LAB1
Sara Sanchez
2025-02-23
#Ejercicio 3
library("MASS")
data("anorexia")
sum(is.na(anorexia)) #0: no hay faltantes## [1] 0names(anorexia)## [1] "Treat" "Prewt" "Postwt"head(anorexia,n=5)## Treat Prewt Postwt
## 1 Cont 80.7 80.2
## 2 Cont 89.4 80.1
## 3 Cont 91.8 86.4
## 4 Cont 74.0 86.3
## 5 Cont 78.1 76.1anorexia_modif<-factor(anorexia$Treat, levels=c("CBT","Cont","FT"), labels=c("Cogn Beh Tr", "Contr","Fam Tr"))#Ejercicio 4 ##4.A.
library(MASS)
head(Melanoma,n=2)## time status sex age year thickness ulcer
## 1 10 3 1 76 1972 6.76 1
## 2 30 3 1 56 1968 0.65 0write.csv(Melanoma, "C:/Users/Sara Sánchez Orgaz/OneDrive/Documentos/Trabajos Software R/LAB1.csv")##4.C.
resumen = summary(Melanoma$age)
capture.output(resumen, file="resumenLAB1.doc")#Ejercicio 5
summary(birthwt)## low age lwt race
## Min. :0.0000 Min. :14.00 Min. : 80.0 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:19.00 1st Qu.:110.0 1st Qu.:1.000
## Median :0.0000 Median :23.00 Median :121.0 Median :1.000
## Mean :0.3122 Mean :23.24 Mean :129.8 Mean :1.847
## 3rd Qu.:1.0000 3rd Qu.:26.00 3rd Qu.:140.0 3rd Qu.:3.000
## Max. :1.0000 Max. :45.00 Max. :250.0 Max. :3.000
## smoke ptl ht ui
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.3915 Mean :0.1958 Mean :0.06349 Mean :0.1481
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :3.0000 Max. :1.00000 Max. :1.0000
## ftv bwt
## Min. :0.0000 Min. : 709
## 1st Qu.:0.0000 1st Qu.:2414
## Median :0.0000 Median :2977
## Mean :0.7937 Mean :2945
## 3rd Qu.:1.0000 3rd Qu.:3487
## Max. :6.0000 Max. :4990##5.a ¿Cuál es la edad máxima de las madres del conjunto de datos?
max(birthwt$age)## [1] 45##5.b. ¿Cuál es la edad mínima de las madres del conjunto de datos?
min(birthwt$age)## [1] 14##5.c ¿Cuál es el rango de edad de las madres?
range(birthwt$age)## [1] 14 45##5.d ¿Fumaba la madre cuyo recién nacido era el de menor peso?
birthwt$smoke[birthwt$bwt==min(birthwt$bwt)]## [1] 1##5.e ¿Cuánto pesó el recién nacido cuya madre tenía la edad máxima?
subset(birthwt, age==max(age), select =bwt)## bwt
## 226 4990##5.f Listad los pesos de los recién nacidos, cuyas madres visitarán menos de dos veces al médico durante el primer trimestre.
