RとQuartoではじめるデータサイエンス

#2 Rの基本的な操作方法(1)

苅谷千尋
kariyach@staff.kanazawa-u.ac.jp

金沢大学

April 15, 2026

0. 本日の目標

本日の目標

  1. データセットの基本的な構造（行・列・変数）を理解する
  2. データの型（数値・文字・文字など）の違いをなんとなく理解する
  3. データを抽出・選択する基本的な操作を行ってみる
  4. データを簡単に計算し、その結果を図にしてみる（詳しい説明、理解は次週以降）
  5. 生成AIを補助ツールとして活用する可能性と注意点をなんとなく理解する

Ⅰ. データセットの構造と型

データセットとは何か

  • データセット = データを表の形でまとめたもの
penguins |> head(3) |> gt()
species island bill_len bill_dep flipper_len body_mass sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007

Excelのシートとの相違点

  • 空白行は入れない
  • 空白セルは基本的に作らない
    • 空白セルは欠損値
  • セルの結合はしない

Excelでやりがちなこと

  • 見やすくするための空白行挿入
  • セルを結合する
  • 見出しを2段にする
    • データ分析ではすべてNG

a

データセットの構造（行と列）

  • 行（row）: 1つの観測（例：1羽のペンギン）
  • 列（column）: 1つの変数（例：体重・種類）
penguins |> head(3) |> gt()
species island bill_len bill_dep flipper_len body_mass sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007

Note |

  • 行が増える = データが増える
  • 列が増える = 情報の種類が増える

変数とは何か

  • 列 = 変数（variable）

例

  • 体重：数値の変数
    • 計算できる
  • 種類：カテゴリの変数
    • 種別できる

👉 重要な考え方
- 「何を測っているか」が変数
- 変数の意味を理解することが出発点

データの型（type）

  • データには種類（型）がある
    • 数値（numeric）：体重、身長など
    • 文字（character）：名前など
    • カテゴリ（factor）：種類、性別など
    • 日付・時刻（date / datetime）：日付や時間（特別な型）
  • 型によって「できる操作」が異なる
    • 数値：計算に向いている
    • 文字：分類に向いている
    • カテゴリ：分類に使われるデータ（順序を持つこともある）

データの型（type）を確認しよう

  • skim()関数を使用
    • base R や tidyverseには含まれていないため、追加パッケージが必要
  1. install.packageで’skimr’パッケージをダウンロード
  2. Setup Chunkにパッケージを追加
  3. 以下のコードを実行
  4. skim_typeを確認
penguins |> skim()
penguins |> skim() |> gt()
skim_type skim_variable n_missing complete_rate factor.ordered factor.n_unique factor.top_counts numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
factor species 0 1.0000000 FALSE 3 Ade: 152, Gen: 124, Chi: 68 NA NA NA NA NA NA NA NA
factor island 0 1.0000000 FALSE 3 Bis: 168, Dre: 124, Tor: 52 NA NA NA NA NA NA NA NA
factor sex 11 0.9680233 FALSE 2 mal: 168, fem: 165 NA NA NA NA NA NA NA NA
numeric bill_len 2 0.9941860 NA NA NA 43.92193 5.4595837 32.1 39.225 44.45 48.5 59.6 ▃▇▇▆▁
numeric bill_dep 2 0.9941860 NA NA NA 17.15117 1.9747932 13.1 15.600 17.30 18.7 21.5 ▅▅▇▇▂
numeric flipper_len 2 0.9941860 NA NA NA 200.91520 14.0617137 172.0 190.000 197.00 213.0 231.0 ▂▇▃▅▂
numeric body_mass 2 0.9941860 NA NA NA 4201.75439 801.9545357 2700.0 3550.000 4050.00 4750.0 6300.0 ▃▇▆▃▂
numeric year 0 1.0000000 NA NA NA 2008.02907 0.8183559 2007.0 2007.000 2008.00 2009.0 2009.0 ▇▁▇▁▇

パッケージのインストール、読み込み方法を忘れた人は、前回の教材を参照しましょう

データの型（type）を確認しよう

skim()関数の利点

  • データの特徴を一度に把握できる
    • 型（numeric / character など）
    • NA（欠損値）の有無
    • 数値の大まかな大きさ（平均・最小・最大など）

Cf. base R関数場合

str(penguins)

a

•   データの「構造」と「型」を確認できる

👉 見るポイント • 各列の名前 • 数値か文字か

👉 完全に理解しなくてOK → 「こうやって確認できるんだな」で十分

⑴ 試行錯誤の大切さ

試行錯誤を始めるときに、2つの点を心に留めてください。1つ目は、何かを試すことには常に価値があるということです。たとえその結果何が起こるか完全にはわかっていなかったとしてもです。コンソールを怖がってはいけません。コードを使ってグラフを作るすばらしい点は、いったん壊したら元に戻せないような操作が含まれていないことです。もし何かがうまくいかなかったら、何が起きているかを特定し、それを修正し、そして作図コードをもう一度実行すればよいのです。

2つ目の点は、ggplotを使った作業の主な流れはいつも同じだということです。それは、テーブル型のデータから始め、位置・色・形といったグラフに表示される審美的要素に当たる変数をマップし、そしてグラフを描画するために1つか2つのgeom_関数を選ぶ、という流れです。コードの上ではこの流れは、まずデータとマッピングに関する基礎的な情報を持ったオブジェクトを作り、そこに必要な情報を重ねたり加えたりする というプロセスで実装されます。この作図法を一度身につけてしまえば、特に審美的要素のマッピングの指定とその継承方法が重要ですが、図を作るのが簡単になります（ヒーリー，キーラン (2021), 124ページ）。

