taller 4
valentina corrales
2024-05-01
punto 2.1
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, unitsdata()
hist(bumpers, main = "Histograma de Bumpers Data", xlab = "Valores")
hist(firstchi, main = "Histograma de Firstchi Data", xlab = "Valores")
hist(math, main = "Histograma de Math Data", xlab = "Valores")
boxplot(bumpers, main = "Boxplot de Bumpers Data")
boxplot(firstchi, main = "Boxplot de Firstchi Data")
boxplot(math, main = "Boxplot de Math Data")
mean_bumpers <- mean(bumpers)
median_bumpers <- median(bumpers)
sd_bumpers <- sd(bumpers)
mean_firstchi <- mean(firstchi)
median_firstchi <- median(firstchi)
sd_firstchi <- sd(firstchi)
mean_math <- mean(math)
median_math <- median(math)
sd_math <- sd(math)cat("Estimaciones visuales para 'bumpers':\n")## Estimaciones visuales para 'bumpers':cat("Media:", mean_bumpers, "\n")## Media: 2122.478cat("Mediana:", median_bumpers, "\n")## Mediana: 2129cat("Desviación Estándar:", sd_bumpers, "\n\n")## Desviación Estándar: 798.4574cat("Estimaciones visuales para 'firstchi':\n")## Estimaciones visuales para 'firstchi':cat("Media:", mean_firstchi, "\n")## Media: 23.97701cat("Mediana:", median_firstchi, "\n")## Mediana: 23cat("Desviación Estándar:", sd_firstchi, "\n\n")## Desviación Estándar: 6.254258cat("Estimaciones visuales para 'math':\n")## Estimaciones visuales para 'math':cat("Media:", mean_math, "\n")## Media: 54.9cat("Mediana:", median_math, "\n")## Mediana: 54cat("Desviación Estándar:", sd_math, "\n")## Desviación Estándar: 9.746264Un histograma sería el grafico que resulta de mayor ayuda ya que todas las distribuciones son aproximadas entre si, por ende el histogra permitiria visualizar la distribución de los datos y la frecuencia con la que ocurren ciertos valores dentro de un rango determinado.
punto 2.2 a)
library(UsingR)
data(brightness)
hist(brightness, main = "Histograma de Brightness Data", xlab = "Valores", col = "lightblue", prob = TRUE)
lines(density(brightness), col = "green", lwd = 2)
b)
outlier = boxplot(brightness)$out
min_outlier = sort(outlier, decreasing = FALSE)
min_outlier## [1] 2.07 2.28 3.88 4.37 4.55 4.61 4.78 4.89 4.99 5.01 5.04 5.13
## [13] 5.24 5.29 5.41 5.42 5.53 5.54 5.55 11.28 11.55 11.55 11.55 11.63
## [25] 11.65 11.67 11.71 11.73 11.79 11.80 11.99 12.04 12.04 12.14 12.17 12.19
## [37] 12.31 12.43min_2 = min_outlier[2]
min_2## [1] 2.28#c)
quantile(brightness)## 0% 25% 50% 75% 100%
## 2.0700 7.7025 8.5000 9.1300 12.4300brightness.sin= brightness[brightness > 7.7025 & brightness < 9.130]
boxplot(brightness.sin)
cat("La nueva variable brightness.sin contiene", length(brightness.sin), "valores.")## La nueva variable brightness.sin contiene 479 valores.punto 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 ...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 1table(UScereal$fat, UScereal$vitamins)##
