Explore rbokeh library in R

rbokeh.R

library(dplyr)
library(ggplot2)
library(rbokeh)

########################
# Layering
########################

# Histogram & Density Plot - Price
ggplot(diamonds) + aes(price, y = ..density..) + 
  geom_histogram(binwidth = 100) + 
  geom_density(color = "red")

figure() %>%
  ly_hist(price, data = diamonds, breaks = 250, freq = FALSE) %>%
  ly_density(price, data = diamonds, color = "red")


########################
# Interaction
########################

# Take a sample of 1000 from diamonds dataset
dsmall <- sample_n(diamonds, 1000)

# Scatterplot - Carat vs Price, with Cut
ggplot(dsmall) + aes(carat, price, color = cut) + 
  geom_point(alpha = 0.5, size = 4)

# Make same scatterplot with rbokeh
figure() %>%
  ly_points(carat, price, data = dsmall, color = cut)

# Make same scatterplot with rbokeh - width = 600
figure(width = 600) %>%
  ly_points(carat, price, data = dsmall, color = cut)

# Add a interactive tool capability - lasso
figure(width = 600) %>%
  ly_points(carat, price, data = dsmall, color = cut) %>%
  tool_lasso_select()

# Add hover labels to the point
figure(width = 600) %>%
  ly_points(carat, price, data = dsmall, color = cut,
            hover = list(carat, price, color))


########################
#  Mapping
########################

# Create a dummy data frame of points
set.seed(500)
df <- round(data.frame(
  lon = jitter(rep( 77.59, 50), amount = .3),
  lat = jitter(rep( 12.97, 50), amount = .3)
), digits = 2)

# Plot these points on the map using ggmap
library(ggmap)
map <- get_map("Bangalore")
ggmap(map)

ggmap(map) + geom_point(data = df, aes(lon, lat), 
                         color = "red", size = 6)

# Plot these points on the map using rbokeh
map1 <- gmap(lat = 12.97, lng = 77.59, zoom = 9, 
             width = 500, height = 500)

map1 %>%
  ly_points(lon, lat, data = df, alpha = 0.8, col = "red",
            hover = c(lon, lat))