Visualización de datos

treemap

#------------------visualización estadisticas oficiales-----------------
library(openxlsx)
library(treemap)

setwd("C:/indices/IVC_ISUP/ENTREGABLES")
bd<-read.xlsx("Historico_IVC_Real_LDP_C_G_D_NOV2017 .xlsx"  , 1)

#---------treemap
        treemap(bd,
        index=c("C", "LDP"),
        vSize="NOV2017",
        vColor="NOV2017",
        type="value", format.legend = list(scientific = FALSE, big.mark = " "))


library(openxlsx)
setwd("C:/indices/IVC_ISUP/ENTREGABLES")
bd<-read.xlsx("Historico_IVC_Real_LDP_C_G_D_NOV2017 .xlsx"  , 1)

library(treemap)
        treemap(bd,
        index=c("C", "LDP"),
        vSize="NOV2017",
        vColor="NOV2017",
        type="value", format.legend = list(scientific = FALSE, big.mark = " "))

#-----------VIM------------------
setwd("C:/indices/IVC_ISUP/BASES_DATOS")
bd<-read.xlsx("BASE_SAS_COMERCIO_Ventas.xlsx"  , 1)

library(VIM)
matrixplot(bd)

install.packages("ggplot2")
library(ggplot2)
# http://had.co.nz/ggplot2

# qplot examples -------------------------------------------------------------

qplot(diamonds$cut, diamonds$carat)
qplot(carat, price, data = diamonds)
qplot(carat, price, data = diamonds, colour=clarity)
qplot(carat, price, data = diamonds, geom=c("point", "smooth"), method=lm)

qplot(carat, data = diamonds,
  geom="histogram")
qplot(carat, data = diamonds,
  geom="histogram", binwidth = 1)
qplot(carat, data = diamonds,
  geom="histogram", binwidth = 0.1)
qplot(carat, data = diamonds,
  geom="histogram", binwidth = 0.01)

# using ggplot() -------------------------------------------------------------
d <- ggplot(diamonds, aes(x=carat, y=price))
d + geom_point()
d + geom_point(aes(colour = carat))
d + geom_point(aes(colour = carat)) + scale_colour_brewer()

ggplot(diamonds) + geom_histogram(aes(x=price))

# Separation of statistcs and geometric elements -----------------------------

p <- ggplot(diamonds, aes(x=price))

p + geom_histogram()
p + stat_bin(geom="area")
p + stat_bin(geom="point")
p + stat_bin(geom="line")

p + geom_histogram(aes(fill = clarity))
p + geom_histogram(aes(y = ..density..))

# Setting vs mapping ---------------------------------------------------------
p <- ggplot(diamonds, aes(x=carat,y=price))

# What will this do?
p + geom_point(aes(colour = "green"))
p + geom_point(colour = "green")
p + geom_point(colour = colour)

###-----------tutorial en Harvard------------------
http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html
http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html