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データ分析入門 / tokupon-ds2022

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

Uryu Shinya

July 09, 2022
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  1. 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
  2. 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
  3. σʔλϑϨʔϜσʔλΛදܗࣜͰ·ͱΊͯදݱͨ͠΋ͷ ಈ෺ʹ͍ͭͯͷ෼ྨ܈ͱ໊শʢछ໊ʣɺମ௕ͱମॏͷͭͷม਺Λه࿥ ৯೑ྨ ྶ௕ྨ ྶ௕ྨ     

     Ϩοαʔύϯμ νϯύϯδʔ Ϛϯτώώ ৯೑ྨ ௗྨ ϥΠΦϯ ϑϯϘϧτϖϯΪϯ     σʔλ෼ੳͰ͸σʔλϑϨʔϜͷܗࣜͰσʔλΛѻ͏ͷ͕Ұൠత 22
  4. 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
  5. 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
  6. ୅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 <- c(1, 2, 4, 6) # ਅΜதͷ྆ྡͷ஋ͷฏۉ஋Λதԝ஋ͱ͢Δ median(x) #> [1] 3 தԝ஋ ۮ਺ͷ৔߹ தԝ஋ 28 1 3 5 7 10 1 2 4 6
  7. 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
  8. x <- c(5, 1, 3, 5, 10, 5, 3, 7)

    # ࠷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
  9. σʔλͷ͹Β͖ͭൣғ ࠷ස஋ ࠷খ஋ɾ࠷େ஋ͷൣғ x <- c(5, 1, 3, 5, 10,

    5, 3, 7) range(x) #> [1] 1 10 min(x) #> [1] 1 max(x) #> [1] 10 32
  10. 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
  11. ෼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 #> <int> #> 1 3750 #> 2 3800 #> 3 3250 #> 4 3450 #> 5 3650 35
  12. ෼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 #> <int> <dbl> #> 1 3750 170 #> 2 3800 220 #> 3 3250 -330 #> 4 3450 -130 #> 5 3650 70 ਖ਼ͷ஋ͱෛͷ஋ͷ྆ํ͕ࠞ͟Δ ߹ܭ͢ΔͱʹͳΔ ภࠩͷಛ௃ ෛͷ஋Ͱ΋৐͢Δͱਖ਼ͷ஋ʹͳΔ
  13. 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 #> <int> <dbl> <dbl> #> 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
  14. ෼෍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
  15. ෼෍Λࢹ֮Խ͢Δ౓਺෼෍ද df_zoo$taxon #> [1] "৯೑ྨ" "ௗྨ" "৯೑ྨ" "ௗྨ" "ྶ௕ྨ" "ྶ௕ྨ"

    #> [7] "ྶ௕ྨ" "৯೑ྨ" "ᴩࣃྨ" "৯೑ྨ" "ௗྨ" "ۮఙྨ" #> [13] "৯೑ྨ" "৯೑ྨ" "ௗྨ" "৯೑ྨ" "ྶ௕ྨ" "ௗྨ" #> [19] "ܵۮఙྨ" "حఙྨ" "ᴩࣃྨ" "ܵۮఙྨ" 40
  16. ෼෍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)
  17. 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
  18. 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")
  19. 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
  20. 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௕͞
  21. ؆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()         
  22. ؆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 #> <int> <dbl> #> 1 181 39.1 #> 2 186 39.5
  23. 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
  24. ๮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")
  25. ๮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௕")
  26. 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