我在每个网格单元格中都有Lat Lon mean_wind和wind_dir的数据框。我试图用背景和风向上的平均风作为每个网格单元格上的箭头制作空间图。
我试过跟随样本数据框架wind.dt
win.plt<- ggplot(wind.dt,aes(x=Lon,y=Lat))+
#Mean wind plot : OK
geom_tile(aes(fill=mean_wind),alpha=1)+
geom_tile(aes(color=mean_wind), fill=NA) +
scale_fill_gradientn(colours=(brewer.pal(9,rev("RdYlGn"))))+
scale_color_gradientn(colours=(brewer.pal(9,rev("RdYlGn"))),guide=F)
#Wind Direction : doesnot work
geom_segment(arrow = arrow(),aes(yend = Lon + wind_dir, xend = Lat + wind_dir))
win.plt
wind.dt<-structure(list(Lon = c(170.25, 171, 171.75, 172.5, 173.25, 174,
174.75, 175.5, 176.25, 177, 177.75, 178.5, 179.25, 180, 180.75,
181.5, 182.25, 183, 183.75, 184.5, 185.25, 186, 186.75, 187.5,
188.25, 189, 189.75, 190.5, 191.25, 192, 192.75, 193.5, 194.25,
170.25, 171, 171.75, 172.5, 173.25, 174, 174.75, 175.5, 176.25,
177, 177.75, 178.5, 179.25, 180, 180.75, 181.5, 182.25, 183,
183.75, 184.5, 185.25, 186, 186.75, 187.5, 188.25, 189, 189.75,
190.5, 191.25, 192, 192.75, 193.5, 194.25, 170.25, 171, 171.75,
172.5, 173.25, 174, 174.75, 175.5, 176.25, 177, 177.75, 178.5,
179.25, 180, 180.75, 181.5, 182.25, 183, 183.75, 184.5, 185.25,
186, 186.75, 187.5, 188.25, 189, 189.75, 190.5, 191.25, 192,
192.75, 193.5, 194.25, 170.25, 171, 171.75, 172.5, 173.25, 174,
174.75, 175.5, 176.25, 177, 177.75, 178.5, 179.25, 180, 180.75,
181.5, 182.25, 183, 183.75, 184.5, 185.25, 186, 186.75, 187.5,
188.25, 189, 189.75, 190.5, 191.25, 192, 192.75, 193.5, 194.25,
170.25, 171, 171.75, 172.5, 173.25, 174, 174.75, 175.5, 176.25,
177, 177.75, 178.5, 179.25, 180, 180.75, 181.5, 182.25, 183,
183.75, 184.5, 185.25, 186, 186.75, 187.5, 188.25, 189, 189.75,
190.5, 191.25, 192, 192.75, 193.5, 194.25, 170.25, 171, 171.75,
172.5, 173.25, 174, 174.75, 175.5, 176.25, 177, 177.75, 178.5,
179.25, 180, 180.75, 181.5, 182.25, 183, 183.75, 184.5, 185.25,
186, 186.75, 187.5, 188.25, 189, 189.75, 190.5, 191.25, 192,
192.75, 193.5, 194.25), Lat = c(14.25, 14.25, 14.25, 14.25, 14.25,
14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25,
14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25,
14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25, 14.25,
14.25, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5,
13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5,
13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5,
13.5, 13.5, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75,
12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75,
12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75,
