# 2.1.a
library(UsingR)
## Loading required package: MASS
## Loading required package: HistData
## Loading required package: Hmisc
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
#2.1.B
data(package = .packages(all.available = TRUE))
data(bumpers)
hist(bumpers)
data("firstchi")
boxplot(firstchi)
data(math)
boxplot(math)
#2.1.C
median(bumpers)
## [1] 2129
## [1] 2129
mean(bumpers)
## [1] 2122.478
## [1] 2122.478
sd(bumpers)
## [1] 798.4574
## [1] 798.4574
hist(bumpers)
median(firstchi)
## [1] 23
## [1] 23
mean(firstchi)
## [1] 23.97701
## [1] 23.97701
sd(firstchi)
## [1] 6.254258
## [1] 6.254258
hist(firstchi)
median(math)
## [1] 54
## [1] 54
mean(math)
## [1] 54.9
## [1] 54.9
sd(math)
## [1] 9.746264
## [1] 9.746264
hist(math)
#2.2.a
hist(brightness, probability = TRUE)
lines(density(brightness), col="red",lwd=3)
#2.2.b
boxplot(brightness)
data("brightness")
hist(brightness)
min(brightness)
## [1] 2.07
## [1] 2.07
min(brightness[brightness > min(brightness)])
## [1] 2.28
## [1] 2.28
#2.2.c
boxplot(brightness)
quantile(brightness)
## 0% 25% 50% 75% 100%
## 2.0700 7.7025 8.5000 9.1300 12.4300
## 0% 25% 50% 75% 100%
## 2.0700 7.7025 8.5000 9.1300 12.4300
brightness.sin <- brightness[brightness > 7.702 & brightness < 9.130]
boxplot(brightness.sin)
library(MASS)
#2.3.a
data("UScereal")
str(UScereal)
## 'data.frame': 65 obs. of 11 variables:
## $ mfr : Factor w/ 6 levels "G","K","N","P",..: 3 2 2 1 2 1 6 4 5 1 ...
## $ calories : num 212 212 100 147 110 ...
## $ protein : num 12.12 12.12 8 2.67 2 ...
## $ fat : num 3.03 3.03 0 2.67 0 ...
## $ sodium : num 394 788 280 240 125 ...
## $ fibre : num 30.3 27.3 28 2 1 ...
## $ carbo : num 15.2 21.2 16 14 11 ...
## $ sugars : num 18.2 15.2 0 13.3 14 ...
## $ shelf : int 3 3 3 1 2 3 1 3 2 1 ...
## $ potassium: num 848.5 969.7 660 93.3 30 ...
## $ vitamins : Factor w/ 3 levels "100%","enriched",..: 2 2 2 2 2 2 2 2 2 2 ...
#2.3.B
table(UScereal$mfr,UScereal$shelf)
##
## 1 2 3
## G 6 7 9
## K 4 7 10
## N 2 0 1
## P 2 1 6
## Q 0 3 2
## R 4 0 1
#2.3.B
table(UScereal$mfr,UScereal$shelf)
##
## 1 2 3
## G 6 7 9
## K 4 7 10
## N 2 0 1
## P 2 1 6
## Q 0 3 2
## R 4 0 1
table(UScereal$vitamins,UScereal$fat)
##
## 0 0.6666667 1 1.1363636 1.3333333 1.4925373 1.6 2 2.6666667
## 100% 1 0 3 0 1 0 0 0 0
## enriched 18 1 7 1 8 4 1 2 3
## none 3 0 0 0 0 0 0 0 0
##
## 2.9850746 3.030303 4 6 9.0909091
## 100% 0 0 0 0 0
## enriched 4 2 4 1 1
## none 0 0 0 0 0
table(UScereal$shelf,UScereal$fat)
##
## 0 0.6666667 1 1.1363636 1.3333333 1.4925373 1.6 2 2.6666667 2.9850746
## 1 10 0 2 0 2 2 1 0 1 0
## 2 3 1 5 0 4 1 0 1 1 1
## 3 9 0 3 1 3 1 0 1 1 3
##
## 3.030303 4 6 9.0909091
## 1 0 0 0 0
## 2 0 1 0 0
## 3 2 3 1 1
table(UScereal$shelf,UScereal$fat)