0. データを切り出す操作

データを切り出す操作

  • slice()関数
  • filter()関数
  • filter_out()関数
  • select()関数

データを絞り込む

  • filter()：指定した変数のデータのみ残す
  • filter_out()：指定した変数のデータを除外する
penguins |> 
  filter(species == "Adelie") |> gt()
species island bill_len bill_dep flipper_len body_mass sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007
Adelie Torgersen NA NA NA NA NA 2007
Adelie Torgersen 36.7 19.3 193 3450 female 2007
Adelie Torgersen 39.3 20.6 190 3650 male 2007
Adelie Torgersen 38.9 17.8 181 3625 female 2007
Adelie Torgersen 39.2 19.6 195 4675 male 2007
Adelie Torgersen 34.1 18.1 193 3475 NA 2007
Adelie Torgersen 42.0 20.2 190 4250 NA 2007
Adelie Torgersen 37.8 17.1 186 3300 NA 2007
Adelie Torgersen 37.8 17.3 180 3700 NA 2007
Adelie Torgersen 41.1 17.6 182 3200 female 2007
Adelie Torgersen 38.6 21.2 191 3800 male 2007
Adelie Torgersen 34.6 21.1 198 4400 male 2007
Adelie Torgersen 36.6 17.8 185 3700 female 2007
Adelie Torgersen 38.7 19.0 195 3450 female 2007
Adelie Torgersen 42.5 20.7 197 4500 male 2007
Adelie Torgersen 34.4 18.4 184 3325 female 2007
Adelie Torgersen 46.0 21.5 194 4200 male 2007
Adelie Biscoe 37.8 18.3 174 3400 female 2007
Adelie Biscoe 37.7 18.7 180 3600 male 2007
Adelie Biscoe 35.9 19.2 189 3800 female 2007
Adelie Biscoe 38.2 18.1 185 3950 male 2007
Adelie Biscoe 38.8 17.2 180 3800 male 2007
Adelie Biscoe 35.3 18.9 187 3800 female 2007
Adelie Biscoe 40.6 18.6 183 3550 male 2007
Adelie Biscoe 40.5 17.9 187 3200 female 2007
Adelie Biscoe 37.9 18.6 172 3150 female 2007
Adelie Biscoe 40.5 18.9 180 3950 male 2007
Adelie Dream 39.5 16.7 178 3250 female 2007
Adelie Dream 37.2 18.1 178 3900 male 2007
Adelie Dream 39.5 17.8 188 3300 female 2007
Adelie Dream 40.9 18.9 184 3900 male 2007
Adelie Dream 36.4 17.0 195 3325 female 2007
Adelie Dream 39.2 21.1 196 4150 male 2007
Adelie Dream 38.8 20.0 190 3950 male 2007
Adelie Dream 42.2 18.5 180 3550 female 2007
Adelie Dream 37.6 19.3 181 3300 female 2007
Adelie Dream 39.8 19.1 184 4650 male 2007
Adelie Dream 36.5 18.0 182 3150 female 2007
Adelie Dream 40.8 18.4 195 3900 male 2007
Adelie Dream 36.0 18.5 186 3100 female 2007
Adelie Dream 44.1 19.7 196 4400 male 2007
Adelie Dream 37.0 16.9 185 3000 female 2007
Adelie Dream 39.6 18.8 190 4600 male 2007
Adelie Dream 41.1 19.0 182 3425 male 2007
Adelie Dream 37.5 18.9 179 2975 NA 2007
Adelie Dream 36.0 17.9 190 3450 female 2007
Adelie Dream 42.3 21.2 191 4150 male 2007
Adelie Biscoe 39.6 17.7 186 3500 female 2008
Adelie Biscoe 40.1 18.9 188 4300 male 2008
Adelie Biscoe 35.0 17.9 190 3450 female 2008
Adelie Biscoe 42.0 19.5 200 4050 male 2008
Adelie Biscoe 34.5 18.1 187 2900 female 2008
Adelie Biscoe 41.4 18.6 191 3700 male 2008
Adelie Biscoe 39.0 17.5 186 3550 female 2008
Adelie Biscoe 40.6 18.8 193 3800 male 2008
Adelie Biscoe 36.5 16.6 181 2850 female 2008
Adelie Biscoe 37.6 19.1 194 3750 male 2008
Adelie Biscoe 35.7 16.9 185 3150 female 2008
Adelie Biscoe 41.3 21.1 195 4400 male 2008
Adelie Biscoe 37.6 17.0 185 3600 female 2008
Adelie Biscoe 41.1 18.2 192 4050 male 2008
Adelie Biscoe 36.4 17.1 184 2850 female 2008
Adelie Biscoe 41.6 18.0 192 3950 male 2008
Adelie Biscoe 35.5 16.2 195 3350 female 2008
Adelie Biscoe 41.1 19.1 188 4100 male 2008
Adelie Torgersen 35.9 16.6 190 3050 female 2008
Adelie Torgersen 41.8 19.4 198 4450 male 2008
Adelie Torgersen 33.5 19.0 190 3600 female 2008
Adelie Torgersen 39.7 18.4 190 3900 male 2008
Adelie Torgersen 39.6 17.2 196 3550 female 2008
Adelie Torgersen 45.8 18.9 197 4150 male 2008
Adelie Torgersen 35.5 17.5 190 3700 female 2008
Adelie Torgersen 42.8 18.5 195 4250 male 2008
Adelie Torgersen 40.9 16.8 191 3700 female 2008
Adelie Torgersen 37.2 19.4 184 3900 male 2008
Adelie Torgersen 36.2 16.1 187 3550 female 2008
Adelie Torgersen 42.1 19.1 195 4000 male 2008
Adelie Torgersen 34.6 17.2 189 3200 female 2008
Adelie Torgersen 42.9 17.6 196 4700 male 2008
Adelie Torgersen 36.7 18.8 187 3800 female 2008
Adelie Torgersen 35.1 19.4 193 4200 male 2008
Adelie Dream 37.3 17.8 191 3350 female 2008
Adelie Dream 41.3 20.3 194 3550 male 2008
Adelie Dream 36.3 19.5 190 3800 male 2008
Adelie Dream 36.9 18.6 189 3500 female 2008
Adelie Dream 38.3 19.2 189 3950 male 2008
Adelie Dream 38.9 18.8 190 3600 female 2008
Adelie Dream 35.7 18.0 202 3550 female 2008
Adelie Dream 41.1 18.1 205 4300 male 2008
Adelie Dream 34.0 17.1 185 3400 female 2008
Adelie Dream 39.6 18.1 186 4450 male 2008
Adelie Dream 36.2 17.3 187 3300 female 2008
Adelie Dream 40.8 18.9 208 4300 male 2008
Adelie Dream 38.1 18.6 190 3700 female 2008
Adelie Dream 40.3 18.5 196 4350 male 2008
Adelie Dream 33.1 16.1 178 2900 female 2008
Adelie Dream 43.2 18.5 192 4100 male 2008
Adelie Biscoe 35.0 17.9 192 3725 female 2009
Adelie Biscoe 41.0 20.0 203 4725 male 2009
Adelie Biscoe 37.7 16.0 183 3075 female 2009
Adelie Biscoe 37.8 20.0 190 4250 male 2009
Adelie Biscoe 37.9 18.6 193 2925 female 2009
Adelie Biscoe 39.7 18.9 184 3550 male 2009
Adelie Biscoe 38.6 17.2 199 3750 female 2009
Adelie Biscoe 38.2 20.0 190 3900 male 2009
Adelie Biscoe 38.1 17.0 181 3175 female 2009
Adelie Biscoe 43.2 19.0 197 4775 male 2009
Adelie Biscoe 38.1 16.5 198 3825 female 2009
Adelie Biscoe 45.6 20.3 191 4600 male 2009
Adelie Biscoe 39.7 17.7 193 3200 female 2009
