Encabezado 1
Tras instalar los paquetes MASS y Survival con los siguientes
comandos: install.packages(“MASS”) install.packages(“Survival”) El
resultado de lanzar library() mediante la consola es:
Packages in library
‘C:/Users/mario/AppData/Local/R/win-library/4.4’ y las librerias
contenidas en este directorio
Packages in library ‘C:/Program Files/R/R-4.4.1/library’ y las
librerias contenidas en este directorio
Para buscad información sobre el paquete Rcmdr desde la consola en
primer lugar se intenta utilizar la función help(Rcmdr), lo que lleva a
la sugerencia de usar ??Rcmdr
Ejercicio 2
Fichero TXT:
library(knitr)
library("datasets")
datosTxt <- read.table("C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/altura_peso.txt", sep = ",", header = TRUE)
kable(head(datosTxt))
| 1 |
170 |
65 |
25 |
M |
| 2 |
160 |
70 |
30 |
F |
| 3 |
180 |
75 |
22 |
M |
| 4 |
175 |
80 |
28 |
F |
| 5 |
165 |
60 |
26 |
M |
summary(datosTxt)
## Individuo Altura Peso Edad Sexo
## Min. :1 Min. :160 Min. :60 Min. :22.0 Length:5
## 1st Qu.:2 1st Qu.:165 1st Qu.:65 1st Qu.:25.0 Class :character
## Median :3 Median :170 Median :70 Median :26.0 Mode :character
## Mean :3 Mean :170 Mean :70 Mean :26.2
## 3rd Qu.:4 3rd Qu.:175 3rd Qu.:75 3rd Qu.:28.0
## Max. :5 Max. :180 Max. :80 Max. :30.0
Fichero csv:
library(knitr)
library("datasets")
datosCsv <- read.csv("C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/vacunas_paises.csv", sep = ";", header = TRUE)
kable(head(datosCsv))
| Espana |
8.0e+07 |
3.5e+07 |
75 |
1.5e+07 |
2024-10-01 |
95 |
Pfizer |
| Francia |
7.0e+07 |
3.2e+07 |
70 |
1.4e+07 |
2024-10-01 |
94 |
Moderna |
| Alemania |
9.0e+07 |
4.0e+07 |
80 |
1.6e+07 |
2024-10-01 |
93 |
AstraZeneca |
| Italia |
6.0e+07 |
2.8e+07 |
65 |
1.2e+07 |
2024-10-01 |
92 |
Johnson&Johnson |
| Reino Unido |
7.5e+07 |
3.3e+07 |
72 |
1.3e+07 |
2024-10-01 |
91 |
Pfizer |
| Estados Unidos |
2.5e+08 |
1.2e+08 |
60 |
5.0e+07 |
2024-10-01 |
90 |
Moderna |
summary(datosCsv)
## Pais Vacunas_Administradas Vacunas_Completas
## Length:16 Min. :1.500e+07 Min. : 7000000
## Class :character 1st Qu.:4.750e+07 1st Qu.: 23750000
## Mode :character Median :7.750e+07 Median : 34000000
## Mean :1.487e+08 Mean : 72187500
## 3rd Qu.:1.050e+08 3rd Qu.: 48750000
## Max. :1.000e+09 Max. :500000000
## Poblacion_Vacunada Dosis_Refuerzo Fecha_Actualizacion Efectividad
## Min. :30.00 Min. : 3000000 Length:16 Min. :80.00
## 1st Qu.:48.75 1st Qu.: 9500000 Class :character 1st Qu.:83.75
## Median :57.50 Median : 14500000 Mode :character Median :87.50
## Mean :56.69 Mean : 29312500 Mean :87.50
## 3rd Qu.:70.00 3rd Qu.: 21000000 3rd Qu.:91.25
## Max. :80.00 Max. :200000000 Max. :95.00
## Fabricante
## Length:16
## Class :character
## Mode :character
##
##
##
fivenum(datosCsv$Efectividad)
