如何将离散色标应用于 R stat_summary_2d()？
How to apply discrete colour scale to R stat_summary_2d()?
我正在尝试将离散色标应用于 R 中 stat_summary_2d 函数创建的图块。这些图块绘制在 ggmap 创建的地图之上，每个图块代表 mean 落在图块边界内的所有点（更具体地说，污染值）。以下是我目前所取得的成就的图片（由于目前我的声誉较低，我无法直接包含图片）：
如您所见，快到了。但是，有两个问题：
- 离散颜色不正确，因为我使用的是渐变，
- 图例并未显示所有类别。
我知道使用渐变不会给我想要的离散中断，但这是我迄今为止管理的最好的。此外，我想要的颜色是基于英国的空气质量指数 (AQI) 等级。这些类别的中断、颜色和标签都在下面的代码中，以防您看不到附件。
下面是我到目前为止编写的代码：
library(ggmap)
library(ggplot2)
# Bounding box for creating map
bbox <- make_bbox(longitude, latitude, pmData[,], f = 0.005)
m <- get_map(bbox, source = "stamen", zoom = 15)
# Plot map
ggmap(m) +
stat_summary_2d(geom = "tile", bins = 75, fun = mean,
aes(x = longitude, y = latitude, z = pm_raw),
data = pmData) +
xlab("Longitude") +
ylab("Latitude") +
guides(fill=guide_legend(keywidth=0.05, keyheight=0.05, default.unit="inch")) +
scale_fill_gradientn(breaks=c(0, 17, 34, 51, 59, 67, 76, 84, 92, 100, Inf),
minor_breaks = NULL,
guide = "legend",
colours = c("palegreen", "green", "green3", "yellow", "yellow3",
"orange", "salmon", "red", "red4", "purple"),
labels = c("Low", "Low", "Low", "Moderate", "Moderate", "Moderate",
"High", "High", "High", "Very high", "Very high"))
由于每个图块的值都是平均值，因此使用 cut 创建离散类别不起作用，因为它会导致 Continuous value supplied to discrete scale 错误。
如果您需要示例数据，请告诉我，我可以尝试发布一些。
谢谢。
structure(list(latitude = c(51.383833333, 51.386423333, 51.387305,
51.388133333, 51.383815, 51.378265, 51.38614, 51.390151667, 51.390126667,
51.380048333, 51.389838333, 51.389863333, 51.386211667, 51.381303333,
51.378036667, 51.387336667, 51.378036667, 51.388873333, 51.387988333,
51.38419, 51.383691667, 51.384888333, 51.377866667, 51.384748333,
51.384993333, 51.377456667, 51.389825, 51.378485, 51.377715,
51.386725, 51.386111667, 51.388356667, 51.383935, 51.383833333,
51.378548333, 51.379861667, 51.386688333, 51.388008333, 51.387793333,
51.383365, 51.38366, 51.387165, 51.384871667, 51.378063333, 51.378378333,
51.377955, 51.37791, 51.388115, 51.384763333, 51.383471667, 51.380998333,
51.37841, 51.380526667, 51.380526667, 51.381601667, 51.378836667,
51.37867, 51.382835, 51.38553, 51.38784, 51.386893333, 51.385325,
51.377981667, 51.383798333, 51.378041667, 51.382858333, 51.38115,
51.3812, 51.383555, 51.38834, 51.384345, 51.381181667, 51.377565,
51.379041667, 51.384736667, 51.384828333, 51.38389, 51.382735,
51.38527, 51.385566667, 51.380448333, 51.378585, 51.387068333,
51.379693333, 51.379535, 51.381738333, 51.386378333, 51.378341667,
51.385281667, 51.385115, 51.37791, 51.378043333, 51.377256667,
51.381213333, 51.386808333, 51.380298333, 51.37851, 51.382393333,
51.381196667, 51.377373333, 51.389178333, 51.385386667, 51.385038333,
51.382931667, 51.389025, 51.384655, 51.38477, 51.378701667, 51.377633333,
51.386478333, 51.383513333, 51.384828333, 51.380108333, 51.382911667,
51.377868333, 51.377931667, 51.382061667, 51.382773333, 51.377656667,
51.384795, 51.377878333, 51.377436667, 51.377338333, 51.382755,
51.389243333, 51.386008333, 51.383578333, 51.381366667, 51.385426667,
51.383373333, 51.38756, 51.385405, 51.384733333, 51.382111667,
51.377853333, 51.377975, 51.385383333, 51.3777, 51.383573333,
51.38748, 51.384433333, 51.389521667, 51.384586667, 51.388643333,
