LAB1 Anàlisi de Dades
Nil Duran Prats
2026-09-28
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num1 = 10 num2 = 23 num1 + num2
#exemple calcul IMC = pes/alçada^2 pes = 57 alçada = 1.69 IMC = pes/(alçada)^2 IMC num1==num2 num1!=num2
#vectors vec <- c(1.5, 2, 3.5, 4) vec is.vector(vec)
#exemple MASS library (MASS) #carreguem el paquet MASS que tenim
prèviament instal·lat
data(“birthwt”)#activem les dades
View(birthwt) #mostra el conjunt de dades en format taula
dim (birthwt) #mostra el nombre d’observacions i el nombre de
variables
library(MASS)## Warning: package 'MASS' was built under R version 4.5.3data("birthwt")
View(birthwt)
dim(birthwt)## [1] 189 10length(birthwt) head(birthwt, n=5) names(birthwt)
#Midataf
genere <- c(1, 2, 1, 1, 1, 2, 2, 2, 1, 2)
edat <- c(24, 25, 26, 24, 25, 27, 21, 22, 25, 26)
fuma <- c("no", "sí", "no", "sí", "no", "no", "sí", "no", "no", "sí")
Midata <- data.frame(genere, edat, fuma)
Midata## genere edat fuma
## 1 1 24 no
## 2 2 25 sí
## 3 1 26 no
## 4 1 24 sí
## 5 1 25 no
## 6 2 27 no
## 7 2 21 sí
## 8 2 22 no
## 9 1 25 no
## 10 2 26 síMidata[[2]][3:5]## [1] 26 24 25Midata$genere## [1] 1 2 1 1 1 2 2 2 1 2mean(Midata$edat[Midata$fuma=="no"])## [1] 24.83333mean(Midata$edat[Midata$fuma=="no"])## [1] 24.83333attach(Midata)## The following objects are masked _by_ .GlobalEnv:
##
## edat, fuma, generetable(fuma, genere)## genere
## fuma 1 2
## no 4 2
## sí 1 3detach(Midata)#exemple 8
Dataf_Enf1 = data.frame(malaltia = c("diabetis", "colesterol", "hipertensio", "hipotensio"), individus = c("ind1", "ind2", "ind3", "ind4"))
Dataf_Enf1## malaltia individus
## 1 diabetis ind1
## 2 colesterol ind2
## 3 hipertensio ind3
## 4 hipotensio ind4Dataf_Enf2 = data.frame(malaltia = c("diabetis", "colesterol", "hipertensio", "hipoteniso"), individus = c("ind21", "ind22", "ind23", "ind24"))
Dataf_Enf2## malaltia individus
## 1 diabetis ind21
## 2 colesterol ind22
## 3 hipertensio ind23
## 4 hipoteniso ind24#combinem els dos dataframes amb rbind()
#ja que tenen les mateixes variables
Data_Enf = rbind(Dataf_Enf1, Dataf_Enf2)
Data_Enf## malaltia individus
## 1 diabetis ind1
## 2 colesterol ind2
## 3 hipertensio ind3
## 4 hipotensio ind4
## 5 diabetis ind21
## 6 colesterol ind22
## 7 hipertensio ind23
## 8 hipoteniso ind24#exemple 9
Indiv1 <- c("I213", "I214", "I215", "I216", "I217")
Medic1 <- c("Paracetamol", "Ibuprofen", "Aspirina", "Ibuprofen", "Paracetamol")
Past_dia <- c(2, 3, 2, 2, 2)
dfmedica1 <- data.frame(Indiv1, Medic1, Past_dia)
dfmedica1## Indiv1 Medic1 Past_dia
## 1 I213 Paracetamol 2
## 2 I214 Ibuprofen 3
## 3 I215 Aspirina 2
## 4 I216 Ibuprofen 2
## 5 I217 Paracetamol 2Indiv2 <- c("I213", "I214", "I215", "I216", "I217")
Medic2 <- c("Paracetamol", "Ibuprofe", "Aspirina", "Ibuprofe", "Paracetamol")
Past_dia <- c(2, 3, 2, 2, 2)
dfmedica2 <- data.frame(Indiv2, Medic2, Past_dia)
dfmedica2## Indiv2 Medic2 Past_dia
## 1 I213 Paracetamol 2
## 2 I214 Ibuprofe 3
## 3 I215 Aspirina 2
## 4 I216 Ibuprofe 2
## 5 I217 Paracetamol 2merge(dfmedica1, dfmedica2)## Past_dia Indiv1 Medic1 Indiv2 Medic2
## 1 2 I213 Paracetamol I213 Paracetamol
## 2 2 I213 Paracetamol I215 Aspirina
## 3 2 I213 Paracetamol I216 Ibuprofe
## 4 2 I213 Paracetamol I217 Paracetamol
## 5 2 I215 Aspirina I213 Paracetamol
## 6 2 I215 Aspirina I215 Aspirina
## 7 2 I215 Aspirina I216 Ibuprofe
## 8 2 I215 Aspirina I217 Paracetamol
## 9 2 I216 Ibuprofen I213 Paracetamol
## 10 2 I216 Ibuprofen I215 Aspirina
## 11 2 I216 Ibuprofen I216 Ibuprofe
## 12 2 I216 Ibuprofen I217 Paracetamol
## 13 2 I217 Paracetamol I213 Paracetamol
## 14 2 I217 Paracetamol I215 Aspirina
## 15 2 I217 Paracetamol I216 Ibuprofe
## 16 2 I217 Paracetamol I217 Paracetamol
## 17 3 I214 Ibuprofen I214 Ibuprofe#exemple 10
set.seed(999)
