Edición de datos
Datos existentes
Utilizamos los siguientes datos existentes.
Al ejecutar con el signo de interrogación delante, se representa la explicación en la zona de Help.
La tecla de atajo de ejecución es [Ctrl]+[Enter].
?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
Presentación de datos
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 2.39Selección de datos
Dt(E=D,r=10,c=0,n=F,rn=F,cn=F,d=’’)
D:Datos, r:número de filas,c:número de columnas,n:Mostrar rótulos de filas/columnas,rn:Mostrar rótulos de finas,cn:Mostrar rótulos de columnas,d:Mostrar el nombre de datos, etc.
D=Ip('x45.txt') #Ejemplo sencillo (Matriz numérica: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) #Mostrar los datos (3 filas)## 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) #Mostrar los datos (3 columnas)## 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) #Mostrar los datos (3 filas: 3 columnas)## 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) #Id+(n: Rótulos de filas/columnas)## 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) #Id+(n: Rótulos de filas)## 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) #Id+(n: Rótulos de columnas)## 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.CMostrar los datos en Source Pane
D=Ip('x45'); Vista() #Ejemplo sencillo (Matriz numérica)## 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) #Matriz grande
Vista(diamonds)
Edición de datos
D=Ip('x45'); D=edit(D); Dt() #Leer x45.txt; editar D; mostar D## 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() #Editar D (por defecto)X=Edit(diamonds) #Editar matriz grande 
Edit(diamonds)
Edición de fichero
Editamos un fichero dentro de Source Pane de RStudio.
File('dle') #Editar y guardar el fichero en Source Pane
File(‘dle’)
—–
(Hiroto Ueda, Universidad de Tokio, 2022)