library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.3
setwd("D:/AVRespaldo/Personal/Especializacion/SEM 2/Comunicacion de los analisis")
anscombe<-read.table("anscombe.txt", sep=";", dec = "," ,header = TRUE)
str(anscombe)
## 'data.frame':    44 obs. of  3 variables:
##  $ Set: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ X  : int  10 8 13 9 11 14 6 4 12 7 ...
##  $ Y  : num  8.04 6.95 7.58 8.81 8.33 ...
summary(anscombe)
##       Set             X            Y         
##  Min.   :1.00   Min.   : 4   Min.   : 3.100  
##  1st Qu.:1.75   1st Qu.: 7   1st Qu.: 6.117  
##  Median :2.50   Median : 8   Median : 7.520  
##  Mean   :2.50   Mean   : 9   Mean   : 7.501  
##  3rd Qu.:3.25   3rd Qu.:11   3rd Qu.: 8.748  
##  Max.   :4.00   Max.   :19   Max.   :12.740
anscombe$Set<-as.factor(anscombe$Set)
##anscombe$X<-as.numeric(anscombe$X)
##anscombe$Y<-as.numeric(anscombe$Y)
str(anscombe$Set)
##  Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
attach(anscombe)

##subconjuntos
uno<-subset(anscombe, Set=="1")
dos<-subset(anscombe, Set=="2")
tres<-subset(anscombe, Set=="3")
cuatro<-subset(anscombe, Set=="4")


##
xprom1<-mean(uno$X)
yprom1<-mean(uno$Y)
xvar1<-var(uno$X)
yvar1<-var(uno$Y)
corr1<-cor(uno$X, uno$Y)

##
summarystatus1<-data.frame(xprom1, yprom1, xvar1, yvar1, corr1)

##rbind(summarystatus1,summarystatus2)

plot.ans<-ggplot(anscombe, aes(X,Y))
plot.ans<-plot.ans+geom_point()
# Regresion lineal
plot.ans <- plot.ans + geom_smooth(method=lm, se=FALSE)
# promedio X
plot.ans <- plot.ans + geom_vline (aes ( xintercept = summarystatus1[1,1]))
# promedio Y
plot.ans <- plot.ans + geom_hline (aes ( yintercept = summarystatus1[1,2]))
# facetas
plot.ans <- plot.ans + facet_grid(. ~ Set)
plot.ans
## `geom_smooth()` using formula 'y ~ x'

```