Exercises Unit 1
2025-10-08
Ejercicio 1
Utilizando las funciones citadas en este Laboratorio, comprobad qué paquetes tenéis instalados en vuestra versión de RStudio e instalad el paquete MASS y el paquete Survival y comprobad la información que contienen.
Buscad información sobre el paquete Rcmdr (R Commander) desde la consola.
- Comprobar paquetes instalados en la sesión de R
library()sessionInfo()## R version 4.3.3 (2024-02-29)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS 15.1.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Madrid
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.5.1 fastmap_1.2.0 xfun_0.49
## [5] cachem_1.1.0 knitr_1.49 htmltools_0.5.8.1 rmarkdown_2.29
## [9] lifecycle_1.0.4 cli_3.6.3 sass_0.4.9 jquerylib_0.1.4
## [13] compiler_4.3.3 rstudioapi_0.17.1 tools_4.3.3 evaluate_1.0.1
## [17] bslib_0.8.0 yaml_2.3.10 rlang_1.1.4 jsonlite_1.8.9Instalar el paquete MASS y survival
Buscar información del paquete Rcmdr (RCommander)
??RcmdrEjercicio 2
- Importad un archivo de texto y buscad un summary() de tres variables que escojáis.
- Importad un archivo «.csv» y buscad un fivenum() de dos variables que os parezcan relevantes para el estudio.
df <- read.csv("/Users/andre/Documents/24genetics/Banzai_Legend/Banzai_v2.6_including_noclientisrael_29092022/K15_15092022_v2.6_ItalyRaveane_from1300_noclientashkenazi.csv", sep = ",")summary(df)## X North_Sea Atlantic Baltic
## Length:3039 Min. : 0.000 Min. : 0.000 Min. : 0.00
## Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Mode :character Median : 1.560 Median : 1.680 Median : 1.06
## Mean : 7.418 Mean : 7.981 Mean : 5.09
## 3rd Qu.:13.140 3rd Qu.:13.040 3rd Qu.: 6.30
## Max. :88.080 Max. :90.000 Max. :99.16
## Eastern_Euro West_Med West_Asian East_Med
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.810 Median : 0.900 Median : 1.380 Median : 0.640
## Mean : 5.527 Mean : 5.943 Mean : 7.789 Mean : 8.597
## 3rd Qu.: 8.275 3rd Qu.: 9.525 3rd Qu.: 9.780 3rd Qu.:12.890
## Max. :91.570 Max. :99.110 Max. :90.730 Max. :88.590
## Red_Sea South_Asian Southeast_Asian Siberian
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.32 Median : 0.780 Median : 0.48 Median : 0.540
## Mean : 3.33 Mean : 5.493 Mean :14.65 Mean : 8.294
## 3rd Qu.: 3.04 3rd Qu.: 4.040 3rd Qu.:12.95 3rd Qu.: 5.265
## Max. :100.00 Max. :88.390 Max. :95.97 Max. :94.520
## Amerindian Oceanian Northeast_African Sub.Saharan
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.350 Median : 0.190 Median : 0.070 Median : 0.050
## Mean : 8.184 Mean : 2.877 Mean : 3.494 Mean : 5.329
## 3rd Qu.: 1.400 3rd Qu.: 0.740 3rd Qu.: 1.020 3rd Qu.: 0.730
## Max. :100.000 Max. :100.000 Max. :96.630 Max. :100.000fivenum(df$North_Sea)## [1] 0.00 0.00 1.56 13.14 88.08fivenum(df$Sub.Saharan)## [1] 0.00 0.00 0.05 0.73 100.00#Ejercicio 3
library(MASS)
data(anorexia)
anorexia_test <- anorexiahead(anorexia)## 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 76.1
## 6 Cont 88.3 78.1summary(anorexia)## Treat Prewt Postwt
## CBT :29 Min. :70.00 Min. : 71.30
## Cont:26 1st Qu.:79.60 1st Qu.: 79.33
## FT :17 Median :82.30 Median : 84.05
## Mean :82.41 Mean : 85.17
## 3rd Qu.:86.00 3rd Qu.: 91.55
## Max. :94.90 Max. :103.60class(anorexia$Treat)## [1] "factor"class(anorexia$Prewt)## [1] "numeric"class(anorexia$Postwt)## [1] "numeric"table(is.na(anorexia))##
## FALSE
## 216table(is.null(anorexia))##
## FALSE
## 1factor(anorexia_test$Treat,levels=c("CBT","Cont","FT"),labels=c("Cogn
Beh Tr","Contr","Fam Tr"))## [1] Contr Contr Contr Contr Contr
