈʹ੄ʢ৲ඌʣ͕௕͍෼෍ʜϩϯάςʔϧܕ
    ୅ද஋͕খ͍͞ํ͔Β࠷ස஋ɺதԝ஋ɺฏۉ஋ͷॱʹฒͿ
    

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45. ෼෍Λࢹ֮Խ͢Δശώήਤ
    ʮശʯͱʮώήʯΛ࢖ͬͯσʔλͷ෼෍Λදݱ͢Δάϥϑ
    ࢛෼Ґ఺ɺ֎Ε஋ͷ৘ใ΋ՄࢹԽ͢Δ͜ͱ͕Ͱ͖Δ
    45
    

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46. 㲔
    ෼෍Λࢹ֮Խ͢Δശώήਤ
    ෳ਺σʔλͷ͹Β͖ͭΛൺֱ͢Δࡍʹ΋༗ޮ
    ശώήਤͰ͸σʔλͷࢄΒ͹Γ͕খ͍͞৔߹ʹ͸খ͘͞ͳΓɺٯʹࢄΒ͹Γ͕େ͖͍࣌ʹ͸େ͖͘ͳΔ
    46
    df_zoo |>
    filter(!is.na(body_length_cm)) |>
    group_by(taxon) |>
    mutate(body_length_median = median(body_length_cm)) |>
    ungroup() |>
    mutate(taxon = forcats::fct_reorder(taxon, body_length_median)) |>
    ggplot(aes(taxon, body_length_cm, color = taxon)) +
    geom_boxplot() +
    coord_flip() +
    scale_colour_tokupon() +
    guides(color = "none") +
    labs(title = "ಈ෺σʔλͷ෼ྨ܈͝ͱͷମ௕ͷശώήਤ")
    

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47. 47
    ౴͑߹Θͤ
    ਓͷΫϥεͰߦΘΕͨςετʢ఺ຬ఺ʣͷฏۉ఺͕఺Ͱͨ͠ɻ
    ͜ͷͱ͖ɺ఺਺͕఺ͩͬͨਓ͸Ϋϥεͷ্Ґਓͷதʹؚ·ΕΔͰ͠ΐ͏͔ɻ
    # Ϋϥεதͷ40ਓͷςετͷ఺਺ʢ఺਺ॱʣ
    x
    #> [1] 16 24 27 31 32 32 33 33 36 36 37 38 39 40 40 42 43 43 43 44 44 45 46 46 48
    #> [26] 50 50 52 52 53 54 65 62 66 70 75 73 82 88 89
    mean(x) # Ϋϥεͷฏۉ఺
    #> [1] 47.975
    median(x) # Ϋϥεͷ఺਺ͷதԝ஋
    #> [1] 44
    x[1:20]
    #> [1] 16 24 27 31 32 32 33 33 36 36 37 38 39 40 40 42 43 43 43 44
    x[21:40]
    #> [1] 44 45 46 46 48 50 50 52 52 53 54 65 62 66 70 75 73 82 88 89
    

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48. ม਺ͷؔ܎Λௐ΂Δ
    ࠓ೔ͷ಺༰
    σʔλ෼ੳͱ͸Կ͔
    σʔλͷछྨͱදݱํ๏
    σʔλͷಛ௃Λଊ͑Δ
    άϥϑͷ࡞੒
    ·ͱΊ
    

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49. σʔλ෼ੳʹ͓͚Δͭͷؔ܎
    ෳ਺ͷม਺͕ͱ΋ʹมԽ͢Δঢ়ଶ
    σʔλ෼ੳͰ͸ɹɹɹɹɹɹͱɹɹɹɹɹɹͷͭͷؔ܎Λѻ͏ʢࣅͯඇͳΔ΋ͷʣ
    49
    ͦ͏͔Μ
    ૬ؔؔ܎
    ҼՌؔ܎
    ҼՌؔ܎ ͋Δग़དྷࣄ΍෺ࣄ͕ݪҼͱͳͬͯɺผͷग़དྷࣄ΍෺ࣄʢ݁Ռʣ͕ى͜Δ΋ͷ
    ٖࣅ૬ؔ ؍ଌ͞Ε͍ͯͳ͍ୈࡾͷཁҼʹΑͬͯ૬ؔؔ܎͕ҼՌؔ܎ͷΑ͏ʹݟ͑Δ΋ͷ
    ૬ؔؔ܎ ͋Δग़དྷࣄ΍෺ࣄͱผͷग़དྷࣄ΍෺ࣄͷؒʹؔ܎͕͋Δ΋ͷ
    ͋Δਫಓձࣾͷར༻ΛࢭΊΔ ਫಓΛར༻͍ͯͨ͠஍ҬͷίϨϥױऀ͕ݮΔ
    Ұਓ౰ͨΓͷνϣίϨʔτͷফඅྔ͕૿͑Δ ϊʔϕϧ৆ड৆ऀ͕૿͑Δ
    Ұਓ౰ͨΓͷ(%1͕૿͑Δ
    ϖϯΪϯݸମͷཌྷͷ௕͞ ϖϯΪϯݸମͷͪ͘͹͠ͷ௕͞
    

