LAB1- Software para el Análisis de Datos
Edward Espejo
2026-10-11
Ejercicio 1:
Para comprobar los paquetes instalados:
sessionInfo()## R version 4.6.1 (2026-06-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## time zone: Europe/Tallinn
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.39 R6_2.6.1 fastmap_1.2.0 xfun_0.61
## [5] cachem_1.1.0 knitr_1.52 htmltools_0.5.9 rmarkdown_2.32
## [9] lifecycle_1.0.5 cli_3.6.6 sass_0.4.10 jquerylib_0.1.4
## [13] compiler_4.6.1 rstudioapi_0.19.0 tools_4.6.1 evaluate_1.0.5
## [17] bslib_0.12.0 yaml_2.3.12 rlang_1.3.0 jsonlite_2.0.0library()Para instalar los paquetes MASS y survival:
install.packages(c("MASS","Survival"))Para comprobrar la información de los paquetes instalados:
packageDescription("MASS")
packageDescription("survival")Para obtener información sobre un paquete específico:
??Rcmdr## starting httpd help server ... doneEjercicio 2:
Para cargar un archivo de texto y buscar un summary() de tres variables:
hg38_RefSeq <- read.csv("~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/hg38_RefSeq.txt", sep="")
summary(hg38_RefSeq[, c("name","strand","cdsStart")])## name strand cdsStart
## Length :172766 Length :172766 Min. : 0
## N.unique :163988 N.unique : 2 1st Qu.: 25925963
## N.blank : 0 N.blank : 0 Median : 56468697
## Min.nchar: 11 Min.nchar: 1 Mean : 70879841
## Max.nchar: 14 Max.nchar: 1 3rd Qu.:108149909
## Max. :248917337Para importar un archivo .csv y buscar un fivenum() de dos variables:
pinguinos <- read.csv("~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/penguins.csv")
fivenum(pinguinos$body_mass_g)## [1] 2700 3550 4050 4750 6300fivenum(pinguinos$flipper_length_mm)## [1] 172 190 197 213 231Ejercicio 3:
Para cargar el conjunto de datos anorexia del paquete MASS y mostrar los tipos de datos que contiene:
library(MASS)
data("anorexia")
str(anorexia)## 'data.frame': 72 obs. of 3 variables:
## $ Treat : Factor w/ 3 levels "CBT","Cont","FT": 2 2 2 2 2 2 2 2 2 2 ...
## $ Prewt : num 80.7 89.4 91.8 74 78.1 88.3 87.3 75.1 80.6 78.4 ...
## $ Postwt: num 80.2 80.1 86.4 86.3 76.1 78.1 75.1 86.7 73.5 84.6 ...summary(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. :103.60Para comprobar si en el conjunto de datos existen los valores NA y NULL:
sum(is.na(anorexia))## [1] 0is.null(anorexia)## [1] FALSEPara transformar los nombres de los valores CBT, Cont y FT en la variable Treat:
levels(anorexia$Treat) <- c("Cogn Beh Tr","Contr","Fam Tr")
str(anorexia)## 'data.frame': 72 obs. of 3 variables:
## $ Treat : Factor w/ 3 levels "Cogn Beh Tr",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Prewt : num 80.7 89.4 91.8 74 78.1 88.3 87.3 75.1 80.6 78.4 ...