Adelie Biscoe 42.2 19.5 197 4275 male 2009
Adelie Biscoe 39.6 20.7 191 3900 female 2009
Adelie Biscoe 42.7 18.3 196 4075 male 2009
Adelie Torgersen 38.6 17.0 188 2900 female 2009
Adelie Torgersen 37.3 20.5 199 3775 male 2009
Adelie Torgersen 35.7 17.0 189 3350 female 2009
Adelie Torgersen 41.1 18.6 189 3325 male 2009
Adelie Torgersen 36.2 17.2 187 3150 female 2009
Adelie Torgersen 37.7 19.8 198 3500 male 2009
Adelie Torgersen 40.2 17.0 176 3450 female 2009
Adelie Torgersen 41.4 18.5 202 3875 male 2009
Adelie Torgersen 35.2 15.9 186 3050 female 2009
Adelie Torgersen 40.6 19.0 199 4000 male 2009
Adelie Torgersen 38.8 17.6 191 3275 female 2009
Adelie Torgersen 41.5 18.3 195 4300 male 2009
Adelie Torgersen 39.0 17.1 191 3050 female 2009
Adelie Torgersen 44.1 18.0 210 4000 male 2009
Adelie Torgersen 38.5 17.9 190 3325 female 2009
Adelie Torgersen 43.1 19.2 197 3500 male 2009
Adelie Dream 36.8 18.5 193 3500 female 2009
Adelie Dream 37.5 18.5 199 4475 male 2009
Adelie Dream 38.1 17.6 187 3425 female 2009
Adelie Dream 41.1 17.5 190 3900 male 2009
Adelie Dream 35.6 17.5 191 3175 female 2009
Adelie Dream 40.2 20.1 200 3975 male 2009
Adelie Dream 37.0 16.5 185 3400 female 2009
Adelie Dream 39.7 17.9 193 4250 male 2009
Adelie Dream 40.2 17.1 193 3400 female 2009
Adelie Dream 40.6 17.2 187 3475 male 2009
Adelie Dream 32.1 15.5 188 3050 female 2009
Adelie Dream 40.7 17.0 190 3725 male 2009
Adelie Dream 37.3 16.8 192 3000 female 2009
Adelie Dream 39.0 18.7 185 3650 male 2009
Adelie Dream 39.2 18.6 190 4250 male 2009
Adelie Dream 36.6 18.4 184 3475 female 2009
Adelie Dream 36.0 17.8 195 3450 female 2009
Adelie Dream 37.8 18.1 193 3750 male 2009
Adelie Dream 36.0 17.1 187 3700 female 2009
Adelie Dream 41.5 18.5 201 4000 male 2009 
penguins |> 
  filter_out(species == "Adelie") |> gt()
species island bill_len bill_dep flipper_len body_mass sex year
Gentoo Biscoe 46.1 13.2 211 4500 female 2007
Gentoo Biscoe 50.0 16.3 230 5700 male 2007
Gentoo Biscoe 48.7 14.1 210 4450 female 2007
Gentoo Biscoe 50.0 15.2 218 5700 male 2007
Gentoo Biscoe 47.6 14.5 215 5400 male 2007
Gentoo Biscoe 46.5 13.5 210 4550 female 2007
Gentoo Biscoe 45.4 14.6 211 4800 female 2007
Gentoo Biscoe 46.7 15.3 219 5200 male 2007
Gentoo Biscoe 43.3 13.4 209 4400 female 2007
Gentoo Biscoe 46.8 15.4 215 5150 male 2007
Gentoo Biscoe 40.9 13.7 214 4650 female 2007
Gentoo Biscoe 49.0 16.1 216 5550 male 2007
Gentoo Biscoe 45.5 13.7 214 4650 female 2007
Gentoo Biscoe 48.4 14.6 213 5850 male 2007
Gentoo Biscoe 45.8 14.6 210 4200 female 2007
Gentoo Biscoe 49.3 15.7 217 5850 male 2007
Gentoo Biscoe 42.0 13.5 210 4150 female 2007
Gentoo Biscoe 49.2 15.2 221 6300 male 2007
Gentoo Biscoe 46.2 14.5 209 4800 female 2007
Gentoo Biscoe 48.7 15.1 222 5350 male 2007
Gentoo Biscoe 50.2 14.3 218 5700 male 2007
Gentoo Biscoe 45.1 14.5 215 5000 female 2007
Gentoo Biscoe 46.5 14.5 213 4400 female 2007
Gentoo Biscoe 46.3 15.8 215 5050 male 2007
Gentoo Biscoe 42.9 13.1 215 5000 female 2007
Gentoo Biscoe 46.1 15.1 215 5100 male 2007
Gentoo Biscoe 44.5 14.3 216 4100 NA 2007
Gentoo Biscoe 47.8 15.0 215 5650 male 2007
Gentoo Biscoe 48.2 14.3 210 4600 female 2007
Gentoo Biscoe 50.0 15.3 220 5550 male 2007
Gentoo Biscoe 47.3 15.3 222 5250 male 2007
Gentoo Biscoe 42.8 14.2 209 4700 female 2007
Gentoo Biscoe 45.1 14.5 207 5050 female 2007
Gentoo Biscoe 59.6 17.0 230 6050 male 2007
Gentoo Biscoe 49.1 14.8 220 5150 female 2008
Gentoo Biscoe 48.4 16.3 220 5400 male 2008
Gentoo Biscoe 42.6 13.7 213 4950 female 2008
Gentoo Biscoe 44.4 17.3 219 5250 male 2008
Gentoo Biscoe 44.0 13.6 208 4350 female 2008
Gentoo Biscoe 48.7 15.7 208 5350 male 2008
Gentoo Biscoe 42.7 13.7 208 3950 female 2008
Gentoo Biscoe 49.6 16.0 225 5700 male 2008
Gentoo Biscoe 45.3 13.7 210 4300 female 2008
Gentoo Biscoe 49.6 15.0 216 4750 male 2008
Gentoo Biscoe 50.5 15.9 222 5550 male 2008
Gentoo Biscoe 43.6 13.9 217 4900 female 2008
Gentoo Biscoe 45.5 13.9 210 4200 female 2008
Gentoo Biscoe 50.5 15.9 225 5400 male 2008
Gentoo Biscoe 44.9 13.3 213 5100 female 2008
Gentoo Biscoe 45.2 15.8 215 5300 male 2008
Gentoo Biscoe 46.6 14.2 210 4850 female 2008
Gentoo Biscoe 48.5 14.1 220 5300 male 2008
Gentoo Biscoe 45.1 14.4 210 4400 female 2008
Gentoo Biscoe 50.1 15.0 225 5000 male 2008
Gentoo Biscoe 46.5 14.4 217 4900 female 2008
Gentoo Biscoe 45.0 15.4 220 5050 male 2008
Gentoo Biscoe 43.8 13.9 208 4300 female 2008
Gentoo Biscoe 45.5 15.0 220 5000 male 2008
Gentoo Biscoe 43.2 14.5 208 4450 female 2008
Gentoo Biscoe 50.4 15.3 224 5550 male 2008
Gentoo Biscoe 45.3 13.8 208 4200 female 2008
Gentoo Biscoe 46.2 14.9 221 5300 male 2008
Gentoo Biscoe 45.7 13.9 214 4400 female 2008
Gentoo Biscoe 54.3 15.7 231 5650 male 2008
Gentoo Biscoe 45.8 14.2 219 4700 female 2008
Gentoo Biscoe 49.8 16.8 230 5700 male 2008
Gentoo Biscoe 46.2 14.4 214 4650 NA 2008
Gentoo Biscoe 49.5 16.2 229 5800 male 2008
Gentoo Biscoe 43.5 14.2 220 4700 female 2008
Gentoo Biscoe 50.7 15.0 223 5550 male 2008
Gentoo Biscoe 47.7 15.0 216 4750 female 2008
Gentoo Biscoe 46.4 15.6 221 5000 male 2008
Gentoo Biscoe 48.2 15.6 221 5100 male 2008
Gentoo Biscoe 46.5 14.8 217 5200 female 2008
Gentoo Biscoe 46.4 15.0 216 4700 female 2008
Gentoo Biscoe 48.6 16.0 230 5800 male 2008