subset(birthwt, ftv<2, select=bwt, row.names=F)## Warning: In subset.data.frame(birthwt, ftv < 2, select = bwt, row.names = F) :
## extra argument 'row.names' will be disregarded## bwt
## 85 2523
## 87 2557
## 89 2600
## 91 2622
## 92 2637
## 93 2637
## 94 2663
## 95 2665
## 96 2722
## 97 2733
## 98 2751
## 100 2769
## 101 2769
## 102 2778
## 104 2807
## 105 2821
## 108 2836
## 109 2863
## 112 2877
## 113 2906
## 115 2920
## 116 2920
## 117 2920
## 118 2948
## 119 2948
## 120 2977
## 121 2977
## 125 2922
## 127 3033
## 130 3062
## 131 3062
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## 133 3062
## 135 3090
## 137 3090
## 138 3100
## 139 3104
## 140 3132
## 142 3175
## 143 3175
## 144 3203
## 145 3203
## 146 3203
## 147 3225
## 148 3225
## 150 3232
## 151 3234
## 154 3260
## 155 3274
## 160 3317
## 162 3317
## 164 3331
## 166 3374
## 167 3374
## 168 3402
## 169 3416
## 172 3444
## 173 3459
## 174 3460
## 175 3473
## 176 3544
## 177 3487
## 179 3544
## 180 3572
## 181 3572
## 182 3586
## 183 3600
## 184 3614
## 185 3614
## 187 3629
## 188 3637
## 189 3643
## 190 3651
## 191 3651
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## 193 3651
## 195 3699
## 196 3728
## 197 3756
## 199 3770
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## 202 3790
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## 204 3827
## 208 3884
## 210 3912
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## 216 3997
## 217 3997
## 218 4054
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## 220 4111
## 223 4174
## 224 4238
## 225 4593
## 226 4990
## 4 709
## 11 1135
## 13 1330
## 15 1474
## 16 1588
## 17 1588
## 18 1701
## 19 1729
## 20 1790
## 22 1818
## 23 1885
## 24 1893
## 25 1899
## 26 1928
## 29 1936
## 30 1970
## 31 2055
## 32 2055
## 34 2084
## 35 2084
## 36 2100
## 37 2125
## 42 2187
## 43 2187
## 44 2211
## 45 2225
## 46 2240
## 47 2240
## 49 2282
## 50 2296
## 51 2296
## 54 2325
## 56 2353
## 57 2353
## 59 2367
## 60 2381
## 61 2381
## 62 2381
## 63 2410
## 65 2410
## 67 2410
## 69 2424
## 75 2442
## 77 2466
## 78 2466
## 82 2495
## 83 2495#Ejercicio 6
data("anorexia")
nrow(anorexia)## [1] 72datos<-matrix(c(anorexia$Prewt,anorexia$Postwt),ncol=2, nrow=72)
datos## [,1] [,2]
## [1,] 80.7 80.2
## [2,] 89.4 80.1
## [3,] 91.8 86.4
## [4,] 74.0 86.3
## [5,] 78.1 76.1
## [6,] 88.3 78.1
## [7,] 87.3 75.1
## [8,] 75.1 86.7
## [9,] 80.6 73.5
## [10,] 78.4 84.6
## [11,] 77.6 77.4
## [12,] 88.7 79.5
## [13,] 81.3 89.6
## [14,] 78.1 81.4
## [15,] 70.5 81.8
## [16,] 77.3 77.3
## [17,] 85.2 84.2
## [18,] 86.0 75.4
## [19,] 84.1 79.5
## [20,] 79.7 73.0
## [21,] 85.5 88.3
## [22,] 84.4 84.7
## [23,] 79.6 81.4
## [24,] 77.5 81.2
## [25,] 72.3 88.2
## [26,] 89.0 78.8
## [27,] 80.5 82.2
## [28,] 84.9 85.6
## [29,] 81.5 81.4
## [30,] 82.6 81.9
## [31,] 79.9 76.4
## [32,] 88.7 103.6
## [33,] 94.9 98.4
## [34,] 76.3 93.4
## [35,] 81.0 73.4
## [36,] 80.5 82.1
## [37,] 85.0 