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
library(MASS)
library(ggplot2)
#2.1
#a) Conjunto de datos de UsingR
packageDescription("UsingR")
## Package: UsingR
## Version: 2.0-7
## Title: Data Sets, Etc. for the Text "Using R for Introductory
## Statistics", Second Edition
## Author: John Verzani <verzani@math.csi.cuny.edu>
## Maintainer: John Verzani <verzani@math.csi.cuny.edu>
## Description: A collection of data sets to accompany the textbook "Using
## R for Introductory Statistics," second edition.
## Depends: R (>= 2.15.0), MASS, HistData, Hmisc
## Suggests: zoo, ggplot2, vcd, lubridate, aplpack
## License: GPL (>= 2)
## LazyData: TRUE
## NeedsCompilation: no
## Packaged: 2022-01-10 19:16:26 UTC; jverzani
## Repository: RSPM
## Date/Publication: 2022-01-11 09:52:45 UTC
## Encoding: UTF-8
## Built: R 4.2.0; ; 2022-04-23 03:09:18 UTC; unix
##
## -- File: /cloud/lib/x86_64-pc-linux-gnu-library/4.2/UsingR/Meta/package.rds
#b) Representa gráficamente los datos contenidos
data(bumpers)
hist(bumpers)
data(firstchi)
hist(firstchi)
data(math)
boxplot(math, main="Math")
#c)Estima visualmente las medias, medianas y desviaciones estándar de cada conjunto
median(bumpers)
## [1] 2129
mean(bumpers)
## [1] 2122.478
sd(bumpers)
## [1] 798.4574
hist(bumpers)
median(firstchi)
## [1] 23
mean(firstchi)
## [1] 23.97701
sd(firstchi)
## [1] 6.254258
hist(firstchi)
median(math)
## [1] 54
mean(math)
## [1] 54.9
sd(math)
## [1] 9.746264
hist(math)
#2.2 Conjunto de datos brightness
#a)Representa estos datos mediante un histograma y un gráfico de densidad superpuesto
data(brightness)
hist(brightness, probability = TRUE)
lines(density(brightness), col="blue",lwd=3)
#b)Representa gráficamente estos datos mediante un diagrama de caja
boxplot(brightness, main="Brightness")
#Si se presentan outliers, y el segundo menor es 2.28
min(brightness[brightness > min(brightness)])
## [1] 2.28
#c)Valores que se encuentran por encima de la primera bisagra y por debajo de la cuarta. Para esto tomamos encuenta el anterior boxplot y identificamos las bisagras superiores e inferiores
quantile(brightness)
## 0% 25% 50% 75% 100%
## 2.0700 7.7025 8.5000 9.1300 12.4300
#Con esto elegimos las bisagras a usar y filtramos los valores del dataset de brightness que esten en ese rango de valores
brightness.sin=brightness[brightness > 7.702 & brightness < 9.130]
boxplot(brightness.sin, main="Brightness nuevo")
#2.3
#a)Datos de cada variable
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 ...
#b)
#i. La relación entre manufacturer y shelf
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
#ii. La relación entre fat y vitamins.
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
#iii. La relación entre fat y shelf
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
#iv. La relación entre carbohydrates y sugars.
table(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 0
#v. La relación entre fibre y manufacturer.
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
#vi. La relación entre sodium y sugars.
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
#a) Correlacion
data(mammals)
cor(mammals$body, mammals$brain)
## [1] 0.9341638
#b)
plot(mammals, main="Mammals")
#c)
plot(log(mammals), main="Mammals con funcion Log")
#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 540
#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
#a)
hist(anorexia)
mean_anorexia=aggregate(anorexia[,2:length(anorexia)], by=list(anorexia$Treat), FUN = mean)
mean_anorexia["diferencia"]=mean_anorexia$Postwt - mean_anorexia$Prewt
mean_anorexia[mean_anorexia$diferencia == max(mean_anorexia$diferencia),]
## Group.1 Prewt Postwt diferencia
## 3 FT 83.22941 90.49412 7.264706
#b)
anorexia["diferencia"]=anorexia$Postwt - anorexia$Prewt
#Total que ganaron peso:
ganaron=length(anorexia[anorexia$diferencia > 0,"diferencia"])
#Total que perdieron peso:
