Sta([D],r=[F]:列/T:行,s:統計値 ex.’sum,mean,sd’,d:小数桁)
s: sum,mean,median,mid,min,max,sd,sdp,var,varp,q1,q2,q3,rng,gini,entropy, gm,gmz,gsd,gsdz,kurt,kurt.r,ldi,pareto,skew,skew.e,skew.r
D=Ip('x45') #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 3S=Sta(s='count,sum'); Dt(S,j=T) #個数, 和：列## Class: matrix, array / Rows: 2 / Columns: 5
##
## A B C D E
## count 4 4 4 4 4
## sum 21 27 27 22 17S=Sta(s='count,sum',r=T); Dt(S) #個数, 和：行(r=T)## Class: matrix, array / Rows: 4 / Columns: 2
##
## count sum
## w1 5 62
## w2 5 29
## w3 5 14
## w4 5 9D=iris; Dt() #データ:iris## 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 setosaD=Dat(c=1:4); Dt() #数値列選択## Class: data.frame / Rows: 150 / Columns: 4
##
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1S=Sta(s='min,q1,median,mean,q3,max',d=3); Dt(S)## Class: matrix, array / Rows: 6 / Columns: 4
##
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## min 4.300 2.000 1.000 0.100
## q1 5.100 2.800 1.600 0.300
## median 5.800 3.000 4.350 1.300
## mean 5.843 3.057 3.758 1.199
## q3 6.400 3.300 5.100 1.800
## max 7.900 4.400 6.900 2.500summary(D) #参考: 比較## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500D=diamonds; Dt() #データ例:ダイヤモンド(df)## 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 2.39D=Dat(c='ca,p,x,y,z'); Dt() #数値列選択## Class: tbl_df, tbl, data.frame / Rows: 53940 / Columns: 5
##
## carat price x y z
## 1 0.23 326 3.95 3.98 2.43
## 2 0.21 326 3.89 3.84 2.31
## 3 0.23 327 4.05 4.07 2.31
## 4 0.29 334 4.20 4.23 2.63
## 5 0.31 335 4.34 4.35 2.75
## 6 0.24 336 3.94 3.96 2.48
## 7 0.24 336 3.95 3.98 2.47
## 8 0.26 337 4.07 4.11 2.53
## 9 0.22 337 3.87 3.78 2.49
## 10 0.23 338 4.00 4.05 2.39S=Sta(s='min,q1,median,mean,q3,max',d=3); Dt(S)## Class: matrix, array / Rows: 6 / Columns: 5
##
## carat price x y z
## min 0.200 326.00 0.000 0.000 0.000
## q1 0.400 950.00 4.710 4.720 2.910
## median 0.700 2401.00 5.700 5.710 3.530
## mean 0.798 3932.80 5.731 5.735 3.539
## q3 1.040 5324.25 6.540 6.540 4.040
## max 5.010 18823.00 10.740 58.900 31.800summary(D) #参考: 比較## carat price x y
## Min. :0.2000 Min. : 326 Min. : 0.000 Min. : 0.000
## 1st Qu.:0.4000 1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720
## Median :0.7000 Median : 2401 Median : 5.700 Median : 5.710
## Mean :0.7979 Mean : 3933 Mean : 5.731 Mean : 5.735
## 3rd Qu.:1.0400 3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540
## Max. :5.0100 Max. :18823 Max. :10.740 Max. :58.900
## z
## Min. : 0.000
## 1st Qu.: 2.910
## Median : 3.530
## Mean : 3.539
## 3rd Qu.: 4.040
## Max. :31.800D=USArrests; Dt() #データ:USArrests## 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 