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50. ૬ؔ
    ͭͷม਺ؒͰى͜Δ܏޲
    ͱͷؔ܎
    50
    ͱͷؔ܎
    ؔ܎͕ݟΒΕͳ͍
    ࢄ෍ਤͱͯ͠άϥϑ্ʹՄࢹԽ͢Δ͜ͱͰ܏޲Λ೺Ѳ͠΍͘͢ͳΔ
    

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51. ؔ܎ͷ਺஋Խ
    ؔ܎ͷڧ͞Λ਺஋Խ͢Δ͜ͱͰผͷม਺ͱͷൺֱ΋ՄೳʹͳΔ
    51
    ਖ਼ͷ૬ؔؔ܎ʹ͋Δ͜ͱ͸Θ͔Δ͚Ͳɺ
    ͦͷؔ܎ͷڧ͞͸Θ͔Βͳ͍Α
    

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52. ؔ܎ͷ਺஋Խڞ෼ࢄDPWBSJBODF
    52
    ͭͷม਺ YͱZ
    ʹ͍ͭͯͷڞ෼ࢄ͸࣍ͷΑ͏ʹٻΊΒΕΔ
    ෼ղͯ͠ߟ͑ͯΈΑ͏
    ม਺Y Z
    ͷ஋͔Βม਺Y Z
    ͷฏۉ஋ΛҾ͘ ภࠩ
    ภࠩͷੵ
    

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53. ؔ܎ͷ਺஋Խڞ෼ࢄDPWBSJBODF
    σʔλͷJ൪໨͔Β
    Oʢ͢΂ͯͷσʔλʣ·ͰӈͷॲཧΛߦ͍ɺͦΕΛ଍͠߹ΘͤΔ
    ม਺Yͱม਺Zͷ֤஋ʹରͯ͠ภࠩΛٻΊɺͦΕΛֻ͚߹Θͤͨ΋ͷΛ଍͢
    O σʔλ਺
    ͰׂΔ
    

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54. ؆୯ͳσʔλͰڞ෼ࢄΛܭࢉ
    54
    df df |>
    mutate(across(everything(),.fns = mean, .names = "{.col}_mean")) |>
    rowwise() |>
    mutate(flipper_length_deviation = flipper_length_mm - flipper_length_mm_mean,
    bill_length_deviation = bill_length_mm - bill_length_mm_mean) |>
    mutate(deviation_cross = flipper_length_deviation * bill_length_deviation) |>
    ungroup()
    
    
    
    
    
    
    
    

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55. ؆୯ͳσʔλͰڞ෼ࢄΛܭࢉ
    55
    # ϖϯΪϯσʔλ͔Β2݅෼ΛऔΓग़ͯ͠ڞ෼ࢄΛٻΊ·͢
    df penguins |>
    slice_head(n = 2) |>
    select(flipper_length_mm, bill_length_mm)
    df
    #> # A tibble: 2 × 2
    #> flipper_length_mm bill_length_mm
    #>
    #> 1 181 39.1
    #> 2 186 39.5
    

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56. ڞ෼ࢄͷಛ௃
    56
    ஋͕େ͖͍΄Ͳม਺ͷؔ܎͕ڧ͍͜ͱΛࣔ͢
    ୹ॴʜม਺ͷ୯Ґʹґଘͯ͠஋͕มΘΔ
    df_mm penguins |>
    select(flipper_length_mm, bill_length_mm) |>
    purrr::set_names(c("flipper_length", "bill_length"))
    cov(df_mm$flipper_length, df_mm$bill_length, use = "complete.obs")
    #> [1] 50.37577
    df_cm df_mm |>
    transmute(across(everything(), .fns = ~ .x / 10))
    cov(df_cm$flipper_length, df_cm$bill_length, use = "complete.obs")
    #> [1] 0.5037577
    ϛϦϝʔτϧͷͱ͖
    ηϯνϝʔτϧͷͱ͖
    3ͷඪ४ؔ਺Ͱ෼ࢄΛٻΊΔ ˞σʔλͷ਺ͰׂΔෆภڞ෼ࢄ
    