12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12.75, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 11.25,
11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25,
11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25,
11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25, 11.25,
11.25, 11.25, 11.25, 11.25, 11.25, 10.5, 10.5, 10.5, 10.5, 10.5,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5,
10.5, 10.5, 10.5, 10.5, 10.5, 10.5), mean_wind = c(8.34, 8.33,
8.31, 8.29, 8.27, 8.24, 8.22, 8.2, 8.19, 8.16, 8.14, 8.13, 8.1,
8.08, 8.06, 8.02, 7.99, 7.96, 7.93, 7.89, 7.85, 7.81, 7.78, 7.73,
7.7, 7.67, 7.63, 7.62, 7.6, 7.58, 7.56, 7.53, 7.54, 8.65, 8.64,
8.61, 8.59, 8.56, 8.53, 8.51, 8.48, 8.46, 8.43, 8.41, 8.39, 8.38,
8.37, 8.33, 8.31, 8.28, 8.24, 8.2, 8.15, 8.12, 8.07, 8.03, 8.01,
7.97, 7.94, 7.92, 7.89, 7.87, 7.85, 7.85, 7.83, 7.8, 8.85, 8.84,
8.81, 8.8, 8.77, 8.74, 8.72, 8.69, 8.67, 8.65, 8.63, 8.61, 8.59,
8.58, 8.55, 8.54, 8.5, 8.46, 8.44, 8.4, 8.37, 8.33, 8.29, 8.26,
8.21, 8.18, 8.16, 8.13, 8.12, 8.09, 8.06, 8.06, 8.03, 9.01, 8.99,
8.96, 8.94, 8.91, 8.89, 8.86, 8.83, 8.82, 8.79, 8.78, 8.77, 8.75,
8.75, 8.73, 8.7, 8.68, 8.66, 8.63, 8.59, 8.55, 8.52, 8.47, 8.43,
8.4, 8.38, 8.35, 8.32, 8.31, 8.29, 8.26, 8.25, 8.23, 9.07, 9.06,
9.04, 9.01, 8.99, 8.97, 8.94, 8.92, 8.91, 8.9, 8.89, 8.88, 8.88,
8.87, 8.86, 8.84, 8.83, 8.8, 8.75, 8.74, 8.7, 8.67, 8.63, 8.59,
8.57, 8.53, 8.52, 8.51, 8.47, 8.47, 8.45, 8.42, 8.41, 9.1, 9.08,
9.06, 9.04, 9.02, 9, 8.98, 8.97, 8.96, 8.96, 8.95, 8.95, 8.97,
8.96, 8.96, 8.94, 8.91, 8.89, 8.86, 8.84, 8.8, 8.76, 8.73, 8.69,
8.67, 8.64, 8.63, 8.63, 8.61, 8.59, 8.57, 8.54, 8.53), wind_dir = c(81.27,
81.34, 81.38, 81.44, 81.47, 81.34, 81.31, 81.51, 81.56, 81.46,
81.54, 81.53, 81.42, 81.53, 81.66, 81.76, 81.86, 81.96, 82.02,
82.28, 82.65, 82.77, 83.07, 83.46, 83.78, 84.15, 84.52, 84.92,
85.39, 85.87, 86.15, 86.38, 86.53, 81.34, 81.34, 81.38, 81.31,
81.2, 81.25, 81.39, 81.36, 81.31, 81.4, 81.47, 81.48, 81.59,
81.64, 81.58, 81.62, 81.75, 81.98, 82.13, 82.26, 82.52, 82.77,
82.97, 83.15, 83.49, 83.74, 84.23, 84.78, 85.04, 85.49, 85.73,
86.05, 86.35, 81.5, 81.41, 81.32, 81.28, 81.32, 81.31, 81.24,
81.17, 81.28, 81.33, 81.24, 81.3, 81.44, 81.46, 81.55, 81.76,
81.8, 81.88, 82.11, 82.31, 82.4, 82.61, 82.88, 82.95, 83.29,
83.59, 83.93, 84.46, 84.8, 85.26, 85.47, 85.78, 86.11, 81.3,
81.29, 81.29, 81.28, 81.32, 81.22, 81.24, 81.32, 81.31, 81.23,
81.34, 81.47, 81.37, 81.42, 81.5, 81.6, 81.78, 81.98, 82.06,
82.26, 82.49, 82.52, 82.7, 82.79, 83.05, 83.46, 83.79, 84.18,