96.7
## [38,] 89.2 95.3
## [39,] 81.3 82.4
## [40,] 76.5 72.5
## [41,] 70.0 90.9
## [42,] 80.4 71.3
## [43,] 83.3 85.4
## [44,] 83.0 81.6
## [45,] 87.7 89.1
## [46,] 84.2 83.9
## [47,] 86.4 82.7
## [48,] 76.5 75.7
## [49,] 80.2 82.6
## [50,] 87.8 100.4
## [51,] 83.3 85.2
## [52,] 79.7 83.6
## [53,] 84.5 84.6
## [54,] 80.8 96.2
## [55,] 87.4 86.7
## [56,] 83.8 95.2
## [57,] 83.3 94.3
## [58,] 86.0 91.5
## [59,] 82.5 91.9
## [60,] 86.7 100.3
## [61,] 79.6 76.7
## [62,] 76.9 76.8
## [63,] 94.2 101.6
## [64,] 73.4 94.9
## [65,] 80.5 75.2
## [66,] 81.6 77.8
## [67,] 82.1 95.5
## [68,] 77.6 90.7
## [69,] 83.5 92.5
## [70,] 89.9 93.8
## [71,] 86.0 91.7
## [72,] 87.3 98.0#Ejercicio 7
Identificador <-
c("I1","I2","I3","I4","I5","I6","I7","I8","I9","I10","I11","I12","I13","I14",
"I15","I16","I17","I18","I19","I20","I21","I22","I23","I24","I25")
Edad <-
c(23,24,21,22,23,25,26,24,21,22,23,25,26,24,22,21,25,26,24,21,25,27,26,22,29)
Sexo <-c(1,2,1,1,1,2,2,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,1,1,2) #1 para mujeres y 2 para hombres
Peso <-
c(76.5,81.2,79.3,59.5,67.3,78.6,67.9,100.2,97.8,56.4,65.4,67.5,87.4,99.7,87.6
,93.4,65.4,73.7,85.1,61.2,54.8,103.4,65.8,71.7,85.0)
Alt <-
c(165,154,178,165,164,175,182,165,178,165,158,183,184,164,189,167,182,179,165
,158,183,184,189,166,175) #altura en cm
Fuma <-
c("SÍ","NO","SÍ","SÍ","NO","NO","NO","SÍ","SÍ","SÍ","NO","NO","SÍ","SÍ","SÍ",
"SÍ","NO","NO","SÍ","SÍ","SÍ","NO","SÍ","NO","SÍ")
Trat_Pulmon <- data.frame(Identificador,Edad,Sexo,Peso,Alt,Fuma)
Trat_Pulmon## Identificador Edad Sexo Peso Alt Fuma
## 1 I1 23 1 76.5 165 SÍ
## 2 I2 24 2 81.2 154 NO
## 3 I3 21 1 79.3 178 SÍ
## 4 I4 22 1 59.5 165 SÍ
## 5 I5 23 1 67.3 164 NO
## 6 I6 25 2 78.6 175 NO
## 7 I7 26 2 67.9 182 NO
## 8 I8 24 2 100.2 165 SÍ
## 9 I9 21 1 97.8 178 SÍ
## 10 I10 22 2 56.4 165 SÍ
## 11 I11 23 1 65.4 158 NO
## 12 I12 25 2 67.5 183 NO
## 13 I13 26 2 87.4 184 SÍ
## 14 I14 24 2 99.7 164 SÍ
## 15 I15 22 1 87.6 189 SÍ
## 16 I16 21 1 93.4 167 SÍ
## 17 I17 25 1 65.4 182 NO
## 18 I18 26 2 73.7 179 NO
## 19 I19 24 2 85.1 165 SÍ
## 20 I20 21 2 61.2 158 SÍ
## 21 I21 25 1 54.8 183 SÍ
## 22 I22 27 2 103.4 184 NO
## 23 I23 26 1 65.8 189 SÍ
## 24 I24 22 1 71.7 166 NO
## 25 I25 29 2 85.0 175 SÍ##7.a Seleccionad los registros con edad > 22.
seleccionA<-subset(Trat_Pulmon,Edad>22)
seleccionA## Identificador Edad Sexo Peso Alt Fuma
## 1 I1 23 1 76.5 165 SÍ
## 2 I2 24 2 81.2 154 NO
## 5 I5 23 1 67.3 164 NO
## 6 I6 25 2 78.6 175 NO
## 7 I7 26 2 67.9 182 NO
## 8 I8 24 2 100.2 165 SÍ
## 11 I11 23 1 65.4 158 NO
## 12 I12 25 2 67.5 183 NO
## 13 I13 26 2 87.4 184 SÍ
## 14 I14 24 2 99.7 164 SÍ
## 17 I17 25 1 65.4 182 NO
## 18 I18 26 2 73.7 179 NO
## 19 I19 24 2 85.1 165 SÍ
## 21 I21 25 1 54.8 183 SÍ
## 22 I22 27 2 103.4 184 NO
## 23 I23 26 1 65.8 189 SÍ
## 25 I25 29 2 85.0 175 SÍ##7.b Seleccionad el elemento 3 de la columna 4 del conjunto de datos (contando el identificador).