## 100% enriched none
## 0 1 18 3
## 0.6666667 0 1 0
## 1 3 7 0
## 1.1363636 0 1 0
## 1.3333333 1 8 0
## 1.4925373 0 4 0
## 1.6 0 1 0
## 2 0 2 0
## 2.6666667 0 3 0
## 2.9850746 0 4 0
## 3.030303 0 2 0
## 4 0 4 0
## 6 0 1 0
## 9.0909091 0 1 0table(UScereal$fat, UScereal$shelf)##
## 1 2 3
## 0 10 3 9
## 0.6666667 0 1 0
## 1 2 5 3
## 1.1363636 0 0 1
## 1.3333333 2 4 3
## 1.4925373 2 1 1
## 1.6 1 0 0
## 2 0 1 1
## 2.6666667 1 1 1
## 2.9850746 0 1 3
## 3.030303 0 0 2
## 4 0 1 3
## 6 0 0 1
## 9.0909091 0 0 1table(UScereal$carbo, UScereal$sugars)##
## 0 0.8 1.769912 2 3 4 4.477612 5.681818 6 6.666667 7.462687 8.270677
## 10.52632 0 0 0 0 0 0 0 0 0 0 0 1
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0
## 12.5 0 0 0 0 0 0 0 0 0 0 0 0
## 13 1 0 0 0 0 0 0 0 0 0 0 0
## 13.6 0 1 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 1 0 0 0 0 0 0 0 0
## 14.66667 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 1 0 0 0
## 15.15152 0 0 0 0 0 0 0 0 0 0 0 0
## 15.33333 0 0 0 0 0 0 0 0 0 0 0 0
## 16 1 0 0 0 2 0 0 0 0 0 0 0
## 16.41791 0 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 1 0 0 0 0 0 0 0
## 17.04545 0 0 0 0 0 0 0 1 0 0 0 0
## 17.33333 0 0 0 0 0 0 0 0 0 0 0 0
## 17.5 0 0 0 0 0 0 0 0 0 0 0 0
## 17.91045 0 0 0 0 0 0 0 0 0 0 0 0
## 18.66667 0 0 0 0 0 0 0 0 0 0 0 0
## 19.40299 0 0 0 0 0 0 0 0 0 0 1 0
## 20 0 0 0 0 1 0 0 0 0 0 0 0
## 20.35398 0 0 1 0 0 0 0 0 0 0 0 0
## 20.89552 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 1 2 0 0 0 0 0 0 0
## 21.21212 0 0 0 0 0 0 0 0 0 0 0 0
## 21.33333 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 2 0 0 0 0 0 0 0
## 22.38806 0 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 1 0 0
## 25.37313 0 0 0 0 0 0 1 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0 0 0 0
## 26.66667 0 0 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 1 0 0 0 0 0 0
## 28.35821 1 0 0 0 0 0 0 0 0 0 0 0
## 29.85075 1 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0
## 31.34328 0 0 0 0 0 0 0 0 0 0 0 0
## 39.39394 0 0 0 0 0 0 0 0 0 0 0 0
## 68 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
## 10.52632 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 1 0
## 12 0 0 0 0 1 1 0 2 0
## 12.5 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 2 0 0 0
## 13.6 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 1
## 14.66667 0 0 0 0 0 0 0 0 1
## 15 0 0 0 0 0 0 0 0 0
## 15.15152 0 0 0 0 0 0 0 0 0
## 15.33333 0 0 0 0 0 0 0 0 1
## 16 0 0 0 0 0 0 0 0 0
## 16.41791 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0
## 17.04545 0 0 0 0 0 0 0 0 0
## 17.33333 0 0 0 0 0 1 0 0 0
## 17.5 1 0 0 0 0 0 0 0 0
## 17.91045 0 1 0 0 0 0 0 0 0
## 18.66667 0 0 0 0 0 0 0 0 0
## 19.40299 0 0 0 0 0 0 0 0 0
## 20 0 0 0 0 0 1 0 0 0
## 20.35398 0 0 0 0 0 0 0 0 0
## 20.89552 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0
## 21.21212 0 0 0 0 0 0 0 0 0
## 21.33333 0 0 0 1 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0
## 22.38806 0 1 0 0 0 0 0 0 0
## 24 0 0 0 1 0 0 0 0 0
## 25.37313 0 0 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0
## 26.66667 0 0 0 0 0 1 0 0 0
## 27 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 1 0 0 0
## 28.35821 0 0 0 0 0 0 0 0 0
## 29.85075 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 1 0 0 0
## 31.34328 0 0 1 0 0 0 0 0 0
## 39.39394 0 0 0 0 0 0 1 0 0
## 68 