25.8D=Dat(c='1,2,4') #数値列選択S=Sta(); Dt(S) #デフォルト：sum,mean,sd ## Class: matrix, array / Rows: 3 / Columns: 3
##
## Murder Assault Rape
## sum 389.4 8538.0 1061.6
## mean 7.8 170.8 21.2
## sd 4.4 83.3 9.4summary(D) #参考: 比較 summary()## Murder Assault Rape
## Min. : 0.800 Min. : 45.0 Min. : 7.30
## 1st Qu.: 4.075 1st Qu.:109.0 1st Qu.:15.07
## Median : 7.250 Median :159.0 Median :20.10
## Mean : 7.788 Mean :170.8 Mean :21.23
## 3rd Qu.:11.250 3rd Qu.:249.0 3rd Qu.:26.18
## Max. :17.400 Max. :337.0 Max. :46.00Cross(df=D,x=0,v=0,n=0,d=0,m=F,t=F)
([df], x:個体列(単複), v:変数列(単複),n:数値列(単),t:入力列名付加)
D=Ip('mozi') #簡単なデータ例 文字行列 (df)## File: mozi.txt / Class: data.frame / Rows: 10 / Columns: 3
##
## A B C
## 1 a1 b1 c2
## 2 a1 b2 c3
## 3 a1 b3 c1
## 4 a1 b1 c3
## 5 a1 b1 c2
## 6 a2 b2 c1
## 7 a2 b2 c3
## 8 a2 b1 c1
## 9 a2 b3 c1
## 10 a2 b2 c2Cross(x=0,v=1:3) #ダミー行列(x=0), 列(v)=1:3## a1 a2 b1 b2 b3 c1 c2 c3
## 1 1 0 1 0 0 0 1 0
## 2 1 0 0 1 0 0 0 1
## 3 1 0 0 0 1 1 0 0
## 4 1 0 1 0 0 0 0 1
## 5 1 0 1 0 0 0 1 0
## 6 0 1 0 1 0 1 0 0
## 7 0 1 0 1 0 0 0 1
## 8 0 1 1 0 0 1 0 0
## 9 0 1 0 0 1 1 0 0
## 10 0 1 0 1 0 0 1 0D=Ip('santander') #Santander 録音調査:多次元文字行列## File: santander.txt / Class: data.frame / Rows: 3962 / Columns: 4
##
## Forma Sexo Edad Nivel
## 1 eh H E1 N1
## 2 eh H E1 N1
## 3 eh H E1 N1
## 4 eh H E1 N1
## 5 eh H E1 N1
## 6 eh H E1 N1
## 7 eh H E1 N1
## 8 eh H E1 N1
## 9 eh H E1 N1
## 10 eh H E1 N1Cross(D,x=1) #個体x=1列## Sum
## ah 75
## ay 48
## bueno 949
## claramente 13
## claro 336
## directamente 20
## efectivamente 30
## eh 1145
## exactamente 46
## generalmente 25
## hm 101
## hola 25
## jo 102
## joder 29
## jolín 13
## madre mía 15
## mm 412
## muchísimo 95
## normalmente 75
## oye 40
## perfectamente 17
## pff 62
## prácticamente 22
## realmente 41
## simplemente 31
## sinceramente 13
## totalmente 43
## uhum 57
## uy 19
## vale 46
## vamos 17Cross(D,x=1,v=2) #個体x=1列, 変数v=2列## H M
## ah 24 51
## ay 8 40
## bueno 488 461
## claramente 13 0
## claro 118 218
## directamente 12 8
## efectivamente 11 19
## eh 828 317
## exactamente 39 7
## generalmente 18 7
## hm 30 71
## hola 17 8
## jo 4 98
## joder 25 4
## jolín 0 13
## madre mía 4 11
## mm 223 189
## muchísimo 19 76
## normalmente 62 13
## oye 30 10
## perfectamente 6 11
## pff 24 38
## prácticamente 14 8
## realmente 29 12
## simplemente 24 7
## sinceramente 8 5
## totalmente 17 26
## uhum 30 27
## uy 3 16
## vale 14 32
## vamos 4 13Cross(D,x=1,v=3) #個体x=1列, 変数v=3列## E1 E2 E3
## ah 16 13 46
## ay 4 1 43
## bueno 280 347 322
## claramente 0 10 3
## claro 54 98 184
## directamente 10 9 1
## efectivamente 0 7 23
## eh 329 456 360
## exactamente 0 26 20
## generalmente 4 14 7
## hm 29 31 41
## hola 4 