##
## 0 0.6666667 1 1.1363636 1.3333333 1.4925373 1.6 2 2.6666667 2.9850746
## 1 10 0 2 0 2 2 1 0 1 0
## 2 3 1 5 0 4 1 0 1 1 1
## 3 9 0 3 1 3 1 0 1 1 3
##
## 3.030303 4 6 9.0909091
## 1 0 0 0 0
## 2 0 1 0 0
## 3 2 3 1 1
table(UScereal$mfr, UScereal$fibre)
##
## 0 1 1.333333 1.6 2 2.666667 2.985075 3 3.409091 3.75 4 4.477612 5 5.970149
## G 9 0 1 1 3 2 0 3 0 0 2 0 1 0
## K 2 7 2 0 0 1 0 0 0 1 1 2 0 0
## N 0 0 0 0 0 0 0 0 0 0 0 1 0 1
## P 3 0 0 0 0 0 0 0 1 0 0 0 0 0
## Q 2 1 0 0 0 0 1 0 0 0 1 0 0 0
## R 2 0 1 0 0 0 0 0 0 0 0 1 0 1
##
## 6.666667 7.462687 8 8.955224 9.090909 12 27.272727 28 30.30303
## G 0 0 0 0 0 0 0 0 0
## K 1 1 1 0 0 0 1 1 0
## N 0 0 0 0 0 0 0 0 1
## P 0 2 0 1 1 1 0 0 0
## Q 0 0 0 0 0 0 0 0 0
## R 0 0 0 0 0 0 0 0 0
table(UScereal$sodium, UScereal$sugars)
##
## 0 0.8 1.769912 2 3 4 4.477612 5.681818 6 6.666667 7.462687 8.270677
## 0 3 0 0 0 0 0 0 0 0 0 0 0
## 51.13636 0 0 0 0 0 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0 0 0 0 0 0
## 93.33333 0 0 0 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0 0 0 0
## 135.33835 0 0 0 0 0 0 0 0 0 0 0 1
## 140 0 0 0 0 0 0 0 0 0 0 0 0
## 159.09091 0 0 0 0 0 0 0 1 0 0 0 0
## 173.33333 0 0 0 1 0 0 0 0 0 0 0 0
## 180 0 0 0 0 0 0 0 0 0 0 0 0
## 186.66667 0 0 0 0 0 0 0 0 0 0 0 0
## 190 0 0 0 0 0 0 0 0 0 0 0 0
## 200 0 0 0 0 3 0 0 0 0 0 0 0
## 212.38938 0 0 1 0 0 0 0 0 0 0 0 0
## 220 0 0 0 0 1 0 0 0 1 0 0 0
## 223.8806 0 0 0 0 0 0 0 0 0 0 0 0
## 226.66667 0 0 0 0 0 0 0 0 0 0 0 0
## 227.27273 0 0 0 0 0 0 0 0 0 0 0 0
## 230 0 0 0 0 1 0 0 0 0 0 0 0
## 232 0 1 0 0 0 0 0 0 0 0 0 0
## 238.80597 0 0 0 0 0 0 0 0 0 0 0 0
## 240 0 0 0 0 0 0 0 0 0 0 0 0
## 253.33333 0 0 0 0 0 0 0 0 0 1 0 0
## 266.66667 0 0 0 0 0 0 0 0 0 0 0 0
## 270 0 0 0 0 0 0 0 0 0 0 0 0
## 280 1 0 0 0 1 0 0 0 0 0 0 0
## 283.58209 0 0 0 0 0 0 0 0 0 0 0 0
## 290 0 0 0 1 1 0 0 0 0 0 0 0
## 293.33333 0 0 0 0 0 0 0 0 0 0 0 0
## 298.50746 0 0 0 0 0 0 0 0 0 0 0 0
## 313.43284 0 0 0 0 0 0 0 0 0 0 1 0
## 320 0 0 0 0 1 0 0 0 0 0 0 0
## 328.35821 0 0 0 0 0 0 0 0 0 0 0 0
## 333.33333 0 0 0 0 0 1 0 0 0 0 0 0
## 340 0 0 0 0 0 0 0 0 0 0 0 0
## 343.28358 0 0 0 0 0 0 1 0 0 0 0 0
## 358.20896 0 0 0 0 0 0 0 0 0 0 0 0
## 373.33333 0 0 0 0 0 0 0 0 0 0 0 0
## 393.93939 0 0 0 0 0 0 0 0 0 0 0 0
## 680 0 0 0 0 0 0 0 0 0 0 0 0
## 787.87879 0 0 0 0 0 0 0 0 0 0 0 0
##
## 8.75 8.955224 10.447761 10.666667 11 12 12.121212 13 13.333333
## 0 1 0 0 0 0 1 0 0 0
## 51.13636 0 0 0 0 0 0 0 0 0
## 90 0 0 0 0 0 1 0 0 0
## 93.33333 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0 1 0
## 135.33835 0 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 1 0 0 0
## 159.09091 0 0 0 0 0 0 0 0 0
## 173.33333 0 0 0 0 0 0 0 0 0
## 180 0 0 0 0 0 1 0 2 0
## 186.66667 0 0 0 0 0 0 0 0 1
## 190 0 0 0 0 0 0 0 0 0
## 200 0 0 0 0 0 0 0 0 0
## 212.38938 0 0 0 0 0 0 0 0 0
## 220 0 0 0 0 1 0 0 0 0
## 223.8806 0 1 0 0 0 0 0 0 0
## 226.66667 0 0 0 0 0 1 0 0 0
## 227.27273 0 0 0 0 0 0 1 0 0
