Lab_1
2025-03-01
library(openxlsx) # Recommended
library(MASS)
library(RColorBrewer)Ejercicio 1 –> Hecho
??Rcmdr##Ejercicio 2
#Importad txt and summary
View(random_data)
head(random_data)## Measurement1 Measurement2 Category Score
## 1 2.1 50 X 78.5
## 2 3.5 60 Y 80.1
## 3 4.0 70 X 82.3
## 4 5.2 80 Z 85.6
## 5 6.3 90 Y 88.9
## 6 7.4 100 X 91.2summary(random_data)## Measurement1 Measurement2 Category Score
## Min. : 2.100 Min. : 50 Length:9 Min. :78.50
## 1st Qu.: 4.000 1st Qu.: 70 Class :character 1st Qu.:82.30
## Median : 6.300 Median : 90 Mode :character Median :88.90
## Mean : 6.233 Mean : 90 Mean :88.04
## 3rd Qu.: 8.500 3rd Qu.:110 3rd Qu.:93.50
## Max. :10.100 Max. :130 Max. :97.30#Importad csv and fivenum
fivenum(random_data$Measurement1)## [1] 2.1 4.0 6.3 8.5 10.1fivenum(random_data$Measurement2)## [1] 50 70 90 110 130Ejercicio 3
data("anorexia")
#Mostrar tipo de datos
dim(anorexia) # numero de observaciones y variables## [1] 72 3names(anorexia) # nombre de las variables## [1] "Treat" "Prewt" "Postwt"# Con esto sabemos que hay 72 observaciones y tres variables con los nombres treat, Prewt and Postwt
#Comprobar si hay null y NA values
table(is.na(anorexia))##
## FALSE
## 216is.null(anorexia)## [1] FALSE# No hay valores Na en ninguna columna y el dataset no es null.
#Cambiar el nombre de los valores
# Convertir Treat a factor con nuevos nombres
anorexia$Treat <- factor(anorexia$Treat,
levels = c("CBT", "Cont", "FT"),
labels = c("Cogn Beh Tr", "Contr", "Fam Tr"))
# Verificar cambios
table(anorexia$Treat)##
## Cogn Beh Tr Contr Fam Tr
## 29 26 17Ejercicio 4
data("biopsy")
#a
write.csv(biopsy, "biopsy_data.csv", row.names = FALSE) # Usamos el False porque no necesitamos en nombre de las filas
#b
data("Melanoma")
write.csv(Melanoma, "Melanoma_data.csv", row.names = FALSE)
write.table(Melanoma, "Melanoma_data.txt", sep ="\t", row.names= FALSE) # "\t" par tabulacion
write.xlsx(Melanoma, "Melanoma_data.xlsx")
# c
summary_age <- summary(Melanoma$age)
write(summary_age, file="melanoma_age_sumary.doc")##Ejercicio 5
data("birthwt")
#a, b y c
max_age <- max(birthwt$age)
max_age## [1] 45min_age <- min(birthwt$age)
min_age## [1] 14age_range <- max_age - min_age
age_range## [1] 31#La edad maxima es 45 y la minima es de 14. La diferencia es de 31 años
#d
madre_d <- subset(birthwt, bwt == min(bwt))$smoke # Primer encuentra el valor minimo y luego mire si fuma.
madre_d## [1] 1# El resultado es 1, por lo que si fuma.