## [1] 80.0 83.5 87.5 91.5 95.0
fivenum(datosCsv$Poblacion_Vacunada)
## [1] 30.0 47.5 57.5 70.0 80.0
Ejercicio 3
library("MASS")
data("anorexia")
#Datos que contiene:
kable(head(anorexia))
| Cont |
80.7 |
80.2 |
| Cont |
89.4 |
80.1 |
| Cont |
91.8 |
86.4 |
| Cont |
74.0 |
86.3 |
| Cont |
78.1 |
76.1 |
| Cont |
88.3 |
78.1 |
#numero de datos:
dimensiones <- dim(anorexia)
print(dimensiones)
## [1] 72 3
#comprobamos si existen valores na
any(is.na(anorexia))
## [1] FALSE
#comprobamos si existen valores null
any(is.null(anorexia))
## [1] FALSE
#cambiamos valores añadiendo los nuevos valores como factores
levels(anorexia$Treat) <- c(levels(anorexia$Treat), "Cogn Beh Tr")
levels(anorexia$Treat) <- c(levels(anorexia$Treat), "Contr")
levels(anorexia$Treat) <- c(levels(anorexia$Treat), "Fam Tr")
anorexia$Treat[anorexia$Treat == "CBT"] <- "Cogn Beh Tr"
anorexia$Treat[anorexia$Treat == "Cont"] <- "Contr"
anorexia$Treat[anorexia$Treat == "FT"] <- "Fam Tr"
#comprobamos que se hayan cambiado
anorexia
## Treat Prewt Postwt
## 1 Contr 80.7 80.2
## 2 Contr 89.4 80.1
## 3 Contr 91.8 86.4
## 4 Contr 74.0 86.3
## 5 Contr 78.1 76.1
## 6 Contr 88.3 78.1
## 7 Contr 87.3 75.1
## 8 Contr 75.1 86.7
## 9 Contr 80.6 73.5
## 10 Contr 78.4 84.6
## 11 Contr 77.6 77.4
## 12 Contr 88.7 79.5
## 13 Contr 81.3 89.6
## 14 Contr 78.1 81.4
## 15 Contr 70.5 81.8
## 16 Contr 77.3 77.3
## 17 Contr 85.2 84.2
## 18 Contr 86.0 75.4
## 19 Contr 84.1 79.5
## 20 Contr 79.7 73.0
## 21 Contr 85.5 88.3
## 22 Contr 84.4 84.7
## 23 Contr 79.6 81.4
## 24 Contr 77.5 81.2
## 25 Contr 72.3 88.2
## 26 Contr 89.0 78.8
## 27 Cogn Beh Tr 80.5 82.2
## 28 Cogn Beh Tr 84.9 85.6
## 29 Cogn Beh Tr 81.5 81.4
## 30 Cogn Beh Tr 82.6 81.9
## 31 Cogn Beh Tr 79.9 76.4
## 32 Cogn Beh Tr 88.7 103.6
## 33 Cogn Beh Tr 94.9 98.4
## 34 Cogn Beh Tr 76.3 93.4
## 35 Cogn Beh Tr 81.0 73.4
## 36 Cogn Beh Tr 80.5 82.1
## 37 Cogn Beh Tr 85.0 96.7
## 38 Cogn Beh Tr 89.2 95.3
## 39 Cogn Beh Tr 81.3 82.4
## 40 Cogn Beh Tr 76.5 72.5
## 41 Cogn Beh Tr 70.0 90.9
## 42 Cogn Beh Tr 80.4 71.3
## 43 Cogn Beh Tr 83.3 85.4
## 44 Cogn Beh Tr 83.0 81.6
## 45 Cogn Beh Tr 87.7 89.1
## 46 Cogn Beh Tr 84.2 83.9
## 47 Cogn Beh Tr 86.4 82.7
## 48 Cogn Beh Tr 76.5 75.7
## 49 Cogn Beh Tr 80.2 82.6
## 50 Cogn Beh Tr 87.8 100.4
## 51 Cogn Beh Tr 83.3 85.2
## 52 Cogn Beh Tr 79.7 83.6
## 53 Cogn Beh Tr 84.5 84.6
## 54 Cogn Beh Tr 80.8 96.2
## 55 Cogn Beh Tr 87.4 86.7
## 56 Fam Tr 83.8 95.2
## 57 Fam Tr 83.3 94.3
## 58 Fam Tr 86.0 91.5
## 59 Fam Tr 82.5 91.9
## 60 Fam Tr 86.7 100.3
## 61 Fam Tr 79.6 76.7
## 62 Fam Tr 76.9 76.8
## 63 Fam Tr 94.2 101.6
## 64 Fam Tr 73.4 94.9
## 65 Fam Tr 80.5 75.2
## 66 Fam Tr 81.6 77.8
## 67 Fam Tr 82.1 95.5
## 68 Fam Tr 77.6 90.7
## 69 Fam Tr 83.5 92.5
## 70 Fam Tr 89.9 93.8
## 71 Fam Tr 86.0 91.7
## 72 Fam Tr 87.3 98.0
Ejercicio 4