0 0 0 0 0 1 0 0 0
##
## 13.432836 14 14.666667 14.925373 15.151515 16 17.045455 17.910448
## 10.52632 0 0 0 0 0 0 0 0
## 11 0 1 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0
## 12.5 0 0 0 0 0 0 1 0
## 13 0 0 0 0 0 0 0 0
## 13.6 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0
## 14.66667 0 0 0 0 0 0 0 0
## 15 0 1 0 0 0 0 0 0
## 15.15152 0 0 0 0 0 0 0 0
## 15.33333 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 1 0 0
## 16.41791 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0
## 17.04545 0 0 0 0 0 0 0 0
## 17.33333 0 0 0 0 0 1 0 0
## 17.5 0 0 0 0 0 0 0 0
## 17.91045 0 0 0 1 0 0 0 0
## 18.66667 0 0 1 0 0 1 0 0
## 19.40299 0 0 0 0 0 0 0 0
## 20 0 1 0 0 0 0 0 0
## 20.35398 0 0 0 0 0 0 0 0
## 20.89552 0 0 0 0 0 0 0 1
## 21 0 0 0 0 0 1 0 0
## 21.21212 0 0 0 0 1 0 0 0
## 21.33333 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0
## 22.38806 1 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0
## 25.37313 0 0 0 0 0 0 0 0
## 26 0 1 0 0 0 0 0 0
## 26.66667 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0
## 28.35821 0 0 0 0 0 0 0 0
## 29.85075 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0
## 31.34328 0 0 0 0 0 0 0 0
## 39.39394 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 0 0 0
##
## 18.181818 19.402985 20 20.895522
## 10.52632 0 0 0 0
## 11 0 0 0 0
## 12 0 0 1 0
## 12.5 0 0 0 0
## 13 0 0 0 0
## 13.6 0 0 0 0
## 14 0 0 0 0
## 14.66667 0 0 0 0
## 15 0 0 0 0
## 15.15152 1 0 0 0
## 15.33333 0 0 0 0
## 16 0 0 0 0
## 16.41791 0 0 0 1
## 17 0 0 0 0
## 17.04545 0 0 0 0
## 17.33333 0 0 0 0
## 17.5 0 0 0 0
## 17.91045 0 0 0 0
## 18.66667 0 0 0 0
## 19.40299 0 0 0 0
## 20 0 0 0 0
## 20.35398 0 0 0 0
## 20.89552 0 0 0 0
## 21 0 0 0 0
## 21.21212 0 0 0 0
## 21.33333 0 0 0 0
## 22 0 0 0 0
## 22.38806 0 0 0 0
## 24 0 0 0 0
## 25.37313 0 1 0 0
## 26 0 0 0 0
## 26.66667 0 0 0 0
## 27 0 0 1 0
## 28 0 0 0 0
## 28.35821 0 0 0 0
## 29.85075 0 0 0 0
## 30 0 0 0 0
## 31.34328 0 0 0 0
## 39.39394 0 0 0 0
## 68 0 0 0 0table(UScereal$fibre, UScereal$mfr)##
## G K N P Q R
## 0 9 2 0 3 2 2
## 1 0 7 0 0 1 0
## 1.333333 1 2 0 0 0 1
## 1.6 1 0 0 0 0 0
## 2 3 0 0 0 0 0
## 2.666667 2 1 0 0 0 0
## 2.985075 0 0 0 0 1 0
## 3 3 0 0 0 0 0
## 3.409091 0 0 0 1 0 0
## 3.75 0 1 0 0 0 0
## 4 2 1 0 0 1 0
## 4.477612 0 2 1 0 0 1
## 5 1 0 0 0 0 0
## 5.970149 0 0 1 0 0 1
## 6.666667 0 1 0 0 0 0
## 7.462687 0 1 0 2 0 0
## 8 0 1 0 0 0 0
## 8.955224 0 0 0 1 0 0
## 9.090909 0 0 0 1 0 0
## 12 0 0 0 1 0 0
## 27.272727 0 1 0 0 0 0
## 28 0 1 0 0 0 0
## 30.30303 0 0 1 0 0 0table(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 0punto 2.4 a)
attach(mammals)
cor (mammals) ## body brain
## body 1.0000000 0.9341638
## brain 0.9341638 1.0000000 plot(mammals)
c)
cor(x=log(mammals$body), y=log(mammals$brain))## [1] 0.9595748plot(log(mammals$body),log(mammals$brain))
c)
# Punto c: Transformar los datos mediante la función log y repetir el estudio
mammals$log_body <- log(mammals$body)
mammals$log_brain <- log(mammals$brain)
# correlación lineal después de la transformación
correlation_log <- cor(mammals$log_brain, mammals$log_body)
