geom_polygon in ggplot2

library(plotly)

ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))

values <- data.frame(
  id = ids,
  value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)

positions <- data.frame(
  id = rep(ids, each = 4),
  x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
  0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
  y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
  2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)

datapoly <- merge(values, positions, by=c("id"))

p <- ggplot(datapoly, aes(x=x, y=y)) + geom_polygon(aes(fill=value, group=id))

fig <- ggplotly(p)

fig

# create data
set.seed(20130226)
n <- 200
x1 <- rnorm(n, mean = 2)
y1 <- 1.5 + 0.4 * x1 + rnorm(n)
x2 <- rnorm(n, mean = -1)
y2 <- 3.5 - 1.2 * x2 + rnorm(n)
class <- rep(c("A", "B"), each = n)
df <- data.frame(x = c(x1, x2), y = c(y1, y2), colour = class)

# get code for "stat_ellipse"
library(devtools)
library(ggplot2)
library(proto) #source_url("https://raw.github.com/JoFrhwld/FAAV/master/r/stat-ellipse.R")

p <- qplot(data = df, x = x, y = y, colour = class) +
  stat_ellipse(geom = "polygon", alpha = 1/2, aes(fill = class))

fig <- ggplotly(p)

fig

library(plotly)

tmp <-  with(mtcars, data.frame(x=c(0, 0, max(wt)*35), y=c(0, max(wt), max(wt))))

p <- ggplot(mtcars, aes(hp, wt)) +
  geom_polygon(data=tmp, aes(x, y), fill="#d8161688") +
  geom_point()

fig <- ggplotly(p)

fig

library(plotly)

library(data.table)
df<-data.table(Product=letters[1:10], minX=1:10, maxX=5:14, minY= 10:1, maxY=14:5)

df.t<-data.table(rbind( df[,list(Product,X=minX,Y=minY)],
       df[,list(Product,X=minX,Y=maxY)],
       df[,list(Product,X=maxX,Y=minY)],
       df[,list(Product,X=maxX,Y=maxY)]))[
      order(Product,X,Y)]

p <- ggplot(df,aes(xmin=minX,xmax=maxX,ymin=minY,ymax=maxY,fill=Product))+
  geom_rect()

fig <- ggplotly(p)

fig

library(plotly)

x=seq(-2,2,length=200)
dat <- data.frame(
  norm = dnorm(x,mean=0,sd=0.2),
  logistic = dlogis(x,location=0,scale=0.2), x = x
)
p <- ggplot(data=dat, aes(x=x)) +
  geom_polygon(aes(y=norm), fill="red", alpha=0.6) +
  geom_polygon(aes(y=logistic), fill="blue", alpha=0.6) +
  xlab("z") + ylab("") +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0))

fig <- ggplotly(p)

fig

library(plotly)
library(RColorBrewer)

# Generate some data
nn <- 500
myData <- data.frame(X = rnorm(nn),
                     Y = rnorm(nn))

setK = 6  # How many clusters?
clusterSolution <- kmeans(myData, centers = setK)

myData$whichCluster <- factor(clusterSolution$cluster)

splitData <- split(myData, myData$whichCluster)
appliedData <- lapply(splitData, function(df){
  df[chull(df), ]  # chull really is useful, even outside of contrived examples.
  })
combinedData <- do.call(rbind, appliedData)

zp3 <- ggplot(data = myData,
                     aes(x = X, y = Y))
zp3 <- zp3 + geom_polygon(data = combinedData,  # This is also a nice example of how to plot
                          aes(x = X, y = Y, fill = whichCluster),  # two superimposed geoms
                          alpha = 1/2)                             # from different data.frames
zp3 <- zp3 + geom_point(size=1)
zp3 <- zp3 + coord_equal()
zp3 <- zp3 + scale_fill_manual(values = colorRampPalette(rev(brewer.pal(11, "Spectral")))(setK))

fig <- ggplotly(zp3)

fig

library(plotly)
library(maps)

county_df <- map_data("county")
state_df <- map_data("state")

# create state boundaries
p <- ggplot(county_df, aes(long, lat, group = group)) +
  geom_polygon(colour = alpha("black", 1/2), fill = NA) +
  geom_polygon(data = state_df, colour = "black", fill = NA) + 
  theme_void()

fig <- ggplotly(p)

fig

library(plotly)
library(dplyr)
library(maps)

# map data
county_df <- map_data("county")
state_df <- map_data("state")

county_df$subregion <- gsub(" ", "", county_df$subregion)

#election data
df <- read.csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/votes.csv")
df <- subset(df, select = c(Obama, Romney, area_name))

df$area_name <- tolower(df$area_name) 
df$area_name <- gsub(" county", "", df$area_name)
df$area_name <- gsub(" ", "", df$area_name)
df$area_name <- gsub("[.]", "", df$area_name)

df$Obama <- df$Obama*100
df$Romney <- df$Romney*100

for (i in 1:length(df[,1])) {
  if (df$Obama[i] > df$Romney[i]) {
    df$Percent[i] = df$Obama[i]
  } else {
    df$Percent[i] = -df$Romney[i]
  }
}

names(df) <- c("Obama", "Romney", "subregion", "Percent")

# join data
US <- inner_join(county_df, df, by = "subregion")
US <- US[!duplicated(US$order), ]

# colorramp
blue <- colorRampPalette(c("navy","royalblue","lightskyblue"))(200)                      
red <- colorRampPalette(c("mistyrose", "red2","darkred"))(200)

#plot
p <- ggplot(US, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = Percent),
               colour = alpha("white", 1/2), size = 0.05)  +
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  ggtitle("2012 US Election") +
  scale_fill_gradientn(colours=c(blue,"white", red))  +
  theme_void()

fig <- ggplotly(p)

fig

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)