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(ggplot2)
# PUNTO 2.1, PAQUETE USINGR:
# PUNTO A) CONJUNTO DE DATOS DEL PAQUETE:
data(package="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: CRAN
## Date/Publication: 2022-01-11 09:52:45 UTC
## Built: R 4.3.0; ; 2023-07-10 06:52:20 UTC; unix
##
## -- File: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/UsingR/Meta/package.rds
data()
data(package=.packages(all.available = TRUE))
length(ls("package:UsingR"))
## [1] 150
# PUNTO B) REPRESENTACION GRAFICA, DATASETS:
data("bumpers")
hist(bumpers, main="REPRESENTACION GRAFICA DATASETS", ylab = "FRECUENCIA", xlab= "BUMPERS", las=1, col= c("thistle1", "pink1"))
boxplot(bumpers, main="REPRESENTACION GRAFICA DATASETS", xlab="BUMPERS", las=1, col= c("lightsteelblue1"))
data("firstchi")
hist(firstchi, main="REPRESENTACION GRAFICA DATASETS", ylab = "FRECUENCIA", xlab= "FIRSTCHI", las=1, col= c("violet", "aquamarine1"))
boxplot(firstchi, main="REPRESENTACION GRAFICA DATASETS", xlab="FIRSTCHI", las=1, col= c("slateblue1"))
data("math")
hist(math, main="REPRESENTACION GRAFICA DATASETS", ylab = "FRECUENCIA", xlab= "MATH", las=1, col= c("turquoise1", "steelblue1"))
boxplot(math, main="REPRESENTACION GRAFICA DATASETS", xlab="MATH", las=1, col= c("salmon1"))
# PUNTO C) MEDIA, MEDIANA Y DESVIACION ESTANDAR:
media_C= mean(bumpers)
mediana_c= median(bumpers)
desviacion_c= sd(bumpers)
media_C
## [1] 2122.478
mediana_c
## [1] 2129
desviacion_c
## [1] 798.4574
media_c1= mean(firstchi)
mediana_c2= median(firstchi)
desviacion_c3=sd(firstchi)
media_c1
## [1] 23.97701
mediana_c2
## [1] 23
desviacion_c3
## [1] 6.254258
media_c1.1= mean(math)
mediana_c2.1= median(math)
desviacion_c3.1= sd(math)
media_c1.1
## [1] 54.9
mediana_c2.1
## [1] 54
desviacion_c3.1
## [1] 9.746264
# PUNTO 2.2, DATOS BRIGHTNESS:
# PUNTO A) HISTOGRAMA Y GRAFICO DENSIDAD:
hist(brightness, probability = TRUE)
lines(density(brightness), col="lightgoldenrod1", lwd=3)
# PUNTO B) DIAGRAMA DE CAJA:
boxplot(brightness, main="REPRESENTACION GRAFICA BRIGHTNESS", xlab="BRIGHTNESS", las=1, col= c("salmon1"))
data("brightness")
hist(brightness, main="REPRESENTACION GRAFICA BRIGHTNESS", xlab= "BRIGHTNESS", las=1, col= c("turquoise1", "steelblue1"))
min(brightness[brightness > min(brightness)])
## [1] 2.28
# PUNTO C) VARIABLE BRIGHTNESS.SIN:
# QUANTILE, BISAGRAS INFERIOR Y SUPERIOR:
# 1 BISAGRA (Q1) 7.7025, 3 BISAGRA (Q3) 9.1300
boxplot(brightness, main="REPRESENTACION GRAFICA BRIGHTNESS", xlab="BRIGHTNESS", las=1, col= c("violet"))
quantile(brightness)
## 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, main="REPRESENTACION GRAFICA BRIGHTNESS", xlab="BRIGHTNESS", las=1, col=c("lightsteelblue1"))
# PUNTO 2.3, DATOS UScereaL:
# PUNTO A) TIPO DE DATOS DE CADA VARIABLE:
class(UScereal[,1])
## [1] "factor"
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 ...
