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library("ggmap")
library(maptools)
library(maps)


visited <- c("SFO", "Chennai", "London", "Melbourne", "Johannesbury, SA")
ll.visited <- geocode(visited)
visit.x <- ll.visited$lon
visit.y <- ll.visited$lat
#> dput(visit.x)

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#Using GGPLOT, plot the Base World Map
mp <- NULL
mapWorld <- borders("world", colour="gray50", fill="gray50") # create a layer of borders
mp <- ggplot() +   mapWorld

#Now Layer the cities on top
mp <- mp+ geom_point(aes(x=visit.x, y=visit.y) ,color="blue", size=3) 
mp

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#USING MAPS
map("world", fill=TRUE, col="white", bg="lightblue", ylim=c(-60, 90), mar=c(0,0,0,0))
points(visit.x,visit.y, col="red", pch=16)

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City ,Lat-Deg,Lat-Min,Long-Deg,Long-Min,Time
"Aberdeen, Scotland ",57,9 N ,2,9 W ,5:00 p.m.
"Adelaide, Australia ",34,55 S ,138,36 E ,2:30 a.m.1
"Algiers, Algeria",36,50 N ,3,0 E ,6:00 p.m.
"Amsterdam, Netherlands",52,22 N ,4,53 E ,6:00 p.m.
"Ankara, Turkey ",39,55 N ,32,55 E ,7:00 p.m.
"Asuncion, Paraguay ",25,15 S ,57,40 W ,1:00 p.m.
"Athens, Greece",37,58 N ,23,43 E ,7:00 p.m.
"Auckland, New Zealand ",36,52 S ,174,45 E ,5:00 a.m.1
"Bangkok, Thailand ",13,45 N ,100,30 E ,12:00am

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#plotting
createXYWalls <- function (cells) {
  #let each cell start at its XY position
  xs<- NULL; ys<-NULL; xe <- NULL; ye<-NULL
  for (i in 1:nrow(cells)) {
    if(cells$W[i])
    {
      xs <- c(xs, cells$x[i]+1)   
      ys <- c(ys, cells$y[i]+0)   
      xe <- c(xe, cells$x[i]+1)   

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BigN<- NULL # BigN is the DISTRIBUTION of N.hats
m <- 80 # initial number of fish that we caught and marked and released
n <- 60 # the fish that were caught again. Our "sample”
for(i in 1:5000) {
  s <- sample.int(n, replace=T) #resampling from our second captured set
  k <- sum(s<14) # how many of them were the marked ones
  N.hat  <- (m * n)/k 
  BigN <- c(N.hat, BigN)  
}
# Let's inspect what we got 

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rm(list=ls())
library(datasets)
library(ggplot2)
str(UCBAdmissions) #Table, but ggplot needs data frames


ucb <- data.frame(UCBAdmissions)
names(ucb)
head(ucb)

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library(ggplot2)
unique(diamonds$cut)
unique(diamonds$clarity)
names(diamonds)
dim(diamonds)
g <- ggplot(diamonds[1:1000,]) + geom_point(aes(x=carat, y=price))
g <- g + facet_grid(. ~ cut) #horizontal stacking
g <- g + facet_grid(cut ~ .) #vertical stacking
g <- g + facet_grid(clarity ~ cut) #vertical stacking
g <- g + facet_wrap(clarity ~ cut) #good for exploring interesting cases

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library(ggplot2)
unique(diamonds$cut)
unique(diamonds$color)
names(diamonds)
h <- ggplot(diamonds[1:1000,], aes(x=cut)) + geom_bar(); h #simple histogram
h <- ggplot(diamonds[1:1000,], aes(x=cut, fill=color)) + geom_bar(); h # stacked by default
h <- ggplot(diamonds[1:1000,], aes(x=cut, fill=color)) + geom_bar(position="dodge"); h #unstack using dodge
h

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states_map <- map_data("state")
region <- tolower(state.name)
count.vector <- 1:50 # just a value
df<- data.frame(region, count.vector)

pal <- colorRampPalette(c('grey10','darkgreen'))(50)
mp <- ggplot(df, aes(map_id=region)) + geom_map(aes(fill=count.vector), map=states_map) + coord_map("polyconic") + expand_limits(x = states_map$long, y = states_map$lat)
mp <- mp + scale_fill_gradient(low='grey90', high='darkgreen', limits=c(0,100))
mp