Trat_Pulmon[3,4]## [1] 79.3##7.c Usad el comando subset() para seleccionar todas las filas que tienen una edad menor que 27 años y sin incluir la columna Alt.
subset(Trat_Pulmon,Edad<27,select=-Alt)## Identificador Edad Sexo Peso Fuma
## 1 I1 23 1 76.5 SÍ
## 2 I2 24 2 81.2 NO
## 3 I3 21 1 79.3 SÍ
## 4 I4 22 1 59.5 SÍ
## 5 I5 23 1 67.3 NO
## 6 I6 25 2 78.6 NO
## 7 I7 26 2 67.9 NO
## 8 I8 24 2 100.2 SÍ
## 9 I9 21 1 97.8 SÍ
## 10 I10 22 2 56.4 SÍ
## 11 I11 23 1 65.4 NO
## 12 I12 25 2 67.5 NO
## 13 I13 26 2 87.4 SÍ
## 14 I14 24 2 99.7 SÍ
## 15 I15 22 1 87.6 SÍ
## 16 I16 21 1 93.4 SÍ
## 17 I17 25 1 65.4 NO
## 18 I18 26 2 73.7 NO
## 19 I19 24 2 85.1 SÍ
## 20 I20 21 2 61.2 SÍ
## 21 I21 25 1 54.8 SÍ
## 23 I23 26 1 65.8 SÍ
## 24 I24 22 1 71.7 NO#Ejercicio 8
##8.a. Incorporad el conjunto de datos ChickWeight del paquete datasets a vuestro entorno de trabajo.
library(datasets)