Gentoo Biscoe 47.5 14.2 209 4600 female 2008
Gentoo Biscoe 51.1 16.3 220 6000 male 2008
Gentoo Biscoe 45.2 13.8 215 4750 female 2008
Gentoo Biscoe 45.2 16.4 223 5950 male 2008
Gentoo Biscoe 49.1 14.5 212 4625 female 2009
Gentoo Biscoe 52.5 15.6 221 5450 male 2009
Gentoo Biscoe 47.4 14.6 212 4725 female 2009
Gentoo Biscoe 50.0 15.9 224 5350 male 2009
Gentoo Biscoe 44.9 13.8 212 4750 female 2009
Gentoo Biscoe 50.8 17.3 228 5600 male 2009
Gentoo Biscoe 43.4 14.4 218 4600 female 2009
Gentoo Biscoe 51.3 14.2 218 5300 male 2009
Gentoo Biscoe 47.5 14.0 212 4875 female 2009
Gentoo Biscoe 52.1 17.0 230 5550 male 2009
Gentoo Biscoe 47.5 15.0 218 4950 female 2009
Gentoo Biscoe 52.2 17.1 228 5400 male 2009
Gentoo Biscoe 45.5 14.5 212 4750 female 2009
Gentoo Biscoe 49.5 16.1 224 5650 male 2009
Gentoo Biscoe 44.5 14.7 214 4850 female 2009
Gentoo Biscoe 50.8 15.7 226 5200 male 2009
Gentoo Biscoe 49.4 15.8 216 4925 male 2009
Gentoo Biscoe 46.9 14.6 222 4875 female 2009
Gentoo Biscoe 48.4 14.4 203 4625 female 2009
Gentoo Biscoe 51.1 16.5 225 5250 male 2009
Gentoo Biscoe 48.5 15.0 219 4850 female 2009
Gentoo Biscoe 55.9 17.0 228 5600 male 2009
Gentoo Biscoe 47.2 15.5 215 4975 female 2009
Gentoo Biscoe 49.1 15.0 228 5500 male 2009
Gentoo Biscoe 47.3 13.8 216 4725 NA 2009
Gentoo Biscoe 46.8 16.1 215 5500 male 2009
Gentoo Biscoe 41.7 14.7 210 4700 female 2009
Gentoo Biscoe 53.4 15.8 219 5500 male 2009
Gentoo Biscoe 43.3 14.0 208 4575 female 2009
Gentoo Biscoe 48.1 15.1 209 5500 male 2009
Gentoo Biscoe 50.5 15.2 216 5000 female 2009
Gentoo Biscoe 49.8 15.9 229 5950 male 2009
Gentoo Biscoe 43.5 15.2 213 4650 female 2009
Gentoo Biscoe 51.5 16.3 230 5500 male 2009
Gentoo Biscoe 46.2 14.1 217 4375 female 2009
Gentoo Biscoe 55.1 16.0 230 5850 male 2009
Gentoo Biscoe 44.5 15.7 217 4875 NA 2009
Gentoo Biscoe 48.8 16.2 222 6000 male 2009
Gentoo Biscoe 47.2 13.7 214 4925 female 2009
Gentoo Biscoe NA NA NA NA NA 2009
Gentoo Biscoe 46.8 14.3 215 4850 female 2009
Gentoo Biscoe 50.4 15.7 222 5750 male 2009
Gentoo Biscoe 45.2 14.8 212 5200 female 2009
Gentoo Biscoe 49.9 16.1 213 5400 male 2009
Chinstrap Dream 46.5 17.9 192 3500 female 2007
Chinstrap Dream 50.0 19.5 196 3900 male 2007
Chinstrap Dream 51.3 19.2 193 3650 male 2007
Chinstrap Dream 45.4 18.7 188 3525 female 2007
Chinstrap Dream 52.7 19.8 197 3725 male 2007
Chinstrap Dream 45.2 17.8 198 3950 female 2007
Chinstrap Dream 46.1 18.2 178 3250 female 2007
Chinstrap Dream 51.3 18.2 197 3750 male 2007
Chinstrap Dream 46.0 18.9 195 4150 female 2007
Chinstrap Dream 51.3 19.9 198 3700 male 2007
Chinstrap Dream 46.6 17.8 193 3800 female 2007
Chinstrap Dream 51.7 20.3 194 3775 male 2007
Chinstrap Dream 47.0 17.3 185 3700 female 2007
Chinstrap Dream 52.0 18.1 201 4050 male 2007
Chinstrap Dream 45.9 17.1 190 3575 female 2007
Chinstrap Dream 50.5 19.6 201 4050 male 2007
Chinstrap Dream 50.3 20.0 197 3300 male 2007
Chinstrap Dream 58.0 17.8 181 3700 female 2007
Chinstrap Dream 46.4 18.6 190 3450 female 2007
Chinstrap Dream 49.2 18.2 195 4400 male 2007
Chinstrap Dream 42.4 17.3 181 3600 female 2007
Chinstrap Dream 48.5 17.5 191 3400 male 2007
Chinstrap Dream 43.2 16.6 187 2900 female 2007
Chinstrap Dream 50.6 19.4 193 3800 male 2007
Chinstrap Dream 46.7 17.9 195 3300 female 2007
Chinstrap Dream 52.0 19.0 197 4150 male 2007
Chinstrap Dream 50.5 18.4 200 3400 female 2008
Chinstrap Dream 49.5 19.0 200 3800 male 2008
Chinstrap Dream 46.4 17.8 191 3700 female 2008
Chinstrap Dream 52.8 20.0 205 4550 male 2008
Chinstrap Dream 40.9 16.6 187 3200 female 2008
Chinstrap Dream 54.2 20.8 201 4300 male 2008
Chinstrap Dream 42.5 16.7 187 3350 female 2008
Chinstrap Dream 51.0 18.8 203 4100 male 2008
Chinstrap Dream 49.7 18.6 195 3600 male 2008
Chinstrap Dream 47.5 16.8 199 3900 female 2008
Chinstrap Dream 47.6 18.3 195 3850 female 2008
Chinstrap Dream 52.0 20.7 210 4800 male 2008
Chinstrap Dream 46.9 16.6 192 2700 female 2008
Chinstrap Dream 53.5 19.9 205 4500 male 2008
Chinstrap Dream 49.0 19.5 210 3950 male 2008
Chinstrap Dream 46.2 17.5 187 3650 female 2008
Chinstrap Dream 50.9 19.1 196 3550 male 2008
Chinstrap Dream 45.5 17.0 196 3500 female 2008
Chinstrap Dream 50.9 17.9 196 3675 female 2009
Chinstrap Dream 50.8 18.5 201 4450 male 2009
Chinstrap Dream 50.1 17.9 190 3400 female 2009
Chinstrap Dream 49.0 19.6 212 4300 male 2009
Chinstrap Dream 51.5 18.7 187 3250 male 2009
Chinstrap Dream 49.8 17.3 198 3675 female 2009
Chinstrap Dream 48.1 16.4 199 3325 female 2009
Chinstrap Dream 51.4 19.0 201 3950 male 2009
Chinstrap Dream 45.7 17.3 193 3600 female 2009
Chinstrap Dream 50.7 19.7 203 4050 male 2009
Chinstrap Dream 42.5 17.3 187 3350 female 2009
Chinstrap Dream 52.2 18.8 197 3450 male 2009
Chinstrap Dream 45.2 16.6 191 3250 female 2009
Chinstrap Dream 49.3 19.9 203 4050 male 2009
Chinstrap Dream 50.2 18.8 202 3800 male 2009
Chinstrap Dream 45.6 19.4 194 3525 female 2009
Chinstrap Dream 51.9 19.5 206 3950 male 2009
Chinstrap Dream 46.8 16.5 189 3650 female 2009
Chinstrap Dream 45.7 17.0 195 3650 female 2009
Chinstrap Dream 55.8 19.8 207 4000 male 2009
Chinstrap Dream 43.5 18.1 202 3400 female 2009
Chinstrap Dream 49.6 18.2 193 3775 male 2009
Chinstrap Dream 50.8 19.0 210 4100 male 2009
Chinstrap Dream 50.2 18.7 198 3775 female 2009