# PUNTO B) ASOCIACIONES ENTRE SUS VARIABLES:
# RELACION 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
# RELACION 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
# RELACION 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
barplot(table(UScereal$fat,UScereal$shelf), beside = T,main = "FAT Y SHELF", las=1, col=c("pink", "purple", "violet"))
# RELACION 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
plot(UScereal$carbo,UScereal$sugars, main = "CARBOHYDRATES Y SUGARS", las=1, col=c("green", "black", "yellow"))
# RELACION 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
# RELACION 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
# PUNTO 2.4, RELACION PESO CORPORAL Y PESO DEL CEREBRO:
# PUNTO A) CORRELACION LINEAL ENTRE LAS VARIABLES:
attach(mammals)
cor (mammals)
## body brain
## body 1.0000000 0.9341638
## brain 0.9341638 1.0000000
cor(mammals$body, mammals$brain)
## [1] 0.9341638
# PUNTO B) REPRESENTACION PLOT:
plot(mammals, main="REPRESENTACION GRAFICA DE VARIABLES", las=1, col=c("blue", "salmon"))
# PUNTO C) FUNCION LOG:
cor(x=log(mammals$body), y=log(mammals$brain))
## [1] 0.9595748
plot(log(mammals), main="REPRESENTACION GRAFICA DE LOG", las=1, col=c("pink", "purple"))
# PUNTO 2.5, EMISSIONS PAQUETE UsingR:
# PUNTO A) RELACION GDP, PERCAPITA Y CO2 DE CADA PAIS:
UsingR::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
## Russia 692000 4727 2000
## Canada 658000 21221 700
## Spain 642400 16401 370
## Australia 394000 20976 480
## Netherlands 343900 21755 240
## Poland 280700 7270 400
## Belgium 236300 23208 145
## Sweden 176200 19773 75
## Austria 174100 21390 80
## Switzerland 172400 23696 54
## Portugal 149500 15074 75
## Greece 137400 12833 125
## Ukraine 124900 2507 420
## Denmark 122500 22868 75
## Norway 120500 27149 56
## Romania 114200 5136 160
## CzechRepublic 111900 10885 150
## Finland 102100 19793 76
## Hungary 73200 7186 85
## Ireland 59900 16488 63
pairs(emissions, main= "REPRESENTACION GRAFICA DE RELACION GDP, PERCAPITA, CO2", las=1, col=c("green", "yellow"))
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
# PUNTO B) MODELO DE REGRESION:
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$CO2, main="REPRESENTACION GRAFICA DE MODELO DE REGRESION")
abline(r_lineal,col = "violet")
## Warning in abline(r_lineal, col = "violet"): only using the first two of 3
## regression coefficients
emissions$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, main="REPRESENTACION GRAFICA", col=c("pink"))
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$CO2,CO2_predict)
## [1] 0.9619321
# PUNTO C) OUTLIERS:
boxplot(emissions, main="REPRESENTACION GRAFICA DE OUTLIERS", col=c("red"))
# PUNTO 2.6, BASE DE DATOS MASS:
data("anorexia")
head(anorexia)
## Treat Prewt Postwt
## 1 Cont 80.7 80.2
## 2 Cont 89.4 80.1
## 3 Cont 91.8 86.4
## 4 Cont 74.0 86.3
## 5 Cont 78.1 76.1
## 6 Cont 88.3 78.1
# PUNTO A) TRATAMIENTO MAS EFECTIVO:
pos_pesos <- which(anorexia$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$Treat))," casos exitosos"))
