我想使用以下数据点生成一个Choropleth贴图:
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的区域和价格较低的区域。它最有可能看起来像这样(示例图像):
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$average_rate_per_night))
ggmap(map, extent = "device") +
stat_contour( data = data, geom="polygon",
aes( x = longitude, y = latitude, z = average_rate_per_night,
fill = ..level.. ) ) +
scale_fill_continuous( name = "Price", low = "yellow", high = "red" )
我收到以下错误消息:
2: Computation failed in `stat_contour()`:
Contour requires single `z` at each combination of `x` and `y`.
对于如何解决此问题或生成这种类型的热图的任何其他方法,我将非常感谢。请注意,我对对价格的权重感兴趣,而不对记录的密度感兴趣。
最佳答案
如果您坚持使用轮廓法,则需要为数据中每个可能的x,y坐标组合提供一个值。为此,我强烈建议对空间进行网格化,并为每个容器生成一些摘要统计信息。
我在下面根据您提供的数据附上一个工作示例:
library(ggmap)
library(data.table)
map <- get_map(location = "austin", zoom = 12)
data <- setDT(read.csv(file.choose(), stringsAsFactors = FALSE))
# convert the rate from string into numbers
data[, average_rate_per_night := as.numeric(gsub(",", "",
substr(average_rate_per_night, 2, nchar(average_rate_per_night))))]
# generate bins for the x, y coordinates
xbreaks <- seq(floor(min(data$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,您可以使用仓的大小和轮廓仓的宽度来获得所需的结果,但是您还可以在网格上应用平滑功能以获得更平滑的轮廓图。