演習：filterでデータを絞り込む

演習問題

  • 「Biscoe島」にいるペンギンだけを取り出し、何羽いるか数えよう（行数＝個数です）

penguins |> 
  filter(          )

ヒント

  • 島の名前が入っている列は island
  • “Biscoe” と一致するものを選ぶ

演習：filterでデータを絞り込む

演習問題

  • 体重が4000g以上のペンギンを取り出し、何羽いるか数えよう（行数＝個数です）

ヒント

  • 体重の列名は body_mass_g
    • 「以上」は >= を使う

aa

penguins |>
  filter(body_mass >= 4000) |> count() |> gt()
n
177

aa

penguins |> 
  filter(island == "Biscoe")
    species island bill_len bill_dep flipper_len body_mass    sex year
1    Adelie Biscoe     37.8     18.3         174      3400 female 2007
2    Adelie Biscoe     37.7     18.7         180      3600   male 2007
3    Adelie Biscoe     35.9     19.2         189      3800 female 2007
4    Adelie Biscoe     38.2     18.1         185      3950   male 2007
5    Adelie Biscoe     38.8     17.2         180      3800   male 2007
6    Adelie Biscoe     35.3     18.9         187      3800 female 2007
7    Adelie Biscoe     40.6     18.6         183      3550   male 2007
8    Adelie Biscoe     40.5     17.9         187      3200 female 2007
9    Adelie Biscoe     37.9     18.6         172      3150 female 2007
10   Adelie Biscoe     40.5     18.9         180      3950   male 2007
11   Adelie Biscoe     39.6     17.7         186      3500 female 2008
12   Adelie Biscoe     40.1     18.9         188      4300   male 2008
13   Adelie Biscoe     35.0     17.9         190      3450 female 2008
14   Adelie Biscoe     42.0     19.5         200      4050   male 2008
15   Adelie Biscoe     34.5     18.1         187      2900 female 2008
16   Adelie Biscoe     41.4     18.6         191      3700   male 2008
17   Adelie Biscoe     39.0     17.5         186      3550 female 2008
18   Adelie Biscoe     40.6     18.8         193      3800   male 2008
19   Adelie Biscoe     36.5     16.6         181      2850 female 2008
20   Adelie Biscoe     37.6     19.1         194      3750   male 2008
21   Adelie Biscoe     35.7     16.9         185      3150 female 2008
22   Adelie Biscoe     41.3     21.1         195      4400   male 2008
23   Adelie Biscoe     37.6     17.0         185      3600 female 2008
24   Adelie Biscoe     41.1     18.2         192      4050   male 2008
25   Adelie Biscoe     36.4     17.1         184      2850 female 2008
26   Adelie Biscoe     41.6     18.0         192      3950   male 2008
27   Adelie Biscoe     35.5     16.2         195      3350 female 2008
28   Adelie Biscoe     41.1     19.1         188      4100   male 2008
29   Adelie Biscoe     35.0     17.9         192      3725 female 2009
30   Adelie Biscoe     41.0     20.0         203      4725   male 2009
31   Adelie Biscoe     37.7     16.0         183      3075 female 2009
32   Adelie Biscoe     37.8     20.0         190      4250   male 2009
33   Adelie Biscoe     37.9     18.6         193      2925 female 2009
34   Adelie Biscoe     39.7     18.9         184      3550   male 2009
35   Adelie Biscoe     38.6     17.2         199      3750 female 2009
36   Adelie Biscoe     38.2     20.0         190      3900   male 2009
37   Adelie Biscoe     38.1     17.0         181      3175 female 2009
38   Adelie Biscoe     43.2     19.0         197      4775   male 2009
39   Adelie Biscoe     38.1     16.5         198      3825 female 2009
40   Adelie Biscoe     45.6     20.3         191      4600   male 2009
41   Adelie Biscoe     39.7     17.7         193      3200 female 2009
42   Adelie Biscoe     42.2     19.5         197      4275   male 2009
43   Adelie Biscoe     39.6     20.7         191      3900 female 2009
44   Adelie Biscoe     42.7     18.3         196      4075   male 2009
45   Gentoo Biscoe     46.1     13.2         211      4500 female 2007
46   Gentoo Biscoe     50.0     16.3         230      5700   male 2007
47   Gentoo Biscoe     48.7     14.1         210      4450 female 2007
48   Gentoo Biscoe     50.0     15.2         218      5700   male 2007
49   Gentoo Biscoe     47.6     14.5         215      5400   male 2007
50   Gentoo Biscoe     46.5     13.5         210      4550 female 2007
51   Gentoo Biscoe     45.4     14.6         211      4800 female 2007
52   Gentoo Biscoe     46.7     15.3         219      5200   male 2007
53   Gentoo Biscoe     43.3     13.4         209      4400 female 2007
54   Gentoo Biscoe     46.8     15.4         215      5150   male 2007
55   Gentoo Biscoe     40.9     13.7         214      4650 female 2007
56   Gentoo Biscoe     49.0     16.1         216      5550   male 2007
57   Gentoo Biscoe     45.5     13.7         214      4650 female 2007
58   Gentoo Biscoe     48.4     14.6         213      5850   male 2007
59   Gentoo Biscoe     45.8     14.6         210      4200 female 2007
60   Gentoo Biscoe     49.3     15.7         217      5850   male 2007
61   Gentoo Biscoe     42.0     13.5         210      4150 female 2007
62   Gentoo Biscoe     49.2     15.2         221      6300   male 2007
63   Gentoo Biscoe     46.2     14.5         209      4800 female 2007
64   Gentoo Biscoe     48.7     15.1         222      5350   male 2007
65   Gentoo Biscoe     50.2     14.3         218      5700   male 2007
66   Gentoo Biscoe     45.1     14.5         215      5000 female 2007
67   Gentoo Biscoe     46.5     14.5         213      4400 female 2007
68   Gentoo Biscoe     46.3     15.8         215      5050   male 2007
69   Gentoo Biscoe     42.9     13.1         215      5000 female 2007
70   Gentoo Biscoe     46.1     15.1         215      5100   male 2007
71   Gentoo Biscoe     44.5     14.3         216      4100   <NA> 2007
72   Gentoo Biscoe     47.8     15.0         215      5650   male 2007
73   Gentoo Biscoe     48.2     14.3         210      4600 female 2007
74   Gentoo Biscoe     50.0     15.3         220      5550   male 2007
75   Gentoo Biscoe     47.3     15.3         222      5250   male 2007
76   Gentoo Biscoe     42.8     14.2         209      4700 female 2007
77   Gentoo Biscoe     45.1     14.5         207      5050 female 2007
78   Gentoo Biscoe     59.6     17.0         230      6050   male 2007
79   Gentoo Biscoe     49.1     14.8         220      5150 female 2008
80   Gentoo Biscoe     48.4     16.3         220      5400   male 2008
81   Gentoo Biscoe     42.6     13.7         213      4950 female 2008