## [6] Contr Contr Contr Contr Contr
## [11] Contr Contr Contr Contr Contr
## [16] Contr Contr Contr Contr Contr
## [21] Contr Contr Contr Contr Contr
## [26] Contr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr
## [31] Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr
## [36] Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr
## [41] Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr
## [46] Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr
## [51] Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr Cogn\nBeh Tr
## [56] Fam Tr Fam Tr Fam Tr Fam Tr Fam Tr
## [61] Fam Tr Fam Tr Fam Tr Fam Tr Fam Tr
## [66] Fam Tr Fam Tr Fam Tr Fam Tr Fam Tr
## [71] Fam Tr Fam Tr
## Levels: Cogn\nBeh Tr Contr Fam TrEjercicio 4
data(biopsy)write.csv(biopsy, "biopsy.csv")data(Melanoma)head(Melanoma)## time status sex age year thickness ulcer
## 1 10 3 1 76 1972 6.76 1
## 2 30 3 1 56 1968 0.65 0
## 3 35 2 1 41 1977 1.34 0
## 4 99 3 0 71 1968 2.90 0
## 5 185 1 1 52 1965 12.08 1
## 6 204 1 1 28 1971 4.84 1write.csv(Melanoma, "./Melanoma.csv")write.table(Melanoma, "./Melanoma.tsv")write.matrix(Melanoma, "./Melanoma.matrix")summary_age <- summary(Melanoma$age)capture.output(summary(Melanoma$age), file = "summary_age_melanoma.txt")#Ejercicio 5
data("birthwt")max(birthwt$age)## [1] 45min(birthwt$age)## [1] 14range(birthwt$age)## [1] 14 45subset(birthwt, lwt==min(lwt))$smoke## [1] 1Sí fumaba
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
##
## select## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionfilter(birthwt, age==max(age))$lwt## [1] 123123 lwt
filter(birthwt, ftv<2)$lwt## [1] 182 105 107 124 118 103 123 113 95 150 95 100 100 98 120 120 202 120
## [19] 167 122 168 113 113 90 121 155 125 124 109 130 160 90 90 132 85 120
## [37] 128 130 115 110 110 153 103 119 119 110 140 133 169 141 112 115 112 135
## [55] 229 140 121 190 131 170 110 127 123 120 105 130 175 125 133 235 95 135
## [73] 135 154 147 147 137 110 184 110 110 120 241 112 169 120 117 170 134 135
## [91] 130 95 158 160 115 129 170 120 116 123 120 187 105 85 150 97 128 132
## [109] 165 105 91 115 130 92 155 103 125 89 112 117 138 130 130 130 80 110
## [127] 105 109 148 110 121 96 102 110 187 122 105 115 120 142 130 110 154 190
## [145] 101 94 142Ejercicio 6
mtr <- matrix(c(anorexia$Prewt, anorexia$Treat), ncol = 2)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 SÍTrat_Pulmon[Trat_Pulmon$Edad>22,]## Identificador Edad Sexo Peso Alt Fuma
## 1 I1 23 1 76.5 165 SÍ
## 2 I2 24 2 81.2 154 NO
## 5 I5 23 1 67.3 164 NO
## 6 I6 25 2 78.6 175 NO
## 7 I7 26 2 67.9 182 NO
## 8 I8 24 2 100.2 165 SÍ
## 11 I11 23 1 65.4 158 NO
## 12 I12 25 2 67.5 183 NO
## 13 I13 26 2 87.4 184 SÍ
## 14 I14 24 2 99.7 164 SÍ
## 17 I17 25 1 65.4 182 NO
## 18 I18 26 2 73.7 179 NO
## 19 I19 24 2 85.1 165 SÍ
## 21 I21 25 1 54.8 183 SÍ
## 22 I22 27 2 103.4 184 NO
## 23 I23 26 1 65.8 189 SÍ
## 25 I25 29 2 85.0 175 SÍTrat_Pulmon[3,4]## [1] 79.3subset(Trat_Pulmon, Edad<27, select= -c(Alt))## Identificador Edad Sexo Peso Fuma
## 1 I1 23 1 76.5 SÍ
## 2 I2 24 2 81.2 NO
## 3 I3 21 1 79.3 SÍ
## 4 I4 22 1 59.5 SÍ
## 5 I5 23 1 67.3 NO
## 6 I6 25 2 78.6 NO
## 7 I7 26 2 67.9 NO
## 8 I8 24 2 100.2 SÍ
## 9 I9 21 1 97.8 SÍ
## 10 I10 22 2 56.4 SÍ
## 11 I11 23 1 65.4 NO
## 12 I12 25 2 67.5 NO
## 13 I13 26 2 87.4 SÍ
## 14 I14 24 2 99.7 SÍ
## 15 I15 22 1 87.6 SÍ
## 16 I16 21 1 93.4 SÍ
## 17 I17 25 1 65.4 NO
## 18 I18 26 2 73.7 NO
## 19 I19 24 2 85.1 SÍ
## 20 I20 21 2 61.2 SÍ
## 21 I21 25 1 54.8 SÍ
## 23 I23 26 1 65.8 SÍ
## 24 I24 22 1 71.7 NO#Ejercicio 8
data("ChickWeight")- Gráfico de dispersión
plot(ChickWeight$weight, col=blues9)
- Boxplot time
boxplot(ChickWeight$Time, col = "lightblue")
Ejercicio 9
anorexia_treat_df <- data.frame("Treat"=anorexia$Treat, "Prewt_Postwt"=anorexia$Prewt-anorexia$Postwt)
anorexia_treat_C_df <- subset(anorexia_treat_df, anorexia_treat_df$Prewt_Postwt>0 & anorexia_treat_df$Treat=="Cont")