19 2
## jo 3 98 1
## joder 4 12 13
## jolín 1 10 2
## madre mía 0 8 7
## mm 133 163 116
## muchísimo 20 52 23
## normalmente 18 14 43
## oye 4 22 14
## perfectamente 2 3 12
## pff 15 27 20
## prácticamente 9 7 6
## realmente 28 4 9
## simplemente 19 6 6
## sinceramente 3 6 4
## totalmente 12 28 3
## uhum 9 40 8
## uy 2 7 10
## vale 9 27 10
## vamos 11 3 3Cross(D,x=1,v=4) #個体x=1列, 変数v=4列## N1 N2 N3
## ah 21 40 14
## ay 7 32 9
## bueno 362 253 334
## claramente 11 1 1
## claro 128 90 118
## directamente 13 6 1
## efectivamente 1 6 23
## eh 505 290 350
## exactamente 17 7 22
## generalmente 0 14 11
## hm 48 17 36
## hola 8 14 3
## jo 5 96 1
## joder 12 16 1
## jolín 2 10 1
## madre mía 1 9 5
## mm 120 132 160
## muchísimo 41 32 22
## normalmente 20 7 48
## oye 17 15 8
## perfectamente 3 6 8
## pff 12 44 6
## prácticamente 2 15 5
## realmente 18 4 19
## simplemente 7 10 14
## sinceramente 6 7 0
## totalmente 1 26 16
## uhum 8 4 45
## uy 8 10 1
## vale 8 31 7
## vamos 2 11 4Cross(D,x=1,v=2:4) #個体x=1列, 変数v=2:4列## H M E1 E2 E3 N1 N2 N3
## ah 24 51 16 13 46 21 40 14
## ay 8 40 4 1 43 7 32 9
## bueno 488 461 280 347 322 362 253 334
## claramente 13 0 0 10 3 11 1 1
## claro 118 218 54 98 184 128 90 118
## directamente 12 8 10 9 1 13 6 1
## efectivamente 11 19 0 7 23 1 6 23
## eh 828 317 329 456 360 505 290 350
## exactamente 39 7 0 26 20 17 7 22
## generalmente 18 7 4 14 7 0 14 11
## hm 30 71 29 31 41 48 17 36
## hola 17 8 4 19 2 8 14 3
## jo 4 98 3 98 1 5 96 1
## joder 25 4 4 12 13 12 16 1
## jolín 0 13 1 10 2 2 10 1
## madre mía 4 11 0 8 7 1 9 5
## mm 223 189 133 163 116 120 132 160
## muchísimo 19 76 20 52 23 41 32 22
## normalmente 62 13 18 14 43 20 7 48
## oye 30 10 4 22 14 17 15 8
## perfectamente 6 11 2 3 12 3 6 8
## pff 24 38 15 27 20 12 44 6
## prácticamente 14 8 9 7 6 2 15 5
## realmente 29 12 28 4 9 18 4 19
## simplemente 24 7 19 6 6 7 10 14
## sinceramente 8 5 3 6 4 6 7 0
## totalmente 17 26 12 28 3 1 26 16
## uhum 30 27 9 40 8 8 4 45
## uy 3 16 2 7 10 8 10 1
## vale 14 32 9 27 10 8 31 7
## vamos 4 13 11 3 3 2 11 4Cross(D,x='F',v='S,E,N') #同: 変数4列を頭文字で指定## H M E1 E2 E3 N1 N2 N3
## ah 24 51 16 13 46 21 40 14
## ay 8 40 4 1 43 7 32 9
## bueno 488 461 280 347 322 362 253 334
## claramente 13 0 0 10 3 11 1 1
## claro 118 218 54 98 184 128 90 118
## directamente 12 8 10 9 1 13 6 1
## efectivamente 11 19 0 7 23 1 6 23
## eh 828 317 329 456 360 505 290 350
## exactamente 39 7 0 26 20 17 7 22
## generalmente 18 7 4 14 7 0 14 11
## hm 30 71 29 31 41 48 17 36
## hola 17 8 4 19 2 8 14 3
## jo 4 98 3 98 1 5 96 1
## joder 25 4 4 12 13 12 16 1
## jolín 0 13 1 10 2 2 10 1
## madre mía 4 11 0 8 7 1 9 5
## mm 223 189 133 163 116 120 