library("MASS")
data("biopsy")
#Datos que contiene:
kable(head(biopsy))
| 1000025 |
5 |
1 |
1 |
1 |
2 |
1 |
3 |
1 |
1 |
benign |
| 1002945 |
5 |
4 |
4 |
5 |
7 |
10 |
3 |
2 |
1 |
benign |
| 1015425 |
3 |
1 |
1 |
1 |
2 |
2 |
3 |
1 |
1 |
benign |
| 1016277 |
6 |
8 |
8 |
1 |
3 |
4 |
3 |
7 |
1 |
benign |
| 1017023 |
4 |
1 |
1 |
3 |
2 |
1 |
3 |
1 |
1 |
benign |
| 1017122 |
8 |
10 |
10 |
8 |
7 |
10 |
9 |
7 |
1 |
malignant |
#Exportamos
write.csv(biopsy, file="C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/Ejercicio4/biopsy.csv")
#Exportamos los datos melanoma en tres ficheros distintos
data(Melanoma, package="MASS")
kable(head(Melanoma))
| 10 |
3 |
1 |
76 |
1972 |
6.76 |
1 |
| 30 |
3 |
1 |
56 |
1968 |
0.65 |
0 |
| 35 |
2 |
1 |
41 |
1977 |
1.34 |
0 |
| 99 |
3 |
0 |
71 |
1968 |
2.90 |
0 |
| 185 |
1 |
1 |
52 |
1965 |
12.08 |
1 |
| 204 |
1 |
1 |
28 |
1971 |
4.84 |
1 |
write.csv(Melanoma, file="C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/Ejercicio4/melanoma.csv")
write.table(Melanoma,"C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/Ejercicio4/melanoma.txt")
library(xlsx)
write.xlsx(Melanoma,"C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/Ejercicio4/melanoma.xlsx")
resumenAge = summary(Melanoma$age)
capture.output(resumenAge, file="C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/Ejercicio4/resumenAge.doc")
#https://hbiostat.org/data/ -> Diabetes data
dataSetDiabetes = read.csv("C:/Users/mario/Desktop/UOC/Software para el análisis de datos/RStudio/Lab1/Ejercicio4/diabetes.csv")
kable(head(dataSetDiabetes))
| 1000 |
203 |
82 |
56 |
3.6 |
4.31 |
Buckingham |
46 |
female |
62 |
121 |
medium |
118 |
59 |
NA |
NA |
29 |
38 |
720 |
| 1001 |
165 |
97 |
24 |
6.9 |
4.44 |
Buckingham |
29 |
female |
64 |
218 |
large |
112 |
68 |
NA |
NA |
46 |
48 |
360 |
| 1002 |
228 |
92 |
37 |
6.2 |
4.64 |
Buckingham |
58 |
female |
61 |
256 |
large |
190 |
92 |
185 |
92 |
49 |
57 |
180 |
| 1003 |
78 |
93 |
12 |
6.5 |
4.63 |
Buckingham |
67 |
male |
67 |
119 |
large |
110 |
50 |
NA |
NA |
33 |
38 |
480 |
| 1005 |
249 |
90 |
28 |
8.9 |
7.72 |
Buckingham |
64 |
male |
68 |
183 |
medium |
138 |
80 |
NA |
NA |
44 |
41 |
300 |
| 1008 |
248 |
94 |
69 |
3.6 |
4.81 |
Buckingham |
34 |
male |
71 |
190 |
large |
132 |
86 |
NA |
NA |
36 |
42 |
195 |
Ejercicio 5
library("MASS")
data("birthwt")
#Datos que contiene:
kable(head(birthwt))
| 85 |
0 |
19 |
182 |
2 |
0 |
0 |
0 |
1 |
0 |
2523 |
| 86 |