#e
madre_e <- subset(birthwt, age == max(age))$bwt
madre_e## [1] 4990#Solucion de la profesore
birthwt$bwt[birthwt$age==max(birthwt$age)]## [1] 4990# Ell resultado es el mismo 4990
#f
list_bebes <- birthwt$bwt[birthwt$ftv<=2]
list_bebes## [1] 2523 2557 2594 2600 2622 2637 2637 2663 2665 2722 2733 2751 2750 2769 2769
## [16] 2778 2807 2821 2835 2836 2863 2877 2877 2906 2920 2920 2920 2920 2948 2948
## [31] 2977 2977 2977 2977 2922 3005 3033 3042 3062 3062 3062 3062 3062 3090 3090
## [46] 3090 3100 3104 3132 3147 3175 3175 3203 3203 3203 3225 3225 3232 3232 3234
## [61] 3260 3274 3274 3317 3317 3317 3321 3331 3374 3374 3402 3416 3444 3459 3460
## [76] 3473 3544 3487 3544 3572 3572 3586 3600 3614 3614 3629 3629 3637 3643 3651
## [91] 3651 3651 3651 3699 3728 3756 3770 3770 3770 3790 3799 3827 3856 3860 3884
## [106] 3884 3912 3940 3941 3941 3969 3983 3997 3997 4054 4054 4111 4153 4167 4174
## [121] 4238 4593 4990 709 1021 1135 1330 1474 1588 1588 1701 1729 1790 1818 1885
## [136] 1893 1899 1928 1928 1928 1936 1970 2055 2055 2082 2084 2084 2100 2125 2187
## [151] 2187 2211 2225 2240 2240 2282 2296 2296 2325 2353 2353 2367 2381 2381 2381
## [166] 2410 2410 2410 2424 2438 2442 2466 2466 2466 2495 2495 2495#Ejercicio 6
data("anorexia")
matrix_anorexia <- matrix(c(anorexia$Prewt,anorexia$Postwt),ncol=2)
matrix_anorexia## [,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","I2","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 I2 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
registros_22 <- subset(Trat_Pulmon, Edad >22)
registros_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 I2 27 2 103.4 184 NO
## 23 I23 26 1 65.8 189 SÍ
## 25 I25 29 2 85.0 175 SÍ#b
Trat_Pulmon[3,4]## [1] 79.3#c
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 NOEjercicio 8
# a --> Hecho
#b
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
## 46 154 18 4 1
## 47 160 20 4 1
## 48 157 21 4 1
## 49 41 0 5 1
## 50 42 2 5 1
## 51 48 4 5 1
## 52 60 6 5 1
## 53 79 8 5 1
## 54 106 10 5 1
## 55 141 12 5 1
## 56 164 14 5 1
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## 60 223 21 5 1
## 61 41 0 6 1
## 62 49 2 6 1
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## 73 41 0 7 1
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## 76 71 6 7 1
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## 512 117 10 45 4
## 513 135 12 45 4
## 514 141 14 45 4
## 515 147 16 45 4
## 516 174 18 45 4
## 517 197 20 45 4
## 518 196 21 45 4
## 519 40 0 46 4
## 520 52 2 46 4
## 521 62 4 46 4
## 522 82 6 46 4
## 523 101 8 46 4
## 524 120 10 46 4
## 525 144 12 46 4
## 526 156 14 46 4
## 527 173 16 46 4
## 528 210 18 46 4
## 529 231 20 46 4
## 530 238 21 46 4
## 531 41 0 47 4
## 532 53 2 47 4
## 533 66 4 47 4
## 534 79 6 47 4
## 535 100 8 47 4
## 536 123 10 47 4
## 537 148 12 47 4
## 538 157 14 47 4
## 539 168 16 47 4
## 540 185 18 47 4
## 541 210 20 47 4
## 542 205 21 47 4
## 543 39 0 48 4
## 544 50 2 48 4
## 545 62 4 48 4
## 546 80 6 48 4
## 547 104 8 48 4
## 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
## 571 105 8 50 4
## 572 122 10 50 4
## 573 155 12 50 4
## 574 175 14 50 4
## 575 205 16 50 4
## 576 234 18 50 4
## 577 264 20 50 4
## 578 264 21 50 4head(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 1plot(ChickWeight$weight, ylab = "Chicken Weight", xlab = "index", col = brewer.pal(9, "Greens"), main="Grafico de dipersion")
#C
boxplot(ChickWeight$Time, ylab="time", col="blue", main="Gráfico 2")
9
# Crear una nueva columna con la diferencia de peso
anorexia_treat_df <- data.frame( Treat = anorexia$Treat, Weight_Diff = anorexia$Postwt - anorexia$Prewt )
# Ver el resultado
head(anorexia_treat_df)## Treat Weight_Diff
## 1 Cont -0.5
## 2 Cont -9.3
## 3 Cont -5.4
## 4 Cont 12.3
## 5 Cont -2.0
## 6 Cont -10.2# Filtrar aquellos que ganaron peso
anorexia_treat_gain_df <- subset(anorexia_treat_df, Weight_Diff > 0)
# Ver el resultado
head(anorexia_treat_gain_df)## Treat Weight_Diff
## 4 Cont 12.3
## 8 Cont 11.6
## 10 Cont 6.2
## 13 Cont 8.3
## 14 Cont 3.3
## 15 Cont 11.3# Filtrar aquellos con tratamiento "Cont" y que ganaron peso
anorexia_treat_C_df <- subset(anorexia_treat_gain_df, Treat == "Cont")
# Ver el resultado
head(anorexia_treat_C_df)## Treat Weight_Diff
## 4 Cont 12.3
## 8 Cont 11.6
## 10 Cont 6.2
## 13 Cont 8.3
## 14 Cont 3.3
## 15 Cont 11.3#Ejercicio 10