使用不同颜色将具有2D密度的多个数据叠加到ggmap上
我觉得我一直在寻找解决方案而无法在任何地方找到解决方案。基本上，我需要将彩色等高线图作为不同的图层（具有不同的颜色）叠加到ggmap上，并且不能让它在我的生活中起作用。我要做的是采用此处所示的示例：Overlay two ggplot2 stat_density2d plots with alpha channels而不是将其映射到ggplot（），将其映射到ggmap（）。我在这里读到：ggmap with geom_map superimposed这部分难度使得从ggmap发送到ggplot的隐含美学无效，但无论我做什么，我总会遇到一系列错误。我遇到的一些错误来自于不承认'群'。当使用与下面相同的格式（z = X3）时，使用geom_contour会出现以下错误：
Error in contourLines(x = sort(unique(data$x)), y = sort(unique(data$y)), :
no proper 'z' matrix specified
部分困难还在于为变量分配不同的颜色。绑定列（如下面和上面链接的示例中所做的那样）并通过在第四列'groups'中使用“a”或“b”来区分变量形式变量B在列X3中似乎没有很好地记录scale_color_manual（）。最终，我正在尝试找到一种为data.frame的每一列分配不同颜色的方法 - 对此的任何想法也会有所帮助。
我已经包含了一个示例数据集和我的一个方法+错误 - 非常感谢任何帮助（非常感谢您的时间/考虑）！如果我能做任何其他事情来帮助我，请告诉我。
library(ggplot2)
library(ggmap)
#location defined by lon/lat coordinates
location <- c(-74.03990, 40.52726, -73.68864, 40.81141)
map <- get_map(location = location, scale="auto", zoom=11)
map2 <- ggmap(map)
map2 + stat_density2d(data=d, geom="density2d", aes(x=d[,1],y=d[,2], z=d[,3], color = group,alpha=..level..),size=2, contour=TRUE) +
scale_color_manual(values=c("a"="#FF0000", "b"="#00FF00"))
#Following error received: Error in if (any(h <= 0)) stop("bandwidths must be strictly positive") :
missing value where TRUE/FALSE needed
#Dataset (d):
d = read.table(header=TRUE, text="
X1 X2 X3 group
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NA NA 33 d
40.78075 -73.8253 1 d
NA NA 8 d
40.76821 -73.8274 2704 d
40.75145 -73.82103 1832 d
40.78485 -73.84128 702 d
40.78639 -73.81086 917 d
40.76047 -73.79637 1160 d
40.79178 -73.77688 11 d
40.78038 -73.78123 361 d
40.76419 -73.77277 1010 d
40.75657 -73.73784 442 d
40.77262 -73.74653 165 d
40.74529 -73.76059 584 d
40.73963 -73.79449 643 d
40.72815 -73.78502 449 d
40.73014 -73.82703 582 d
40.75172 -73.85182 1277 d
40.76336 -73.87237 280 d
40.76539 -73.89324 297 d
40.77389 -73.87348 95 d
40.75169 -73.88364 1629 d
40.73884 -73.87853 1498 d
40.72642 -73.86153 1089 d
40.72093 -73.84615 2060 d
40.74482 -73.90516 1492 d
40.72474 -73.90964 1101 d
40.71675 -73.8796 543 d
NA NA 2 d
40.70067 -73.88943 1742 d
NA NA 2 d
NA NA 2 d
40.69402 -73.73622 140 d
40.6981 -73.75899 239 d
40.67166 -73.75257 433 d
40.6576 -73.8448 438 d
40.70792 -73.82821 419 d
40.68465 -73.84955 393 d
40.67645 -73.84444 391 d
40.70027 -73.83597 718 d
40.68867 -73.82292 682 d
40.67358 -73.81773 373 d
40.69406 -73.85863 443 d
40.66006 -73.73601 307 d
40.71561 -73.76847 359 d
40.7143 -73.82726 7 d
40.60775 -74.02394 3 d
40.73642 -73.72238 248 d
40.7309 -73.74566 237 d
40.72102 -73.74224 310 d
40.70977 -73.73865 222 d
40.64696 -73.78481 342 d
NA NA 13 d
40.71536 -73.79307 1220 d
40.69816 -73.78689 298 d
40.67681 -73.77643 939 d
40.70126 -73.8096 823 d
40.67581 -73.79662 98 d
NA NA 27 d
40.70128 -73.79597 3 d
NA NA 9 d
40.60128 -73.76165 490 d
40.59409 -73.7929 90 d
40.59069 -73.80975 132 d
40.57827 -73.84476 330 d
NA NA 2 d
40.55569 -73.92066 48 d")
1 个答案:
答案 0 :(得分：1)
使用x和y美学的正确列似乎可以解决主要问题：
p = map2 +
stat_density2d(aes(x=X2 ,y=X1, z=X3, color=group, alpha=..level..),
data=d, size=2, contour=TRUE)
ggsave("map.png", plot=p, height=7, width=7)
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