0 |
33 |
155 |
3 |
0 |
0 |
0 |
0 |
3 |
2551 |
| 87 |
0 |
20 |
105 |
1 |
1 |
0 |
0 |
0 |
1 |
2557 |
| 88 |
0 |
21 |
108 |
1 |
1 |
0 |
0 |
1 |
2 |
2594 |
| 89 |
0 |
18 |
107 |
1 |
1 |
0 |
0 |
1 |
0 |
2600 |
| 91 |
0 |
21 |
124 |
3 |
0 |
0 |
0 |
0 |
0 |
2622 |
#a) ¿Cuál es la edad máxima de las madres del conjunto de datos?
max(birthwt$age)
## [1] 45
#b) ¿Cuál es la edad mínima de las madres del conjunto de datos?
min(birthwt$age)
## [1] 14
#c) ¿Cuál es el rango de edad de las madres?
max(birthwt$age)-min(birthwt$age)
## [1] 31
#d) ¿Fumaba la madre cuyo recién nacido era el de menor peso?
birthwt$smoke[birthwt$bwt==min(birthwt$bwt)]
## [1] 1
#e) ¿Cuánto pesó el recién nacido cuya madre tenía la edad máxima?
birthwt$bwt[birthwt$age==max(birthwt$age)]
## [1] 4990
#f) Listad los pesos de los recién nacidos, cuyas madres visitarán menos de dos veces al médico durante el primer trimestre.
birthwt$bwt[birthwt$ftv<=2]
## [1] 2523 2557 2594 2600 2622 2637 2637 2663 2665 2722 2733 2751 2750 2769 2769
## [16] 2778 2807 2821 2835 2836 2863 2877 2877 2906 2920 2920 2920 2920 2948 2948
## [31] 2977 2977 2977 2977 2922 3005 3033 3042 3062 3062 3062 3062 3062 3090 3090
## [46] 3090 3100 3104 3132 3147 3175 3175 3203 3203 3203 3225 3225 3232 3232 3234
## [61] 3260 3274 3274 3317 3317 3317 3321 3331 3374 3374 3402 3416 3444 3459 3460
## [76] 3473 3544 3487 3544 3572 3572 3586 3600 3614 3614 3629 3629 3637 3643 3651
## [91] 3651 3651 3651 3699 3728 3756 3770 3770 3770 3790 3799 3827 3856 3860 3884
## [106] 3884 3912 3940 3941 3941 3969 3983 3997 3997 4054 4054 4111 4153 4167 4174
## [121] 4238 4593 4990 709 1021 1135 1330 1474 1588 1588 1701 1729 1790 1818 1885
## [136] 1893 1899 1928 1928 1928 1936 1970 2055 2055 2082 2084 2084 2100 2125 2187
## [151] 2187 2211 2225 2240 2240 2282 2296 2296 2325 2353 2353 2367 2381 2381 2381
## [166] 2410 2410 2410 2424 2438 2442 2466 2466 2466 2495 2495 2495
Ejercicio 6
#Datos que contiene:
kable(head(anorexia))
| Contr |
80.7 |
80.2 |
| Contr |
89.4 |
80.1 |
| Contr |
91.8 |
86.4 |
| Contr |
74.0 |
86.3 |
| Contr |
78.1 |
76.1 |
| Contr |
88.3 |
78.1 |
summary(anorexia)
## Treat Prewt Postwt
## CBT : 0 Min. :70.00 Min. : 71.30
## Cont : 0 1st Qu.:79.60 1st Qu.: 79.33
## FT : 0 Median :82.30 Median : 84.05
## Cogn Beh Tr:29 Mean :82.41 Mean : 85.17
## Contr :26 3rd Qu.:86.00 3rd Qu.: 91.55
## Fam Tr :17 Max. :94.90 Max. :103.60
length(anorexia$Prewt)
## [1] 72
length(anorexia$Postwt)
## [1] 72
matrix(c(anorexia$Prewt, anorexia$Postwt), ncol = 2)
## [,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)
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
#a) Seleccionad los registros con edad > 22.