51.388745, 51.389503333, 51.384065, 51.385931667, 51.3902, 51.3902,
51.377628333, 51.377165, 51.388163333, 51.38431, 51.381613333
), longitude = c(-2.363555, -2.351465, -2.352908333, -2.354518333,
-2.363075, -2.357106667, -2.36021, -2.356771667, -2.356616667,
-2.357106667, -2.356616667, -2.357891667, -2.360146667, -2.374723333,
-2.361418333, -2.359806667, -2.35918, -2.35918, -2.359898333,
-2.371886667, -2.36423, -2.353118333, -2.357285, -2.353675, -2.361558333,
-2.36048, -2.356543333, -2.365641667, -2.362033333, -2.351465,
-2.351508333, -2.354675, -2.363166667, -2.381021667, -2.366575,
-2.371295, -2.351558333, -2.371886667, -2.376568333, -2.367488333,
-2.36423, -2.359898333, -2.38118, -2.36133, -2.359806667, -2.35902,
-2.35746, -2.35464, -2.362713333, -2.381021667, -2.357285, -2.359898333,
-2.357106667, -2.357106667, -2.377308333, -2.361121667, -2.367248333,
-2.3812, -2.377001667, -2.354101667, -2.359915, -2.376568333,
-2.363448333, -2.363593333, -2.357106667, -2.358393333, -2.372698333,
-2.372431667, -2.364931667, -2.359806667, -2.354518333, -2.357375,
-2.361121667, -2.363593333, -2.381628333, -2.37422, -2.363448333,
-2.358081667, -2.352161667, -2.37829, -2.372305, -2.360146667,
-2.352568333, -2.357285, -2.360146667, -2.35762, -2.373406667,
-2.36133, -2.37422, -2.36133, -2.357845, -2.359806667, -2.361418333,
-2.357375, -2.359806667, -2.370346667, -2.365983333, -2.379583333,
-2.357436667, -2.36013, -2.355635, -2.380135, -2.381021667, -2.388205,
-2.355141667, -2.371321667, -2.372501667, -2.368555, -2.363548333,
-2.351465, -2.364931667, -2.362295, -2.37045, -2.38118, -2.357533333,
-2.36389, -2.38118, -2.358116667, -2.361878333, -2.363166667,
-2.357891667, -2.36048, -2.361558333, -2.358843333, -2.358843333,
-2.36021, -2.368918333, -2.377361667, -2.376808333, -2.365816667,
-2.353393333, -2.377001667, -2.363166667, -2.380608333, -2.357106667,
-2.356938333, -2.376808333, -2.362295, -2.369111667, -2.35344,
-2.354408333, -2.35856, -2.363448333, -2.359675, -2.35517, -2.35856,
-2.38118, -2.36021, -2.356781667, -2.356781667, -2.36048, -2.36133,
-2.354521667, -2.372698333, -2.377001667), pm_raw = c(20.2856490204,
77.8877903749, 71.7933099292, 98.9227746379, 13.168840034, 25.1495305964,
18.5911875492, 11.5532213795, 11.5532213795, 34.6047765217, 34.2986999054,
38.1823784449, 60.2354155746, 19.6998286117, 24.3481168666, 18.4068794706,
92.4024502724, 20.8700066963, 53.7655811403, 26.56884172, 25.9563524058,
41.8398168697, 21.204229175, 18.4985229695, 69.4438433391, 35.0603153227,
52.5171448444, 37.6010327173, 11.3101753017, 50.9286177028, 17.8909927779,
16.4572391933, 39.9021155572, 8.86286052724, 23.9431598443, 39.7689915216,
30.8634678742, 18.1143232738, 18.3941479353, 9.46628056132, 14.412527522,
27.7423289978, 10.7671408538, 15.9341080291, 20.6349163328, 15.4269963603,
5.83588000809, 16.1941855148, 68.6352334409, 14.4015212512, 102.822361444,
26.5387481, 60.5308607077, 6.97155968363, 234.155664235, 26.7571613942,
12.5228906193, 9.15531699502, 28.963860631, 11.1517290682, 76.4919317928,
11.5337240599, 16.9191620121, 14.9634160546, 7.48538606606, 7.36565315293,
6.58305090626, 2.88802757122, 4.80066803954, 14.3716849029, 21.3111785588,
16.1417055686, 8.91166650308, 14.6912815119, 13.6515088126, 34.6758160409,