82   Gentoo Biscoe     44.4     17.3         219      5250   male 2008
83   Gentoo Biscoe     44.0     13.6         208      4350 female 2008
84   Gentoo Biscoe     48.7     15.7         208      5350   male 2008
85   Gentoo Biscoe     42.7     13.7         208      3950 female 2008
86   Gentoo Biscoe     49.6     16.0         225      5700   male 2008
87   Gentoo Biscoe     45.3     13.7         210      4300 female 2008
88   Gentoo Biscoe     49.6     15.0         216      4750   male 2008
89   Gentoo Biscoe     50.5     15.9         222      5550   male 2008
90   Gentoo Biscoe     43.6     13.9         217      4900 female 2008
91   Gentoo Biscoe     45.5     13.9         210      4200 female 2008
92   Gentoo Biscoe     50.5     15.9         225      5400   male 2008
93   Gentoo Biscoe     44.9     13.3         213      5100 female 2008
94   Gentoo Biscoe     45.2     15.8         215      5300   male 2008
95   Gentoo Biscoe     46.6     14.2         210      4850 female 2008
96   Gentoo Biscoe     48.5     14.1         220      5300   male 2008
97   Gentoo Biscoe     45.1     14.4         210      4400 female 2008
98   Gentoo Biscoe     50.1     15.0         225      5000   male 2008
99   Gentoo Biscoe     46.5     14.4         217      4900 female 2008
100  Gentoo Biscoe     45.0     15.4         220      5050   male 2008
101  Gentoo Biscoe     43.8     13.9         208      4300 female 2008
102  Gentoo Biscoe     45.5     15.0         220      5000   male 2008
103  Gentoo Biscoe     43.2     14.5         208      4450 female 2008
104  Gentoo Biscoe     50.4     15.3         224      5550   male 2008
105  Gentoo Biscoe     45.3     13.8         208      4200 female 2008
106  Gentoo Biscoe     46.2     14.9         221      5300   male 2008
107  Gentoo Biscoe     45.7     13.9         214      4400 female 2008
108  Gentoo Biscoe     54.3     15.7         231      5650   male 2008
109  Gentoo Biscoe     45.8     14.2         219      4700 female 2008
110  Gentoo Biscoe     49.8     16.8         230      5700   male 2008
111  Gentoo Biscoe     46.2     14.4         214      4650   <NA> 2008
112  Gentoo Biscoe     49.5     16.2         229      5800   male 2008
113  Gentoo Biscoe     43.5     14.2         220      4700 female 2008
114  Gentoo Biscoe     50.7     15.0         223      5550   male 2008
115  Gentoo Biscoe     47.7     15.0         216      4750 female 2008
116  Gentoo Biscoe     46.4     15.6         221      5000   male 2008
117  Gentoo Biscoe     48.2     15.6         221      5100   male 2008
118  Gentoo Biscoe     46.5     14.8         217      5200 female 2008
119  Gentoo Biscoe     46.4     15.0         216      4700 female 2008
120  Gentoo Biscoe     48.6     16.0         230      5800   male 2008
121  Gentoo Biscoe     47.5     14.2         209      4600 female 2008
122  Gentoo Biscoe     51.1     16.3         220      6000   male 2008
123  Gentoo Biscoe     45.2     13.8         215      4750 female 2008
124  Gentoo Biscoe     45.2     16.4         223      5950   male 2008
125  Gentoo Biscoe     49.1     14.5         212      4625 female 2009
126  Gentoo Biscoe     52.5     15.6         221      5450   male 2009
127  Gentoo Biscoe     47.4     14.6         212      4725 female 2009
128  Gentoo Biscoe     50.0     15.9         224      5350   male 2009
129  Gentoo Biscoe     44.9     13.8         212      4750 female 2009
130  Gentoo Biscoe     50.8     17.3         228      5600   male 2009
131  Gentoo Biscoe     43.4     14.4         218      4600 female 2009
132  Gentoo Biscoe     51.3     14.2         218      5300   male 2009
133  Gentoo Biscoe     47.5     14.0         212      4875 female 2009
134  Gentoo Biscoe     52.1     17.0         230      5550   male 2009
135  Gentoo Biscoe     47.5     15.0         218      4950 female 2009
136  Gentoo Biscoe     52.2     17.1         228      5400   male 2009
137  Gentoo Biscoe     45.5     14.5         212      4750 female 2009
138  Gentoo Biscoe     49.5     16.1         224      5650   male 2009
139  Gentoo Biscoe     44.5     14.7         214      4850 female 2009
140  Gentoo Biscoe     50.8     15.7         226      5200   male 2009
141  Gentoo Biscoe     49.4     15.8         216      4925   male 2009
142  Gentoo Biscoe     46.9     14.6         222      4875 female 2009
143  Gentoo Biscoe     48.4     14.4         203      4625 female 2009
144  Gentoo Biscoe     51.1     16.5         225      5250   male 2009
145  Gentoo Biscoe     48.5     15.0         219      4850 female 2009
146  Gentoo Biscoe     55.9     17.0         228      5600   male 2009
147  Gentoo Biscoe     47.2     15.5         215      4975 female 2009
148  Gentoo Biscoe     49.1     15.0         228      5500   male 2009
149  Gentoo Biscoe     47.3     13.8         216      4725   <NA> 2009
150  Gentoo Biscoe     46.8     16.1         215      5500   male 2009
151  Gentoo Biscoe     41.7     14.7         210      4700 female 2009
152  Gentoo Biscoe     53.4     15.8         219      5500   male 2009
153  Gentoo Biscoe     43.3     14.0         208      4575 female 2009
154  Gentoo Biscoe     48.1     15.1         209      5500   male 2009
155  Gentoo Biscoe     50.5     15.2         216      5000 female 2009
156  Gentoo Biscoe     49.8     15.9         229      5950   male 2009
157  Gentoo Biscoe     43.5     15.2         213      4650 female 2009
158  Gentoo Biscoe     51.5     16.3         230      5500   male 2009
159  Gentoo Biscoe     46.2     14.1         217      4375 female 2009
160  Gentoo Biscoe     55.1     16.0         230      5850   male 2009
161  Gentoo Biscoe     44.5     15.7         217      4875   <NA> 2009
162  Gentoo Biscoe     48.8     16.2         222      6000   male 2009
163  Gentoo Biscoe     47.2     13.7         214      4925 female 2009
164  Gentoo Biscoe       NA       NA          NA        NA   <NA> 2009
165  Gentoo Biscoe     46.8     14.3         215      4850 female 2009
166  Gentoo Biscoe     50.4     15.7         222      5750   male 2009
167  Gentoo Biscoe     45.2     14.8         212      5200 female 2009
168  Gentoo Biscoe     49.9     16.1         213      5400   male 2009