subset(Trat_Pulmon, Edad > 22)
## 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
#b) Seleccionad el elemento 3 de la columna 4 del conjunto de datos (contando elidentificador).
Trat_Pulmon[3, 4]
## [1] 79.3
#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 = c(Identificador,Edad,Sexo,Peso,Fuma))
## 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
#Datos que contiene:
data("ChickWeight")
kable(head(ChickWeight))
| 42 |
0 |
1 |
1 |
| 51 |
2 |
1 |
1 |
| 59 |
4 |
1 |
1 |
| 64 |
6 |
1 |
1 |
| 76 |
8 |
1 |
1 |
| 93 |
10 |
1 |
1 |
summary(ChickWeight)
## weight Time Chick Diet
## Min. : 35.0 Min. : 0.00 13 : 12 1:220
## 1st Qu.: 63.0 1st Qu.: 4.00 9 : 12 2:120
## Median :103.0 Median :10.00 20 : 12 3:120
## Mean :121.8 Mean :10.72 10 : 12 4:118
## 3rd Qu.:163.8 3rd Qu.:16.00 17 : 12
## Max. :373.0 Max. :21.00 19 : 12
## (Other):506
plot(ChickWeight$weight)
boxplot(ChickWeight$Time)
Ejercicio 9
data("anorexia")
anorexia_treat_df<-data.frame(anorexia$Treat,c(anorexia$Postwt-anorexia$Prewt))
colnames(anorexia_treat_df) <- c("Treat", "WeightChange")
anorexia_treat_df
## Treat WeightChange
## 1 Cont -0.5
## 2 Cont -9.3
## 3 Cont -5.4
## 4 Cont 12.3
## 5 Cont -2.0
## 6 Cont -10.2
## 7 Cont -12.2
## 8 Cont 11.6
## 9 Cont -7.1
## 10 Cont 6.2
## 11 Cont -0.2
## 12 Cont -9.2
## 13 Cont 8.3
## 14 Cont 3.3
## 15 Cont 11.3
## 16 Cont 0.0
## 17 Cont -1.0
## 18 Cont -10.6
## 19 Cont -4.6
## 20 Cont -6.7
## 21 Cont 2.8
## 22 Cont 0.3
## 23 Cont 1.8
## 24 Cont 3.7
## 25 Cont 15.9
## 26 Cont -10.2
## 27 CBT 1.7
## 28 CBT 0.7
## 29 CBT -0.1
## 30 CBT -0.7
## 31 CBT -3.5
## 32 CBT 14.9
## 33 CBT 3.5
## 34 CBT 17.1
## 35 CBT -7.6
## 36 CBT 1.6
## 37 CBT 11.7
## 38 CBT 6.1
## 39 CBT 1.1
## 40 CBT -4.0
## 41 CBT 20.9
## 42 CBT -9.1
## 43 CBT 2.1
## 44 CBT -1.4
## 45 CBT 1.4
## 46 CBT -0.3
## 47 CBT -3.7
## 48 CBT -0.8
## 49 CBT 2.4
## 50 CBT 12.6
## 51 CBT 1.9
## 52 CBT 3.9
## 53 CBT 0.1
## 54 CBT 15.4
## 55 CBT -0.7
## 56 FT 11.4
## 57 FT 11.0
## 58 FT 5.5
## 59 FT 9.4
## 60 FT 13.6
## 61 FT -2.9
## 62 FT -0.1
## 63 FT 7.4
## 64 FT 21.5
## 65 FT -5.3
## 66 FT -3.8
## 67 FT 13.4
## 68 FT 13.1
## 69 FT 9.0
## 70 FT 3.9
## 71 FT 5.7
## 72 FT 10.7
anorexia_treat_C_df<-subset(anorexia_treat_df,Treat == "Cont" & WeightChange > 0 )