table(casos_exito$Treat)
##
## CBT Cont FT
## 18 11 13
mejor_tratamiento
## [1] "CBT : \n 18 casos exitosos"
# PUNTO B) PACIENTES QUE GANARON Y PERDIERON PESO:
# pacientes que ganaron peso
anorexia["diferencia"] <- anorexia$Postwt - anorexia$Prewt
ganaron <- length(anorexia[anorexia$diferencia > 0,"diferencia"])
ganaron
## [1] 42
# pacientes que perdieron peso
perdieron <- length(anorexia[anorexia$diferencia < 0,"diferencia"])
perdieron
## [1] 29
# pacientes con el mismo peso
igual <- length(anorexia[anorexia$diferencia == 0,"diferencia"])
igual
## [1] 1
# PUNTO C) GRAFICA DEL PUNTO B:
barplot(c(ganaron,perdieron), main = "REPRESENTACION GRAFICA DE GANANCIA Y PERDIDA DE PESO", ylab = "NUMERO DE PACIENTES", col = c("darkorange","skyblue"))
legend("topright", legend = c(paste("Ganaron peso",ganaron),paste("Perdieron peso: ",perdieron)), fill = c("darkorange","skyblue"))
# PUNTO 2.7, MELANOMA:
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 0
# PUNTO A) NUMERO DE FALLECIDOS:
Melanoma$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 0
N_fallecidos = nrow(Melanoma[Melanoma$status==1,]) + nrow(Melanoma[Melanoma$status==3,])
N_fallecidos
## [1] 71
# PUNTO B) PRESENCIA Y AUSENCIA:
table(Melanoma[,"ulcer"])
##
## 0 1
## 115 90
# presencia
sum(Melanoma$ulcer=="1")
## [1] 90
# ausencia
sum(Melanoma$ulcer=="0")
## [1] 115
# PUNTO C) TAMAÑO DEL TUMOR Y MUERTE:
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
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$status,s2,0)
c( paste ("TAMAÑO DEL TUMOR Y MUERTE", cor (Melanoma1$thickness,Melanoma1$status)))
## [1] "TAMAÑO DEL TUMOR Y MUERTE 0.314179811783222"
# PUNTO D) GRAFICA DEL PUNTO B:
barplot(table(Melanoma$ulcer), main="REPRESENTACION GRAFICA DE LA PRESENCIA Y AUSENCIA DE MELANOMA", ylab = "NUMERO DE PACIENTES", beside = TRUE, legend.text = c("FALLECIDOS","VIVOS"), col=c("grey","pink"))
# PUNTO 2.8, BABYBOOM:
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
# PUNTO A) NUMERO DE NIÑAS Y NIÑOS:
table(babyboom$gender)
##
## girl boy
## 18 26
# PUNTO B) CANTIDAD DE NIÑOS NACIDOS EN LAS PRIMERAS 12H:
nacidos12h = nrow(babyboom[babyboom$clock.time<708,])
nacidos12h
## [1] 9
# PUNTO C) NIÑOS QUE NACIERON POR DEBAJO DE 3000GR:
ninos_bajo_peso= nrow(babyboom[babyboom$gender=='boy' & babyboom$wt<3000,])
ninos_bajo_peso
## [1] 4
# PUNTO D) RELACION DE PESO POR DEBAJO DE 3000GR Y SEXO:
barplot(table(babyboom$gender,babyboom$wt<3000),beside = T,col = c("pink","turquoise"),xlab="GENERO",ylab="NUMERO DE PACIENTES", main="REPRESENTACION GRAFICA PESO Y SEXO")
# PUNTO E) GRAFICA PROMEDIO PESO TOTAL, DE NIÑOS Y NIÑAS:
niños = median(babyboom$wt[babyboom$gender=='boy'])
niños
## [1] 3404
niñas = median(babyboom$wt[babyboom$gender=='girl'])
niñas
## [1] 3381
boxplot(babyboom$wt,ylab = "Peso (gr)",main = "PROMEDIO PESO TOTAL (niños y niñas)")
points(niños, col = "blue", pch = 15)
points(niñas, col = "purple", pch = 15)
legend(x = "topleft", legend = c(paste("NIÑOS", niños),paste("NIÑAS", niñas)), fill = c("blue", "purple"),title = "PROMEDIO DE LOS PESOS")