## $ Postwt: num 80.2 80.1 86.4 86.3 76.1 78.1 75.1 86.7 73.5 84.6 ...summary(anorexia$Treat)## Cogn Beh Tr Contr Fam Tr
## 29 26 17Ejercicio 4:
Para exportar los datos biopsy del paquete MASS a un archivo .csv:
library(MASS)
data("biopsy")
head(biopsy)## ID V1 V2 V3 V4 V5 V6 V7 V8 V9 class
## 1 1000025 5 1 1 1 2 1 3 1 1 benign
## 2 1002945 5 4 4 5 7 10 3 2 1 benign
## 3 1015425 3 1 1 1 2 2 3 1 1 benign
## 4 1016277 6 8 8 1 3 4 3 7 1 benign
## 5 1017023 4 1 1 3 2 1 3 1 1 benign
## 6 1017122 8 10 10 8 7 10 9 7 1 malignantwrite.csv(biopsy,file="~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/biopsy.csv")Para generar tres archivos con diferentes formatos de los datos Melanoma del paquete MASS:
library(MASS)
data("Melanoma")
head(Melanoma)## time status sex age year thickness ulcer
## 1 10 3 1 76 1972 6.76 1
## 2 30 3 1 56 1968 0.65 0
## 3 35 2 1 41 1977 1.34 0
## 4 99 3 0 71 1968 2.90 0
## 5 185 1 1 52 1965 12.08 1
## 6 204 1 1 28 1971 4.84 1write.table(Melanoma,file="~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/Melanoma1.txt")
write.csv(Melanoma,file="~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/Melanoma2.csv")
library(foreign)
write.foreign(Melanoma,"~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/Melanoma1.txt","~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/Melanoma3", package="SAS")
Imagen 1. Comprobación de la creación de
los archivos con diferentes formatos
Para generar un archivo .doc luego de generar un resumen de la variable Age:
Age = summary(Melanoma$age)
capture.output(Age,file="Age_Melanoma.doc")Para importar el conjunto de datos .csv obtenido a partir del repositorio https://ouhsc.edu/bserdac/dthompso/web/statres.htm sobre el consumo de alcohol en el estado de Alabama en el año 1999:
datos <- read.csv("~/UOC/Semestre 2/Software para el Análisis de datos/Reto1_InstalacionEstadisticaDescriptiva/LAB1/NCCD_COVE_API.csv")
head(datos)## X95..CI BreakOutCategoryId BreakOutId Category Category_DisplayOrder
## 1 2.1-3.9 CAT1 BO1 Overall 1
## 2 1.8-3.4 CAT1 BO1 Overall 1
## 3 2.2-4.0 CAT1 BO1 Overall 1
## 4 96.1-97.9 CAT1 BO1 Overall 1
## 5 96.6-98.2 CAT1 BO1 Overall 1
## 6 96.0-97.8 CAT1 BO1 Overall 1
## Class ClassId Class_DisplayOrder Createdon
## 1 Alcohol Consumption CLASS01 1 Tue, 15 Sep 2026 13:58:03 GMT
## 2 Alcohol Consumption CLASS01 1 Tue, 15 Sep 2026 13:58:03 GMT
## 3 Alcohol Consumption CLASS01 1 Tue, 15 Sep 2026 13:58:03 GMT
## 4 Alcohol Consumption CLASS01 1 Tue, 15 Sep 2026 13:58:03 GMT
## 5 Alcohol Consumption CLASS01 1 Tue, 15 Sep 2026 13:58:03 GMT
## 6 Alcohol Consumption CLASS01 1 Tue, 15 Sep 2026 13:58:03 GMT
## DataSource Data_Type Data_Value_Format Dataset Footnotes
## 1 BRFSS Crude Prevalence 3.0 Nationwide NA
## 2 BRFSS Crude Prevalence 2.6 Nationwide NA
## 3 BRFSS Crude Prevalence 3.1 Nationwide NA
## 4 BRFSS Crude Prevalence 97.0 Nationwide NA
## 5 BRFSS Crude Prevalence 97.4 Nationwide NA
## 6 BRFSS Crude Prevalence 96.9 Nationwide NA
## Footnotes_Symbol GeoLocation_Lat GeoLocation_Long High_CI LocationCategory
## 1 NA 32.84057 -86.63186 3.9 States
## 2 NA 32.84057 -86.63186 3.4 States
## 3 NA 32.84057 -86.63186 4.0 States
## 4 NA 32.84057 -86.63186 97.9 States
## 5 NA 32.84057 -86.63186 98.2 States
## 6 NA 32.84057 -86.63186 97.8 States
## LocationId Location_DisplayOrder Low_CI Percentage Percentage_Format
## 1 1 3 2.1 3.0 3.0
## 2 1 3 1.8 2.6 2.6
## 3 1 3 2.2 3.1 3.1
## 4 1 3 96.1 97.0 97.0
## 5 1 3 96.6 97.4 97.4
## 6 1 3 96.0 96.9 96.9
## Question
## 1 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 2 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 3 