anorexia_treat_C_df
## Treat WeightChange
## 4 Cont 12.3
## 8 Cont 11.6
## 10 Cont 6.2
## 13 Cont 8.3
## 14 Cont 3.3
## 15 Cont 11.3
## 21 Cont 2.8
## 22 Cont 0.3
## 23 Cont 1.8
## 24 Cont 3.7
## 25 Cont 15.9
Caso práctico
caso_practico <- data.frame(Id = c("ID01", "ID02", "ID03", "ID04", "ID05", "ID06", "ID07", "ID08", "ID09", "ID10","ID11", "ID12", "ID13", "ID14", "ID15", "ID16", "ID17", "ID18", "ID19", "ID20","ID21", "ID22", "ID23", "ID24", "ID25", "ID26", "ID27", "ID28", "ID29", "ID30"),
Edad = c(56, 46, 32, 60, 25, 38, 56, 36, 40, 28, 28, 41, 53, 57, 41, 20, 39, 19, 41, 61,47, 55, 19, 38, 50, 29, 39, 61, 42, 44),
Gene = c(1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2),
Trat = c("B", "B", "A", "B", "A", "B", "C", "C", "A", "C", "C", "B", "A", "B", "B", "B", "B", "B", "B", "B","A", "C", "B", "B", "B", "B", "B", "B", "C", "C"),
Peso = c(54.4, 59.8, 52.3, 66.3, 69.4, 63.6, 91.4, 67.8, 64.0, 77.1, 57.0, 90.1, 53.7, 99.3, 88.6, 59.9, 50.3, 90.8, 85.3, 86.5,88.6, 53.7, 67.9, 55.8, 93.2, 81.2, 66.5, 53.2, 65.5, 66.3),
Alt = c(186.5, 181.9, 194.4, 173.6, 156.0, 185.7, 188.0, 178.1, 188.5, 174.7, 176.1, 171.4, 151.3, 155.4, 151.6, 181.8, 165.7, 175.4, 195.4, 162.5,170.5, 187.8, 161.4, 153.8, 164.5, 158.1, 196.5, 190.4, 181.7, 193.6))
caso_practico
## Id Edad Gene Trat Peso Alt
## 1 ID01 56 1 B 54.4 186.5
## 2 ID02 46 1 B 59.8 181.9
## 3 ID03 32 2 A 52.3 194.4
## 4 ID04 60 2 B 66.3 173.6
## 5 ID05 25 2 A 69.4 156.0
## 6 ID06 38 2 B 63.6 185.7
## 7 ID07 56 2 C 91.4 188.0
## 8 ID08 36 1 C 67.8 178.1
## 9 ID09 40 2 A 64.0 188.5
## 10 ID10 28 2 C 77.1 174.7
## 11 ID11 28 1 C 57.0 176.1
## 12 ID12 41 2 B 90.1 171.4
## 13 ID13 53 1 A 53.7 151.3
## 14 ID14 57 2 B 99.3 155.4
## 15 ID15 41 1 B 88.6 151.6
## 16 ID16 20 2 B 59.9 181.8
## 17 ID17 39 2 B 50.3 165.7
## 18 ID18 19 1 B 90.8 175.4
## 19 ID19 41 1 B 85.3 195.4
## 20 ID20 61 1 B 86.5 162.5
## 21 ID21 47 1 A 88.6 170.5
## 22 ID22 55 1 C 53.7 187.8
## 23 ID23 19 1 B 67.9 161.4
## 24 ID24 38 1 B 55.8 153.8
## 25 ID25 50 1 B 93.2 164.5
## 26 ID26 29 2 B 81.2 158.1
## 27 ID27 39 2 B 66.5 196.5
## 28 ID28 61 1 B 53.2 190.4
## 29 ID29 42 2 C 65.5 181.7
## 30 ID30 44 2 C 66.3 193.6
summary(caso_practico)