132 160
## muchísimo 19 76 20 52 23 41 32 22
## normalmente 62 13 18 14 43 20 7 48
## oye 30 10 4 22 14 17 15 8
## perfectamente 6 11 2 3 12 3 6 8
## pff 24 38 15 27 20 12 44 6
## prácticamente 14 8 9 7 6 2 15 5
## realmente 29 12 28 4 9 18 4 19
## simplemente 24 7 19 6 6 7 10 14
## sinceramente 8 5 3 6 4 6 7 0
## totalmente 17 26 12 28 3 1 26 16
## uhum 30 27 9 40 8 8 4 45
## uy 3 16 2 7 10 8 10 1
## vale 14 32 9 27 10 8 31 7
## vamos 4 13 11 3 3 2 11 4Cross(D,x=1:2,v=3:4) #個体x=1:2列, 変数v=3:4列## E1 E2 E3 N1 N2 N3
## ah_H 6 6 12 3 12 9
## ah_M 10 7 34 18 28 5
## ay_H 1 0 7 0 4 4
## ay_M 3 1 36 7 28 5
## bueno_H 177 204 107 191 148 149
## bueno_M 103 143 215 171 105 185
## claramente_H 0 10 3 11 1 1
## claro_H 6 64 48 23 57 38
## claro_M 48 34 136 105 33 80
## directamente_H 4 7 1 8 3 1
## directamente_M 6 2 0 5 3 0
## efectivamente_H 0 1 10 1 0 10
## efectivamente_M 0 6 13 0 6 13
## eh_H 281 279 268 424 147 257
## eh_M 48 177 92 81 143 93
## exactamente_H 0 21 18 17 4 18
## exactamente_M 0 5 2 0 3 4
## generalmente_H 4 14 0 0 14 4
## generalmente_M 0 0 7 0 0 7
## hm_H 3 6 21 5 2 23
## hm_M 26 25 20 43 15 13
## hola_H 0 16 1 2 13 2
## hola_M 4 3 1 6 1 1
## jo_H 1 2 1 1 2 1
## jo_M 2 96 0 4 94 0
## joder_H 1 11 13 9 15 1
## joder_M 3 1 0 3 1 0
## jolín_M 1 10 2 2 10 1
## madre mía_H 0 4 0 1 3 0
## madre mía_M 0 4 7 0 6 5
## mm_H 79 105 39 100 43 80
## mm_M 54 58 77 20 89 80
## muchísimo_H 6 4 9 7 10 2
## muchísimo_M 14 48 14 34 22 20
## normalmente_H 10 12 40 20 4 38
## normalmente_M 8 2 3 0 3 10
## oye_H 4 20 6 15 13 2
## oye_M 0 2 8 2 2 6
## perfectamente_H 2 0 4 1 4 1
## perfectamente_M 0 3 8 2 2 7
## pff_H 0 6 18 3 15 6
## pff_M 15 21 2 9 29 0
## prácticamente_H 8 3 3 0 12 2
## prácticamente_M 1 4 3 2 3 3
## realmente_H 23 2 4 18 1 10
## realmente_M 5 2 5 0 3 9
## simplemente_H 17 2 5 5 8 11
## simplemente_M 2 4 1 2 2 3
## sinceramente_H 2 3 3 5 3 0
## sinceramente_M 1 3 1 1 4 0
## totalmente_H 12 3 2 1 1 15
## totalmente_M 0 25 1 0 25 1
## uhum_H 4 21 5 5 0 25
## uhum_M 5 19 3 3 4 20
## uy_H 1 1 1 2 1 0
## uy_M 1 6 9 6 9 1
## vale_H 0 6 8 3 6 5
## vale_M 9 21 2 5 25 2
## vamos_H 0 2 2 1 3 0
## vamos_M 11 1 1 1 8 4Cross(D,x=2:1,v=3:4) #個体x=2:1列 順番を変える## E1 E2 E3 N1 N2 N3
## H_ah 6 6 12 3 12 9
## H_ay 1 0 7 0 4 4
## H_bueno 177 204 107 191 148 149
## H_claramente 0 10 3 11 1 1
## H_claro 6 64 48 23 57 38
## H_directamente 4 7 1 8 3 1
## H_efectivamente 0 1 10 1 0 10
## H_eh 281 279 268 424 147 257
## H_exactamente 0 21 18 17 4 18
## H_generalmente 4 14 0 0 14 4
## H_hm 3 6 21 5 2 23
## H_hola 0 16 1 2 13 2
## H_jo 1 2 1 1 2 1
## H_joder 1 11 13 9 15 1
## H_madre mía 0 4 0 1 3 0
## H_mm 79 105 39 100 43 80
## H_muchísimo 