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 4 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 5 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 6 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## QuestionId
## 1 _RFDRCHR
## 2 _RFDRCHR
## 3 _RFDRCHR
## 4 _RFDRCHR
## 5 _RFDRCHR
## 6 _RFDRCHR
## Question_Code
## 1 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 2 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 3 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 4 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 5 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## 6 Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calculated from one or more BRFSS questions)
## Question_DisplayOrder Question_Year Response ResponseId Response_DisplayOrder
## 1 1 1999 Yes RESP046 1
## 2 1 1999 Yes RESP046 1
## 3 1 1999 Yes RESP046 1
## 4 1 1999 No RESP054 9
## 5 1 1999 No RESP054 9
## 6 1 1999 No RESP054 9
## Response_Filter Series Series_DisplayOrder State State_Abbr State_Code
## 1 All Responses Yes 1 Alabama AL Alabama
## 2 All Responses Yes 1 Alabama AL Alabama
## 3 All Responses Yes 1 Alabama AL Alabama
## 4 All Responses No 9 Alabama AL Alabama
## 5 All Responses No 9 Alabama AL Alabama
## 6 All Responses No 9 Alabama AL Alabama
## Subcategory Subcategory_DisplayOrder Topic TopicId
## 1 Overall 1 Chronic Drinking TOPIC13
## 2 Overall 1 Chronic Drinking TOPIC13
## 3 Overall 1 Chronic Drinking TOPIC13
## 4 Overall 1 Chronic Drinking TOPIC13
## 5 Overall 1 Chronic Drinking TOPIC13
## 6 Overall 1 Chronic Drinking TOPIC13
## Topic_DisplayOrder Year Year_Pivot_Display n
## 1 3 1997 Year 55
## 2 3 1995 Year 45
## 3 3 1999 Year 64
## 4 3 1997 Year 2059
## 5 3 1995 Year 1705
## 6 3 1999 Year 1994str(datos)## 'data.frame': 6 obs. of 46 variables:
## $ X95..CI : chr "2.1-3.9" "1.8-3.4" "2.2-4.0" "96.1-97.9" ...
## $ BreakOutCategoryId : chr "CAT1" "CAT1" "CAT1" "CAT1" ...
## $ BreakOutId : chr "BO1" "BO1" "BO1" "BO1" ...
## $ Category : chr "Overall" "Overall" "Overall" "Overall" ...
## $ Category_DisplayOrder : int 1 1 1 1 1 1
## $ Class : chr "Alcohol Consumption" "Alcohol Consumption" "Alcohol Consumption" "Alcohol Consumption" ...
## $ ClassId : chr "CLASS01" "CLASS01" "CLASS01" "CLASS01" ...
## $ Class_DisplayOrder : int 1 1 1 1 1 1
## $ Createdon : chr "Tue, 15 Sep 2026 13:58:03 GMT" "Tue, 15 Sep 2026 13:58:03 GMT" "Tue, 15 Sep 2026 13:58:03 GMT" "Tue, 15 Sep 2026 13:58:03 GMT" ...
## $ DataSource : chr "BRFSS" "BRFSS" "BRFSS" "BRFSS" ...
## $ Data_Type : chr "Crude Prevalence" "Crude Prevalence" "Crude Prevalence" "Crude Prevalence" ...
## $ Data_Value_Format : num 3 2.6 3.1 97 97.4 96.9
## $ Dataset : chr "Nationwide" "Nationwide" "Nationwide" "Nationwide" ...
## $ Footnotes : logi NA NA NA NA NA NA
## $ Footnotes_Symbol : logi NA NA NA NA NA NA
## $ GeoLocation_Lat : num 32.8 32.8 32.8 32.8 32.8 ...
## $ GeoLocation_Long : num -86.6 -86.6 -86.6 -86.6 -86.6 ...
## $ High_CI : num 3.9 3.4 4 97.9 98.2 97.8
## $ LocationCategory : chr "States" "States" "States" "States" ...
## $ LocationId : int 1 1 1 1 1 1
## $ Location_DisplayOrder : int 3 3 3 3 3 3
## $ Low_CI : num 2.1 1.8 2.2 96.1 96.6 96
## $ Percentage : num 3 2.6 3.1 97 97.4 96.9
## $ Percentage_Format : num 3 2.6 3.1 97 97.4 96.9
## $ Question : chr "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ ...
## $ QuestionId : chr "_RFDRCHR" "_RFDRCHR" "_RFDRCHR" "_RFDRCHR" ...
## $ Question_Code : chr "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ "Chronic drinkers (adult respondents having more than 60 alcoholic beverages in the past month) (variable calcul"| __truncated__ ...
