データの編集
既存データ
次の既存データを使用します。
クエスチョンマークを前につけて実行すると説明がHelp画面に表示されます。
実行のショートカットは[Ctrl]+[Enter]です。
?iris #Edgar Anderson’s Iris Data
?caith #Colours of Eyes and Hair of People in Caithness
?HairEyeColor #Hair and Eye Color of Statistics Students
?mtcars #Motor Trend Car Road Tests
?USArrests #Violent Crime Rates by US State
?decathlon2 #Athletes’ performance in decathlon ?Titanic #Survival of passengers on the Titanic
?diamonds #Prices of over 50,000 round cut diamonds
データの表示
Dt(iris) #Class: data.frame / Rows: 150 / Columns: 5## Class: data.frame / Rows: 150 / Columns: 5
##
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosaDt(caith) #Class: data.frame / Rows: 4 / Columns: 5## Class: data.frame / Rows: 4 / Columns: 5
##
## fair red medium dark black
## blue 326 38 241 110 3
## light 688 116 584 188 4
## medium 343 84 909 412 26
## dark 98 48 403 681 85Dt(HairEyeColor) #Class: table / Rows: 4 / Columns: 4## Class: table / Rows: 4 / Columns: 4
##
## Hair Eye Sex Freq
## 1 Black Brown Male 32
## 2 Brown Brown Male 53
## 3 Red Brown Male 10
## 4 Blond Brown Male 3
## 5 Black Blue Male 11
## 6 Brown Blue Male 50
## 7 Red Blue Male 10
## 8 Blond Blue Male 30
## 9 Black Hazel Male 10
## 10 Brown Hazel Male 25Dt(mtcars) #Class: data.frame / Rows: 32 / Columns: 11## Class: data.frame / Rows: 32 / Columns: 11
##
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4Dt(USArrests) #Class: data.frame / Rows: 50 / Columns: 4## Class: data.frame / Rows: 50 / Columns: 4
##
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
## Connecticut 3.3 110 77 11.1
## Delaware 5.9 238 72 15.8
## Florida 15.4 335 80 31.9
## Georgia 17.4 211 60 25.8Dt(decathlon2) #Class: data.frame / Rows: 27 / Columns: 13## Class: data.frame / Rows: 27 / Columns: 13
##
## X100m Long.jump Shot.put High.jump X400m X110m.hurdle Discus
## SEBRLE 11.04 7.58 14.83 2.07 49.81 14.69 43.75
## CLAY 10.76 7.40 14.26 1.86 49.37 14.05 50.72
## BERNARD 11.02 7.23 14.25 1.92 48.93 14.99 40.87
## YURKOV 11.34 7.09 15.19 2.10 50.42 15.31 46.26
## ZSIVOCZKY 11.13 7.30 13.48 2.01 48.62 14.17 45.67
## McMULLEN 10.83 7.31 13.76 2.13 49.91 14.38 44.41
## MARTINEAU 11.64 6.81 14.57 1.95 50.14 14.93 47.60
## HERNU 11.37 7.56 14.41 1.86 51.10 15.06 44.99
## BARRAS 11.33 6.97 14.09 1.95 49.48 14.48 42.10
## NOOL 11.33 7.27 12.68 1.98 49.20 15.29 37.92
## Pole.vault Javeline X1500m Rank Points Competition
## SEBRLE 5.02 63.19 291.7 1 8217 Decastar
## CLAY 4.92 60.15 301.5 2 8122 Decastar
## BERNARD 5.32 62.77 280.1 4 8067 Decastar
## YURKOV 4.72 63.44 276.4 5 8036 Decastar
## ZSIVOCZKY 4.42 55.37 268.0 7 8004 Decastar
## McMULLEN 4.42 56.37 285.1 8 7995 Decastar
## MARTINEAU 4.92 52.33 262.1 9 7802 Decastar
## HERNU 4.82 57.19 285.1 10 7733 Decastar
## BARRAS 4.72 55.40 282.0 11 7708 Decastar
## NOOL 4.62 57.44 266.6 12 7651 DecastarDt(Titanic) #Class: table / Rows: 4 / Columns: 2## Class: table / Rows: 4 / Columns: 2
##
## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
## 7 3rd Female Child No 17
## 8 Crew Female Child No 0
## 9 1st Male Adult No 118
## 10 2nd Male Adult No 154Dt(diamonds) #Class: tbl_df, tbl, data.frame / Rows: 53940 / Columns: 10## Class: tbl_df, tbl, data.frame / Rows: 53940 / Columns: 10
##
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39データの選択
Dt(E=D,r=10,c=0,n=F,rn=F,cn=F,d=’’)
D:データ, r:行数,c:列数,n:行/列名表示,rn:行名表示,cn:列名表示,d:データ名など表示
D=Ip('x45.txt') #簡単な例(数値行列:4x5)## File: x45.txt / Class: data.frame / Rows: 4 / Columns: 5
##
## A B C D E
## w1 10 19 14 7 12
## w2 11 7 10 0 1
## w3 0 0 1 12 1
## w4 0 1 2 3 3Dt(r=3) #データ表示(3行row)## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C D E
## w1 10 19 14 7 12
## w2 11 7 10 0 1
## w3 0 0 1 12 1Dt(c=3) #データ表示(3列col)## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C
## w1 10 19 14
## w2 11 7 10
## w3 0 0 1
## w4 0 1 2Dt(r=3,c=3) #データ表示(3行,3列)## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C
## w1 10 19 14
## w2 11 7 10
## w3 0 0 1Dt(r=3,c=3,n=T) #同+(行/列名=T)## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C
## w1 10 19 14
## w2 11 7 10
## w3 0 0 1
##
## Row names: 1.w1 2.w2 3.w3
##
## Column names: 1.A 2.B 3.CDt(r=3,c=3,rn=T) #同+(行/列名=T)## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C
## w1 10 19 14
## w2 11 7 10
## w3 0 0 1
##
## Row names: 1.w1 2.w2 3.w3Dt(r=3,c=3,cn=T) #同+(行/列名=T)## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C
## w1 10 19 14
## w2 11 7 10
## w3 0 0 1
##
## Column names: 1.A 2.B 3.Cスクリプト画面に表示
D=Ip('x45'); Vista() #簡単なデータ例(数値行列)## File: x45.txt / Class: data.frame / Rows: 4 / Columns: 5
##
## A B C D E
## w1 10 19 14 7 12
## w2 11 7 10 0 1
## w3 0 0 1 12 1
## w4 0 1 2 3 3
Vista()
Vista(diamonds) #大きな行列
Vista(diamonds)
データの編集
D=Ip('x45'); D=edit(D); Dt() #ファイルを読み込み，編集して，表示## File: x45.txt / Class: data.frame / Rows: 4 / Columns: 5
##
## A B C D E
## w1 10 19 14 7 12
## w2 11 7 10 0 1
## w3 0 0 1 12 1
## w4 0 1 2 3 3## Class: data.frame / Rows: 4 / Columns: 5
##
## A B C D E
## w1 10 19 14 7 12
## w2 11 7 10 0 1
## w3 0 0 1 12 1
## w4 0 1 2 3 3
edit()
D=Edit() #Dを編集 (デフォルト:D)X=Edit(diamonds) #大きな行列を編集
Edit(diamonds)