107.411084295, 8.34201752814, 43.5562737658, 13.1949460218, 22.5121677682,
7.94588103399, 13.7452057672, 16.4227477955, 24.6492704894, 27.7137110021,
14.1559453375, 32.1195214536, 22.1462601372, 29.8239718823, 60.8699820745,
71.4253002227, 35.8562060293, 82.0146924171, 38.3981186095, 15.3572590305,
8.77474869292, 41.4601384099, 19.6070387258, 41.9414449, 29.3322623223,
120.145146039, 72.294045077, 21.6956980653, 9.83379481087, 9.61218271598,
6.18969929985, 15.3210540005, 48.9376058474, 56.9003135671, 28.1088565396,
6.59098222239, 22.6884614987, 52.7084659288, 38.4823403648, 58.7704971854,
19.9000537492, 0.000966813590044, 0.000966813590044, 26.0864635077,
36.8963060363, 39.9757260957, 42.790215098, 79.8085095756, 47.0276739845,
31.4995803181, 133.024483283, 31.2866736476, 92.7349765429, 11.1185166794,
31.1820240423, 8.08385762269, 48.4851751962, 63.9069236938, 27.1244461233,
60.3187940712, 20.5374151595, 28.4407989289, 65.8097519874, 91.1719973116,
42.3963928042, 18.0173416637, 85.6945673673, 20.9880757421, 53.375218596,
26.4525339035, 47.8580307084, 23.9389236402, 4.46424583036, 12.5901938763,
17.1264828579, 25.9273498878, 92.1563825857, 11.1875766427, 28.5205111082
)), row.names = c(NA, 155L), class = "data.frame")
看看下面的方法是否适合你。代码中注释的解释：
# define the cut points (I changed 100 to 101)
fill.breaks <- c(0, 17, 34, 51, 59, 67, 76, 84, 92, 101, Inf)
# obtain the correct factor names output by this specific set of cutpoints
# (less error-prone than typing them out manually)
fill.names <- levels(cut(seq(0, 120), breaks = fill.breaks, right = FALSE))
# define the labels & colours associated with the cutpoints
fill.labels <- c(rep(c("Low", "Moderate", "High"), each = 3), "Very High")
fill.colors <- c("palegreen", "green", "green3", "yellow", "yellow3",
"orange", "salmon", "red", "red4", "purple")
names(fill.labels) <- fill.names
names(fill.colors) <- fill.names
# define the function in stat_summary_2d to return the results of mean as
# appropriately cut factor levels, & use scale_fill_manual to match each
# level to its corresponding color / label
ggplot(pmData,
aes(x = longitude, y = latitude, z = pm_raw)) +
stat_summary_2d(geom = "tile", bins = 75,
fun = function(x) cut(mean(x),
breaks = fill.breaks,
right = FALSE)) +
scale_fill_manual(values = fill.colors,
labels = fill.labels) +
guides(fill=guide_legend(keywidth=0.05, keyheight=0.05, default.unit="inch"))
我正在尝试将离散色标应用于 R 中 stat_summary_2d 函数创建的图块。这些图块绘制在 ggmap 创建的地图之上，每个图块代表 mean 落在图块边界内的所有点（更具体地说，污染值）。以下是我目前所取得的成就的图片（由于目前我的声誉较低，我无法直接包含图片）：
如您所见，快到了。但是，有两个问题：
- 离散颜色不正确，因为我使用的是渐变，
- 图例并未显示所有类别。
我知道使用渐变不会给我想要的离散中断，但这是我迄今为止管理的最好的。此外，我想要的颜色是基于英国的空气质量指数 (AQI) 等级。这些类别的中断、颜色和标签都在下面的代码中，以防您看不到附件。
下面是我到目前为止编写的代码：
library(ggmap)
library(ggplot2)
# Bounding box for creating map
bbox <- make_bbox(longitude, latitude, pmData[,], f = 0.005)
m <- get_map(bbox, source = "stamen", zoom = 15)
# Plot map
ggmap(m) +
stat_summary_2d(geom = "tile", bins = 75, fun = mean,
aes(x = longitude, y = latitude, z = pm_raw),
data = pmData) +
xlab("Longitude") +
ylab("Latitude") +
guides(fill=guide_legend(keywidth=0.05, keyheight=0.05, default.unit="inch")) +
scale_fill_gradientn(breaks=c(0, 17, 34, 51, 59, 67, 76, 84, 92, 100, Inf),