## 230 0 0 0 0 0 0 0 0 0
## 232 0 0 0 0 0 0 0 0 0
## 238.80597 0 0 0 0 0 0 0 0 0
## 240 0 0 0 0 0 0 0 0 1
## 253.33333 0 0 0 0 0 0 0 0 0
## 266.66667 0 0 0 1 0 0 0 0 0
## 270 0 0 0 0 0 1 0 0 0
## 280 0 0 0 1 0 1 0 0 0
## 283.58209 0 0 0 0 0 0 0 0 0
## 290 0 0 0 0 0 0 0 0 0
## 293.33333 0 0 0 0 0 0 0 0 0
## 298.50746 0 1 0 0 0 0 0 0 0
## 313.43284 0 0 0 0 0 0 0 0 0
## 320 0 0 0 0 0 0 0 0 0
## 328.35821 0 0 1 0 0 0 0 0 0
## 333.33333 0 0 0 0 0 0 0 0 1
## 340 0 0 0 0 0 0 0 0 0
## 343.28358 0 0 0 0 0 0 0 0 0
## 358.20896 0 0 0 0 0 0 0 0 0
## 373.33333 0 0 0 0 0 1 0 0 0
## 393.93939 0 0 0 0 0 0 0 0 0
## 680 0 0 0 0 0 1 0 0 0
## 787.87879 0 0 0 0 0 0 0 0 0
##
## 13.432836 14 14.666667 14.925373 15.151515 16 17.045455 17.910448
## 0 0 0 0 0 0 0 0 0
## 51.13636 0 0 0 0 0 0 1 0
## 90 0 0 0 0 0 0 0 0
## 93.33333 0 0 0 0 0 0 0 0
## 125 0 1 0 0 0 0 0 0
## 135.33835 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 0 0
## 159.09091 0 0 0 0 0 0 0 0
## 173.33333 0 0 0 0 0 0 0 0
## 180 0 0 0 0 0 1 0 0
## 186.66667 0 0 0 0 0 0 0 0
## 190 0 1 0 0 0 0 0 0
## 200 0 0 0 0 0 0 0 0
## 212.38938 0 0 0 0 0 0 0 0
## 220 0 0 0 0 0 0 0 0
## 223.8806 0 0 0 0 0 0 0 0
## 226.66667 0 0 0 0 0 0 0 0
## 227.27273 0 0 0 0 0 0 0 0
## 230 0 0 0 0 0 0 0 0
## 232 0 0 0 0 0 0 0 0
## 238.80597 0 0 0 1 0 0 0 0
## 240 0 0 0 0 0 0 0 0
## 253.33333 0 0 0 0 0 0 0 0
## 266.66667 0 0 1 0 0 0 0 0
## 270 0 0 0 0 0 0 0 0
## 280 0 2 0 0 0 2 0 0
## 283.58209 1 0 0 0 0 0 0 0
## 290 0 0 0 0 0 0 0 0
## 293.33333 0 0 0 0 0 1 0 0
## 298.50746 0 0 0 0 0 0 0 0
## 313.43284 0 0 0 0 0 0 0 0
## 320 0 0 0 0 0 0 0 0
## 328.35821 0 0 0 0 0 0 0 0
## 333.33333 0 0 0 0 0 0 0 0
## 340 0 0 0 0 0 0 0 0
## 343.28358 0 0 0 0 0 0 0 0
## 358.20896 0 0 0 0 0 0 0 1
## 373.33333 0 0 0 0 0 0 0 0
## 393.93939 0 0 0 0 0 0 0 0
## 680 0 0 0 0 0 0 0 0
## 787.87879 0 0 0 0 1 0 0 0
##
## 18.181818 19.402985 20 20.895522
## 0 0 0 0 0
## 51.13636 0 0 0 0
## 90 0 0 0 0
## 93.33333 0 0 1 0
## 125 0 0 0 0
## 135.33835 0 0 0 0
## 140 0 0 0 0
## 159.09091 0 0 0 0
## 173.33333 0 0 0 0
## 180 0 0 0 0
## 186.66667 0 0 0 0
## 190 0 0 0 0
## 200 0 0 0 0
## 212.38938 0 0 0 0
## 220 0 0 0 0
## 223.8806 0 1 0 0
## 226.66667 0 0 0 0
## 227.27273 0 0 0 0
## 230 0 0 0 0
## 232 0 0 0 0
## 238.80597 0 0 0 0
## 240 0 0 0 0
## 253.33333 0 0 0 0
## 266.66667 0 0 0 0
## 270 0 0 0 0
## 280 0 0 0 0
## 283.58209 0 0 0 0
## 290 0 0 0 0
## 293.33333 0 0 0 0
## 298.50746 0 0 0 1
## 313.43284 0 0 0 0
## 320 0 0 0 0
## 328.35821 0 0 0 0
## 333.33333 0 0 0 0
## 340 0 0 1 0
## 343.28358 0 0 0 0
## 358.20896 0 0 0 0
## 373.33333 0 0 0 0
## 393.93939 1 0 0 0
## 680 0 0 0 0
## 787.87879 0 0 0 0
#2.4
cor(mammals$body, mammals$brain)
## [1] 0.9341638
## [1] 0.9341638
plot(mammals, main="Datos")
plot(log(mammals), main="Resultado")
#2.5
cor(emissions)