cat("Correlación lineal después de la transformación:", correlation_log, "\n")## Correlación lineal después de la transformación: 0.9595748plot(mammals$log_body, mammals$log_brain, main = "Relación entre Log(Peso Corporal) y Log(Peso del Cerebro)", xlab = "Log(Peso Corporal)", ylab = "Log(Peso del Cerebro)")
punto 2.5
data("emissions")
head(emissions)## GDP perCapita CO2
## UnitedStates 8083000 29647 6750
## Japan 3080000 24409 1320
## Germany 1740000 21197 1740
## France 1320000 22381 550
## UnitedKingdom 1242000 21010 675
## Italy 1240000 21856 540pairs(emissions)
cor(emissions)## GDP perCapita CO2
## GDP 1.0000000 0.4325303 0.9501753
## perCapita 0.4325303 1.0000000 0.2757962
## CO2 0.9501753 0.2757962 1.0000000lineal_regre = lm(emissions$CO2 ~ emissions$GDP + emissions$perCapita, data = emissions)
summary(lineal_regre)##
## Call:
## lm(formula = emissions$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-13plot(emissions$GDP+emissions$perCapita,emissions$CO2)
abline(lineal_regre,col = "red")## Warning in abline(lineal_regre, col = "red"): only using the first two of 3
## regression coefficients
emissions$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 63prediccion_CO2 = predict(lineal_regre,emissions)
plot(emissions$GDP+emissions$perCapita,prediccion_CO2)
prediccion_CO2## 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
## 59.239930cor(emissions$CO2,prediccion_CO2)## [1] 0.9619321boxplot(emissions)
boxplot(emissions$CO2)
quantile(emissions$CO2)## 0% 25% 50% 75% 100%
## 54.0 77.0 200.0 547.5 6750.0CO2_sin = emissions$CO2[emissions$CO2 > 77.0 & emissions$CO2 < 547.5]
boxplot(CO2_sin, col = "#90EE90")
Punto 2.6 a)
MASS::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 98.0data("anorexia")
anorexia[3,1:3]## Treat Prewt Postwt
## 3 Cont 91.8 86.4pos_casos_exito <- which(anorexia$Postwt>anorexia$Prewt) #Arroja las posiciones en los casos donde se subió de peso
casos_exito = anorexia[c(pos_casos_exito),]
mejor_tratamiento = which.max(table(casos_exito$Treat))
mejor_tratamiento = c(paste(names(mejor_tratamiento),": ",max(table(casos_exito$Treat))," casos de éxito"))
table(casos_exito$Treat)##
## CBT Cont FT
## 18 11 13mejor_tratamiento## [1] "CBT : 18 casos de éxito"pos_casos_fracaso <- which(anorexia$Postwt<anorexia$Prewt)
casos_fracaso = anorexia[c(pos_casos_fracaso),]
ganaron_peso = length(pos_casos_exito);ganaron_peso ## [1] 42perdieron_peso = nrow(casos_fracaso)
barplot(c(ganaron_peso,perdieron_peso), main = "Pacientes que ganaron vs los que perdieron peso",
ylab = "Cantidad de pacientes", col = c("#00FF00","#9370DB"))
legend("topright", legend = c(paste("Ganaron peso: ",ganaron_peso),paste("Perdieron peso: ",perdieron_peso)),
fill = c("#00FF00","#9370DB"))
punto 2.7
MASS::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 1