metge.id <- 1:10
metge.nom <- c("Ona", "Jordi", "Oriol", "Pau", "Ester", "Xavi", "Jan", "Marta", "Anna", "Abril") #variables amb el nom de cada metge
metge_sal <- round(rnorm(10, mean = 1500, sd = 200)) #salari estimat aleatori de cada un
metge_edat <- round(rnorm(10, mean = 50, sd = 8))
metge_espec <- c("Neuro", "Orto", "Gine", "Trauma", rep("General", 6))
df_Med_1 <- data.frame(id = metge.id[1:8], nom = metge.nom [1:8], salari = metge_sal[1:8])
df_Med_2 <- data.frame(id = metge.id[-5], nom = metge.nom[-5], edat = metge_edat[-5], posicio = metge_espec[-5])
df_Med_1## id nom salari
## 1 1 Ona 1444
## 2 2 Jordi 1237
## 3 3 Oriol 1659
## 4 4 Pau 1554
## 5 5 Ester 1445
## 6 6 Xavi 1387
## 7 7 Jan 1124
## 8 8 Marta 1247df_Med_2## id nom edat posicio
## 1 1 Ona 61 Neuro
## 2 2 Jordi 51 Orto
## 3 3 Oriol 58 Gine
## 4 4 Pau 51 Trauma
## 5 6 Xavi 39 General
## 6 7 Jan 51 General
## 7 8 Marta 51 General
## 8 9 Anna 57 General
## 9 10 Abril 33 Generalmerge(df_Med_1, df_Med_2)## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 6 Xavi 1387 39 General
## 6 7 Jan 1124 51 General
## 7 8 Marta 1247 51 Generalmerge(x=df_Med_1, y=df_Med_2, all = TRUE)## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 5 Ester 1445 NA <NA>
## 6 6 Xavi 1387 39 General
## 7 7 Jan 1124 51 General
## 8 8 Marta 1247 51 General
## 9 9 Anna NA 57 General
## 10 10 Abril NA 33 Generalmerge (x=df_Med_1, y=df_Med_2, all = TRUE) ## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 5 Ester 1445 NA <NA>
## 6 6 Xavi 1387 39 General
## 7 7 Jan 1124 51 General
## 8 8 Marta 1247 51 General
## 9 9 Anna NA 57 General
## 10 10 Abril NA 33 Generalmerge(df_Med_1, df_Med_2)## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 6 Xavi 1387 39 General
## 6 7 Jan 1124 51 General
## 7 8 Marta 1247 51 Generalmerge(x=df_Med_1, y=df_Med_2, all = TRUE)## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 5 Ester 1445 NA <NA>
## 6 6 Xavi 1387 39 General
## 7 7 Jan 1124 51 General
## 8 8 Marta 1247 51 General
## 9 9 Anna NA 57 General
## 10 10 Abril NA 33 Generalmerge(df_Med_1, df_Med_2)## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 6 Xavi 1387 39 General
## 6 7 Jan 1124 51 General
## 7 8 Marta 1247 51 Generalmerge(x=df_Med_1, y=df_Med_2, all = TRUE)## id nom salari edat posicio
## 1 1 Ona 1444 61 Neuro
## 2 2 Jordi 1237 51 Orto
## 3 3 Oriol 1659 58 Gine
## 4 4 Pau 1554 51 Trauma
## 5 5 Ester 1445 NA <NA>
## 6 6 Xavi 1387 39 General
## 7 7 Jan 1124 51 General
## 8 8 Marta 1247 51 General
## 9 9 Anna NA 57 General
## 10 10 Abril NA 33 General#Seleccionar i filtrar registres
Id<-
c("I1","I2","I3","I4","I5","I6","I7","I8","I9","I10","I11","I12","I13","I14",
"I15","I16","I17","I18","I19","I20","I21","I22")
Edat <- c(23,24,21,22,23,25,26,24,21,22,23,25,26,24,22,21,25,26,24,21,25,27)
Sexe <-c(1,2,1,1,1,2,2,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2)
Pes <-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)
Altura <-
c(165,154,178,165,164,175,182,165,178,165,158,183,184,164,189,167,182,179,165
,158,183,184)
Pacients <- data.frame (Id, Edat, Sexe, Pes, Altura) #seleccionar un nou conjunt de dades anomenat Prova1 amb nomes les variables Id, Edat i sexe
Prova1 <- subset(Pacients, select = c(Id, Edat, Sexe))