## GDP perCapita CO2
## GDP 1.0000000 0.4325303 0.9501753
## perCapita 0.4325303 1.0000000 0.2757962
## CO2 0.9501753 0.2757962 1.0000000
pairs(emissions)
r_lineal= lm(emissions$CO2 ~ emissions$GDP + emissions$perCapita, data = emissions)
summary(r_lineal)
##
## Call:
## lm(formula = emissions$CO2 ~ emissions$GDP + emissions$perCapita,
## data = emissions)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1037.3 -167.4 10.8 153.2 1052.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.100e+02 2.044e+02 2.495 0.0202 *
## emissions$GDP 8.406e-04 5.198e-05 16.172 4.68e-14 ***
## emissions$perCapita -3.039e-02 1.155e-02 -2.631 0.0149 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 382.8 on 23 degrees of freedom
## Multiple R-squared: 0.9253, Adjusted R-squared: 0.9188
## F-statistic: 142.5 on 2 and 23 DF, p-value: 1.102e-13
plot(emissions$GDP+emissions$perCapita,emissions$CO2)
abline(r_lineal,col = "red")
## Warning in abline(r_lineal, col = "red"): only using the first two of 3
## regression coefficients
emissions$CO2
## [1] 6750 1320 1740 550 675 540 2000 700 370 480 240 400 145 75 80
## [16] 54 75 125 420 75 56 160 150 76 85 63
CO2_predict <- predict(r_lineal,emissions)
plot(emissions$GDP+emissions$perCapita,CO2_predict)
CO2_predict
## UnitedStates Japan Germany France UnitedKingdom
## 6403.720110 2357.274571 1328.457202 939.412260 915.510264
## Italy Russia Canada Spain Australia
## 888.117914 948.030553 418.174003 551.546727 203.695487
## Netherlands Poland Belgium Sweden Austria
## 137.905405 524.997147 3.295727 57.168681 6.260516
## Switzerland Portugal Greece Ukraine Denmark
## -65.251059 177.533136 235.468677 538.782254 -82.034073
## Norway Romania CzechRepublic Finland Hungary
## -213.820805 449.888660 273.235200 -5.729292 353.120804
## Ireland
## 59.239930
cor(emissions$CO2,CO2_predict)
## [1] 0.9619321
#2.6
data("anorexia")
head(anorexia)
## Treat Prewt Postwt
## 1 Cont 80.7 80.2
## 2 Cont 89.4 80.1
## 3 Cont 91.8 86.4
## 4 Cont 74.0 86.3
## 5 Cont 78.1 76.1
## 6 Cont 88.3 78.1
hist(anorexia)
pos_pesos <- which(anorexia$Postwt>anorexia$Prewt)
casos_exito = anorexia[c(pos_pesos),]
mejor_tratamiento = which.max(table(casos_exito$Treat))
mejor_tratamiento = c(paste(names(mejor_tratamiento),": ",max(table(casos_exito$Treat))," Casos positivos logrando el mejor tratamiento"))
table(casos_exito$Treat)
##
## CBT Cont FT
## 18 11 13
mejor_tratamiento
## [1] "CBT : 18 Casos positivos logrando el mejor tratamiento"
#B
pos_perdida_peso <- which(anorexia$Postwt<anorexia$Prewt)
casos_fracaso = anorexia[c(pos_perdida_peso),]
ganaron_peso = length(pos_pesos)
ganaron_peso
## [1] 42
perdieron_peso = nrow(casos_fracaso)
perdieron_peso
## [1] 29
#C
barplot(c(ganaron_peso,perdieron_peso), main = "Pacientes que ganaron y que perdieron peso",
ylab = "Cantidad de pacientes", col = c("red","green"))