## Id Edad Gene Trat
## Length:30 Min. :19.00 Min. :1.0 Length:30
## Class :character 1st Qu.:33.00 1st Qu.:1.0 Class :character
## Mode :character Median :41.00 Median :1.5 Mode :character
## Mean :41.37 Mean :1.5
## 3rd Qu.:52.25 3rd Qu.:2.0
## Max. :61.00 Max. :2.0
## Peso Alt
## Min. :50.30 Min. :151.3
## 1st Qu.:57.70 1st Qu.:163.0
## Median :66.40 Median :175.8
## Mean :70.65 Mean :175.1
## 3rd Qu.:86.20 3rd Qu.:187.5
## Max. :99.30 Max. :196.5
caso_practico$IMC <- caso_practico$Peso/(caso_practico$Alt/100)^2
caso_practico
## Id Edad Gene Trat Peso Alt IMC
## 1 ID01 56 1 B 54.4 186.5 15.64016
## 2 ID02 46 1 B 59.8 181.9 18.07323
## 3 ID03 32 2 A 52.3 194.4 13.83914
## 4 ID04 60 2 B 66.3 173.6 21.99956
## 5 ID05 25 2 A 69.4 156.0 28.51742
## 6 ID06 38 2 B 63.6 185.7 18.44307
## 7 ID07 56 2 C 91.4 188.0 25.86012
## 8 ID08 36 1 C 67.8 178.1 21.37479
## 9 ID09 40 2 A 64.0 188.5 18.01181
## 10 ID10 28 2 C 77.1 174.7 25.26205
## 11 ID11 28 1 C 57.0 176.1 18.38045
## 12 ID12 41 2 B 90.1 171.4 30.66925
## 13 ID13 53 1 A 53.7 151.3 23.45829
## 14 ID14 57 2 B 99.3 155.4 41.11944
## 15 ID15 41 1 B 88.6 151.6 38.55097
## 16 ID16 20 2 B 59.9 181.8 18.12337
## 17 ID17 39 2 B 50.3 165.7 18.31989
## 18 ID18 19 1 B 90.8 175.4 29.51390
## 19 ID19 41 1 B 85.3 195.4 22.34086
## 20 ID20 61 1 B 86.5 162.5 32.75740
## 21 ID21 47 1 A 88.6 170.5 30.47789
## 22 ID22 55 1 C 53.7 187.8 15.22590
## 23 ID23 19 1 B 67.9 161.4 26.06530
## 24 ID24 38 1 B 55.8 153.8 23.58965
## 25 ID25 50 1 B 93.2 164.5 34.44166
## 26 ID26 29 2 B 81.2 158.1 32.48570
## 27 ID27 39 2 B 66.5 196.5 17.22251
## 28 ID28 61 1 B 53.2 190.4 14.67499
## 29 ID29 42 2 C 65.5 181.7 19.83953
## 30 ID30 44 2 C 66.3 193.6 17.68898
Df_Hombres <- subset(caso_practico, Gene == 2)
Df_Hombres