Prova1## Id Edat Sexe
## 1 I1 23 1
## 2 I2 24 2
## 3 I3 21 1
## 4 I4 22 1
## 5 I5 23 1
## 6 I6 25 2
## 7 I7 26 2
## 8 I8 24 2
## 9 I9 21 1
## 10 I10 22 2
## 11 I11 23 1
## 12 I12 25 2
## 13 I13 26 2
## 14 I14 24 2
## 15 I15 22 1
## 16 I16 21 1
## 17 I17 25 1
## 18 I18 26 2
## 19 I19 24 2
## 20 I20 21 2
## 21 I21 25 1
## 22 I22 27 2#seleccionar totes les diles que tenen una edat mes gran o igual que 24 anys en un conjunt anomenat prova2
Prova2 <- subset(Pacients, Edat >= 24)
Prova2## Id Edat Sexe Pes Altura
## 2 I2 24 2 81.2 154
## 6 I6 25 2 78.6 175
## 7 I7 26 2 67.9 182
## 8 I8 24 2 100.2 165
## 12 I12 25 2 67.5 183
## 13 I13 26 2 87.4 184
## 14 I14 24 2 99.7 164
## 17 I17 25 1 65.4 182
## 18 I18 26 2 73.7 179
## 19 I19 24 2 85.1 165
## 21 I21 25 1 54.8 183
## 22 I22 27 2 103.4 184#prova3 seleccionar totes les files que tenen una edat mes petita a 25 i no volem incloure la columna sexe
Prova3 <- subset(Pacients, Edat < 25, select = -c(Sexe))
Prova3## Id Edat Pes Altura
## 1 I1 23 76.5 165
## 2 I2 24 81.2 154
## 3 I3 21 79.3 178
## 4 I4 22 59.5 165
## 5 I5 23 67.3 164
## 8 I8 24 100.2 165
## 9 I9 21 97.8 178
## 10 I10 22 56.4 165
## 11 I11 23 65.4 158
## 14 I14 24 99.7 164
## 15 I15 22 87.6 189
## 16 I16 21 93.4 167
## 19 I19 24 85.1 165
## 20 I20 21 61.2 158#Prova 4 nomes els registres que tenen una alçada mes petita o igual que 165 o mes gran que 175cm
Prova4 <- subset(Pacients,Altura <= 165 | Altura > 175)#exemple 12
set.seed(999)
f <- nrow(Pacients)
n <- 3
i <- sample(1:f, n, replace = FALSE)
Prova5 <- Pacients[i,]
Prova5## Id Edat Sexe Pes Altura
## 4 I4 22 1 59.5 165
## 7 I7 26 2 67.9 182
## 9 I9 21 1 97.8 178library(dplyr)##
## Adjuntando el paquete: '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, uniondata("women")
Prova6 <- filter(women, height > 58)
Prova6## height weight
## 1 59 117
## 2 60 120
## 3 61 123
## 4 62 126
## 5 63 129
## 6 64 132
## 7 65 135
## 8 66 139
## 9 67 142
## 10 68 146
## 11 69 150
## 12 70 154
## 13 71 159
## 14 72 164#Visualització de Gràfics
plot(iris)
hist(iris$Sepal.Length)
hist(iris$Sepal.Length, breaks = c(4,5,6,7,8), main = "Histograma", xlab= "cm",
ylab= "Freq", xlim = c(2,10), ylim = c(0,60), col = "blue")
boxplot(iris)
boxplot(iris[ ,-5], main = "Diagrames de caixa", xlab= "Dimensions", ylab = "cm", col = c("red", "blue", "yellow", "orange"))
#exercicis
#ex1
sessionInfo()## R version 4.5.0 (2025-04-11 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=Spanish_Spain.utf8 LC_CTYPE=Spanish_Spain.utf8
## [3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Spain.utf8
##
## time zone: Europe/Madrid
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.1.4 MASS_7.3-66
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.6.1 fastmap_1.2.0 tidyselect_1.2.1
## [5] xfun_0.52 magrittr_2.0.3 glue_1.8.0 cachem_1.1.0
## [9] tibble_3.2.1 knitr_1.52 pkgconfig_2.0.3 htmltools_0.5.8.1
## [13] generics_0.1.4 rmarkdown_2.29 lifecycle_1.0.5 cli_3.6.5
## [17] vctrs_0.6.5 sass_0.4.10 jquerylib_0.1.4 compiler_4.5.0
## [21] tools_4.5.0 pillar_1.10.2 evaluate_1.0.3 bslib_0.9.0
## [25] yaml_2.3.10 rlang_1.1.6 jsonlite_2.0.0library("MASS")
library("survival")## Warning: package 'survival' was built under R version 4.5.3sessionInfo()## R version 4.5.0 (2025-04-11 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=Spanish_Spain.utf8 LC_CTYPE=Spanish_Spain.utf8