minor_breaks = NULL,
guide = "legend",
colours = c("palegreen", "green", "green3", "yellow", "yellow3",
"orange", "salmon", "red", "red4", "purple"),
labels = c("Low", "Low", "Low", "Moderate", "Moderate", "Moderate",
"High", "High", "High", "Very high", "Very high"))
由于每个图块的值都是平均值，因此使用 cut 创建离散类别不起作用，因为它会导致 Continuous value supplied to discrete scale 错误。
如果您需要示例数据，请告诉我，我可以尝试发布一些。
谢谢。
structure(list(latitude = c(51.383833333, 51.386423333, 51.387305,
51.388133333, 51.383815, 51.378265, 51.38614, 51.390151667, 51.390126667,
51.380048333, 51.389838333, 51.389863333, 51.386211667, 51.381303333,
51.378036667, 51.387336667, 51.378036667, 51.388873333, 51.387988333,
51.38419, 51.383691667, 51.384888333, 51.377866667, 51.384748333,
51.384993333, 51.377456667, 51.389825, 51.378485, 51.377715,
51.386725, 51.386111667, 51.388356667, 51.383935, 51.383833333,
51.378548333, 51.379861667, 51.386688333, 51.388008333, 51.387793333,
51.383365, 51.38366, 51.387165, 51.384871667, 51.378063333, 51.378378333,
51.377955, 51.37791, 51.388115, 51.384763333, 51.383471667, 51.380998333,
51.37841, 51.380526667, 51.380526667, 51.381601667, 51.378836667,
51.37867, 51.382835, 51.38553, 51.38784, 51.386893333, 51.385325,
51.377981667, 51.383798333, 51.378041667, 51.382858333, 51.38115,
51.3812, 51.383555, 51.38834, 51.384345, 51.381181667, 51.377565,
51.379041667, 51.384736667, 51.384828333, 51.38389, 51.382735,
51.38527, 51.385566667, 51.380448333, 51.378585, 51.387068333,
51.379693333, 51.379535, 51.381738333, 51.386378333, 51.378341667,
51.385281667, 51.385115, 51.37791, 51.378043333, 51.377256667,
51.381213333, 51.386808333, 51.380298333, 51.37851, 51.382393333,
51.381196667, 51.377373333, 51.389178333, 51.385386667, 51.385038333,
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51.386478333, 51.383513333, 51.384828333, 51.380108333, 51.382911667,
51.377868333, 51.377931667, 51.382061667, 51.382773333, 51.377656667,
51.384795, 51.377878333, 51.377436667, 51.377338333, 51.382755,
51.389243333, 51.386008333, 51.383578333, 51.381366667, 51.385426667,
51.383373333, 51.38756, 51.385405, 51.384733333, 51.382111667,
51.377853333, 51.377975, 51.385383333, 51.3777, 51.383573333,
51.38748, 51.384433333, 51.389521667, 51.384586667, 51.388643333,
51.388745, 51.389503333, 51.384065, 51.385931667, 51.3902, 51.3902,
51.377628333, 51.377165, 51.388163333, 51.38431, 51.381613333
), longitude = c(-2.363555, -2.351465, -2.352908333, -2.354518333,
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-2.36021, -2.368918333, -2.377361667, -2.376808333, -2.365816667,
-2.353393333, -2.377001667, -2.363166667, -2.380608333, -2.357106667,
-2.356938333, -2.376808333, -2.362295, -2.369111667, -2.35344,
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-2.38118, -2.36021, -2.356781667, -2.356781667, -2.36048, -2.36133,
-2.354521667, -2.372698333, -2.377001667), pm_raw = c(20.2856490204,
77.8877903749, 71.7933099292, 98.9227746379, 13.168840034, 25.1495305964,
18.5911875492, 11.5532213795, 11.5532213795, 34.6047765217, 34.2986999054,
38.1823784449, 60.2354155746, 19.6998286117, 24.3481168666, 18.4068794706,
92.4024502724, 20.8700066963, 53.7655811403, 26.56884172, 25.9563524058,
