Dat(D,r,c)
D:Matriz de datos,r=filas asignadas,c=columnas asignadas
D=Ip('s4') #Ejemplo sencillo: Puntaje de 4 personas## File: s4.txt / Class: data.frame / Rows: 4 / Columns: 3
##
## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i2.Juan 17 7 11
## i3.Mary 15 13 14
## i4.Ken 5 18 8Dat(r='1,3') #r: Asignar número(s) de fila## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i3.Mary 15 13 14Dat(r='i1,i3') #r: Asignar inicial(es) de fila## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i3.Mary 15 13 14Dat(c='1,2') #c: Asignar número(s) de columna## v1.English v2.Physics
## i1.Ana 9 14
## i2.Juan 17 7
## i3.Mary 15 13
## i4.Ken 5 18D=diamonds; Dt() #Datos: diamonds## 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 2.39Dat(D,'ca>=0.5') # Condición: ca(rat)>=0.5## # A tibble: 36,266 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.7 Ideal E SI1 62.5 57 2757 5.7 5.72 3.57
## 2 0.86 Fair E SI2 55.1 69 2757 6.45 6.33 3.52
## 3 0.7 Ideal G VS2 61.6 56 2757 5.7 5.67 3.5
## 4 0.71 Very Good E VS2 62.4 57 2759 5.68 5.73 3.56
## 5 0.78 Very Good G SI2 63.8 56 2759 5.81 5.85 3.72
## 6 0.7 Good E VS2 57.5 58 2759 5.85 5.9 3.38
## 7 0.7 Good F VS1 59.4 62 2759 5.71 5.76 3.4
## 8 0.96 Fair F SI2 66.3 62 2759 6.27 5.95 4.07
## 9 0.73 Very Good E SI1 61.6 59 2760 5.77 5.78 3.56
## 10 0.8 Premium H SI1 61.5 58 2760 5.97 5.93 3.66
## # ... with 36,256 more rowsDat(D,'v1>=0.5') # (Id)## # A tibble: 36,266 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.7 Ideal E SI1 62.5 57 2757 5.7 5.72 3.57
## 2 0.86 Fair E SI2 55.1 69 2757 6.45 6.33 3.52
## 3 0.7 Ideal G VS2 61.6 56 2757 5.7 5.67 3.5
## 4 0.71 Very Good E VS2 62.4 57 2759 5.68 5.73 3.56
## 5 0.78 Very Good G SI2 63.8 56 2759 5.81 5.85 3.72
## 6 0.7 Good E VS2 57.5 58 2759 5.85 5.9 3.38
## 7 0.7 Good F VS1 59.4 62 2759 5.71 5.76 3.4
## 8 0.96 Fair F SI2 66.3 62 2759 6.27 5.95 4.07
## 9 0.73 Very Good E SI1 61.6 59 2760 5.77 5.78 3.56
## 10 0.8 Premium H SI1 61.5 58 2760 5.97 5.93 3.66
## # ... with 36,256 more rowsDat(D,'ca>=0.5,ca<0.8') # Condición múltiple: ca dentro del rango [0.5,0.8]## # A tibble: 13,453 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.7 Ideal E SI1 62.5 57 2757 5.7 5.72 3.57
## 2 0.7 Ideal G VS2 61.6 56 2757 5.7 5.67 3.5
## 3 0.71 Very Good E VS2 62.4 57 2759 5.68 5.73 3.56
## 4 0.78 Very Good G SI2 63.8 56 2759 5.81 5.85 3.72
## 5 0.7 Good E VS2 57.5 58 2759 5.85 5.9 3.38
## 6 0.7 Good F VS1 59.4 62 2759 5.71 5.76 3.4
## 7 0.73 Very Good E SI1 61.6 59 2760 5.77 5.78 3.56
## 8 0.75 Very Good D SI1 63.2 56 2760 5.8 5.75 3.65
## 9 0.75 Premium E SI1 59.9 54 2760 6 5.96 3.58
## 10 0.74 Ideal G SI1 61.6 55 2760 5.8 5.85 3.59
## # ... with 13,443 more