## [3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Spain.utf8
##
## time zone: Europe/Madrid
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] survival_3.8-12 dplyr_1.1.4 MASS_7.3-66
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.5 cli_3.6.5 knitr_1.52 rlang_1.1.6
## [5] xfun_0.52 generics_0.1.4 jsonlite_2.0.0 glue_1.8.0
## [9] htmltools_0.5.8.1 sass_0.4.10 rmarkdown_2.29 grid_4.5.0
## [13] evaluate_1.0.3 jquerylib_0.1.4 tibble_3.2.1 fastmap_1.2.0
## [17] yaml_2.3.10 lifecycle_1.0.5 compiler_4.5.0 pkgconfig_2.0.3
## [21] lattice_0.22-6 digest_0.6.37 R6_2.6.1 tidyselect_1.2.1
## [25] pillar_1.10.2 splines_4.5.0 magrittr_2.0.3 Matrix_1.7-3
## [29] bslib_0.9.0 tools_4.5.0 cachem_1.1.0#exercici2
setwd("C:/Users/nl172/Downloads")
arxiu1 <- read.table("pacients_exercici.txt", header = TRUE, sep = "\t")
taula1 <- read.table("pacients_exercici.txt", header = TRUE, sep = "\t")
summary(taula1$Edat)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 21.00 23.75 26.50 26.75 29.25 35.00taula2 <- read.table("estudi_clinic_exercici.csv", header = TRUE, sep = ",")
fivenum(taula2$Colesterol)## [1] 175 189 205 220 242#exercici3
library("MASS")
data("anorexia")
View(anorexia)
is.na(anorexia)## Treat Prewt Postwt
## 1 FALSE FALSE FALSE
## 2 FALSE FALSE FALSE
## 3 FALSE FALSE FALSE
## 4 FALSE FALSE FALSE
## 5 FALSE FALSE FALSE
## 6 FALSE FALSE FALSE
## 7 FALSE FALSE FALSE
## 8 FALSE FALSE FALSE
## 9 FALSE FALSE FALSE
## 10 FALSE FALSE FALSE
## 11 FALSE FALSE FALSE
## 12 FALSE FALSE FALSE
## 13 FALSE FALSE FALSE
## 14 FALSE FALSE FALSE
## 15 FALSE FALSE FALSE
## 16 FALSE FALSE FALSE
## 17 FALSE FALSE FALSE
## 18 FALSE FALSE FALSE
## 19 FALSE FALSE FALSE
## 20 FALSE FALSE FALSE
## 21 FALSE FALSE FALSE
## 22 FALSE FALSE FALSE
## 23 FALSE FALSE FALSE
## 24 FALSE FALSE FALSE
## 25 FALSE FALSE FALSE
## 26 FALSE FALSE FALSE
## 27 FALSE FALSE FALSE
## 28 FALSE FALSE FALSE
## 29 FALSE FALSE FALSE
## 30 FALSE FALSE FALSE
## 31 FALSE FALSE FALSE
## 32 FALSE FALSE FALSE
## 33 FALSE FALSE FALSE
## 34 FALSE FALSE FALSE
## 35 FALSE FALSE FALSE
## 36 FALSE FALSE FALSE
## 37 FALSE FALSE FALSE
## 38 FALSE FALSE FALSE
## 39 FALSE FALSE FALSE
## 40 FALSE FALSE FALSE
## 41 FALSE FALSE FALSE
## 42 FALSE FALSE FALSE
## 43 FALSE FALSE FALSE
## 44 FALSE FALSE FALSE
## 45 FALSE FALSE FALSE
## 46 FALSE FALSE FALSE
## 47 FALSE FALSE FALSE
## 48 FALSE FALSE FALSE
## 49 FALSE FALSE FALSE
## 50 FALSE FALSE FALSE
## 51 FALSE FALSE FALSE
## 52 FALSE FALSE FALSE
## 53 FALSE FALSE FALSE
## 54 FALSE FALSE FALSE
## 55 FALSE FALSE FALSE
## 56 FALSE FALSE FALSE
## 57 FALSE FALSE FALSE
## 58 FALSE FALSE FALSE
## 59 FALSE FALSE FALSE
## 60 FALSE FALSE FALSE
## 61 FALSE FALSE FALSE
## 62 FALSE FALSE FALSE
## 63 FALSE FALSE FALSE
## 64 FALSE FALSE FALSE
## 65 FALSE FALSE FALSE
## 66 FALSE FALSE FALSE
## 67 FALSE FALSE FALSE
## 68 FALSE FALSE FALSE
## 69 FALSE FALSE FALSE
## 70 FALSE FALSE FALSE
## 71 FALSE FALSE FALSE
## 72 FALSE FALSE FALSEany(is.na(anorexia))## [1] FALSElibrary ("MASS")
data("anorexia")
anorexia$Treat <- as.character(anorexia$Treat)
anorexia$Treat[anorexia$Treat == "FT"] <- "ft"
anorexia$Treat <- factor(anorexia$Treat)
anorexia## Treat Prewt Postwt