## $ Question_DisplayOrder : int 1 1 1 1 1 1
## $ Question_Year : int 1999 1999 1999 1999 1999 1999
## $ Response : chr "Yes" "Yes" "Yes" "No" ...
## $ ResponseId : chr "RESP046" "RESP046" "RESP046" "RESP054" ...
## $ Response_DisplayOrder : int 1 1 1 9 9 9
## $ Response_Filter : chr "All Responses" "All Responses" "All Responses" "All Responses" ...
## $ Series : chr "Yes" "Yes" "Yes" "No" ...
## $ Series_DisplayOrder : int 1 1 1 9 9 9
## $ State : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ State_Abbr : chr "AL" "AL" "AL" "AL" ...
## $ State_Code : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ Subcategory : chr "Overall" "Overall" "Overall" "Overall" ...
## $ Subcategory_DisplayOrder: int 1 1 1 1 1 1
## $ Topic : chr "Chronic Drinking" "Chronic Drinking" "Chronic Drinking" "Chronic Drinking" ...
## $ TopicId : chr "TOPIC13" "TOPIC13" "TOPIC13" "TOPIC13" ...
## $ Topic_DisplayOrder : int 3 3 3 3 3 3
## $ Year : int 1997 1995 1999 1997 1995 1999
## $ Year_Pivot_Display : chr "Year" "Year" "Year" "Year" ...
## $ n : int 55 45 64 2059 1705 1994Ejercicio 5:
Para cargar el conjunto de datos birthwt:
data("birthwt")
head(birthwt)## low age lwt race smoke ptl ht ui ftv bwt
## 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 2622Para saber la edad máxima de las madres en el conjunto de datos:
max(birthwt$age)## [1] 45Para saber la edad mínima de las madres en el conjunto de datos:
min(birthwt$age)## [1] 14Para saber el rango de edad de las madres en el conjunto de datos:
range(birthwt$age)## [1] 14 45Para saber si la madre, cuyo recién nacido fue el de menor peso, fumaba:
birthwt$smoke[birthwt$bwt==min(birthwt$bwt)]## [1] 1Para saber cuánto pesó el recién nacido cuya madre tenía la edad máxima:
birthwt$bwt[birthwt$age==max(birthwt$age)]## [1] 4990Para saber los pesos de los recién nacidos cuyas madres visitarán menos de dos veces al médico durante el primer trimestre:
birthwt$bwt[birthwt$ftv < 2]## [1] 2523 2557 2600 2622 2637 2637 2663 2665 2722 2733 2751 2769 2769 2778 2807
## [16] 2821 2836 2863 2877 2906 2920 2920 2920 2948 2948 2977 2977 2922 3033 3062
## [31] 3062 3062 3062 3090 3090 3100 3104 3132 3175 3175 3203 3203 3203 3225 3225
## [46] 3232 3234 3260 3274 3317 3317 3331 3374 3374 3402 3416 3444 3459 3460 3473
## [61] 3544 3487 3544 3572 3572 3586 3600 3614 3614 3629 3637 3643 3651 3651 3651
## [76] 3651 3699 3728 3756 3770 3770 3770 3790 3799 3827 3884 3912 3940 3941 3941
## [91] 3969 3997 3997 4054 4054 4111 4174 4238 4593 4990 709 1135 1330 1474 1588
## [106] 1588 1701 1729 1790 1818 1885 1893 1899 1928 1936 1970 2055 2055 2084 2084
## [121] 2100 2125 2187 2187 2211 2225 2240 2240 2282 2296 2296 2325 2353 2353 2367
## [136] 2381 2381 2381 2410 2410 2410 2424 2442 2466 2466 2495 2495Ejercicio 6:
Para crear una matriz de dos columnas con los valores de Prewt y Postwt del conjunto de datos anorexia, en donde cada fila sean los valores correspondientes para cada posición:
