library(dslabs)
data("murders")
#install.packages(ggplot2)
library(ggplot2)
ggplot(data = murders) + 
    geom_point(mapping = aes(x = total, y = region))

barplot(murders$total)

barplot(murders$total, names.arg = murders$abb)

barplot(murders$total, names.arg = murders$state,col= "green")

barplot(murders$total, names.arg = murders$state,col= "green",
        ylim=c(0,max(murders$total)*1.2),mai='Crímenes totales por estado',xlab='Estate')

#No todos los gráficos se adaptan a todos los datos y viceversa
par(las=1)
barplot(murders$total, names.arg = murders$abb,col= "magenta",
        ylim=c(0,max(murders$total)*1.2),main='Crímenes totales por estado',xlab='Estado',horiz = TRUE)

#Gráfico de geom_point

library(ggplot2)
ggplot(data = murders) + 
    geom_point(mapping = aes(x = total, y = region, color=state))

#Gráfico vertical estilizado

library(ggplot2)
ggplot(data = murders) + 
    geom_point(mapping = aes(x = region, y = total, color="red"),col="red")

#Gráfico de FACETS
ggplot(data = murders) + 
    geom_point(mapping = aes(x = region, y = total))+facet_wrap(~abb,nrow = 4)

library(ggplot2)
x<-murders$population
y<-murders$total
ggplot(data = murders) + 
    geom_point(mapping = aes(x = population, y = total))

#Gráfico de suavización exponencial.

x<-mes
y<-ventas
ggplot(data = murders) + 
    geom_smooth(mapping = aes(x = population, y = total))

#Gráfico de suavización exponencial e histograma

library(ggplot2)
x<-murders$population
y<-murders$total
ggplot(data = murders) + 
    geom_point(mapping = aes(x = population, y = total))+
    geom_smooth(mapping = aes(x = population, y = total))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

library(ggplot2)
ggplot(data = murders) + 
    geom_histogram(mapping = aes(x = population,)) #+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

    #xlab("Población") + ylab("Número de habitantes") +
    #ggtitle(“HISTOGRAMA\nPoblación”) +
    #theme_light()

```