rowsDat(D,'ca>=0.5,ca<0.8,co=E') #Id. + co(lor)=E## # A tibble: 3,022 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.7 Ideal E SI1 62.5 57 2757 5.7 5.72 3.57
## 2 0.71 Very Good E VS2 62.4 57 2759 5.68 5.73 3.56
## 3 0.7 Good E VS2 57.5 58 2759 5.85 5.9 3.38
## 4 0.73 Very Good E SI1 61.6 59 2760 5.77 5.78 3.56
## 5 0.75 Premium E SI1 59.9 54 2760 6 5.96 3.58
## 6 0.59 Ideal E VVS2 62 55 2761 5.38 5.43 3.35
## 7 0.74 Ideal E SI2 62.2 56 2761 5.8 5.84 3.62
## 8 0.7 Ideal E VS2 60.7 58 2762 5.73 5.76 3.49
## 9 0.74 Ideal E SI1 62.3 54 2762 5.8 5.83 3.62
## 10 0.7 Very Good E VS2 62.6 60 2765 5.62 5.65 3.53
## # ... with 3,012 more rowsDat(D,'cl#^S') # cl(arity) ExpReg.=^S## # A tibble: 22,259 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 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 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 4 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 5 0.3 Good J SI1 64 55 339 4.25 4.28 2.73
## 6 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
## 7 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
## 8 0.2 Premium E SI2 60.2 62 345 3.79 3.75 2.27
## 9 0.3 Ideal I SI2 62 54 348 4.31 4.34 2.68
## 10 0.3 Good J SI1 63.4 54 351 4.23 4.29 2.7
## # ... with 22,249 more rowsD=Ip('dle') #Datos: dle.txt (lemas de DLE(RAE))## File: dle.txt / Class: data.frame / Rows: 87532 / Columns: 4
##
## Forma Ac CV Fin
## 1 a 0 V a
## 2 aba 2 V a
## 3 abaá 1 V a
## 4 ababol 1 C l
## 5 abacá 1 V a
## 6 abacal 1 C l
## 7 abacalero 2 V o
## 8 abacería 2 V a
## 9 abacero 2 V o
## 10 abacial 1 C lE=Dat(D,'Fo#a$'); Dt(E) #Exp.Reg.: Fo(rma) con (a$)## Class: data.frame / Rows: 18531 / Columns: 4
##
## Forma Ac CV Fin
## 1 a 0 V a
## 2 aba 2 V a
## 8 abacería 2 V a
## 12 abacora 2 V a
## 15 abada 2 V a
## 19 abadesa 2 V a
## 21 abadía 2 V a
## 28 abajera 2 V a
## 31 abalada 2 V a
## 43 ábana 3 V aE=Dat(D,'Fo#graf'); Dt(E) #Exp.Reg.: Fo(rma) con (a$)## Class: data.frame / Rows: 292 / Columns: 4
##
## Forma Ac CV Fin
## 1983 aerocriptografía 2 V a
## 1998 aerofotografía 2 V a
## 2000 aerografía 2 V a
## 2002 aerógrafo 3 V o
## 2532 agrafe 2 V e
## 2533 agrafia 2 V a
## 2534 ágrafo 3 V o
## 5516 ampelografía 2 V a
## 5518 ampelógrafo 3 V o
## 5938 anemografía 2 V aE=Dat(s='Fo#graf'); Dt(E) #Exp.Reg.: Fo(rma) con (graf)## Class: data.frame / Rows: 292 / Columns: 4
##
## Forma Ac CV Fin
## 1983 aerocriptografía 2 V a
## 1998 aerofotografía 2 V a
## 2000 aerografía 2 V a
## 2002 aerógrafo 3 V o
## 2532 agrafe 2 V e
## 2533 agrafia 2 V a
## 2534 ágrafo 3 V o
## 5516 ampelografía 2 V a
## 5518 ampelógrafo 3 V o
## 5938 anemografía 2 V aE=Dat(D,'Ac=1,CV=V'); Dt(E) #Exp.Reg.: A con 1, CV con V## Class: data.frame / Rows: 971 / Columns: 4
##
## Forma Ac CV Fin
## 3 abaá 1 V a
## 5 abacá 1 V a
## 20 abadí 1 V i
## 30 abakuá 1 V a
## 102 abasí 1 V i
## 117 abatí 1 V i
## 134 abecé 1 V e
## 283 abonaré 1 V e
## 482 abudabí 1 V i
## 538 acá 1 V aReordenación de filas/columnas
D=Ip('x45') #Ejemplo sencillo: Matriz(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 3Dat(r='2,1,3,4') #Reordenación de filas## A B C D E
## w2 11 7 10 0 1
## w1 10 19 14 7 12
## w3 0 0 1 12 1
## w4 0 1 2 3 3Dat(c='2,1,3,4,5') #Reordenación de columnas## B A C D E
## w1 19 10 14 7 12
## w2 7 11 10 0 1
## w3 0 0 1 12 1
## w4 1 0 2 3 3Dat(c='B,A,C,D,E') #Id.## B A C D E
## w1 19 10 14 7 12
## w2 7 11 10 0 1
## w3 0 0 1 12 1
## w4 1 0 2 3 3Dat(c=5:1) #Reordenación inversa (5:1=5,4,3,2,1)## E D C B A
## w1 12 7 14 19 10
## w2 1 0 10 7 11
## w3 1 12 1 0 0
## w4 3 3 2 1 0D=iris; Dt(cn=T) #Datos: iris, representar rótulos de columna## 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 setosa
##
## Column names: 1.Sepal.Length 2.Sepal.Width 3.Petal.Length 4.Petal.Width 5.SpeciesD=Dat(c='5,1,2'); Dt() #Reordenación y selección## Class: data.frame / Rows: 150 / Columns: 3
##
## Species Sepal.Length Sepal.Width
## 1 setosa 5.1 3.5
## 2 setosa 4.9 3.0
## 3 setosa 4.7 3.2
## 4 setosa 4.6 3.1
## 5 setosa 5.0 3.6
## 6 setosa 5.4 3.9
## 7 setosa 4.6 3.4
## 8 setosa 5.0 3.4
## 9 setosa 4.4 2.9
## 10 setosa 4.9 3.1Ordenación de filas/columnas
Dat([D],sr=NULL,sc=NULL,d=F)
D:Matriz,sr:Número de fila,sc:Número de column,d=T:Ordenación descendente
D=Ip('s4'); Dt() #Ejemplo sencillo: Pntaje de 4 personas## File: s4.txt / Class: data.frame / Rows: 4 / Columns: 3
##
## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i2.Juan 17 7 11
## i3.Mary 15 13 14
## i4.Ken 5 18 8## Class: data.frame / Rows: 4 / Columns: 3
##
## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i2.Juan 17 7 11
## i3.Mary 15 13 14
## i4.Ken 5 18 8Dat(sc=1) #Columna:1, Ordenación ascendente## v1.English v2.Physics v3.Latin
## i4.Ken 5 18 8
## i1.Ana 9 14 18
## i3.Mary 15 13 14
## i2.Juan 17 7 11Dat(sc='v1') #Id.## v1.English v2.Physics v3.Latin
## i4.Ken 5 18 8
## i1.Ana 9 14 18
## i3.Mary 15 13 14
## i2.Juan 17 7 11Dat(sc=1,d=T)#Columna:1, Ordenación descendente## v1.English v2.Physics v3.Latin
## i2.Juan 17 7 11
## i3.Mary 15 13 14
## i1.Ana 9 14 18
## i4.Ken 5 18 8Dat(sr=2) ##Columna:2, Ordenación ascendente## v2.Physics v3.Latin v1.English
## i1.Ana 14 18 9
## i2.Juan 7 11 17
## i3.Mary 13 14 15
## i4.Ken 18 8 5Dat(sr='i2') #Id.## v2.Physics v3.Latin v1.English
## i1.Ana 14 18 9
## i2.Juan 7 11 17
## i3.Mary 13 14 15
## i4.Ken 18 8 5Dat(sr=2,d=T)#Columna:2, Ordenación descendente## v1.English v3.Latin v2.Physics
## i1.Ana 9 18 14
## i2.Juan 17 11 7
## i3.Mary 15 14 13
## i4.Ken 5 8 18D=iris; Dt() #Datos: 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(sc=1); Dt() #Columna:1, Ordenación ascendente## Class: data.frame / Rows: 150 / Columns: 5
##
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosaD=Dat(sc=1,d=T); Dt() #Columna:1, Ordenación descendente## Class: data.frame / Rows: 150 / Columns: 5