## 7 210 1 1 77 1972 5.16 1
## 8 232 3 0 60 1974 3.22 1
## 9 232 1 1 49 1968 12.88 1
## 10 279 1 0 68 1971 7.41 1
## 11 295 1 0 53 1969 4.19 1
## 12 355 3 0 64 1972 0.16 1
## 13 386 1 0 68 1965 3.87 1
## 14 426 1 1 63 1970 4.84 1
## 15 469 1 0 14 1969 2.42 1
## 16 493 3 1 72 1971 12.56 1
## 17 529 1 1 46 1971 5.80 1
## 18 621 1 1 72 1972 7.06 1
## 19 629 1 1 95 1968 5.48 1
## 20 659 1 1 54 1972 7.73 1
## 21 667 1 0 89 1968 13.85 1
## 22 718 1 1 25 1967 2.34 1
## 23 752 1 1 37 1973 4.19 1
## 24 779 1 1 43 1967 4.04 1
## 25 793 1 1 68 1970 4.84 1
## 26 817 1 0 67 1966 0.32 0
## 27 826 3 0 86 1965 8.54 1
## 28 833 1 0 56 1971 2.58 1
## 29 858 1 0 16 1967 3.56 0
## 30 869 1 0 42 1965 3.54 0
## 31 872 1 0 65 1968 0.97 0
## 32 967 1 1 52 1970 4.83 1
## 33 977 1 1 58 1967 1.62 1
## 34 982 1 0 60 1970 6.44 1
## 35 1041 1 1 68 1967 14.66 0
## 36 1055 1 0 75 1967 2.58 1
## 37 1062 1 1 19 1966 3.87 1
## 38 1075 1 1 66 1971 3.54 1
## 39 1156 1 0 56 1970 1.34 1
## 40 1228 1 1 46 1973 2.24 1
## 41 1252 1 0 58 1971 3.87 1
## 42 1271 1 0 74 1971 3.54 1
## 43 1312 1 0 65 1970 17.42 1
## 44 1427 3 1 64 1972 1.29 0
## 45 1435 1 1 27 1969 3.22 0
## 46 1499 2 1 73 1973 1.29 0
## 47 1506 1 1 56 1970 4.51 1
## 48 1508 2 1 63 1973 8.38 1
## 49 1510 2 0 69 1973 1.94 0
## 50 1512 2 0 77 1973 0.16 0
## 51 1516 1 1 80 1968 2.58 1
## 52 1525 3 0 76 1970 1.29 1
## 53 1542 2 0 65 1973 0.16 0
## 54 1548 1 0 61 1972 1.62 0
## 55 1557 2 0 26 1973 1.29 0
## 56 1560 1 0 57 1973 2.10 0
## 57 1563 2 0 45 1973 0.32 0
## 58 1584 1 1 31 1970 0.81 0
## 59 1605 2 0 36 1973 1.13 0
## 60 1621 1 0 46 1972 5.16 1
## 61 1627 2 0 43 1973 1.62 0
## 62 1634 2 0 68 1973 1.37 0
## 63 1641 2 1 57 1973 0.24 0
## 64 1641 2 0 57 1973 0.81 0
## 65 1648 2 0 55 1973 1.29 0
## 66 1652 2 0 58 1973 1.29 0
## 67 1654 2 1 20 1973 0.97 0
## 68 1654 2 0 67 1973 1.13 0
## 69 1667 1 0 44 1971 5.80 1
## 70 1678 2 0 59 1973 1.29 0
## 71 1685 2 0 32 1973 0.48 0
## 72 1690 1 1 83 1971 1.62 0
## 73 1710 2 0 55 1973 2.26 0
## 74 1710 2 1 15 1973 0.58 0
## 75 1726 1 0 58 1970 0.97 1
## 76 1745 2 0 47 1973 2.58 1
## 77 1762 2 0 54 1973 0.81 0
## 78 1779 2 1 55 1973 3.54 1
## 79 1787 2 1 38 1973 0.97 0
## 80 1787 2 0 41 1973 1.78 1
## 81 1793 2 0 56 1973 1.94 0
## 82 1804 2 0 48 1973 1.29 0
## 83 1812 2 1 44 1973 3.22 1
## 84 1836 2 0 70 1972 1.53 0
## 85 1839 2 0 40 1972 1.29 0
## 86 1839 2 1 53 1972 1.62 1
## 87 1854 2 0 65 1972 1.62 1
## 88 1856 2 1 54 1972 0.32 0
## 89 1860 3 1 71 1969 4.84 1
## 90 1864 2 0 49 1972 1.29 0
## 91 1899 2 0 55 1972 0.97 0
## 92 1914 2 0 69 1972 3.06 0
## 93 1919 2 1 83 1972 3.54 0
## 94 1920 2 1 60 1972 1.62 1
## 95 1927 2 1 40 1972 2.58 1
## 96 1933 1 0 77 1972 1.94 0
## 97 1942 2 0 35 1972 0.81 0
## 98 1955 2 0 46 1972 7.73 1
## 99 1956 2 0 34 1972 0.97 0
## 100 1958 2 0 69 1972 12.88 0
## 101 1963 2 0 60 1972 2.58 0
## 102 1970 2 1 84 1972 4.09 1
## 103 2005 2 0 66 1972 0.64 0
## 104 2007 2 1 56 1972 0.97 0
## 105 2011 2 0 75 1972 3.22 1
## 106 2024 2 0 36 1972 1.62 0
## 107 2028 2 1 52 1972 3.87 1
## 108 2038 2 0 58 1972 0.32 1
## 109 2056 2 0 39 1972 0.32 0
## 110 2059 2 1 68 1972 3.22 1
## 111 2061 1 1 71 1968 2.26 0
## 112 2062 1 0 52 1965 3.06 0
## 113 2075 2 1 55 1972 2.58 1
## 114 2085 3 0 66 1970 0.65 0
## 115 2102 2 1 35 1972 1.13 0
## 116 2103 1 1 44 1966 0.81 0
## 117 2104 2 0 72 1972 0.97 0
## 118 2108 1 0 