library("MASS")
data("anorexia")
head(anorexia)## Treat Prewt Postwt
## 1 Cont 80.7 80.2
## 2 Cont 89.4 80.1
## 3 Cont 91.8 86.4
## 4 Cont 74.0 86.3
## 5 Cont 78.1 76.1
## 6 Cont 88.3 78.1matriz_pesos <- cbind(Prewt=anorexia$Prewt, Postwt=anorexia$Postwt)
head(matriz_pesos)## Prewt Postwt
## [1,] 80.7 80.2
## [2,] 89.4 80.1
## [3,] 91.8 86.4
## [4,] 74.0 86.3
## [5,] 78.1 76.1
## [6,] 88.3 78.1class(matriz_pesos)## [1] "matrix" "array"Ejercicio 7:
Para generar un data frame con 25 registros y 6 variables:
Identificador <- c("I1","I2","I3","I4","I5","I6","I7","I8","I9","I10","I11","I12","I13","I14","I15","I16","I17","I18","I19","I20","I21","I22","I23","I24","I25")
Edad <- c(23,24,21,22,23,25,26,24,21,22,23,25,26,24,22,21,25,26,24,21,25,27,26,22,29)
Sexo <- c(1,2,1,1,1,2,2,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,1,1,2) #1 para mujeres y 2 para hombres
Peso <- c(76.5,81.2,79.3,59.5,67.3,78.6,67.9,100.2,97.8,56.4,65.4,67.5,87.4,99.7,87.6,93.4,65.4,73.7,85.1,61.2,54.8,103.4,65.8,71.7,85.0)
Alt <- c(165,154,178,165,164,175,182,165,178,165,158,183,184,164,189,167,182,179,165,158,183,184,189,166,175) #altura en cm
Fuma <- c("SÍ","NO","SÍ","SÍ","NO","NO","NO","SÍ","SÍ","SÍ","NO","NO","SÍ","SÍ","SÍ","SÍ","NO","NO","SÍ","SÍ","SÍ","NO","SÍ","NO","SÍ")
Trat_Pulmon <- data.frame(Identificador,Edad,Sexo,Peso,Alt,Fuma)
Trat_Pulmon## Identificador Edad Sexo Peso Alt Fuma
## 1 I1 23 1 76.5 165 SÍ
## 2 I2 24 2 81.2 154 NO
## 3 I3 21 1 79.3 178 SÍ
## 4 I4 22 1 59.5 165 SÍ
## 5 I5 23 1 67.3 164 NO
## 6 I6 25 2 78.6 175 NO
## 7 I7 26 2 67.9 182 NO
## 8 I8 24 2 100.2 165 SÍ
## 9 I9 21 1 97.8 178 SÍ
## 10 I10 22 2 56.4 165 SÍ
## 11 I11 23 1 65.4 158 NO
## 12 I12 25 2 67.5 183 NO
## 13 I13 26 2 87.4 184 SÍ
## 14 I14 24 2 99.7 164 SÍ
## 15 I15 22 1 87.6 189 SÍ
## 16 I16 21 1 93.4 167 SÍ
## 17 I17 25 1 65.4 182 NO
## 18 I18 26 2 73.7 179 NO
## 19 I19 24 2 85.1 165 SÍ
## 20 I20 21 2 61.2 158 SÍ
## 21 I21 25 1 54.8 183 SÍ
## 22 I22 27 2 103.4 184 NO
## 23 I23 26 1 65.8 189 SÍ
## 24 I24 22 1 71.7 166 NO
## 25 I25 29 2 85.0 175 SÍPara seleccionar los resgistros con edad > 22:
Trat_Pulmon[Trat_Pulmon$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 SÍPara seleccionar el elemento 3 de la columna 4 del conjunto de datos:
Trat_Pulmon[3,4]## [1] 79.3Para seleccionar todas las filas que tienen una edad < 27 y sin incluir la columna Alt:
Trat_Pulmon2 <- subset(Trat_Pulmon, Edad<27, select=-c(Alt))
Trat_Pulmon2## 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:
Para incorporar el conjunto de datos ChickWeight del paquete datasets:
library(datasets)
data("ChickWeight")
head(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 1Para generar un gráfico de dispersión con la variable weight:
plot(ChickWeight$weight, main="Diagrama de dispersión de la variable Weight", ylab="Weight (g)")
