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# Histogram 
library(RColorBrewer)
data(VADeaths)
par(mfrow=c(2,3))
hist(VADeaths,breaks=10, col=brewer.pal(3,"Set3"),main="Set3 3 colors")
hist(VADeaths,breaks=3 ,col=brewer.pal(3,"Set2"),main="Set2 3 colors")
hist(VADeaths,breaks=7, col=brewer.pal(3,"Set1"),main="Set1 3 colors")
hist(VADeaths,breaks= 2, col=brewer.pal(8,"Set3"),main="Set3 8 colors")
hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8 colors")
hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8 colors")

# Bar/ Line Chart 
plot(AirPassengers,type="l")  #Simple Line Plot
barplot(iris$Petal.Length) #Creating simple Bar Graph
barplot(iris$Sepal.Length,col  = brewer.pal(3,"Set1"))
barplot(table(iris$Species,iris$Sepal.Length),col  = brewer.pal(3,"Set1")) #Stacked Plot

# Box Plot ( including group-by option )
boxplot(iris$Petal.Length~iris$Species) #Creating Box Plot between two variable

data(iris)
par(mfrow=c(2,2))

boxplot(iris$Sepal.Length,col="red")
boxplot(iris$Sepal.Length~iris$Species,col="red")
boxplot(iris$Sepal.Length~iris$Species,col=heat.colors(3))
boxplot(iris$Sepal.Length~iris$Species,col=topo.colors(3))

# Scatter Plot (including 3D and other features)
plot(x=iris$Petal.Length) #Simple Scatter Plot
plot(x=iris$Petal.Length,y=iris$Species) #Multivariate Scatter Plot

plot(iris,col=brewer.pal(3,"Set1"))

pie(table(iris$Species))

# Advanced Visualizations
# Hexbin Binning
library(ggplot2)
library(hexbin)
a=hexbin(diamonds$price,diamonds$carat,xbins=40)
plot(a)

library(RColorBrewer)
rf <- colorRampPalette(rev(brewer.pal(40,'Set3')))
## Warning in brewer.pal(40, "Set3"): n too large, allowed maximum for palette Set3 is 12
## Returning the palette you asked for with that many colors
hexbinplot(diamonds$price~diamonds$carat, data=diamonds, colramp=rf)

# Mosaic Plot 
data(HairEyeColor)
mosaicplot(HairEyeColor)

# Heat Map 
heatmap(as.matrix(mtcars))

image(as.matrix(mtcars[2:7]))

# How to summarize lots of data ? 
# Map Visualization
library(magrittr)
library(leaflet)
m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=77.2310, lat=28.6560, popup="The delicious food of chandni chowk")
m  # Print the map
# Leaflet for R
library(leaflet)
library(magrittr)

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m  # Print the map
m <- leaflet()
m <- addTiles(m)
m <- addMarkers(m, lng=174.768, lat=-36.852, popup="The birthplace of R")
m
# 3D Graphs
# install.packages('Rcmdr')
# library(Rcmdr)
# library(RcmdrMisc)
# install.packages("rgl")
# data(iris, package="datasets")
# library(brglm)
# scatter3d(Petal.Width~Petal.Length+Sepal.Length|Species, data=iris, fit="linear",
#           residuals=TRUE, parallel=FALSE, bg="black", axis.scales=TRUE, grid=TRUE, ellipsoid=FALSE)
# 
# attach(iris)# 3d scatterplot by factor level
# cloud(Sepal.Length~Sepal.Width*Petal.Length|Species, main="3D Scatterplot by Species")
# xyplot(Sepal.Width ~ Sepal.Length, iris, groups = iris$Species, pch= 20)
# library(Rcmdr)

# Correlogram (GUIs)

library(corrplot)
## corrplot 0.84 loaded
library(corrgram)
cor(iris[1:4])
##              Sepal.Length Sepal.Width Petal.Length Petal.Width
## Sepal.Length    1.0000000  -0.1175698    0.8717538   0.8179411
## Sepal.Width    -0.1175698   1.0000000   -0.4284401  -0.3661259
## Petal.Length    0.8717538  -0.4284401    1.0000000   0.9628654
## Petal.Width     0.8179411  -0.3661259    0.9628654   1.0000000
corrgram(iris)

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