Creating a spatial heatmap via ggmap in R based on value

Question

I would like to create a choropleth map using the following data points:

• Longitude
• Locator
• Price

Here is the dataset - https://www.dropbox.com/s/0s05cl34bko7ggm/sample_data.csv?dl=0 .

I would like to see areas on the map where the price is higher and where the price is lower. This should probably look like this (sample image):

Here is my code:

library(ggmap)

map <- get_map(location = "austin", zoom = 9)
data <- read.csv(file.choose(), stringsAsFactors = FALSE)
data$average_rate_per_night <- as.numeric(gsub("[\\$,]", "", 
data$average_rate_per_night))
ggmap(map, extent = "device") + 
stat_contour( data = data, geom="polygon", 
            aes( x = longitude, y = latitude, z = average_rate_per_night, 
fill = ..level.. ) ) +
scale_fill_continuous( name = "Price", low = "yellow", high = "red" )

The following error message appears:

2: Computation failed in `stat_contour()`:
Contour requires single `z` at each combination of `x` and `y`. 

I would really appreciate any help on how this can be fixed or any other method for creating this type of heat map. Please note that I am interested in the weight of the price , not in the density of entries.

Answer 1

, x, y, . .

:

library(ggmap)
library(data.table)

map <- get_map(location = "austin", zoom = 12)
data <- setDT(read.csv(file.choose(), stringsAsFactors = FALSE))

# convert the rate from string into numbers
data[, average_rate_per_night := as.numeric(gsub(",", "", 
       substr(average_rate_per_night, 2, nchar(average_rate_per_night))))]

# generate bins for the x, y coordinates
xbreaks <- seq(floor(min(data$latitude)), ceiling(max(data$latitude)), by = 0.01)
ybreaks <- seq(floor(min(data$longitude)), ceiling(max(data$longitude)), by = 0.01)

# allocate the data points into the bins
data$latbin <- xbreaks[cut(data$latitude, breaks = xbreaks, labels=F)]
data$longbin <- ybreaks[cut(data$longitude, breaks = ybreaks, labels=F)]

# Summarise the data for each bin
datamat <- data[, list(average_rate_per_night = mean(average_rate_per_night)), 
                 by = c("latbin", "longbin")]

# Merge the summarised data with all possible x, y coordinate combinations to get 
# a value for every bin
datamat <- merge(setDT(expand.grid(latbin = xbreaks, longbin = ybreaks)), datamat, 
                 by = c("latbin", "longbin"), all.x = TRUE, all.y = FALSE)

# Fill up the empty bins 0 to smooth the contour plot
datamat[is.na(average_rate_per_night), ]$average_rate_per_night <- 0

# Plot the contours
ggmap(map, extent = "device") +
  stat_contour(data = datamat, aes(x = longbin, y = latbin, z = average_rate_per_night, 
               fill = ..level.., alpha = ..level..), geom = 'polygon', binwidth = 100) +
  scale_fill_gradient(name = "Price", low = "green", high = "red") +
  guides(alpha = FALSE)

, , , .

Answer 2

stat_summary_2d() stat_summary_hex() . ( x y), z . , .

. average_rate_per_night , ( $ ).

library(ggmap)
library(data.table)

map <- get_map(location = "austin", zoom = 12)
data <- setDT(read.csv(file.choose(), stringsAsFactors = FALSE))
data[, average_rate_per_night := as.numeric(gsub(",", "",
    substr(average_rate_per_night, 2, nchar(average_rate_per_night))))]

ggmap(map, extent = "device") +
    stat_summary_2d(data = data, aes(x = longitude, y = latitude, 
        z = average_rate_per_night), fun = mean, alpha = 0.6, bins = 30) +
    scale_fill_gradient(name = "Price", low = "green", high = "red")