Para generar un diagrama de caja con la variable Time:
boxplot(ChickWeight$Time, main="Diagrama de caja de la variable Time", ylab="Time (days)", col="orange")
Ejercicio 9:
Para cargar los datos del conjunto de datos anorexia:
library(MASS)
data("anorexia")
head(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 78.1Para crear un data frame en el que se incorpore la variable Treat y una nueva variable correspondiente a la diferencia Prewt-Postwt:
diferencia_peso <- anorexia$Prewt-anorexia$Postwt
anorexia_treat_df <- data.frame(Treat=anorexia$Treat, Df=diferencia_peso)
anorexia_treat_df## Treat Df
## 1 Cont 0.5
## 2 Cont 9.3
## 3 Cont 5.4
## 4 Cont -12.3
## 5 Cont 2.0
## 6 Cont 10.2
## 7 Cont 12.2
## 8 Cont -11.6
## 9 Cont 7.1
## 10 Cont -6.2
## 11 Cont 0.2
## 12 Cont 9.2
## 13 Cont -8.3
## 14 Cont -3.3
## 15 Cont -11.3
## 16 Cont 0.0
## 17 Cont 1.0
## 18 Cont 10.6
## 19 Cont 4.6
## 20 Cont 6.7
## 21 Cont -2.8
## 22 Cont -0.3
## 23 Cont -1.8
## 24 Cont -3.7
## 25 Cont -15.9
## 26 Cont 10.2
## 27 CBT -1.7
## 28 CBT -0.7
## 29 CBT 0.1
## 30 CBT 0.7
## 31 CBT 3.5
## 32 CBT -14.9
## 33 CBT -3.5
## 34 CBT -17.1
## 35 CBT 7.6
## 36 CBT -1.6
## 37 CBT -11.7
## 38 CBT -6.1
## 39 CBT -1.1
## 40 CBT 4.0
## 41 CBT -20.9
## 42 CBT 9.1
## 43 CBT -2.1
## 44 CBT 1.4
## 45 CBT -1.4
## 46 CBT 0.3
## 47 CBT 3.7
## 48 CBT 0.8
## 49 CBT -2.4
## 50 CBT -12.6
## 51 CBT -1.9
## 52 CBT -3.9
## 53 CBT -0.1
## 54 CBT -15.4
## 55 CBT 0.7
## 56 FT -11.4
## 57 FT -11.0
## 58 FT -5.5
## 59 FT -9.4
## 60 FT -13.6
## 61 FT 2.9
## 62 FT 0.1
## 63 FT -7.4
## 64 FT -21.5
## 65 FT 5.3
## 66 FT 3.8
## 67 FT -13.4
## 68 FT -13.1
## 69 FT -9.0
## 70 FT -3.9
## 71 FT -5.7
## 72 FT -10.7Para seleccionar los individuos que han ganado peso después del tratamiento:
anorexia_treat_df[anorexia_treat_df$Df>0,]## Treat Df
## 1 Cont 0.5
## 2 Cont 9.3
## 3 Cont 5.4
## 5 Cont 2.0
## 6 Cont 10.2
## 7 Cont 12.2
## 9 Cont 7.1
## 11 Cont 0.2
## 12 Cont 9.2
## 17 Cont 1.0
## 18 Cont 10.6
## 19 Cont 4.6
## 20 Cont 6.7
## 26 Cont 10.2
## 29 CBT 0.1
## 30 CBT 0.7
## 31 CBT 3.5
## 35 CBT 7.6
## 40 CBT 4.0
## 42 CBT 9.1
## 44 CBT 1.4
## 46 CBT 0.3
## 47 CBT 3.7
## 48 CBT 0.8
## 55 CBT 0.7
## 61 FT 2.9
## 62 FT 0.1
## 65 FT 5.3
## 66 FT 3.8Para crear un nuevo conjunto que contenga solo los datos de los individuos que han ganado peso y los que han seguido el tratamiento Cont:
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, unionanorexia_treat_C_df <- filter(anorexia_treat_df, Df >0 & Treat=="Cont")
anorexia_treat_C_df## Treat Df
## 1 Cont 0.5
## 2 Cont 9.3
## 3 Cont 5.4
## 4 Cont 2.0
## 5 Cont 10.2
## 6 Cont 12.2
## 7 Cont 7.1
## 8 Cont 0.2
## 9 Cont 9.2
## 10 Cont 1.0
## 11 Cont 10.6
## 12 Cont 4.6
## 13 Cont 6.7
## 14 Cont 10.2Caso práctico:
Para crear un conjunto de datos inventado con 30 observaciones (15 para hombres y 15 para mujeres) y 6 variables.