演習：filterでデータを絞り込む

演習問題

penguins |> 
  filter_out(island == "Biscoe") |> view()

演習：filterでデータを絞り込む

演習問題

  • 次のすべてを満たすペンギンを取り出し、個数をカウントして下さい
    • Biscoe島にいる
    • Gentoo種ではない
    • 体重が4000g以上
    • メス
    • くちばしの長さが45mm以上

演習：filterでデータを絞り込む

penguins |> 
  filter(island == "Biscoe") |> 
  filter_out(species == "Gentoo") |> 
  filter(body_mass >= 3000) |> 
  filter(sex == "female") |> 
  filter(bill_len > 25) |> view()
penguins |> 
  filter(island == "Biscoe" &
         body_mass >= 3000 &
         sex == "female" &
         bill_len > 25) |> 
  filter_out(species == "Gentoo") |> view()

欠損値

  • 欠損値（NA）とは
    • データが入っていない値のこと
    • Rでは「NA」と表示される
    • 例：体重が測定されていない；性別が不明
  • 欠損値があると困ること
    • 計算できないことがある
    • 結果が正しく出ないことがある
    • ➡︎ 欠損値を除外するのが鉄則
  • 除外の仕方
    • is.na() → NAかどうかを調べる
    • ! → 否定（〜ではない）

欠損値

penguins |> 
  filter(!is.na(body_mass)) |> skim()
Data summary
Name filter(penguins, !is.na(b…
Number of rows 342
Number of columns 8
_______________________
Column type frequency:
factor 3
numeric 5
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
species 0 1.00 FALSE 3 Ade: 151, Gen: 123, Chi: 68
island 0 1.00 FALSE 3 Bis: 167, Dre: 124, Tor: 51
sex 9 0.97 FALSE 2 mal: 168, fem: 165