58 1969 1.76 1
## 119 2112 2 0 54 1972 1.94 1
## 120 2150 2 0 33 1972 0.65 0
## 121 2156 2 0 45 1972 0.97 0
## 122 2165 2 1 62 1972 5.64 0
## 123 2209 2 0 72 1971 9.66 0
## 124 2227 2 0 51 1971 0.10 0
## 125 2227 2 1 77 1971 5.48 1
## 126 2256 1 0 43 1971 2.26 1
## 127 2264 2 0 65 1971 4.83 1
## 128 2339 2 0 63 1971 0.97 0
## 129 2361 2 1 60 1971 0.97 0
## 130 2387 2 0 50 1971 5.16 1
## 131 2388 1 1 40 1966 0.81 0
## 132 2403 2 0 67 1971 2.90 1
## 133 2426 2 0 69 1971 3.87 0
## 134 2426 2 0 74 1971 1.94 1
## 135 2431 2 0 49 1971 0.16 0
## 136 2460 2 0 47 1971 0.64 0
## 137 2467 1 0 42 1965 2.26 1
## 138 2492 2 0 54 1971 1.45 0
## 139 2493 2 1 72 1971 4.82 1
## 140 2521 2 0 45 1971 1.29 1
## 141 2542 2 1 67 1971 7.89 1
## 142 2559 2 0 48 1970 0.81 1
## 143 2565 1 1 34 1970 3.54 1
## 144 2570 2 0 44 1970 1.29 0
## 145 2660 2 0 31 1970 0.64 0
## 146 2666 2 0 42 1970 3.22 1
## 147 2676 2 0 24 1970 1.45 1
## 148 2738 2 0 58 1970 0.48 0
## 149 2782 1 1 78 1969 1.94 0
## 150 2787 2 1 62 1970 0.16 0
## 151 2984 2 1 70 1969 0.16 0
## 152 3032 2 0 35 1969 1.29 0
## 153 3040 2 0 61 1969 1.94 0
## 154 3042 1 0 54 1967 3.54 1
## 155 3067 2 0 29 1969 0.81 0
## 156 3079 2 1 64 1969 0.65 0
## 157 3101 2 1 47 1969 7.09 0
## 158 3144 2 1 62 1969 0.16 0
## 159 3152 2 0 32 1969 1.62 0
## 160 3154 3 1 49 1969 1.62 0
## 161 3180 2 0 25 1969 1.29 0
## 162 3182 3 1 49 1966 6.12 0
## 163 3185 2 0 64 1969 0.48 0
## 164 3199 2 0 36 1969 0.64 0
## 165 3228 2 0 58 1969 3.22 1
## 166 3229 2 0 37 1969 1.94 0
## 167 3278 2 1 54 1969 2.58 0
## 168 3297 2 0 61 1968 2.58 1
## 169 3328 2 1 31 1968 0.81 0
## 170 3330 2 1 61 1968 0.81 1
## 171 3338 1 0 60 1967 3.22 1
## 172 3383 2 0 43 1968 0.32 0
## 173 3384 2 0 68 1968 3.22 1
## 174 3385 2 0 4 1968 2.74 0
## 175 3388 2 1 60 1968 4.84 1
## 176 3402 2 1 50 1968 1.62 0
## 177 3441 2 0 20 1968 0.65 0
## 178 3458 3 0 54 1967 1.45 0
## 179 3459 2 0 29 1968 0.65 0
## 180 3459 2 1 56 1968 1.29 1
## 181 3476 2 0 60 1968 1.62 0
## 182 3523 2 0 46 1968 3.54 0
## 183 3667 2 0 42 1967 3.22 0
## 184 3695 2 0 34 1967 0.65 0
## 185 3695 2 0 56 1967 1.03 0
## 186 3776 2 1 12 1967 7.09 1
## 187 3776 2 0 21 1967 1.29 1
## 188 3830 2 1 46 1967 0.65 0
## 189 3856 2 0 49 1967 1.78 0
## 190 3872 2 0 35 1967 12.24 1
## 191 3909 2 1 42 1967 8.06 1
## 192 3968 2 0 47 1967 0.81 0
## 193 4001 2 0 69 1967 2.10 0
## 194 4103 2 0 52 1966 3.87 0
## 195 4119 2 1 52 1966 0.65 0
## 196 4124 2 0 30 1966 1.94 1
## 197 4207 2 1 22 1966 0.65 0
## 198 4310 2 1 55 1966 2.10 0
## 199 4390 2 0 26 1965 1.94 1
## 200 4479 2 0 19 1965 1.13 1
## 201 4492 2 1 29 1965 7.06 1
## 202 4668 2 0 40 1965 6.12 0
## 203 4688 2 0 42 1965 0.48 0
## 204 4926 2 0 50 1964 2.26 0
## 205 5565 2 0 41 1962 2.90 0Num_fallecidos = nrow(Melanoma[Melanoma$status==1,]) + nrow(Melanoma[Melanoma$status==3,])
Num_fallecidos## [1] 71Melanoma$ulcer## [1] 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 1