## 1 Cont 80.7 80.2
## 2 Cont 89.4 80.1
## 3 Cont 91.8 86.4
## 4 Cont 74.0 86.3
## 5 Cont 78.1 76.1
## 6 Cont 88.3 78.1
## 7 Cont 87.3 75.1
## 8 Cont 75.1 86.7
## 9 Cont 80.6 73.5
## 10 Cont 78.4 84.6
## 11 Cont 77.6 77.4
## 12 Cont 88.7 79.5
## 13 Cont 81.3 89.6
## 14 Cont 78.1 81.4
## 15 Cont 70.5 81.8
## 16 Cont 77.3 77.3
## 17 Cont 85.2 84.2
## 18 Cont 86.0 75.4
## 19 Cont 84.1 79.5
## 20 Cont 79.7 73.0
## 21 Cont 85.5 88.3
## 22 Cont 84.4 84.7
## 23 Cont 79.6 81.4
## 24 Cont 77.5 81.2
## 25 Cont 72.3 88.2
## 26 Cont 89.0 78.8
## 27 CBT 80.5 82.2
## 28 CBT 84.9 85.6
## 29 CBT 81.5 81.4
## 30 CBT 82.6 81.9
## 31 CBT 79.9 76.4
## 32 CBT 88.7 103.6
## 33 CBT 94.9 98.4
## 34 CBT 76.3 93.4
## 35 CBT 81.0 73.4
## 36 CBT 80.5 82.1
## 37 CBT 85.0 96.7
## 38 CBT 89.2 95.3
## 39 CBT 81.3 82.4
## 40 CBT 76.5 72.5
## 41 CBT 70.0 90.9
## 42 CBT 80.4 71.3
## 43 CBT 83.3 85.4
## 44 CBT 83.0 81.6
## 45 CBT 87.7 89.1
## 46 CBT 84.2 83.9
## 47 CBT 86.4 82.7
## 48 CBT 76.5 75.7
## 49 CBT 80.2 82.6
## 50 CBT 87.8 100.4
## 51 CBT 83.3 85.2
## 52 CBT 79.7 83.6
## 53 CBT 84.5 84.6
## 54 CBT 80.8 96.2
## 55 CBT 87.4 86.7
## 56 ft 83.8 95.2
## 57 ft 83.3 94.3
## 58 ft 86.0 91.5
## 59 ft 82.5 91.9
## 60 ft 86.7 100.3
## 61 ft 79.6 76.7
## 62 ft 76.9 76.8
## 63 ft 94.2 101.6
## 64 ft 73.4 94.9
## 65 ft 80.5 75.2
## 66 ft 81.6 77.8
## 67 ft 82.1 95.5
## 68 ft 77.6 90.7
## 69 ft 83.5 92.5
## 70 ft 89.9 93.8
## 71 ft 86.0 91.7
## 72 ft 87.3 98.0anorexia$Treat <- as.character(anorexia$Treat)
anorexia$Treat[anorexia$Treat == "Cont"] <- "Contr"
anorexia$Treat <- as.factor(anorexia$Treat)
anorexia## Treat Prewt Postwt
## 1 Contr 80.7 80.2
## 2 Contr 89.4 80.1
## 3 Contr 91.8 86.4
## 4 Contr 74.0 86.3
## 5 Contr 78.1 76.1
## 6 Contr 88.3 78.1
## 7 Contr 87.3 75.1
## 8 Contr 75.1 86.7
## 9 Contr 80.6 73.5
## 10 Contr 78.4 84.6
## 11 Contr 77.6 77.4
## 12 Contr 88.7 79.5
## 13 Contr 81.3 89.6
## 14 Contr 78.1 81.4
## 15 Contr 70.5 81.8
## 16 Contr 77.3 77.3
## 17 Contr 85.2 84.2
## 18 Contr 86.0 75.4
## 19 Contr 84.1 79.5
## 20 Contr 79.7 73.0
## 21 Contr 85.5 88.3
## 22 Contr 84.4 84.7
## 23 Contr 79.6 81.4
## 24 Contr 77.5 81.2
## 25 Contr 72.3 88.2
## 26 Contr 89.0 78.8
## 27 CBT 80.5 82.2
## 28 CBT 84.9 85.6
## 29 CBT 81.5 81.4
## 30 CBT 82.6 81.9
## 31 CBT 79.9 76.4
## 32 CBT 88.7 103.6
## 33 CBT 94.9 98.4
## 34 CBT 76.3 93.4
## 35 CBT 81.0 73.4
## 36 CBT 80.5 82.1
## 37 CBT 85.0 96.7
## 38 CBT 89.2 95.3
## 39 CBT 81.3 82.4
## 40 CBT 76.5 72.5
## 41 CBT 70.0 90.9
## 42 CBT 80.4 71.3
## 43 CBT 83.3 85.4
## 44 CBT 83.0 81.6
## 45 CBT 87.7 89.1
## 46 CBT 84.2 83.9
## 47 CBT 86.4 82.7
## 48 CBT 76.5 75.7
## 49 CBT 80.2 82.6
## 50 CBT 87.8 100.4
## 51 CBT 83.3 85.2
## 52 CBT 79.7 83.6
## 53 CBT 84.5 84.6
## 54 CBT 80.8 96.2
## 55 CBT 87.4 86.7
## 56 ft 83.8 95.2
## 57 ft 83.3 94.3
## 58 ft 86.0 91.5
## 59 ft 82.5 91.9
## 60 ft 86.7 100.3
## 61 ft 79.6 76.7
## 62 ft 76.9 76.8
## 63 ft 94.2 101.6
## 64 ft 73.4 94.9
## 65 ft 80.5 75.2
## 66 ft 81.6 77.8
## 67 ft 82.1 95.5
## 68 ft 77.6 90.7
## 69 ft 83.5 92.5
## 70 ft 89.9 93.8
## 71 ft 86.0 91.7
## 72 ft 87.3 98.0anorexia$Treat <- as.character(anorexia$Treat)
anorexia$Treat[anorexia$Treat == "CBT"] <- "CbbT"
anorexia$Treat <- as.factor(anorexia$Treat)
anorexia## Treat Prewt Postwt
## 1 Contr 80.7 80.2
## 2 Contr 89.4 80.1
## 3 Contr 91.8 86.4
## 4 Contr 74.0 86.3
## 5 Contr 78.1 76.1
## 6 Contr 88.3 78.1
## 7 Contr 87.3 75.1
## 8 Contr 75.1 86.7
## 9 Contr 80.6 73.5
## 10 Contr 78.4 84.6
## 11 Contr 77.6 77.4
## 12 Contr 88.7 79.5
## 13 Contr 81.3 89.6
## 14 Contr 78.1 81.4
## 15 Contr 70.5 81.8
## 16 Contr 77.3 77.3
## 17 Contr 85.2 84.2
## 18 Contr 86.0 75.4
## 19 Contr 84.1 79.5
## 20 Contr 79.7 73.0
## 21 Contr 85.5 88.3
## 22 Contr 84.4 84.7
## 23 Contr 79.6 81.4
## 24 Contr 77.5 81.2
## 25 Contr 72.3 88.2
## 26 Contr 89.0 78.8
## 27 CbbT 80.5 82.2
## 28 CbbT 84.9 