6 4 9 7 10 2
## H_normalmente 10 12 40 20 4 38
## H_oye 4 20 6 15 13 2
## H_perfectamente 2 0 4 1 4 1
## H_pff 0 6 18 3 15 6
## H_prácticamente 8 3 3 0 12 2
## H_realmente 23 2 4 18 1 10
## H_simplemente 17 2 5 5 8 11
## H_sinceramente 2 3 3 5 3 0
## H_totalmente 12 3 2 1 1 15
## H_uhum 4 21 5 5 0 25
## H_uy 1 1 1 2 1 0
## H_vale 0 6 8 3 6 5
## H_vamos 0 2 2 1 3 0
## M_ah 10 7 34 18 28 5
## M_ay 3 1 36 7 28 5
## M_bueno 103 143 215 171 105 185
## M_claro 48 34 136 105 33 80
## M_directamente 6 2 0 5 3 0
## M_efectivamente 0 6 13 0 6 13
## M_eh 48 177 92 81 143 93
## M_exactamente 0 5 2 0 3 4
## M_generalmente 0 0 7 0 0 7
## M_hm 26 25 20 43 15 13
## M_hola 4 3 1 6 1 1
## M_jo 2 96 0 4 94 0
## M_joder 3 1 0 3 1 0
## M_jolín 1 10 2 2 10 1
## M_madre mía 0 4 7 0 6 5
## M_mm 54 58 77 20 89 80
## M_muchísimo 14 48 14 34 22 20
## M_normalmente 8 2 3 0 3 10
## M_oye 0 2 8 2 2 6
## M_perfectamente 0 3 8 2 2 7
## M_pff 15 21 2 9 29 0
## M_prácticamente 1 4 3 2 3 3
## M_realmente 5 2 5 0 3 9
## M_simplemente 2 4 1 2 2 3
## M_sinceramente 1 3 1 1 4 0
## M_totalmente 0 25 1 0 25 1
## M_uhum 5 19 3 3 4 20
## M_uy 1 6 9 6 9 1
## M_vale 9 21 2 5 25 2
## M_vamos 11 1 1 1 8 4D=HairEyeColor #イギリス人の髪と目の色 Datos: HairEyeColor
Cross(x=0,v=1:3,n=4)#個体x=0:行名, 変数v=1:3列, n=4列合計## Black Brown Red Blond Brown Blue Hazel Green Male Female
## 1 32 0 0 0 32 0 0 0 32 0
## 2 0 53 0 0 53 0 0 0 53 0
## 3 0 0 10 0 10 0 0 0 10 0
## 4 0 0 0 3 3 0 0 0 3 0
## 5 11 0 0 0 0 11 0 0 11 0
## 6 0 50 0 0 0 50 0 0 50 0
## 7 0 0 10 0 0 10 0 0 10 0
## 8 0 0 0 30 0 30 0 0 30 0
## 9 10 0 0 0 0 0 10 0 10 0
## 10 0 25 0 0 0 0 25 0 25 0
## 11 0 0 7 0 0 0 7 0 7 0
## 12 0 0 0 5 0 0 5 0 5 0
## 13 3 0 0 0 0 0 0 3 3 0
## 14 0 15 0 0 0 0 0 15 15 0
## 15 0 0 7 0 0 0 0 7 7 0
## 16 0 0 0 8 0 0 0 8 8 0
## 17 36 0 0 0 36 0 0 0 0 36
## 18 0 66 0 0 66 0 0 0 0 66
## 19 0 0 16 0 16 0 0 0 0 16
## 20 0 0 0 4 4 0 0 0 0 4
## 21 9 0 0 0 0 9 0 0 0 9
## 22 0 34 0 0 0 34 0 0 0 34
## 23 0 0 7 0 0 7 0 0 0 7
## 24 0 0 0 64 0 64 0 0 0 64
## 25 5 0 0 0 0 0 5 0 0 5
## 26 0 29 0 0 0 0 29 0 0 29
## 27 0 0 7 0 0 0 7 0 0 7
## 28 0 0 0 5 0 0 5 0 0 5
## 29 2 0 0 0 0 0 0 2 0 2
## 30 0 14 0 0 0 0 0 14 0 14
## 31 0 0 7 0 0 0 0 7 0 7
## 32 0 0 0 8 0 0 0 8 0 8Cross(x=0,v=1:3,n=4,t=T) #同 + t=T:入力列名付加)## Hair.Black Hair.Brown Hair.Red Hair.Blond Eye.Brown Eye.Blue Eye.Hazel
## 1 32 0 0 0 32 0 0
## 2 0 53 0 0 53 0 0
## 3 0 0 10 0 10 0 0
## 4 0 0 0 3 3 0 0
## 5 11 0 0 0 0 11 0
## 6 0 50 0 0 0 50 0
## 7 0 0 10 0 0 10 0
## 8 0 0 0 30 0 30 0
## 9 10 0 0 0 0 0 10
## 10 0 25 0 0 0 0 25
## 11 0 0 7 0 0 0 7
## 12 0 0 0 5 0 0 5
## 13 3 0 0 0 0 0 0
## 14 0 15 0 0 0 0 0
## 15 0 0 7 0 0 0 0
## 16 0 0 0 8 0 0 0
## 17 36 