##
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 132 7.9 3.8 6.4 2.0 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 126 7.2 3.2 6.0 1.8 virginicaD=Ip('dle.txt') #Datos: DLE (RAE)## File: dle.txt / Class: data.frame / Rows: 87532 / Columns: 4
##
## Forma Ac CV Fin
## 1 a 0 V a
## 2 aba 2 V a
## 3 abaá 1 V a
## 4 ababol 1 C l
## 5 abacá 1 V a
## 6 abacal 1 C l
## 7 abacalero 2 V o
## 8 abacería 2 V a
## 9 abacero 2 V o
## 10 abacial 1 C lD=Dat(D,r=1:20); Dt(r=20) #Seleccionar 1:20## Class: data.frame / Rows: 20 / Columns: 4
##
## Forma Ac CV Fin
## 1 a 0 V a
## 2 aba 2 V a
## 3 abaá 1 V a
## 4 ababol 1 C l
## 5 abacá 1 V a
## 6 abacal 1 C l
## 7 abacalero 2 V o
## 8 abacería 2 V a
## 9 abacero 2 V o
## 10 abacial 1 C l
## 11 ábaco 3 V o
## 12 abacora 2 V a
## 13 abacorar 1 C r
## 14 abad 1 C d
## 15 abada 2 V a
## 16 abadejo 2 V o
## 17 abadengo 2 V o
## 18 abadernar 1 C r
## 19 abadesa 2 V a
## 20 abadí 1 V iS=Dat(sc=2); Dt(S,r=20) #Columna:2, Ordenación ascendente## Class: data.frame / Rows: 20 / Columns: 4
##
## Forma Ac CV Fin
## 1 a 0 V a
## 3 abaá 1 V a
## 4 ababol 1 C l
## 5 abacá 1 V a
## 6 abacal 1 C l
## 10 abacial 1 C l
## 13 abacorar 1 C r
## 14 abad 1 C d
## 18 abadernar 1 C r
## 20 abadí 1 V i
## 2 aba 2 V a
## 7 abacalero 2 V o
## 8 abacería 2 V a
## 9 abacero 2 V o
## 12 abacora 2 V a
## 15 abada 2 V a
## 16 abadejo 2 V o
## 17 abadengo 2 V o
## 19 abadesa 2 V a
## 11 ábaco 3 V oS=Dat(sc=2:3); Dt(S,r=20) #Columna:2:3, Ordenación ascendente## Class: data.frame / Rows: 20 / Columns: 4
##
## Forma Ac CV Fin
## 1 a 0 V a
## 4 ababol 1 C l
## 6 abacal 1 C l
## 10 abacial 1 C l
## 13 abacorar 1 C r
## 14 abad 1 C d
## 18 abadernar 1 C r
## 3 abaá 1 V a
## 5 abacá 1 V a
## 20 abadí 1 V i
## 2 aba 2 V a
## 7 abacalero 2 V o
## 8 abacería 2 V a
## 9 abacero 2 V o
## 12 abacora 2 V a
## 15 abada 2 V a
## 16 abadejo 2 V o
## 17 abadengo 2 V o
## 19 abadesa 2 V a
## 11 ábaco 3 V oD=diamonds; Dt() #Datos: diamonds## 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=Ip('iris'); Dt(j=T) #Dt(j=T): Justificación derecha## File: iris.txt / 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 setosa## 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=Ip('diamonds')## File: diamonds.txt / Class: 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 