## [38] 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## [75] 1 1 0 1 0 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 0
## [112] 0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 1 1 1 1 1 0 0 1 1 0
## [149] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0
## [186] 1 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 0 0 0sum(Melanoma$ulcer=="0")## [1] 115sum(Melanoma$ulcer=="1")## [1] 90library(MASS)
data("Melanoma")
table(Melanoma$thickness, Melanoma$status)##
## 1 2 3
## 0.1 0 1 0
## 0.16 0 6 1
## 0.24 0 1 0
## 0.32 1 5 0
## 0.48 0 4 0
## 0.58 0 1 0
## 0.64 0 4 0
## 0.65 0 8 2
## 0.81 3 8 0
## 0.97 2 9 0
## 1.03 0 1 0
## 1.13 0 4 0
## 1.29 0 14 2
## 1.34 1 1 0
## 1.37 0 1 0
## 1.45 0 2 1
## 1.53 0 1 0
## 1.62 3 8 1
## 1.76 1 0 0
## 1.78 0 2 0
## 1.94 2 8 0
## 2.1 1 2 0
## 2.24 1 0 0
## 2.26 3 2 0
## 2.34 1 0 0
## 2.42 1 0 0
## 2.58 3 6 0
## 2.74 0 1 0
## 2.9 0 2 1
## 3.06 1 1 0
## 3.22 2 7 1
## 3.54 5 3 0
## 3.56 1 0 0
## 3.87 3 3 0
## 4.04 1 0 0
## 4.09 0 1 0
## 4.19 2 0 0
## 4.51 1 0 0
## 4.82 0 1 0
## 4.83 1 1 0
## 4.84 3 1 1
## 5.16 2 1 0
## 5.48 1 1 0
## 5.64 0 1 0
## 5.8 2 0 0
## 6.12 0 1 1
## 6.44 1 0 0
## 6.76 0 0 1
## 7.06 1 1 0
## 7.09 0 2 0
## 7.41 1 0 0
## 7.73 1 1 0
## 7.89 0 1 0
## 8.06 0 1 0
## 8.38 0 1 0
## 8.54 0 0 1
## 9.66 0 1 0
## 12.08 1 0 0
## 12.24 0 1 0
## 12.56 0 0 1
## 12.88 1 1 0
## 13.85 1 0 0
## 14.66 1 0 0
## 17.42 1 0 0barplot(table(Melanoma$ulcer), main="Presencia y ausencia de Melanoma", xlab= "ulcera", ylab = "Cantidad", beside = TRUE, legend.text = c("Presencia","Ausencia"), col=c("#40E0D0","#FFA07A"))
2.8
UsingR::babyboom## clock.time gender wt running.time
## 1 5 girl 3837 5
## 2 104 girl 3334 64
## 3 118 boy 3554 78
## 4 155 boy 3838 115
## 5 257 boy 3625 177
## 6 405 girl 2208 245
## 7 407 girl 1745 247
## 8 422 boy 2846 262
## 9 431 boy 3166 271
## 10 708 boy 3520 428
## 11 735 boy 3380 455
## 12 812 boy 3294 492
## 13 814 girl 2576 494
## 14 909 girl 3208 549
## 15 1035 boy 3521 635
## 16 1049 girl 3746 649
## 17 1053 girl 3523 653
## 18 1133 boy 2902 693
## 19 1209 boy 2635 729
## 20 1256 boy 3920 776
## 21 1305 boy 3690 785
## 22 1406 girl 3430 846
## 23 1407 girl 3480 847
## 24 1433 girl 3116 873
## 25 1446 girl 3428 886
## 26 1514 boy 3783 914
## 27 1631 boy 3345 991
## 28 1657 boy 3034 1017
## 29 1742 girl 2184 1062
## 30 1807 boy 3300 1087
## 31 1825 girl 2383 1105
## 32 1854 boy 3428 1134
## 33 1909 boy 4162 1149
## 34 1947 boy 3630 1187
## 35 1949 boy 3406 1189
## 36 1951 boy 3402 1191
## 37 2010 girl 3500 1210
## 38 2037 boy 3736 1237
## 39 2051 boy 3370 1251
## 40 2104 boy 2121 1264
## 41 2123 boy 3150 1283
## 42 2217 girl 3866 1337
## 43 2327 girl 3542 1407
## 44 2355 girl 3278 1435attach(babyboom)
boy= nrow(babyboom[gender == "boy",])
boy## [1] 26girl= nrow(babyboom[gender == "girl",])
girl## [1] 18num_nacidos= nrow(babyboom[clock.time<=708,])
p12horas= nrow(babyboom[clock.time< 708,])
paste("Niños nacidos en las primeras 12 horas: ", p12horas)## [1] "Niños nacidos en las primeras 12 horas: 9"niños_menos3000gr= nrow(babyboom[wt<3000,])
paste("Niños con peso menor a los 3000gr: ", niños_menos3000gr)## [1] "Niños con peso menor a los 3000gr: 9"generoninos = babyboom[babyboom$wt < 3000,]