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57. ؔ܎ͷ਺஋Խ૬ؔ܎਺
    ڞ෼ࢄΛ֤ม਺ͷඪ४ภࠩͷੵͰׂΔ͜ͱͰࢉग़͞ΕΔ
    ڞ෼ࢄͷ୯Ґґଘͷ໰୊Λղফ͢Δࢦඪ
    57
    ͔Β·Ͱͷ஋ΛͱΔɻม਺ͷؔ܎͕ڧ͍΄Ͳઈର஋͕ʹۙͮ͘
    cor(penguins$flipper_length_mm, penguins$bill_length_mm, use = "complete.obs")
    #> [1] 0.6561813
    cor(df_cm$flipper_length, df_cm$bill_length, use = "complete.obs")
    #> [1] 0.6561813
    

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58. ΞϯείϜͷྫ
    σʔλՄࢹԽͷॏཁੑΛઆ໌͢Δྫ
    هड़౷ܭྔ΍૬ؔ܎਺͕΄΅ಉ͡஋Ͱ͋ͬͯ΋ɺத਎ͷσʔλ͕ҟͳΔ͜ͱΛࣔ͢
    58
    ΞϯείϜͷྫͱͯࣔ͠͞ΕΔσʔλ
    YͱZ
    YͱZ
    YͱZ
    YͱZ
    ͷϖΞͰ౷ܭྔɺ૬ؔ܎਺Λग़͢ͱ͍ͣΕͷϖΞͰ΋΄΅ಉ͡஋ʹͳΔ
    ࢄ෍ਤΛඳ͍ͯΈΔͱʜ
    

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59. ࠓ೔ͷ಺༰
    σʔλ෼ੳͱ͸Կ͔
    σʔλͷछྨͱදݱํ๏
    σʔλͷಛ௃Λଊ͑Δ
    ม਺ͷؔ܎Λௐ΂Δ
    άϥϑͷ࡞੒
    ·ͱΊ
    

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60. ๮άϥϑ
    60
    σʔλͷେখΛ๮ͷߴ͞Ͱදݱ͢Δάϥϑ
    ෳ਺ͷ߲໨ؒͰͷ஋ͷҧ͍Λൺֱ͢Δͷʹద͢Δ
    ஫ҙ ߲໨ͷฒͼ
    ๮ͷߴ͞͸ݪ఺͔Β։࢝
    

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61. ๮άϥϑΛվળͯ͠ΈΑ͏
    61
    ͜ͷάϥϑͷΑ͘ͳ͍఺͸Ͳ͔͜ͳ
    मਖ਼͢Δͱͨ͠ΒͲ͜Λม͑Α͏͔
    df_zoo |>
    count(taxon) |>
    mutate(prop = n / sum(n) * 100) |>
    ggplot(aes(x = "", y = prop, fill = taxon)) +
    geom_bar(stat = "identity", width = 1) +
    scale_fill_tokupon() +
    coord_polar("y")
    

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62. ๮άϥϑΛվળͯ͠ΈΑ͏
    62
    มߋ఺ ߲໨ͷฒͼ
    ԣ͔ΒॎʹೖΕସ͑
    ஋͕େ͖͍΋ͷ͔Βฒ΂Δ
    df_zoo |>
    filter(!is.na(body_length_cm)) |>
    ggplot(aes(forcats::fct_reorder(name, body_length_cm),
    body_length_cm, fill = taxon)) +
    geom_bar(stat = "identity") +
    scale_fill_tokupon() +
    coord_flip() +
    xlab(NULL) +
    ylab("ମ௕ (cm)") +
    labs(title = "ͱ͘͠·ಈ෺ԂͰࣂҭ͞ΕΔಈ෺ͷඪ४తͳମ௕")
    