perdieron=length(anorexia[anorexia$diferencia < 0,"diferencia"])
#c)
pie(c(ganaron, perdieron), labels = c("Ganaron","Perdieron"), col = c("#CAFF70","#FF6A6A"),main = "Personas que ganaron/perdieron peso")
#2.7
#a)
length(Melanoma[Melanoma$status == 1 | Melanoma$status == 3,"status"])
## [1] 71
#b)
table(Melanoma[,"ulcer"])
##
## 0 1
## 115 90
#c)
table(Melanoma$thickness, Melanoma$status)
##
## 1 2 3
## 0.1 0 1 0
## 0.16 0 6 1
## 0.24 0 1 0
## 0.32 1 5 0
## 0.48 0 4 0
## 0.58 0 1 0
## 0.64 0 4 0
## 0.65 0 8 2
## 0.81 3 8 0
## 0.97 2 9 0
## 1.03 0 1 0
## 1.13 0 4 0
## 1.29 0 14 2
## 1.34 1 1 0
## 1.37 0 1 0
## 1.45 0 2 1
## 1.53 0 1 0
## 1.62 3 8 1
## 1.76 1 0 0
## 1.78 0 2 0
## 1.94 2 8 0
## 2.1 1 2 0
## 2.24 1 0 0
## 2.26 3 2 0
## 2.34 1 0 0
## 2.42 1 0 0
## 2.58 3 6 0
## 2.74 0 1 0
## 2.9 0 2 1
## 3.06 1 1 0
## 3.22 2 7 1
## 3.54 5 3 0
## 3.56 1 0 0
## 3.87 3 3 0
## 4.04 1 0 0
## 4.09 0 1 0
## 4.19 2 0 0
## 4.51 1 0 0
## 4.82 0 1 0
## 4.83 1 1 0
## 4.84 3 1 1
## 5.16 2 1 0
## 5.48 1 1 0
## 5.64 0 1 0
## 5.8 2 0 0
## 6.12 0 1 1
## 6.44 1 0 0
## 6.76 0 0 1
## 7.06 1 1 0
## 7.09 0 2 0
## 7.41 1 0 0
## 7.73 1 1 0
## 7.89 0 1 0
## 8.06 0 1 0
## 8.38 0 1 0
## 8.54 0 0 1
## 9.66 0 1 0
## 12.08 1 0 0
## 12.24 0 1 0
## 12.56 0 0 1
## 12.88 1 1 0
## 13.85 1 0 0
## 14.66 1 0 0
## 17.42 1 0 0
#d)
result=aggregate(Melanoma[,"ulcer"], by=list(Melanoma[,"ulcer"]), FUN=length)
presencia=length(Melanoma[Melanoma$ulcer == 1,"ulcer"])
ausencia=length(Melanoma[Melanoma$ulcer == 0,"ulcer"])
barplot(c(presencia, ausencia), col = c("#FF6A6A","#CAFF70"), legend.text = c("presencia","ausencia"), main = "Presencia y ausencia de melanoma")
#2.8
data(babyboom)
head(babyboom)
## clock.time gender wt running.time
## 1 5 girl 3837 5
## 2 104 girl 3334 64
## 3 118 boy 3554 78
## 4 155 boy 3838 115
## 5 257 boy 3625 177
## 6 405 girl 2208 245
#a)
aggregate(babyboom[,"gender"], by = list(babyboom[,"gender"]), FUN = length)
## Group.1 x
## 1 girl 18
## 2 boy 26
#b)
babyboom[(babyboom$clock.time / 60) <= 12,]
## 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
length(babyboom[(babyboom$clock.time / 60) <= 12,"gender"])
## [1] 10
#c)
babyboom[babyboom$wt < 3000,]
## clock.time gender wt running.time
## 6 405 girl 2208 245
## 7 407 girl 1745 247
## 8 422 boy 2846 262
## 13 814 girl 2576 494
## 18 1133 boy 2902 693
## 19 1209 boy 2635 729
## 29 1742 girl 2184 1062
## 31 1825 girl 2383 1105
## 40 2104 boy 2121 1264
#d)
genders <- babyboom[babyboom$wt < 3000,]
table(genders$gender,genders$wt)
##
## 1745 2121 2184 2208 2383 2576 2635 2846 2902
## girl 1 0 1 1 1 1 0 0 0
## boy 0 1 0 0 0 0 1 1 1
#e)
aggregate(babyboom$wt, by = list(babyboom[,"gender"]), FUN = mean)
## Group.1 x
## 1 girl 3132.444
## 2 boy 3375.308
#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
#a)
aggregate(Aids2$state, by=list(Aids2[,"state"]), FUN=length)
## Group.1 x
## 1 NSW 1780
## 2 Other 249
## 3 QLD 226
## 4 VIC 588
#b)
totalMuertos=Aids2[Aids2$status == "D",]
length(totalMuertos$state)
## [1] 1761
#c)
tipos=table(Aids2$sex, Aids2$T.categ)
#d)
tipos=aggregate(Aids2$T.categ, by=list(Aids2[,"T.categ"]), FUN=length)
ggplot(tipos, aes(x=Group.1,y=x)) + geom_bar(stat = "identity")
#2.10
data(crime)
head(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
#a)
mean(crime$y1993)
## [1] 606.8294
mean(crime$y1983)
## [1] 437.5196
# Podemos observar que la tasa en 1993 fue mayor que la de 1983
#b)
crime[crime$y1993 == max(crime$y1993),]
## y1983 y1993
## DC 1985.4 2832.8
crime[crime$y1983 == max(crime$y1983),]
## y1983 y1993
## DC 1985.4 2832.8
# El estado DC presenta la mayor tasa de crímenes en cada año
#c)
crime["acumulado"]=crime$y1983 + crime$y1993
crime[crime$acumulado == max(crime$acumulado),]
## y1983 y1993 acumulado
## DC 1985.4 2832.8 4818.2
#El estado DC presenta la mayor tasa de crimen acumulado en ambos años
#d)
ggplot(crime, aes(y=acumulado,x=row.names(crime)))+geom_bar(position="stack", stat="identity")