85.6
## 29 CbbT 81.5 81.4
## 30 CbbT 82.6 81.9
## 31 CbbT 79.9 76.4
## 32 CbbT 88.7 103.6
## 33 CbbT 94.9 98.4
## 34 CbbT 76.3 93.4
## 35 CbbT 81.0 73.4
## 36 CbbT 80.5 82.1
## 37 CbbT 85.0 96.7
## 38 CbbT 89.2 95.3
## 39 CbbT 81.3 82.4
## 40 CbbT 76.5 72.5
## 41 CbbT 70.0 90.9
## 42 CbbT 80.4 71.3
## 43 CbbT 83.3 85.4
## 44 CbbT 83.0 81.6
## 45 CbbT 87.7 89.1
## 46 CbbT 84.2 83.9
## 47 CbbT 86.4 82.7
## 48 CbbT 76.5 75.7
## 49 CbbT 80.2 82.6
## 50 CbbT 87.8 100.4
## 51 CbbT 83.3 85.2
## 52 CbbT 79.7 83.6
## 53 CbbT 84.5 84.6
## 54 CbbT 80.8 96.2
## 55 CbbT 87.4 86.7
## 56 ft 83.8 95.2
## 57 ft 83.3 94.3
## 58 ft 86.0 91.5
## 59 ft 82.5 91.9
## 60 ft 86.7 100.3
## 61 ft 79.6 76.7
## 62 ft 76.9 76.8
## 63 ft 94.2 101.6
## 64 ft 73.4 94.9
## 65 ft 80.5 75.2
## 66 ft 81.6 77.8
## 67 ft 82.1 95.5
## 68 ft 77.6 90.7
## 69 ft 83.5 92.5
## 70 ft 89.9 93.8
## 71 ft 86.0 91.7
## 72 ft 87.3 98.0#exercici4
library(MASS)
data("biopsy")
write.csv("biopsy", "biopsy.csv")
data("Melanoma")
library("openxlsx")## Warning: package 'openxlsx' was built under R version 4.5.3write.csv(Melanoma, "melanoma.csv")
write.table(Melanoma, "melanoma2.txt")
write.xlsx(Melanoma, "melanoma2.xlsx")
data("Melanoma")
resumelanoma <- summary(Melanoma$age)
resum_df <- data.frame(Estadistic = names(resumelanoma),Valor = as.numeric(resumelanoma))
write.csv(resum_df, file = "resum_df.doc")#exercici 5
library(MASS)
data("birthwt")
#edat maxima
max(birthwt$age)## [1] 45min(birthwt$age)## [1] 14range(birthwt$age)## [1] 14 45birthwt## 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 2622
## 92 0 22 118 1 0 0 0 0 1 2637
## 93 0 17 103 3 0 0 0 0 1 2637
## 94 0 29 123 1 1 0 0 0 1 2663
## 95 0 26 113 1 1 0 0 0 0 2665
## 96 0 19 95 3 0 0 0 0 0 2722
## 97 0 19 150 3 0 0 0 0 1 2733
## 98 0 22 95 3 0 0 1 0 0 2751
## 99 0 30 107 3 0 1 0 1 2 2750
## 100 0 18 100 1 1 0 0 0 0 2769
## 101 0 18 100 1 1 0 0 0 0 2769
## 102 0 15 98 2 0 0 0 0 0 2778
## 103 0 25 118 1 1 0 0 0 3 2782
## 104 0 20 120 3 0 0 0 1 0 2807
## 105 0 28 120 1 1 0 0 0 1 2821
## 106 0 32 121 3 0 0 0 0 2 2835
## 107 0 31 100 1 0 0 0 1 3 2835
## 108 0 36 202 1 0 0 0 0 1 2836
## 109 0 28 120 3 0 0 0 0 0 2863
## 111 0 25 120 3 0 0 0 1 2 2877
## 112 0 28 167 1 0 0 0 0 0 2877
## 113 0 17 122 1 1 0 0 0 0 2906
## 114 0 29 150 1 0 0 0 0 2 2920
## 115 0 26 168 2 1 0 0 0 0 2920
## 116 0 17 113 2 0 0 0 0 1 2920
## 117 0 17 113 2 0 0 0 0 1 2920
## 118 0 24 90 1 1 1 0 0 1 2948
## 119 0 35 121 2 1 1 0 0 1 2948
## 120 0 25 155 1 0 0 0 0 1 2977
## 121 0 25 125 2 0 0 0 0 0 2977
## 123 0 29 140 1 1 0 0 0 2 2977
## 124 0 19 138 1 1 0 0 0 2 2977
## 125 0 27 124 1 1 0 0 0 0 2922
## 126 0 31 215 1 1 0 0 0 2 3005
## 127 0 33 109 1 1 0 0 0 1 3033
## 128 0 21 185 2 1 0 0 0 2 3042
## 129 0 19 189 1 0 0 0 0 2 3062
## 130 0 23 130 2 0 0 0 0 1 3062
## 131 0 21 160 1 0 0 0 0 0 3062
## 132 0 18 90 1 1 0 0 1 0 3062
## 133 0 18 90 1 1 0 0 1 0 3062
## 134 0 32 132 1 0 0 0 0 4 3080
## 135 0 19 132 3 0 0 0 0 0 3090
## 136 0 24 115 1 0 0 0 0 2 3090
## 137 0 22 85 3 1 0 0 0 0 3090
## 138 0 22 120 1 0 0 1 0 1 3100
## 139 0 23 128 3 0 0 0 0 0 3104
## 140 0 22 130 1 1 0 0 0 0 3132
## 141 0 30 95 1 1 0 0 0 2 3147
## 142 0 19 115 3 0 0 0 0 0 3175
## 143 0 16 110 3 0 0 0 0 0 3175
## 144 0 21 110 3 1 0 0 1 0 3203
## 145 0 30 153 3 0 0 0 0 0 3203
## 146 0 20 103 3 0 0 0 0 0 3203
## 147 0 17 119 3 0 0 0 0 0 3225
## 148 0 17 119 3 0 0 0 0 0 3225
## 149 0 23 119 3 0 0 0 0 2 3232
## 150 0 24 110 3 0 0 0 0 0 3232
## 151 0 28 140 1 0 0 0 0 0 