2.39N=Dat(c='ca,p,x,y,z'); Dt(N) #Seleccionar columnas numérica## Class: 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(N,s='sum,min,q1,median,mean,q3,max',d=3); Dt(S)## Class: matrix, array / Rows: 7 / Columns: 5
##
## carat price x y z
## sum 43040.870 212135217.00 309138.620 309320.330 190879.300
## 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 31.800Round(S,'m=1') #Dígito decimal de la matriz=1## carat price x y z
## sum 43040.9 212135217.0 309138.6 309320.3 190879.3
## min 0.2 326.0 0.0 0.0 0.0
## q1 0.4 950.0 4.7 4.7 2.9
## median 0.7 2401.0 5.7 5.7 3.5
## mean 0.8 3932.8 5.7 5.7 3.5
## q3 1.0 5324.2 6.5 6.5 4.0
## max 5.0 18823.0 10.7 58.9 31.8Round(S,'r1=0') #Dígito decimal de la fila=1## carat price x y z
## sum 43041.000 212135217.00 309139.000 309320.000 190879.000
## 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 31.800Round(S,'c1=1,c2=3') #Dígitos decimales de las columnas 1,2=1,3## carat price x y z
## sum 43040.9 212135217.00 309138.620 309320.330 190879.300
## min 0.2 326.00 0.000 0.000 0.000
## q1 0.4 950.00 4.710 4.720 2.910
## median 0.7 2401.00 5.700 5.710 3.530
## mean 0.8 3932.80 5.731 5.735 3.539
## q3 1.0 5324.25 6.540 6.540 4.040
## max 5.0 18823.00 10.740 58.900 31.800Round(S,'m=2,r1=1,c2=1') #Dígitos decimales. matriz=2, de fila=1,de columna=1## carat price x y z
## sum 43040.90 212135217.0 309138.60 309320.30 190879.30
## min 0.20 326.0 0.00 0.00 0.00
## q1 0.40 950.0 4.71 4.72 2.91
## median 0.70 2401.0 5.70 5.71 3.53
## mean 0.80 3932.8 5.73 5.74 3.54
## q3 1.04 5324.2 6.54 6.54 4.04
## max 5.01 18823.0 10.74 58.90 31.80D=diamonds; Dt() #Datos: diamonds## 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='cu,co,p'); Dt()## Class: tbl_df, tbl, data.frame / Rows: 53940 / Columns: 3
##
## cut color price
## 1 Ideal E 326
## 2 Premium E 326
## 3 Good E 327
## 4 Premium I 334
## 5 Good J 335
## 6 Very Good J 336
## 7 Very Good I 336
## 8 Very Good H 337
## 9 Fair E 337
## 10 Very Good H 338D=mtcars; Dt() #Datos: 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()## 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() #Reemplzar la columna '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() #Reemplzar la columna '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 19.2D=Titanic; Dt() #Datos: 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 154E=T2D(); Dt(E) #table => df## Class: data.frame / Rows: 32 / Columns: 5
##
## 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 154E=T2D(); Dt(E) #table => df## Class: data.frame / Rows: 32 / Columns: 5
##
## 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 154—–
(Hiroto Ueda, Universidad de Tokio, 2022)