plotly
Show Sidebar Hide Sidebar
Fork on Github

geom_polygon in ggplot2

Examples of geom_polygon in R.

New to Plotly?

Plotly's R library is free and open source!
Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode.
We also have a quick-reference cheatsheet (new!) to help you get started!

Version Check

Version 4 of Plotly's R package is now available!
Check out this post for more information on breaking changes and new features available in this version.

library(plotly)
packageVersion('plotly')

## [1] '4.5.6.9000'

Basic Ploygon

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))

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/basic")
chart_link

Inspired by ggplot2 docs

Ellipses

# 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))

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/ellipses")
chart_link

Highlighting

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()

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/highlight")
chart_link

Inspired by Stack Overflow

Vertical Conversion

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()

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/vertical")
chart_link

Inspired by Stack Overflow

Distributions

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))

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/distributions")
chart_link

Inspired by Stack Overflow

Convex Hull

library(plotly)

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("RColorBrewer")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# 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))

p <- ggplotly(zp3)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/convex")
chart_link

Inspired by is.R()

County-Level Boundaries

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()

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/county-level-boundaries")
chart_link

County-Level Choropleths

library(plotly)
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), limits = c(100, -100))  +
  theme_void()

p <- ggplotly(p)

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = plotly_POST(p, filename="geom_polygon/county-level-choropleth")
chart_link