3234
## 154 0 26 133 3 1 2 0 0 0 3260
## 155 0 20 169 3 0 1 0 1 1 3274
## 156 0 24 115 3 0 0 0 0 2 3274
## 159 0 28 250 3 1 0 0 0 6 3303
## 160 0 20 141 1 0 2 0 1 1 3317
## 161 0 22 158 2 0 1 0 0 2 3317
## 162 0 22 112 1 1 2 0 0 0 3317
## 163 0 31 150 3 1 0 0 0 2 3321
## 164 0 23 115 3 1 0 0 0 1 3331
## 166 0 16 112 2 0 0 0 0 0 3374
## 167 0 16 135 1 1 0 0 0 0 3374
## 168 0 18 229 2 0 0 0 0 0 3402
## 169 0 25 140 1 0 0 0 0 1 3416
## 170 0 32 134 1 1 1 0 0 4 3430
## 172 0 20 121 2 1 0 0 0 0 3444
## 173 0 23 190 1 0 0 0 0 0 3459
## 174 0 22 131 1 0 0 0 0 1 3460
## 175 0 32 170 1 0 0 0 0 0 3473
## 176 0 30 110 3 0 0 0 0 0 3544
## 177 0 20 127 3 0 0 0 0 0 3487
## 179 0 23 123 3 0 0 0 0 0 3544
## 180 0 17 120 3 1 0 0 0 0 3572
## 181 0 19 105 3 0 0 0 0 0 3572
## 182 0 23 130 1 0 0 0 0 0 3586
## 183 0 36 175 1 0 0 0 0 0 3600
## 184 0 22 125 1 0 0 0 0 1 3614
## 185 0 24 133 1 0 0 0 0 0 3614
## 186 0 21 134 3 0 0 0 0 2 3629
## 187 0 19 235 1 1 0 1 0 0 3629
## 188 0 25 95 1 1 3 0 1 0 3637
## 189 0 16 135 1 1 0 0 0 0 3643
## 190 0 29 135 1 0 0 0 0 1 3651
## 191 0 29 154 1 0 0 0 0 1 3651
## 192 0 19 147 1 1 0 0 0 0 3651
## 193 0 19 147 1 1 0 0 0 0 3651
## 195 0 30 137 1 0 0 0 0 1 3699
## 196 0 24 110 1 0 0 0 0 1 3728
## 197 0 19 184 1 1 0 1 0 0 3756
## 199 0 24 110 3 0 1 0 0 0 3770
## 200 0 23 110 1 0 0 0 0 1 3770
## 201 0 20 120 3 0 0 0 0 0 3770
## 202 0 25 241 2 0 0 1 0 0 3790
## 203 0 30 112 1 0 0 0 0 1 3799
## 204 0 22 169 1 0 0 0 0 0 3827
## 205 0 18 120 1 1 0 0 0 2 3856
## 206 0 16 170 2 0 0 0 0 4 3860
## 207 0 32 186 1 0 0 0 0 2 3860
## 208 0 18 120 3 0 0 0 0 1 3884
## 209 0 29 130 1 1 0 0 0 2 3884
## 210 0 33 117 1 0 0 0 1 1 3912
## 211 0 20 170 1 1 0 0 0 0 3940
## 212 0 28 134 3 0 0 0 0 1 3941
## 213 0 14 135 1 0 0 0 0 0 3941
## 214 0 28 130 3 0 0 0 0 0 3969
## 215 0 25 120 1 0 0 0 0 2 3983
## 216 0 16 95 3 0 0 0 0 1 3997
## 217 0 20 158 1 0 0 0 0 1 3997
## 218 0 26 160 3 0 0 0 0 0 4054
## 219 0 21 115 1 0 0 0 0 1 4054
## 220 0 22 129 1 0 0 0 0 0 4111
## 221 0 25 130 1 0 0 0 0 2 4153
## 222 0 31 120 1 0 0 0 0 2 4167
## 223 0 35 170 1 0 1 0 0 1 4174
## 224 0 19 120 1 1 0 0 0 0 4238
## 225 0 24 116 1 0 0 0 0 1 4593
## 226 0 45 123 1 0 0 0 0 1 4990
## 4 1 28 120 3 1 1 0 1 0 709
## 10 1 29 130 1 0 0 0 1 2 1021
## 11 1 34 187 2 1 0 1 0 0 1135
## 13 1 25 105 3 0 1 1 0 0 1330
## 15 1 25 85 3 0 0 0 1 0 1474
## 16 1 27 150 3 0 0 0 0 0 1588
## 17 1 23 97 3 0 0 0 1 1 1588
## 18 1 24 128 2 0 1 0 0 1 1701
## 19 1 24 132 3 0 0 1 0 0 1729
## 20 1 21 165 1 1 0 1 0 1 1790
## 22 1 32 105 1 1 0 0 0 0 1818
## 23 1 19 91 1 1 2 0 1 0 1885
## 24 1 25 115 3 0 0 0 0 0 1893
## 25 1 16 130 3 0 0 0 0 1 1899
## 26 1 25 92 1 1 0 0 0 0 1928
## 27 1 20 150 1 1 0 0 0 2 1928
## 28 1 21 200 2 0 0 0 1 2 1928
## 29 1 24 155 1 1 1 0 0 0 1936
## 30 1 21 103 3 0 0 0 0 0 1970
## 31 1 20 125 3 0 0 0 1 0 2055
## 32 1 25 89 3 0 2 0 0 1 2055
## 33 1 19 102 1 0 0 0 0 2 2082
## 34 1 19 112 1 1 0 0 1 0 2084
## 35 1 26 117 1 1 1 0 0 0 2084
## 36 1 24 138 1 0 0 0 0 0 2100
## 37 1 17 130 3 1 1 0 1 0 2125
## 40 1 20 120 2 1 0 0 0 3 2126
## 42 1 22 130 1 1 1 0 1 1 2187
## 43 1 27 130 2 0 0 0 1 0 2187
## 44 1 20 80 3 1 0 0 1 0 2211
## 45 1 17 110 1 1 0 0 0 0 2225
## 46 1 25 105 3 0 1 0 0 1 2240
## 47 1 20 109 3 0 0 0 0 0 2240
## 49 1 18 148 3 0 0 0 0 0 2282
## 50 1 18 110 2 1 1 0 0 0 2296
## 51 1 20 121 1 1 1 0 1 0 2296
## 52 1 21 100 3 0 1 0 0 4 2301
## 54 1 26 96 3 0 0 0 0 0 2325
## 56 1 31 102 1 1 1 0 0 1 2353
## 57 1 15 110 1 0 0 0 0 0 2353
## 59 1 23 187 2 1 0 