41.8398168697, 21.204229175, 18.4985229695, 69.4438433391, 35.0603153227,
52.5171448444, 37.6010327173, 11.3101753017, 50.9286177028, 17.8909927779,
16.4572391933, 39.9021155572, 8.86286052724, 23.9431598443, 39.7689915216,
30.8634678742, 18.1143232738, 18.3941479353, 9.46628056132, 14.412527522,
27.7423289978, 10.7671408538, 15.9341080291, 20.6349163328, 15.4269963603,
5.83588000809, 16.1941855148, 68.6352334409, 14.4015212512, 102.822361444,
26.5387481, 60.5308607077, 6.97155968363, 234.155664235, 26.7571613942,
12.5228906193, 9.15531699502, 28.963860631, 11.1517290682, 76.4919317928,
11.5337240599, 16.9191620121, 14.9634160546, 7.48538606606, 7.36565315293,
6.58305090626, 2.88802757122, 4.80066803954, 14.3716849029, 21.3111785588,
16.1417055686, 8.91166650308, 14.6912815119, 13.6515088126, 34.6758160409,
107.411084295, 8.34201752814, 43.5562737658, 13.1949460218, 22.5121677682,
7.94588103399, 13.7452057672, 16.4227477955, 24.6492704894, 27.7137110021,
14.1559453375, 32.1195214536, 22.1462601372, 29.8239718823, 60.8699820745,
71.4253002227, 35.8562060293, 82.0146924171, 38.3981186095, 15.3572590305,
8.77474869292, 41.4601384099, 19.6070387258, 41.9414449, 29.3322623223,
120.145146039, 72.294045077, 21.6956980653, 9.83379481087, 9.61218271598,
6.18969929985, 15.3210540005, 48.9376058474, 56.9003135671, 28.1088565396,
6.59098222239, 22.6884614987, 52.7084659288, 38.4823403648, 58.7704971854,
19.9000537492, 0.000966813590044, 0.000966813590044, 26.0864635077,
36.8963060363, 39.9757260957, 42.790215098, 79.8085095756, 47.0276739845,
31.4995803181, 133.024483283, 31.2866736476, 92.7349765429, 11.1185166794,
31.1820240423, 8.08385762269, 48.4851751962, 63.9069236938, 27.1244461233,
60.3187940712, 20.5374151595, 28.4407989289, 65.8097519874, 91.1719973116,
42.3963928042, 18.0173416637, 85.6945673673, 20.9880757421, 53.375218596,
26.4525339035, 47.8580307084, 23.9389236402, 4.46424583036, 12.5901938763,
17.1264828579, 25.9273498878, 92.1563825857, 11.1875766427, 28.5205111082
)), row.names = c(NA, 155L), class = "data.frame")
看看下面的方法是否适合你。代码中注释的解释：
# define the cut points (I changed 100 to 101)
fill.breaks <- c(0, 17, 34, 51, 59, 67, 76, 84, 92, 101, Inf)
# obtain the correct factor names output by this specific set of cutpoints
# (less error-prone than typing them out manually)
fill.names <- levels(cut(seq(0, 120), breaks = fill.breaks, right = FALSE))
# define the labels & colours associated with the cutpoints
fill.labels <- c(rep(c("Low", "Moderate", "High"), each = 3), "Very High")
fill.colors <- c("palegreen", "green", "green3", "yellow", "yellow3",
"orange", "salmon", "red", "red4", "purple")
names(fill.labels) <- fill.names
names(fill.colors) <- fill.names
# define the function in stat_summary_2d to return the results of mean as
# appropriately cut factor levels, & use scale_fill_manual to match each
# level to its corresponding color / label
ggplot(pmData,
aes(x = longitude, y = latitude, z = pm_raw)) +
stat_summary_2d(geom = "tile", bins = 75,
fun = function(x) cut(mean(x),
breaks = fill.breaks,
right = FALSE)) +
scale_fill_manual(values = fill.colors,
labels = fill.labels) +
guides(fill=guide_legend(keywidth=0.05, keyheight=0.05, default.unit="inch"))