基于值通过 R 中的 ggmap 生成空间热图

Question

我想使用以下数据点生成等值线图：

• 经度
• 纬度
• 价格

这是数据集 - https://www.dropbox.com/s/0s05cl34bko7ggm/sample_data.csv?dl=0。

我希望地图显示价格较高和价格较低的区域。它应该看起来像这样（示例图片）：

这是我的代码：

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" )

我收到以下错误消息：

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

非常感谢有关如何解决此问题或生成此类热图的任何其他方法的任何帮助。请注意，我感兴趣的是价格的权重，而不是记录的密度。

Answer 1

如果您坚持使用等高线方法，则需要为数据中每个可能的 x,y 坐标组合提供一个值。为了实现这一点，我强烈建议对空间进行网格化并为每个 bin 生成一些汇总统计信息。

我根据您提供的数据在下面附上了一个工作示例：

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)

然后，您可以调整 bin 大小和轮廓 binwidth 以获得所需的结果，但您还可以在网格上应用平滑函数以获得更平滑的轮廓图。

Answer 2

您可以使用 stat_summary_2d() 或 stat_summary_hex() 函数来获得类似的结果。这些函数将数据划分为 bin（由 x 和 y 定义），然后根据给定函数汇总每个 bin 的 z 值。在下面的示例中，我选择了均值作为聚合函数，并且地图基本上显示了每个 bin 中的平均价格。

注意：我需要适当地处理您的 average_rate_per_night 变量，以便将其转换为数字（删除 $ 符号和逗号）。

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")