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63. ԁάϥϑ
    63
    άϥϑʹඳ͍ͨԁͷதʹσʔλͷׂ߹Λද͢άϥϑ
    ԁશମͰͷߏ੒ɻσʔλશମΛ઎ΊΔ಺༁Λදݱ͢Δͷʹద͢Δ
    ஫ҙ ߲໨ͷى఺͸࣌ܭͷ࣌ͷҐஔ
    શମͰͱͳΔׂ߹Λѻ͏͜ͱ
    σʔλؒͷൺֱʹ͸ద͞ͳ͍ɻσʔλ಺Ͱͷ૬ରతͳൺֱ͸0,
    มߋ఺
    ׂ߹ͷେ͖͞ͷॱʹදࣔ
    ׂ߹ͷগͳ͍߲໨Λ·ͱΊΔ
    ʮͦͷଞʯͱͯ͠දࣔ
    

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64. ࠓ೔ͷ಺༰
    σʔλ෼ੳͱ͸Կ͔
    σʔλͷछྨͱදݱํ๏
    σʔλͷಛ௃Λଊ͑Δ
    ม਺ͷؔ܎Λௐ΂Δ
    άϥϑͷ࡞੒
    ·ͱΊ
    

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65. ࢀߟจݙɾ63-
    65
    w ೔ຊ౷ܭֶձ2020σʔλͷ෼ੳ೔ຊ౷ܭֶձެࣜೝఆ౷ܭݕఆڃରԠվగ൛౦ژਤॻ
    w ౢాਖ਼࿨ɺѨ෦ਅਓ20173ͰֶͿ౷ܭֶೖ໳౦ژԽֶಉਓ
    w ಺ా੣ҰΒ2021ڭཆͱͯ͠ͷσʔλαΠΤϯεߨஊࣾ
    w ߐ࡚و༟2020෼ੳऀͷͨΊͷσʔλղऍֶೖ໳σʔλͷຊ࣭ΛͱΒ͑Δٕज़ιγϜ
    w ࣎լେֶσʔλαΠΤϯεֶ෦௕࡚େֶ৘ใσʔλՊֶ෦ڞฤ2022σʔλαΠΤϯεͷา͖ํֶज़ਤॻग़൛ࣾ
    w ஛಺܆2014౷ܭͷׂ͸΢ιੈքʹ͸ͼ͜Δʮ਺ࣈτϦοΫʯΛݟഁΔٕज़ಙؒॻళ
    w ౦ژେֶڭཆֶ෦౷ܭֶڭࣨฤ1991جૅ౷ܭֶ ౷ܭֶೖ໳
    ౦ژେֶग़൛ձ
    w ੢಺ܒ2013౷ܭֶ͕࠷ڧͷֶ໰Ͱ͋ΔσʔλࣾձΛੜ͖ൈͨ͘Ίͷ෢ثͱڭཆμΠϠϞϯυࣾ
    w ΩʔϥϯɾώʔϦʔ ӝੜਅ໵ ߐޱ఩࢙ ࡾଜڤੜ༁
    2021σʔλ෼ੳͷͨΊͷσʔλՄࢹԽೖ໳ߨஊࣾ
    w Ѩ෦ਅਓ2021౷ܭֶೖ໳σʔλ෼ੳʹඞਢͷ஌ࣝɾߟ͑ํԾઆݕఆ͔Β౷ܭϞσϦϯά·ͰॏཁτϐοΫΛ׬શ໢ཏιγϜ
    w দຊ݈ଠ࿠2017άϥϑΛͭ͘ΔલʹಡΉຊҰॠͰ఻ΘΔදݱ͸ͲͷΑ͏ʹੜ·Εͨͷ͔ٕज़ධ࿦ࣾ
    w ΞϧϕϧτɾΧΠϩ ༅Ҫਅ੅༁
    2020άϥϑͷ΢ιΛݟഁΔٕज़ϚΠΞϛେֶϏδϡΞϧɾδϟʔφϦζϜߨ࠲μΠϠϞϯυࣾ
    w ϚΠέϧɾϑϨϯυϦʔϋϫʔυɾ΢ΣΠφʔ ൧ౢوࢠ༁
    2021σʔλࢹ֮Խͷਓྨ࢙άϥϑͷൃ໌͔Β࣌ؒͱۭؒͷՄࢹԽ·Ͱ੨౔ࣾ
    w 4UFWFO44LJFOB ௕ඌߴ߂༁
    2020σʔλαΠΤϯεઃܭϚχϡΞϧΦϥΠϦʔɾδϟύϯ
    

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