set.seed(123)
Id <- 1:30
Edad <- round(rnorm(30, mean=35, sd=5))
Gene <- sample(rep(c(1,2),each=15))
Trat <- sample(c("A", "B", "C"), size=30, replace=TRUE)
Peso <- round(rnorm(30, mean=70, sd=10))
Alt <- round(rnorm(30, mean=170, sd=8))
Midataf <- data.frame(Id, Edad, Gene, Trat, Peso, Alt)
Midataf## Id Edad Gene Trat Peso Alt
## 1 1 32 1 B 65 164
## 2 2 34 1 A 47 172
## 3 3 43 1 B 80 168
## 4 4 35 1 A 63 167
## 5 5 36 2 C 63 162
## 6 6 44 1 C 80 170
## 7 7 37 1 B 67 164
## 8 8 29 2 C 58 157
## 9 9 32 1 A 72 167
## 10 10 33 1 B 69 177
## 11 11 41 2 B 70 165
## 12 12 37 2 C 74 175
## 13 13 37 2 B 66 157
## 14 14 36 1 A 76 170
## 15 15 32 2 C 68 174
## 16 16 44 2 C 73 172
## 17 17 37 2 C 81 171
## 18 18 25 1 B 74 165
## 19 19 39 2 B 67 163
## 20 20 33 1 C 81 162
## 21 21 30 1 A 80 171
## 22 22 34 2 A 75 162
## 23 23 30 1 C 72 166
## 24 24 31 1 B 64 168
## 25 25 32 2 B 84 185
## 26 26 27 2 B 64 165
## 27 27 39 2 B 92 172
## 28 28 36 1 B 85 171
## 29 29 29 2 B 68 162
## 30 30 41 2 C 60 169Para visualizar la información del conjunto de datos:
dim(Midataf)## [1] 30 6length(Midataf)## [1] 6head(Midataf)## Id Edad Gene Trat Peso Alt
## 1 1 32 1 B 65 164
## 2 2 34 1 A 47 172
## 3 3 43 1 B 80 168
## 4 4 35 1 A 63 167
## 5 5 36 2 C 63 162
## 6 6 44 1 C 80 170names(Midataf)## [1] "Id" "Edad" "Gene" "Trat" "Peso" "Alt"str(Midataf)## 'data.frame': 30 obs. of 6 variables:
## $ Id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Edad: num 32 34 43 35 36 44 37 29 32 33 ...
## $ Gene: num 1 1 1 1 2 1 1 2 1 1 ...
## $ Trat: chr "B" "A" "B" "A" ...
## $ Peso: num 65 47 80 63 63 80 67 58 72 69 ...
## $ Alt : num 164 172 168 167 162 170 164 157 167 177 ...summary(Midataf)## Id Edad Gene Trat Peso
## Min. : 1.00 Min. :25.00 Min. :1.0 Length :30 Min. :47.00
## 1st Qu.: 8.25 1st Qu.:32.00 1st Qu.:1.0 N.unique : 3 1st Qu.:65.25
## Median :15.50 Median :34.50 Median :1.5 N.blank : 0 Median :71.00
## Mean :15.50 Mean :34.83 Mean :1.5 Min.nchar: 1 Mean :71.27
## 3rd Qu.:22.75 3rd Qu.:37.00 3rd Qu.:2.0 Max.nchar: 1 3rd Qu.:79.00
## Max. :30.00 Max. :44.00 Max. :2.0 Max. :92.00
## Alt
## Min. :157.0
## 1st Qu.:164.0
## Median :167.5
## Mean :167.8
## 3rd Qu.:171.0
## Max. :185.0Para crear una nueva variable llamada IMC, usando variables existentes, e incorporarla en el conjunto de datos original:
Midataf$IMC <- Midataf$Peso/((Midataf$Alt/100)^2)
Midataf## Id Edad Gene Trat Peso Alt IMC