## Id Edad Gene Trat Peso Alt IMC
## 3 ID03 32 2 A 52.3 194.4 13.83914
## 4 ID04 60 2 B 66.3 173.6 21.99956
## 5 ID05 25 2 A 69.4 156.0 28.51742
## 6 ID06 38 2 B 63.6 185.7 18.44307
## 7 ID07 56 2 C 91.4 188.0 25.86012
## 9 ID09 40 2 A 64.0 188.5 18.01181
## 10 ID10 28 2 C 77.1 174.7 25.26205
## 12 ID12 41 2 B 90.1 171.4 30.66925
## 14 ID14 57 2 B 99.3 155.4 41.11944
## 16 ID16 20 2 B 59.9 181.8 18.12337
## 17 ID17 39 2 B 50.3 165.7 18.31989
## 26 ID26 29 2 B 81.2 158.1 32.48570
## 27 ID27 39 2 B 66.5 196.5 17.22251
## 29 ID29 42 2 C 65.5 181.7 19.83953
## 30 ID30 44 2 C 66.3 193.6 17.68898
Df_Mujeres <- subset(caso_practico, Gene == 1)
Df_Mujeres
## Id Edad Gene Trat Peso Alt IMC
## 1 ID01 56 1 B 54.4 186.5 15.64016
## 2 ID02 46 1 B 59.8 181.9 18.07323
## 8 ID08 36 1 C 67.8 178.1 21.37479
## 11 ID11 28 1 C 57.0 176.1 18.38045
## 13 ID13 53 1 A 53.7 151.3 23.45829
## 15 ID15 41 1 B 88.6 151.6 38.55097
## 18 ID18 19 1 B 90.8 175.4 29.51390
## 19 ID19 41 1 B 85.3 195.4 22.34086
## 20 ID20 61 1 B 86.5 162.5 32.75740
## 21 ID21 47 1 A 88.6 170.5 30.47789
## 22 ID22 55 1 C 53.7 187.8 15.22590
## 23 ID23 19 1 B 67.9 161.4 26.06530
## 24 ID24 38 1 B 55.8 153.8 23.58965
## 25 ID25 50 1 B 93.2 164.5 34.44166
## 28 ID28 61 1 B 53.2 190.4 14.67499
mix<-rbind(Df_Hombres, Df_Mujeres)
mix
## Id Edad Gene Trat Peso Alt IMC
## 3 ID03 32 2 A 52.3 194.4 13.83914
## 4 ID04 60 2 B 66.3 173.6 21.99956
## 5 ID05 25 2 A 69.4 156.0 28.51742
## 6 ID06 38 2 B 63.6 185.7 18.44307
## 7 ID07 56 2 C 91.4 188.0 25.86012
## 9 ID09 40 2 A 64.0 188.5 18.01181
## 10 ID10 28 2 C 77.1 174.7 25.26205
## 12 ID12 41 2 B 90.1 171.4 30.66925
## 14 ID14 57 2 B 99.3 155.4 41.11944
## 16 ID16 20 2 B 59.9 181.8 18.12337
## 17 ID17 39 2 B 50.3 165.7 18.31989
## 26 ID26 29 2 B 81.2 158.1 32.48570
## 27 ID27 39 2 B 66.5 196.5 17.22251
## 29 ID29 42 2 C 65.5 181.7 19.83953
## 30 ID30 44 2 C 66.3 193.6 17.68898
## 1 ID01 56 1 B 54.4 186.5 15.64016
## 2 ID02 46 1 B 59.8 181.9 18.07323
## 8 ID08 36 1 C 67.8 178.1 21.37479
## 11 ID11 28 1 C 57.0 176.1 18.38045
## 13 ID13 53 1 A 53.7 151.3 23.45829
## 15 ID15 41 1 B 88.6 151.6 38.55097
## 18 ID18 19 1 B 90.8 175.4 29.51390
## 19 ID19 41 1 B 85.3 195.4 22.34086
## 20 ID20 61 1 B 86.5 162.5 32.75740
## 21 ID21 47 1 A 88.6 170.5 30.47789
## 22 ID22 55 1 C 53.7 187.8 15.22590
## 23 ID23 19 1 B 67.9 161.4 26.06530
## 24 ID24 38 1 B 55.8 153.8 23.58965
## 25 ID25 50 1 B 93.2 164.5 34.44166
## 28 ID28 61 1 B 53.2 190.4 14.67499
)