table(generoninos$gender,generoninos$wt)##
## 1745 2121 2184 2208 2383 2576 2635 2846 2902
## girl 1 0 1 1 1 1 0 0 0
## boy 0 1 0 0 0 0 1 1 1promedioninos= aggregate(babyboom$wt, by = list(babyboom[,"gender"]), FUN = mean)
promedioninos## Group.1 x
## 1 girl 3132.444
## 2 boy 3375.308barplot(c(mean(babyboom$wt[babyboom$gender=="girl"]), mean(babyboom$wt[babyboom$gender=="boy"])),names.arg=c("Niña", "Niño"), col = c("#FFFF99", "#B0C4DE"), main = "Promedio peso", ylab="peso", xlab="", las=1, ylim=c(0,3500))
2.9
data("Aids2")
head(Aids2)## state sex diag death status T.categ age
## 1 NSW M 10905 11081 D hs 35
## 2 NSW M 11029 11096 D hs 53
## 3 NSW M 9551 9983 D hs 42
## 4 NSW M 9577 9654 D haem 44
## 5 NSW M 10015 10290 D hs 39
## 6 NSW M 9971 10344 D hs 36- contagios por estado
aggregate(Aids2$state, by=list(Aids2[,"state"]), FUN=length)## Group.1 x
## 1 NSW 1780
## 2 Other 249
## 3 QLD 226
## 4 VIC 588- numero de muertos
muertos_sida = Aids2[Aids2$status == "D",]
length(muertos_sida)## [1] 7c)relación entre sexo y tipo de transmisión
table(Aids2$sex, Aids2$T.categ)##
## hs hsid id het haem blood mother other
## F 1 0 20 20 0 37 4 7
## M 2464 72 28 21 46 57 3 63types = table(Aids2$T.categ)
barplot(types, main = "Tipos de contagio", col = c("#FFD700", "#FF69B4", "#00CED1", "#9370DB", "#FF4500", "#32CD32", "#800080", "#FF8C00"), ylab = "No. Personas", ylim = c(0,2500))
UsingR::crime## y1983 y1993
## Alabama 416.0 871.7
## Alaska 613.8 660.5
## Arizona 494.2 670.8
## Arkansas 297.7 576.5
## California 772.6 1119.7
## Colorado 476.4 578.8
## Connecticut 375.0 495.3
## Delaware 453.1 621.2
## DC 1985.4 2832.8
## Florida 826.7 1207.2
## Georgia 456.7 733.2
## Hawaii 252.1 258.4
## Idaho 238.7 281.4
## Illinois 553.0 977.3
## Indiana 283.8 508.3
## Iowa 181.1 278.0
## Kansas 326.6 510.8
## Kentucky 322.2 535.5
## Louisiana 640.9 984.6
## Maine 159.6 130.9
## Maryland 807.1 1000.1
## Massachusetts 576.8 779.0
## Michigan 716.7 770.1
## Minnesota 190.9 338.0
## Mississippi 280.4 411.7
## Missour 477.2 740.4
## Montana 212.6 169.9
## Nebraska 217.7 348.6
## Nevada 655.2 696.8
## New Hampshire 125.1 125.7
## New Jersey 553.1 625.8
## New Mexico 686.8 934.9
## New York 914.1 1122.1
## North Carolina 409.6 681.0
## North Dakota 53.7 83.3
## Ohio 397.9 525.9
## Oklahoma 423.4 622.8
## Oregon 487.8 510.2
## Pennsylvania 342.8 427.0
## Rhode Island 355.2 394.5
## South Carolina 616.8 944.5
## South Dakota 120.0 194.5
## Tennessee 402.0 746.2
## Texas 512.2 806.3
## Utah 256.0 290.5
## Vermont 132.6 109.5
## Virginia 292.5 374.9
## Washington 371.8 534.5
## West Virginia 171.8 211.5
## Wisconsin 190.9 275.7
## Wyoming 237.2 319.5total83 = round(sum(crime$y1983))
total93 = round(sum(crime$y1993))crimen83 =round(max(crime$y1983))
crimen93 = round(max(crime$y1993))crime["acumulado"] <- crime$y1983 + crime$y1993
crime[crime$acumulado == max(crime$acumulado),]## y1983 y1993 acumulado
## DC 1985.4 2832.8 4818.2anos = c("1983","1993")
datos = c(total83,total93)
pie(datos, anos,
main = "Mayor tasa de crímenes por año",
sub = "Año 1983: 1985 - Año 1993: 2833")