## 1 1 32 1 B 65 164 24.16716
## 2 2 34 1 A 47 172 15.88697
## 3 3 43 1 B 80 168 28.34467
## 4 4 35 1 A 63 167 22.58955
## 5 5 36 2 C 63 162 24.00549
## 6 6 44 1 C 80 170 27.68166
## 7 7 37 1 B 67 164 24.91077
## 8 8 29 2 C 58 157 23.53037
## 9 9 32 1 A 72 167 25.81663
## 10 10 33 1 B 69 177 22.02432
## 11 11 41 2 B 70 165 25.71166
## 12 12 37 2 C 74 175 24.16327
## 13 13 37 2 B 66 157 26.77593
## 14 14 36 1 A 76 170 26.29758
## 15 15 32 2 C 68 174 22.46003
## 16 16 44 2 C 73 172 24.67550
## 17 17 37 2 C 81 171 27.70083
## 18 18 25 1 B 74 165 27.18090
## 19 19 39 2 B 67 163 25.21736
## 20 20 33 1 C 81 162 30.86420
## 21 21 30 1 A 80 171 27.35885
## 22 22 34 2 A 75 162 28.57796
## 23 23 30 1 C 72 166 26.12861
## 24 24 31 1 B 64 168 22.67574
## 25 25 32 2 B 84 185 24.54346
## 26 26 27 2 B 64 165 23.50781
## 27 27 39 2 B 92 172 31.09789
## 28 28 36 1 B 85 171 29.06877
## 29 29 29 2 B 68 162 25.91068
## 30 30 41 2 C 60 169 21.00767Para crear dos conjuntos de datos diferenciados para hombres y mujeres:
Df_Mujeres <- Midataf[Midataf$Gene == 1,]
Df_Mujeres## Id Edad Gene Trat Peso Alt IMC
## 1 1 32 1 B 65 164 24.16716
## 2 2 34 1 A 47 172 15.88697
## 3 3 43 1 B 80 168 28.34467
## 4 4 35 1 A 63 167 22.58955
## 6 6 44 1 C 80 170 27.68166
## 7 7 37 1 B 67 164 24.91077
## 9 9 32 1 A 72 167 25.81663
## 10 10 33 1 B 69 177 22.02432
## 14 14 36 1 A 76 170 26.29758
## 18 18 25 1 B 74 165 27.18090
## 20 20 33 1 C 81 162 30.86420
## 21 21 30 1 A 80 171 27.35885
## 23 23 30 1 C 72 166 26.12861
## 24 24 31 1 B 64 168 22.67574
## 28 28 36 1 B 85 171 29.06877Df_Hombres <- Midataf[Midataf$Gene == 2,]
Df_Hombres## Id Edad Gene Trat Peso Alt IMC
## 5 5 36 2 C 63 162 24.00549
## 8 8 29 2 C 58 157 23.53037
## 11 11 41 2 B 70 165 25.71166
## 12 12 37 2 C 74 175 24.16327
## 13 13 37 2 B 66 157 26.77593
## 15 15 32 2 C 68 174 22.46003
## 16 16 44 2 C 73 172 24.67550
## 17 17 37 2 C 81 171 27.70083
## 19 19 39 2 B 67 163 25.21736
## 22 22 34 2 A 75 162 28.57796
## 25 25 32 2 B 84 185 24.54346
## 26 26 27 2 B 64 165 23.50781
## 27 27 39 2 B 92 172 31.09789
## 29 29 29 2 B 68 162 25.91068
## 30 30 41 2 C 60 169 21.00767Para usar el comando rbind() y combinar los ficheros creados anteriormente:
Midataf_ordenado <- rbind(Df_Mujeres, Df_Hombres)
Midataf_ordenado## Id Edad Gene Trat Peso Alt IMC
## 1 1 32 1 B 65 164 24.16716
## 2 2 34 1 A 47 172 15.88697
## 3 3 43 1 B 80 168 28.34467
## 4 4 35 1 A 63 167 22.58955
## 6 6 44 1 C 80 170 27.68166
## 7 7 37 1 B 67 164 24.91077
## 9 9 32 1 A 72 167 25.81663
## 10 10 33 1 B 69 177 22.02432
## 14 14 36 1 A 76 170 26.29758
## 18 18 25 1 B 74 165 27.18090
## 20 20 33 1 C 81 162 30.86420
## 21 21 30 1 A 80 171 27.35885
## 23 23 30 1 C 72 166 26.12861
## 24 24 31 1 B 64 168 22.67574
## 28 28 36 1 B 85 171 29.06877
## 5 5 36 2 C 63 162 24.00549
## 8 8 29 2 C 58 157 23.53037
## 11 11 41 2 B 70 165 25.71166
## 12 12 37 2 C 74 175 24.16327
## 13 13 37 2 B 66 157 26.77593
## 15 15 32 2 C 68 174 22.46003
## 16 16 44 2 C 73 172 24.67550
## 17 17 37 2 C 81 171 27.70083
## 19 19 39 2 B 67 163 25.21736
## 22 22 34 2 A 75 162 28.57796
## 25 25 32 2 B 84 185 24.54346
## 26 26 27 2 B 64 165 23.50781
## 27 27 39 2 B 92 172 31.09789
## 29 29 29 2 B 68 162 25.91068
## 30 30 41 2 C 60 169 21.00767