data.frame(ChickWeight)## weight Time Chick Diet
## 1 42 0 1 1
## 2 51 2 1 1
## 3 59 4 1 1
## 4 64 6 1 1
## 5 76 8 1 1
## 6 93 10 1 1
## 7 106 12 1 1
## 8 125 14 1 1
## 9 149 16 1 1
## 10 171 18 1 1
## 11 199 20 1 1
## 12 205 21 1 1
## 13 40 0 2 1
## 14 49 2 2 1
## 15 58 4 2 1
## 16 72 6 2 1
## 17 84 8 2 1
## 18 103 10 2 1
## 19 122 12 2 1
## 20 138 14 2 1
## 21 162 16 2 1
## 22 187 18 2 1
## 23 209 20 2 1
## 24 215 21 2 1
## 25 43 0 3 1
## 26 39 2 3 1
## 27 55 4 3 1
## 28 67 6 3 1
## 29 84 8 3 1
## 30 99 10 3 1
## 31 115 12 3 1
## 32 138 14 3 1
## 33 163 16 3 1
## 34 187 18 3 1
## 35 198 20 3 1
## 36 202 21 3 1
## 37 42 0 4 1
## 38 49 2 4 1
## 39 56 4 4 1
## 40 67 6 4 1
## 41 74 8 4 1
## 42 87 10 4 1
## 43 102 12 4 1
## 44 108 14 4 1
## 45 136 16 4 1
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## 461 42 0 41 4
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## 464 85 6 41 4
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## 466 124 10 41 4
## 467 155 12 41 4
## 468 153 14 41 4
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## 470 184 18 41 4
## 471 199 20 41 4
## 472 204 21 41 4
## 473 42 0 42 4
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## 475 63 4 42 4
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## 548 125 10 48 4
## 549 154 12 48 4
## 550 170 14 48 4
## 551 222 16 48 4
## 552 261 18 48 4
## 553 303 20 48 4
## 554 322 21 48 4
## 555 40 0 49 4
## 556 53 2 49 4
## 557 64 4 49 4
## 558 85 6 49 4
## 559 108 8 49 4
## 560 128 10 49 4
## 561 152 12 49 4
## 562 166 14 49 4
## 563 184 16 49 4
## 564 203 18 49 4
## 565 233 20 49 4
## 566 237 21 49 4
## 567 41 0 50 4
## 568 54 2 50 4
## 569 67 4 50 4
## 570 84 6 50 4
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## 572 122 10 50 4
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## 574 175 14 50 4
## 575 205 16 50 4
## 576 234 18 50 4
## 577 264 20 50 4
## 578 264 21 50 4##8.b Generad un gráfico de dispersión de la variable weight.
plot(ChickWeight$weight)
##7.c Cread un diagrama de caja con la variable Time.
boxplot(ChickWeight$Time)
#CASO PRACTICO ##1 Variable Nombre Características Identificador Id
carácter Edad Edad numérica
Genero Gene 2 valores 1 = mujer, 2 = hombre
Tratamiento Trat Factor. Tres tipos de tratamiento (A, B y C) Peso Peso
numérica (en kg) Estatura Alt numérica (en cm)
varA<-letters
varB<-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2)
Identificador<-paste(varA,varB,sep="")
Identificador## [1] "a1" "b1" "c1" "d1" "e1" "f1" "g1" "h1" "i1" "j1" "k1" "l1" "m1" "n1" "o1"
## [16] "p1" "q1" "r1" "s1" "t1" "u1" "v1" "w1" "x1" "y1" "z1" "a2" "b2" "c2" "d2"Edad<-sample(18:75, 30, replace = TRUE)
Gene<-sample(1:2,30,replace=TRUE)
Peso<-round(runif(30, min = 40, max = 130),2)
Altura<-round(runif(30,min=120,max=210),0)
TratPREV<-sample(1:3,30,replace=TRUE)
TRat<-factor(TratPREV, levels=1:3, labels=c("A","B","C"))
datosCP<-data.frame(Identificador,Edad,Gene,Peso,Altura,TRat)##2 IMC = peso(kg)/[estatura(m)]2
IMC<-Peso/((Altura/100)^2)
datosCP<-data.frame(datosCP,IMC)
datosCP## Identificador Edad Gene Peso Altura TRat IMC
## 1 a1 40 2 81.34 133 C 45.98338
## 2 b1 33 2 49.88 175 B 16.28735
## 3 c1 24 2 86.36 172 A 29.19145
## 4 d1 32 2 92.99 122 C 62.47648
## 5 e1 49 2 73.61 170 B 25.47059
## 6 f1 67 1 80.44 193 C 21.59521
## 7 g1 65 1 97.00 186 A 28.03792
## 8 h1 42 2 73.54 132 B 42.20615
## 9 i1 52 2 69.21 179 B 21.60045
## 10 j1 53 2 84.62 168 B 29.98158
## 11 k1 64 1 42.39 129 A 25.47323
## 12 l1 21 2 75.54 177 A 24.11185
## 13 m1 62 1 107.77 180 A 33.26235
## 14 n1 32 2 92.31 188 B 26.11759
## 15 o1 27 1 127.23 129 A 76.45574
## 16 p1 52 2 47.14 197 B 12.14667
## 17 q1 72 1 63.14 139 C 32.67947
## 18 r1 41 1 76.45 137 B 40.73206
## 19 s1 59 2 52.54 141 C 26.42724
## 20 t1 18 1 107.96 195 C 28.39185
## 21 u1 56 2 101.36 203 B 24.59657
## 22 v1 29 1 99.68 182 C 30.09298
## 23 w1 21 1 105.24 182 B 31.77153
## 24 x1 29 1 75.48 145 A 35.90012
## 25 y1 71 2 84.77 135 A 46.51303
## 26 z1 25 1 117.05 172 C 39.56531
## 27 a2 73 2 55.12 123 A 36.43334
## 28 b2 71 2 97.74 131 C 56.95472
## 29 c2 34 1 120.05 190 A 33.25485
## 30 d2 45 1 99.25 172 A 33.54854##3 Cread dos data frames diferenciados para hombres y mujeres con dos nombres diferentes: Df_Hombres y Df_Mujeres.
