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set.seed(123)
x = rlnorm(n = 120, meanlog = 0.5, sdlog = 0.6)
range(x)
## [1] 0.4125073 6.1251289
plot(density(x))

hist(x)
rug(x, col = 'red')
abline(v = mean(x), col = 'blue')

boxplot(x)
points(c(1,1), quantile(x, c(0.25, 0.75)), pch=15, col='red')
abline(h = quantile(x,0.25)-1.5*IQR(x), pch=15, col='blue')
abline(h = quantile(x,0.75)+1.5*IQR(x), pch=15, col='blue')

abline(h = quantile(x,0.25)-3*IQR(x), pch=15, col='green')
abline(h = quantile(x,0.75)+3*IQR(x), pch=15, col='green')

library(outliers)

grubbs.test(x, type = 10)
## 
##  Grubbs test for one outlier
## 
## data:  x
## G = 3.73990, U = 0.88148, p-value = 0.00708
## alternative hypothesis: highest value 6.12512886755297 is an outlier
mean(x)
## [1] 1.924438
mean(x, trim = 0.05)
## [1] 1.815529
set.seed(12)
e = rnorm(120, 3, 0.3)
e = e - mean(e)
boxplot(e)

var(e)
## [1] 0.06949205
grubbs.test(e)
## 
##  Grubbs test for one outlier
## 
## data:  e
## G = 2.44484, U = 0.94935, p-value = 0.8076
## alternative hypothesis: lowest value -0.644492012655156 is an outlier
plot(e, pch = 16, type = 'o')

Asignacion 1

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
set.seed(123)
xx1=replicate(15,rnorm(120, 3, 0.3))
xx = apply(xx1, 2, function(xi1){xi1-mean(xi1)})
set.seed(123)
mi=c();ma=c();mi120=c();mi1=c();mxx=c();mtxx=c()
for(i in 1:dim(xx1)[2]){
  mi[i]=min(xx1[,i])
  ma[i]=max(xx1[,i])
  mi120[i]=(xx1[120,i])
  mi1[i]=(xx1[1,i])
  mxx[i]=mean(xx1[,i])
  mtxx[i]=mean(xx1[,i],trim=0.2)
  ee=sd(mxx)
  eet=sd(mtxx)
}
covar<-cov(xx1) #la matriz de varianza y covarianza de xx1
dimencovar<-dim(covar)
d<-det(covar) #determinante de las covarianzas y varianzas
correxx1<-cor(xx1) #la matriz de correlacion de xx1
d_2<-det(correxx1)#determinante de la matriz de correlacion de xx1
m<-c(1)
r<-data.frame(m)
df<-mutate(r,determinante.mat.covar.var=d,determinante.mat.cor=d_2)
df<-select(df,-m)
df
##   determinante.mat.covar.var determinante.mat.cor
## 1               6.407645e-17            0.3871051
set.seed(123)
xx1<-data.frame(xx1)
NEWxx1<-sample_n(xx1,dim(xx1)*0.90)
NEWxx2<-sample_n(xx1,dim(xx1)*0.80)
covarNEWxx1<-cov(NEWxx1) #la matriz de varianza y covarianza de xx1
A_1<-det(covarNEWxx1)
correNEWxx1<-cor(NEWxx1) #la matriz de correlacion de xx1
A_2<-det(correNEWxx1)#determinante de la matriz de correlacion de xx1
covarNEWxx2<-cov(NEWxx2) #la matriz de varianza y covarianza de xx1
A_3<-det(covarNEWxx2)
correNEWxx2<-cor(NEWxx2) #la matriz de correlacion de xx1
A_4<-det(correNEWxx2)#determinante de la matriz de correlacion de xx1
truncado<-c("0%","10%","20%")
f<-data.frame(truncado)
df_1<-mutate(f,determinante.covar.var=c(d,A_1,A_3),determinante.mat.cor=c(d_2,A_2,A_4))
df_1
##   truncado determinante.covar.var determinante.mat.cor
## 1       0%           6.407645e-17            0.3871051
## 2      10%           6.648825e-17            0.3370250
## 3      20%           5.677846e-17            0.3245548

Como se uso la funcion sample_n al truncar los datos se escogieron datos aleatorios, sin embargo se concluye que la varianza generalizada vario en todos los casos esto gracias a que el eliminar datos supone un cambio en el calculo de determinante de la matriz, lo cual en un contexto aplicado puede significar bastante, ya que supone un error a la hora de interpretar los datos.

Asignacion 2: #Coordenadas parques de atracciones grandes #Parque del chicamocha 6.790616931827782, -73.00353873491119 #Parque del cafe: 4.639957251189179, -74.4487202165974 #Salitre Magico 4.665183168612629, -74.0919973607779 #Mundo aventura 4.6222940512890185, -74.13496691845005 #Piscilago 4.21370132198462, -74.68215942814662

library(leaflet)
Mapa_parques<-data.frame(
  lat=c(6.790616931827782,4.665183168612629,4.639957251189179,4.21370132198462,4.6222940512890185),
  lon=c(-73.00353873491119,-74.0919973607779,-74.4487202165974,-74.68215942814662,-74.13496691845005)
)
Mapa_parques_popup= popup=c("Parque del chicamocha","Salitre Magico","Parque del cafe","Piscilago","Mundo Aventura")
Mapa_parques%>%
  leaflet()%>%
  addTiles()%>%
  addMarkers(popup = Mapa_parques_popup, clusterOptions= markerClusterOptions())

Finalizacion de actividad compartida