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
bill_len 0 1 43.92 5.46 32.1 39.23 44.45 48.5 59.6 ▃▇▇▆▁
bill_dep 0 1 17.15 1.97 13.1 15.60 17.30 18.7 21.5 ▅▅▇▇▂
flipper_len 0 1 200.92 14.06 172.0 190.00 197.00 213.0 231.0 ▂▇▃▅▂
body_mass 0 1 4201.75 801.95 2700.0 3550.00 4050.00 4750.0 6300.0 ▃▇▆▃▂
year 0 1 2008.03 0.82 2007.0 2007.00 2008.00 2009.0 2009.0 ▇▁▇▁▇ 
penguins |> 
  filter(!if_any(everything(), is.na)) |> skim()
Data summary
Name filter(penguins, !if_any(…
Number of rows 333
Number of columns 8
_______________________
Column type frequency:
factor 3
numeric 5
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
species 0 1 FALSE 3 Ade: 146, Gen: 119, Chi: 68
island 0 1 FALSE 3 Bis: 163, Dre: 123, Tor: 47
sex 0 1 FALSE 2 mal: 168, fem: 165

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
bill_len 0 1 43.99 5.47 32.1 39.5 44.5 48.6 59.6 ▃▇▇▆▁
bill_dep 0 1 17.16 1.97 13.1 15.6 17.3 18.7 21.5 ▅▆▇▇▂
flipper_len 0 1 200.97 14.02 172.0 190.0 197.0 213.0 231.0 ▂▇▃▅▃
body_mass 0 1 4207.06 805.22 2700.0 3550.0 4050.0 4775.0 6300.0 ▃▇▅▃▂
year 0 1 2008.04 0.81 2007.0 2007.0 2008.0 2009.0 2009.0 ▇▁▇▁▇

データを変える・まとめる

  • mutate()関数
  • arrange()関数
  • summarise()関数
  • group_by()関数

aaa

penguins |> 
  filter(!if_any(everything(), is.na)) |> 
  summarise(avg_body_mass = mean(body_mass)) |> view()

aaa

penguins |> 
  filter(!if_any(everything(), is.na)) |> 
  group_by(species) |>
  summarise(avg = mean(body_mass)) |> view()

aaaaaaa

penguins |> 
  filter(!if_any(everything(), is.na)) |> 
  group_by(species, sex) |>
  summarise(avg = mean(body_mass)) |> tt()
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by species and sex.
ℹ Output is grouped by species.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(species, sex))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.
species sex avg
Adelie female 3368.836
Adelie male 4043.493
Chinstrap female 3527.206
Chinstrap male 3938.971
Gentoo female 4679.741
Gentoo male 5484.836

棒グラフを作ってみる

  • 先に作成したデータを使って、棒グラフを作ってみよう
    • 詳しい解説は次週、行います。まずはコードを動かして実感して下さい
penguins |> 
  filter(!if_any(everything(), is.na)) |> 
  group_by(species, sex) |>
  summarise(avg = mean(body_mass)) |>
  ggplot(aes(x = species)) +
  geom_col(aes(y = avg)) +
  facet_wrap(~ sex)
penguins |> 
  filter(!if_any(everything(), is.na)) |> 
  group_by(species, sex) |>
  summarise(avg = mean(body_mass)) |>
  ggplot(aes(x = species)) +
  geom_col(aes(y = avg)) +
  facet_wrap(~ sex)
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by species and sex.
ℹ Output is grouped by species.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(species, sex))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.

aa

library(crosstable)

次のパッケージを付け加えます: 'crosstable'
以下のオブジェクトは 'package:purrr' からマスクされています:

    compact
penguins |>
  select(species, island) |> 
  filter(!if_any(everything(), is.na)) |>
  crosstable(by = species,
             cols = island,
             total = "row",
             funs = list(prop = ~prop.table(.))) |> 
  gt()
.id label variable Adelie Chinstrap Gentoo Total
island island Biscoe 44 (26.19%) 0 (0%) 124 (73.81%) 168 (48.84%)
island island Dream 56 (45.16%) 68 (54.84%) 0 (0%) 124 (36.05%)
island island Torgersen 52 (100.00%) 0 (0%) 0 (0%) 52 (15.12%)

列の計算（縦） - sum()：全部足す - mean()：平均を出す

行の計算（横） - rowSums()：横に足す

df <- data.frame(
  math = c(70, 80),
  english = c(60, 90)
)

rowSums(df)
[1] 130 170
  1. Rでのデータセットの基本的な考え方

Rでは、データは基本的に データフレーム（data.frame / tibble） で表されます。 • 列（variable） が「変数」 • 例：age、sex、score • 行（observation） が「1つの観測」 • 例：1人の生徒、1羽のペンギン

つまり R は 列（変数）単位で分析することを前提 に設計されています。

•   列全体を足す → 「変数の合計」
•   「平均」「最大値」「分布」など統計的指標も列単位

👉 これが Rで最も自然で簡単な集計方法 です

  1. 行方向の合計はあまりない理由

行方向の合計が少ないのは理由があります： 1. 1行は1つの観測 • 体重＋くちばしの長さを足す意味は通常ない 2. 分析の中心は変数の比較 • Rでは「列ごとの統計量」「列間の関係」を見ることが多い 3. 行方向の処理は特殊 • 試験の合計点のように意味がある場合だけ • 基本は rowSums() を使う

library(tinytable) https://zenn.dev/nicetak/articles/r-tips-tinytable-2024

penguins |>
  distinct(island)
     island
1 Torgersen
2    Biscoe
3     Dream

1

library(janitor)

次のパッケージを付け加えます: 'janitor'
以下のオブジェクトは 'package:stats' からマスクされています:

    chisq.test, fisher.test
penguins |> tabyl(species)
   species   n   percent
    Adelie 152 0.4418605
 Chinstrap  68 0.1976744
    Gentoo 124 0.3604651

2

library(tinytable)

penguins |> tabyl(species, sex) |> tt()
species female male NA_
Adelie 73 73 6
Chinstrap 34 34 0
Gentoo 58 61 5

4

•   「一番大きいペンギンは？」がすぐできる

penguins |> arrange(desc(body_mass_g)) |> slice(1)

penguins |> summarise(avg_weight = mean(body_mass_g, na.rm = TRUE))

Ⅷ. 次回の授業と宿題

次回の授業と宿題

次回：4月22日（水）

  • Rの基本的な操作方法(1)

宿題

  • 授業の感想：
    • 回答先：Google Forms
    • 締め切り：4月17日（金）23時59分
  • 初回授業アンケート：
    • 回答先：Google Forms
    • 締め切り：4月17日（金）23時59分

演習の宿題はありません

引用文献

引用文献

ヒーリー，キーラン, 2021. データ分析のためのデータ可視化入門, 瓜生真也・江口哲史・三村喬生 訳. 講談社.