Df_Mujeres<-subset(datosCP,Gene==1)
Df_Hombres<-subset(datosCP,Gene=2)## Warning: In subset.data.frame(datosCP, Gene = 2) :
## extra argument 'Gene' will be disregardedDf_Mujeres## Identificador Edad Gene Peso Altura TRat IMC
## 6 f1 67 1 80.44 193 C 21.59521
## 7 g1 65 1 97.00 186 A 28.03792
## 11 k1 64 1 42.39 129 A 25.47323
## 13 m1 62 1 107.77 180 A 33.26235
## 15 o1 27 1 127.23 129 A 76.45574
## 17 q1 72 1 63.14 139 C 32.67947
## 18 r1 41 1 76.45 137 B 40.73206
## 20 t1 18 1 107.96 195 C 28.39185
## 22 v1 29 1 99.68 182 C 30.09298
## 23 w1 21 1 105.24 182 B 31.77153
## 24 x1 29 1 75.48 145 A 35.90012
## 26 z1 25 1 117.05 172 C 39.56531
## 29 c2 34 1 120.05 190 A 33.25485
## 30 d2 45 1 99.25 172 A 33.54854##4
DF_UNIDO<-rbind(Df_Hombres,Df_Mujeres)
DF_UNIDO## Identificador Edad Gene Peso Altura TRat IMC
## 1 a1 40 2 81.34 133 C 45.98338
## 2 b1 33 2 49.88 175 B 16.28735
## 3 c1 24 2 86.36 172 A 29.19145
## 4 d1 32 2 92.99 122 C 62.47648
## 5 e1 49 2 73.61 170 B 25.47059
## 6 f1 67 1 80.44 193 C 21.59521
## 7 g1 65 1 97.00 186 A 28.03792
## 8 h1 42 2 73.54 132 B 42.20615
## 9 i1 52 2 69.21 179 B 21.60045
## 10 j1 53 2 84.62 168 B 29.98158
## 11 k1 64 1 42.39 129 A 25.47323
## 12 l1 21 2 75.54 177 A 24.11185
## 13 m1 62 1 107.77 180 A 33.26235
## 14 n1 32 2 92.31 188 B 26.11759
## 15 o1 27 1 127.23 129 A 76.45574
## 16 p1 52 2 47.14 197 B 12.14667
## 17 q1 72 1 63.14 139 C 32.67947
## 18 r1 41 1 76.45 137 B 40.73206
## 19 s1 59 2 52.54 141 C 26.42724
## 20 t1 18 1 107.96 195 C 28.39185
## 21 u1 56 2 101.36 203 B 24.59657
## 22 v1 29 1 99.68 182 C 30.09298
## 23 w1 21 1 105.24 182 B 31.77153
## 24 x1 29 1 75.48 145 A 35.90012
## 25 y1 71 2 84.77 135 A 46.51303
## 26 z1 25 1 117.05 172 C 39.56531
## 27 a2 73 2 55.12 123 A 36.43334
## 28 b2 71 2 97.74 131 C 56.95472
## 29 c2 34 1 120.05 190 A 33.25485
## 30 d2 45 1 99.25 172 A 33.54854
## 61 f1 67 1 80.44 193 C 21.59521
## 71 g1 65 1 97.00 186 A 28.03792
## 111 k1 64 1 42.39 129 A 25.47323
## 131 m1 62 1 107.77 180 A 33.26235
## 151 o1 27 1 127.23 129 A 76.45574
## 171 q1 72 1 63.14 139 C 32.67947
## 181 r1 41 1 76.45 137 B 40.73206
## 201 t1 18 1 107.96 195 C 28.39185
## 221 v1 29 1 99.68 182 C 30.09298
## 231 w1 21 1 105.24 182 B 31.77153
## 241 x1 29 1 75.48 145 A 35.90012
## 261 z1 25 1 117.05 172 C 39.56531
## 291 c2 34 1 120.05 190 A 33.25485
## 301 d2 45 1 99.25 172 A 33.54854