0 0 1 2367
## 60 1 20 122 2 1 0 0 0 0 2381
## 61 1 24 105 2 1 0 0 0 0 2381
## 62 1 15 115 3 0 0 0 1 0 2381
## 63 1 23 120 3 0 0 0 0 0 2410
## 65 1 30 142 1 1 1 0 0 0 2410
## 67 1 22 130 1 1 0 0 0 1 2410
## 68 1 17 120 1 1 0 0 0 3 2414
## 69 1 23 110 1 1 1 0 0 0 2424
## 71 1 17 120 2 0 0 0 0 2 2438
## 75 1 26 154 3 0 1 1 0 1 2442
## 76 1 20 105 3 0 0 0 0 3 2450
## 77 1 26 190 1 1 0 0 0 0 2466
## 78 1 14 101 3 1 1 0 0 0 2466
## 79 1 28 95 1 1 0 0 0 2 2466
## 81 1 14 100 3 0 0 0 0 2 2495
## 82 1 23 94 3 1 0 0 0 0 2495
## 83 1 17 142 2 0 0 1 0 0 2495
## 84 1 21 130 1 1 0 1 0 3 2495min(birthwt$bwt)## [1] 709birthwt$smoke[birthwt$bwt == min(birthwt$bwt)]## [1] 1birthwt$bwt[birthwt$age == max(birthwt$age)]## [1] 4990subset(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 2495#exerici 6
library(MASS)
data("anorexia")
matriu <- as.matrix(anorexia[, c("Prewt", "Postwt")])
matriu## 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 78.1
## 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#exercici 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")
Edat <-
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)
Sexe <-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 per a dones i 2 per a homes
Pes <-
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")
Tract_Pulmo <- data.frame(Identificador,Edat,Sexe,Pes,Alt,Fuma)
Tract_Pulmo## Identificador Edat Sexe Pes 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 SÍsubset(Tract_Pulmo, Edat > 22)## Identificador Edat Sexe Pes 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 SÍTract_Pulmo[3, 4]## [1] 79.3subset(Tract_Pulmo, Edat < 27, -c(Alt))## Identificador Edat Sexe Pes 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#exercici 8
data("ChickWeight")
plot(ChickWeight$weight)
boxplot(ChickWeight$Time)
#exercici 9
library(MASS)
data("anorexia")
anorexia_treat_df <- data.frame(anorexia$Treat, c((anorexia$Postwt)-(anorexia$Prewt)))
anorexia_treat_df## anorexia.Treat c..anorexia.Postwt.....anorexia.Prewt..
## 1 Cont -0.5
## 2 Cont -9.3
## 3 Cont -5.4
## 4 Cont 12.3
## 5 Cont -2.0
## 6 Cont -10.2
## 7 Cont -12.2
## 8 Cont 11.6
## 9 Cont -7.1
## 10 Cont 6.2
## 11 Cont -0.2
## 12 Cont -9.2
## 13 Cont 8.3
## 14 Cont 3.3
## 15 Cont 11.3
## 16 Cont 0.0
## 17 Cont -1.0
## 18 Cont -10.6
## 19 Cont -4.6
## 20 Cont -6.7
## 21 Cont 2.8
## 22 Cont 0.3
## 23 Cont 1.8
## 24 Cont 3.7
## 25 Cont 15.9
## 26 Cont -10.2
## 27 CBT 1.7
## 28 CBT 0.7
## 29 CBT -0.1
## 30 CBT -0.7
## 31 CBT -3.5
## 32 CBT 14.9
## 33 CBT 3.5
## 34 CBT 17.1
## 35 CBT -7.6
## 36 CBT 1.6
## 37 CBT 11.7
## 38 CBT 6.1
## 39 CBT 1.1
## 40 CBT -4.0
## 41 CBT 20.9
## 42 CBT -9.1
## 43 CBT 2.1
## 44 CBT -1.4
## 45 CBT 1.4
## 46 CBT -0.3
## 47 CBT -3.7
## 48 CBT -0.8
## 49 CBT 2.4
## 50 CBT 12.6
## 51 CBT 1.9
## 52 CBT 3.9
## 53 CBT 0.1
## 54 CBT 15.4
## 55 CBT -0.7
## 56 FT 11.4
## 57 FT 11.0
## 58 FT 5.5
## 59 FT 9.4
## 60 FT 13.6
## 61 FT -2.9
## 62 FT -0.1
## 63 FT 7.4
## 64 FT 21.5
## 65 FT -5.3
## 66 FT -3.8
## 67 FT 13.4
## 68 FT 13.1
## 69 FT 9.0
## 70 FT 3.9
## 71 FT 5.7
## 72 FT 10.7colnames(anorexia_treat_df) <- c("Treat", "Dif")
head(anorexia_treat_df)## Treat Dif
## 1 Cont -0.5
## 2 Cont -9.3
## 3 Cont -5.4
## 4 Cont 12.3
## 5 Cont -2.0
## 6 Cont -10.2anorexia_treat_C_df<- subset(anorexia_treat_df, anorexia_treat_df$Treat == "Cont"&anorexia_treat_df$Dif >0)
head(anorexia_treat_C_df)## Treat Dif
## 4 Cont 12.3
## 8 Cont 11.6
## 10 Cont 6.2
## 13 Cont 8.3
## 14 Cont 3.3
## 15 Cont 11.3