次を参考にしてRのdata.tableを学びます。
base vs tidyverse vs data.table
Tableのシンタクスは次です。
dt[行, 列, by]
試験結果のサンプル・データ（CSV）をデータ・フレームで読み込み、dta.tableに変換します。
# import a library
library(data.table)
# load the data
df <- read.csv("https://pastebin.com/raw/nWkAe1qR")
# convert the data frme frame to data.table
dt <- as.data.table(df)
dt## id english japanese nationality department year gender
## 1: 1 17.8 75.6 japanese literature 1 male
## 2: 2 64.4 53.3 nepal literature 1 male
## 3: 3 86.7 31.1 nepal literature 1 male
## 4: 4 60.0 62.2 indonesia literature 1 male
## 5: 5 42.2 80.0 japanese literature 1 male
## ---
## 196: 196 66.7 55.6 nepal economics 1 male
## 197: 197 44.4 80.0 china economics 1 male
## 198: 198 57.8 48.9 vietnam economics 1 female
## 199: 199 86.7 26.7 vietnam economics 1 male
## 200: 200 24.4 68.9 japanese economics 1 maleenglishが60より高い行を取り出すスクリプトは次です。
dt[english >60,]## id english japanese nationality department year gender
## 1: 2 64.4 53.3 nepal literature 1 male
## 2: 3 86.7 31.1 nepal literature 1 male
## 3: 14 62.2 86.7 japanese literature 1 male
## 4: 15 62.2 71.1 japanese literature 1 male
## 5: 16 62.2 86.7 japanese literature 1 male
## 6: 18 84.4 57.8 nepal literature 1 male
## 7: 19 73.3 84.4 china literature 1 female
## 8: 21 64.4 31.1 nepal literature 1 male
## 9: 22 71.1 75.6 japanese literature 1 female
## 10: 25 84.4 33.3 nepal literature 1 male
## 11: 26 77.8 77.8 nepal literature 1 male
## 12: 31 66.7 28.9 nepal literature 1 male
## 13: 35 62.2 82.2 japanese literature 1 male
## 14: 37 71.1 60.0 vietnam literature 1 male
## 15: 45 80.0 57.8 nepal literature 1 male
## 16: 46 93.3 55.6 nepal literature 1 male
## 17: 54 62.2 82.2 japanese literature 1 male
## 18: 55 64.4 82.2 japanese literature 1 female
## 19: 60 82.2 77.8 nepal literature 1 male
## 20: 67 80.0 37.8 nepal economics 1 male
## 21: 89 64.4 86.7 japanese economics 1 female
## 22: 94 88.9 33.3 nepal economics 1 male
## 23: 103 62.2 55.6 vietnam economics 1 female
## 24: 119 86.7 64.4 nepal economics 1 male
## 25: 132 71.1 84.4 japanese economics 1 female
## 26: 133 64.4 53.3 nepal economics 1 male
## 27: 134 80.0 53.3 nepal economics 1 male
## 28: 135 75.6 68.9 nepal economics 1 male
## 29: 139 73.3 77.8 vietnam economics 1 female
## 30: 141 86.7 80.0 china economics 1 male
## 31: 144 62.2 55.6 vietnam economics 1 female
## 32: 147 62.2 77.8 japanese economics 1 male
## 33: 195 84.4 28.9 nepal economics 1 male
## 34: 196 66.7 55.6 nepal economics 1 male
## 35: 199 86.7 26.7 vietnam economics 1 male
## id english japanese nationality department year gendernationalityがnepalの学生を取り出すスクリプトは次です。
特定の値は==です。=でないことに注意してください。=は「名付け」を意味します。
列名に" "は不要です。
dt[nationality == "nepal"]## id english japanese nationality department year gender
## 1: 2 64.4 53.3 nepal literature 1 male
## 2: 3 86.7 31.1 nepal literature 1 male
## 3: 18 84.4 57.8 nepal literature 1 male
## 4: 21 64.4 31.1 nepal literature 1 male
## 5: 25 84.4 33.3 nepal literature 1 male
## 6: 26 77.8 77.8 nepal literature 1 male
## 7: 31 66.7 28.9 nepal literature 1 male
## 8: 45 80.0 57.8 nepal literature 1 male
## 9: 46 93.3 55.6 nepal literature 1 male
## 10: 60 82.2 77.8 nepal literature 1 male
## 11: 67 80.0 37.8 nepal economics 1 male
## 12: 68 35.6 35.6 nepal economics 1 male
## 13: 94 88.9 33.3 nepal economics 1 male
## 14: 98 48.9 40.0 nepal economics 1 male
## 15: 119 86.7 64.4 nepal economics 1 male
## 16: 120 42.2 24.4 nepal economics 1 male
## 17: 133 64.4 53.3 nepal economics 1 male
## 18: 134 80.0 53.3 nepal economics 1 male
## 19: 135 75.6 68.9 nepal economics 1 male
## 20: 161 57.8 60.0 nepal economics 1 male
## 21: 162 48.9 60.0 nepal economics 1 male
## 22: 172 44.4 20.0 nepal economics 1 male
## 23: 173 60.0 22.2 nepal economics 1 male
## 24: 185 53.3 28.9 nepal economics 1 male
## 25: 195 84.4 28.9 nepal economics 1 male
## 26: 196 66.7 55.6 nepal economics 1 male
## id english japanese nationality department year genderenglishとjapaneseとnationality列を取り出すスクリプトは次です。
dt[,.(english, japanese, nationality)]## english japanese nationality
## 1: 17.8 75.6 japanese
## 2: 64.4 53.3 nepal
## 3: 86.7 31.1 nepal
## 4: 60.0 62.2 indonesia
## 5: 42.2 80.0 japanese
## ---
## 196: 66.7 55.6 nepal
## 197: 44.4 80.0 china
## 198: 57.8 48.9 vietnam
## 199: 86.7 26.7 vietnam
## 200: 24.4 68.9 japaneseenglishの平均を算出します。
dt[, .(mean = mean(english))]## mean
## 1: 42.931japaneseの平均を算出します。
dt[, .(mean = mean(japanese))]## mean
## 1: 63.454To be continued.