# PUNTO 2.9, AIDS2:
data("Aids2")
head(Aids2)
## state sex diag death status T.categ age
## 1 NSW M 10905 11081 D hs 35
## 2 NSW M 11029 11096 D hs 53
## 3 NSW M 9551 9983 D hs 42
## 4 NSW M 9577 9654 D haem 44
## 5 NSW M 10015 10290 D hs 39
## 6 NSW M 9971 10344 D hs 36
# PUNTO A) NUMERO DE CONTAGIOS POR ESTADO:
table(Aids2$state,Aids2$T.categ)
##
## hs hsid id het haem blood mother other
## NSW 1539 50 28 18 30 70 3 42
## Other 204 4 12 8 6 5 2 8
## QLD 186 7 4 5 4 15 1 4
## VIC 536 11 4 10 6 4 1 16
# PUNTO B) NUMERO DE FALLECIDOS:
fallecidos= sum(Aids2$status == "D")
paste("NUMERO DE FALLECIDOS",fallecidos)
## [1] "NUMERO DE FALLECIDOS 1761"
# PUNTO C) RELACION SEXO Y TIPO DE TRANSMISION:
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
barplot(table(Aids2$sex,Aids2$T.categ),beside = T,col = c("salmon1", "lightgoldenrod1"),xlab="REPRESENTACION GRAFICA DE TIPOS DE TRANSMISION",ylab="NUMERO DE PACIENTES")
legend("topright",levels(Aids2$sex),fill = c("salmon1", "lightgoldenrod1"))
relacion_F_trans = Aids2[Aids2$sex=='F',]
barplot(table(relacion_F_trans$sex=='F',relacion_F_trans$T.categ), main = "REPRESENTACION GRAFICA ENTRE MUJERES Y TIPO DE TRANSMISION",ylab = "NUMERO DE PACIENTES", col=c("pink1"))
relacion_M_trans = Aids2[Aids2$sex=='M',]
barplot(table(relacion_M_trans$sex=='M',relacion_M_trans$T.categ),
main = "REPRESENTACION GRAFICA ENTRE HOMBRES Y TIPO DE TRANSMISION",ylab = "NUMERO DE PACIENTES", col=c("steelblue"))
# PUNTO D) GRAFICA DE TIPOS DE TRANSMISION:
table(Aids2$T.categ)
##
## hs hsid id het haem blood mother other
## 2465 72 48 41 46 94 7 70
colores = c("pink","skyblue","blueviolet","tomato","darkgreen","thistle","darkorange","gold")
pie(table(Aids2$T.categ),col = colores,labels = table(Aids2$T.categ), main = "REPRESENTACION GRAFICA NUMERO DE PACIENTES Y TIPOS DE TRANSMISION")
legend("topright",legend = levels(Aids2$T.categ),fill = colores)
# PUNTO 2.10 CRIME:
data(crime)
head(crime)
## y1983 y1993
## Alabama 416.0 871.7
## Alaska 613.8 660.5
## Arizona 494.2 670.8
## Arkansas 297.7 576.5
## California 772.6 1119.7
## Colorado 476.4 578.8
# PUNTO A) TASA TOTAL EN 1993:
total83 = round(sum(crime$y1983))
total83
## [1] 22313
total93 = round(sum(crime$y1993))
total93
## [1] 30948
# la tasa total en 1993 fue mayor que 1983
# PUNTO B) ESTADO CON MAYOR TASA DE CRIMENES EN CADA AÑO:
crime[crime$y1993 == max(crime$y1993),]
## y1983 y1993
## DC 1985.4 2832.8
crime[crime$y1983 == max(crime$y1983),]
## y1983 y1993
## DC 1985.4 2832.8
# PUNTO C) ESTADO CON MAYOR TASA DE CRIMENES ACUMULADOS:
crime["acumulado"] <- crime$y1983 + crime$y1993
crime[crime$acumulado == max(crime$acumulado),]
## y1983 y1993 acumulado
## DC 1985.4 2832.8 4818.2
# PUNTO D) GRAFICA DEL PUNTO B:
años = c("1983","1993")
años
## [1] "1983" "1993"
datos = c(total93, total83)
datos
## [1] 30948 22313
pie(datos, años, main = "GRAFICA DE MAYOR TASA DE CRIMENES POR AÑOS",sub = "Año 1983: 1985 - Año 1993: 2833", col=c("purple", "blue"))