0 0 0 36 0 0
## 18 0 66 0 0 66 0 0
## 19 0 0 16 0 16 0 0
## 20 0 0 0 4 4 0 0
## 21 9 0 0 0 0 9 0
## 22 0 34 0 0 0 34 0
## 23 0 0 7 0 0 7 0
## 24 0 0 0 64 0 64 0
## 25 5 0 0 0 0 0 5
## 26 0 29 0 0 0 0 29
## 27 0 0 7 0 0 0 7
## 28 0 0 0 5 0 0 5
## 29 2 0 0 0 0 0 0
## 30 0 14 0 0 0 0 0
## 31 0 0 7 0 0 0 0
## 32 0 0 0 8 0 0 0
## Eye.Green Sex.Male Sex.Female
## 1 0 32 0
## 2 0 53 0
## 3 0 10 0
## 4 0 3 0
## 5 0 11 0
## 6 0 50 0
## 7 0 10 0
## 8 0 30 0
## 9 0 10 0
## 10 0 25 0
## 11 0 7 0
## 12 0 5 0
## 13 3 3 0
## 14 15 15 0
## 15 7 7 0
## 16 8 8 0
## 17 0 0 36
## 18 0 0 66
## 19 0 0 16
## 20 0 0 4
## 21 0 0 9
## 22 0 0 34
## 23 0 0 7
## 24 0 0 64
## 25 0 0 5
## 26 0 0 29
## 27 0 0 7
## 28 0 0 5
## 29 2 0 2
## 30 14 0 14
## 31 7 0 7
## 32 8 0 8D=mtcars; Dt() #データ例:mtcars## 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 4D=Dat(c='v,a,m'); Dt() #列選択## Class: data.frame / Rows: 32 / Columns: 3
##
## vs am mpg
## Mazda RX4 0 1 21.0
## Mazda RX4 Wag 0 1 21.0
## Datsun 710 1 1 22.8
## Hornet 4 Drive 1 0 21.4
## Hornet Sportabout 0 0 18.7
## Valiant 1 0 18.1
## Duster 360 0 0 14.3
## Merc 240D 1 0 24.4
## Merc 230 1 0 22.8
## Merc 280 1 0 19.2D=ReplaceCol(D,'vs','0=>V-shaped,1=>Straight'); Dt(D) #列置換: 'vs' ## Class: data.frame / Rows: 32 / Columns: 3
##
## vs am mpg
## Mazda RX4 V-shaped 1 21.0
## Mazda RX4 Wag V-shaped 1 21.0
## Datsun 710 Straight 1 22.8
## Hornet 4 Drive Straight 0 21.4
## Hornet Sportabout V-shaped 0 18.7
## Valiant Straight 0 18.1
## Duster 360 V-shaped 0 14.3
## Merc 240D Straight 0 24.4
## Merc 230 Straight 0 22.8
## Merc 280 Straight 0 19.2D=ReplaceCol(D,'am','0=>Automatic,1=>Manual') ; Dt(D) #列置換: 'am'## Class: data.frame / Rows: 32 / Columns: 3
##
## vs am mpg
## Mazda RX4 V-shaped Manual 21.0
## Mazda RX4 Wag V-shaped Manual 21.0
## Datsun 710 Straight Manual 22.8
## Hornet 4 Drive Straight Automatic 21.4
## Hornet Sportabout V-shaped Automatic 18.7
## Valiant Straight Automatic 18.1
## Duster 360 V-shaped Automatic 14.3
## Merc 240D Straight Automatic 24.4
## Merc 230 Straight Automatic 22.8
## Merc 280 Straight Automatic 19.2Cross(D,x=1,v=2) #個数## Automatic Manual
## Straight 7 7
## V-shaped 12 6Cross(D,x=1,v=2,n=3) #和## Automatic Manual
## Straight 145 199
## V-shaped 181 118Cross(D,x=1,v=2,n=3,m=T) #m=T平均## Automatic Manual
## Straight 21 28
## V-shaped 15 20Cross(D,x=1,v=2,n=3,m=T,d=1) #d=1小数桁=1## Automatic Manual
## Straight 20.7 28.4
## V-shaped 15.0 19.8D=Titanic; Dt() #データ:Titanic## 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 154Cross(x=1,v=4,n='F') #クロス集計(1:Class,4:Survived)## No Yes
## 1st 122 203
## 2nd 167 118
## 3rd 528 178
## Crew 673 212—–
(東京大学 上田博人 Hiroto Ueda, 2022)