legend("topright", legend = c(paste("Ganaron peso: ",ganaron_peso),paste("Perdieron peso: ",perdieron_peso)),
fill = c("red","green"))
#2.7
#a
fallecidos = nrow(Melanoma[Melanoma$status==1,]) + nrow(Melanoma[Melanoma$status==3,])
fallecidos
## [1] 71
barplot(table(Melanoma$status), main = "Presencia y ausencia de Melanoma en una base de datos de 205 personas",
ylab = "Unidad por persona", col = c("red","blue","green"))
legend("topright",legend = c(paste("MuertexMelanoma:", nrow(Melanoma[Melanoma$status==1,])),paste("Vivo:",nrow(Melanoma[Melanoma$status==2,])),paste("MuertexOtras causas",nrow(Melanoma[Melanoma$status==3,]))),
fill = c("red","blue","green"))
#C
Melanoma1 = Melanoma
s1 <- which((Melanoma1$status==1))
s2 <- which((Melanoma1$status==2))
s3 <- which((Melanoma1$status==3))
Melanoma1$status = replace(Melanoma1$status,s3,1)
Melanoma1$status = replace(Melanoma1$status,s2,0)
c(paste("tumor/muerte: ",cor(Melanoma1$thickness,Melanoma1$status),3))
## [1] "tumor/muerte: 0.314179811783222 3"
#D
remplazo = replace(Melanoma1$status,Melanoma1$status==0,"Vivos")
remplazo = replace(remplazo,Melanoma1$status==1,"Fallecidos")
boxplot(Melanoma1$thickness~remplazo, main = "Relación entre tamaño de tumor y muerte",
ylab = "Tamaño del tumor (mm)",xlab = "Estado del paciente")
#2.8
#a
table(babyboom$gender)
##
## girl boy
## 18 26
#b
sum(with(babyboom, clock.time <= 1200))
## [1] 18
#C
pesoNiños=sum(with(babyboom,gender == "boy" & wt<3000))
pesoNiños
## [1] 4
#D
barplot(table(babyboom$gender,babyboom$wt<3000),beside = T,col = c("red","green"),xlab="Género",ylab="cantidad")
#e
p_ninos = median(babyboom$wt[babyboom$gender=='boy'])
p_ninas = median(babyboom$wt[babyboom$gender=='girl'])
barplot(c(ganaron_peso,perdieron_peso), main = "Pacientes que ganaron y que perdieron peso",
ylab = "Cantidad de pacientes", col = c("darkorange","skyblue"))
legend("topright", legend = c(paste("Ganaron peso: ",p_ninos),paste("Perdieron peso: ",p_ninas)),
fill = c("darkorange","skyblue"))
#2.9
#a
aggregate(Aids2$state, by=list(Aids2[,"state"]), FUN=length)
## Group.1 x
## 1 NSW 1780
## 2 Other 249
## 3 QLD 226
## 4 VIC 588
table(Aids2$status=='D')
##
## FALSE TRUE
## 1082 1761
table(Aids2$sex, Aids2$T.categ)
##
## hs hsid id het haem blood mother other
## F 1 0 20 20 0 37 4 7
## M 2464 72 28 21 46 57 3 63
colores = c("Red","skyblue","blueviolet","tomato","darkgreen","thistle","darkorange","gold")
pie(table(Aids2$T.categ),col = colores,labels = table(Aids2$T.categ),
main = "Cantidad y tipos de transmisión")
legend("topright",legend = levels(Aids2$T.categ),fill = colores)
#2.10
#A
if(mean(crime$y1993) > mean(crime$y1983)){
print("FUE MAYOR 1993")
}else{
print("NO FUE MAYOR 1993 a 1983")
}
## [1] "FUE MAYOR 1993"
#B
crime[crime$y1993 == max(crime$y1993),]
## y1983 y1993
## DC 1985.4 2832.8