84.5, 84.91, 85.23, 85.49, 85.7, 81.31, 81.33, 81.28, 81.19,
81.26, 81.29, 81.36, 81.24, 81.16, 81.18, 81.23, 81.23, 81.23,
81.47, 81.5, 81.55, 81.73, 81.99, 82.14, 82.18, 82.41, 82.46,
82.63, 82.83, 82.97, 83.27, 83.62, 84.01, 84.34, 84.64, 85.01,
85.38, 85.55, 81.14, 81.14, 81.1, 81.15, 81.2, 81.1, 81.14, 81.06,
81.21, 81.26, 81.13, 81.16, 81.17, 81.22, 81.28, 81.63, 81.71,
81.77, 82.13, 82.22, 82.37, 82.48, 82.56, 82.7, 82.92, 83.19,
83.43, 83.74, 84.15, 84.59, 84.89, 85.22, 85.39)), row.names = c(NA,
-198L), .Names = c("Lon", "Lat", "mean_wind", "wind_dir"), class = c("tbl_df",
"tbl", "data.frame"))
geom_spoke是为这种特殊情节而制作的。清理了一点，
library(ggplot2)
ggplot(wind.dt,
aes(x = Lon ,
y = Lat,
fill = mean_wind,
angle = wind_dir,
radius = scales::rescale(mean_wind, c(.2, .8)))) +
geom_raster() +
geom_spoke(arrow = arrow(length = unit(.05, 'inches'))) +
scale_fill_distiller(palette = "RdYlGn") +
coord_equal(expand = 0) +
theme(legend.position = 'bottom',
legend.direction = 'horizontal')
根据需要调整缩放和大小。
为了调整箭头的数量，一条快速而肮脏的路线是将传递给geom_spoke的美学之一用一个回收矢量作为子集，该回收矢量会导致某些行被丢弃，例如：
library(ggplot2)
ggplot(wind.dt,
aes(x = Lon ,
y = Lat,
fill = mean_wind,
angle = wind_dir[c(TRUE, NA, NA, NA, NA)], # causes some values not to plot
radius = scales::rescale(mean_wind, c(.2, .8)))) +
geom_raster() +
geom_spoke(arrow = arrow(length = unit(.05, 'inches'))) +
scale_fill_distiller(palette = "RdYlGn") +
coord_equal(expand = 0) +
theme(legend.position = 'bottom',
legend.direction = 'horizontal')
#> Warning: Removed 158 rows containing missing values (geom_spoke).
这取决于您的数据框架是否有序并且不是无限灵活，但如果它以最小的努力为您提供了一个很好的情节，那么它仍然是无用的。
一个更强大的方法是制作一个子集化的数据框供geom_spoke使用，比如选择Lon和Lat的每个其他值，这里使用不同值向量的回收子集：
library(dplyr)
wind.arrows <- wind.dt %>%
filter(Lon %in% sort(unique(Lon))[c(TRUE, FALSE)],
Lat %in% sort(unique(Lat))[c(TRUE, FALSE)])
ggplot(wind.dt,
aes(x = Lon ,
y = Lat,
fill = mean_wind,
angle = wind_dir,
radius = scales::rescale(mean_wind, c(.2, .8)))) +
geom_raster() +
geom_spoke(data = wind.arrows, # this is the only difference in the plotting code
arrow = arrow(length = unit(.05, 'inches'))) +
scale_fill_distiller(palette = "RdYlGn") +
coord_equal(expand = 0) +
theme(legend.position = 'bottom',
legend.direction = 'horizontal')
这种方法使得获取（和缩放）网格相当容易，但获得菱形模式将需要更多逻辑：
wind.arrows <- wind.dt %>%
filter(( Lon %in% sort(unique(Lon))[c(TRUE, FALSE)] &
Lat %in% sort(unique(Lat))[c(TRUE, FALSE)] ) |
( Lon %in% sort(unique(Lon))[c(FALSE, TRUE)] &
Lat %in% sort(unique(Lat))[c(FALSE, TRUE)] ))