R 如何在ggplot2中使用纬度和箭头绘制风向

我在每个网格单元中都有一个带有

Lat

Lon

mean\u wind

和

wind\u dir

的数据框。 我试图用背景中的平均风和每个网格单元上的箭头方向绘制一个空间图

我在示例数据框

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"))

---

win.plt
geom_spoke
是为这种特殊类型的绘图制作的。清理了一点,

库（ggplot2）
ggplot（wind.dt，
aes（x=Lon，
y=纬度，
填充=平均风，
角度=风向，
半径=比例：：重新缩放（平均风，c（.2，.8）））+
geom_光栅（）+
几何辐条（箭头=箭头（长度=单位（.05，'英寸'））+
刻度填充蒸馏器（调色板=“RdYlGn”）+
坐标等于（展开=0）+
主题（legend.position='bottom'，
legend.direction=‘水平’）


根据需要调整缩放比例和大小


编辑：控制箭头的数量
为了调整箭头的数量，一种快速而肮脏的方法是使用循环向量将传递给
geom_spoke
的一个箭头子集，该循环向量将导致某些行被丢弃，例如

库（ggplot2）
ggplot（wind.dt，
aes（x=Lon，
y=纬度，
填充=平均风，
角度=风方向[c（真，NA，NA，NA，NA）]，#导致某些值无法绘制
半径=比例：：重新缩放（平均风，c（.2，.8）））+
geom_光栅（）+
几何辐条（箭头=箭头（长度=单位（.05，'英寸'））+
刻度填充蒸馏器（调色板=“RdYlGn”）+
坐标等于（展开=0）+
主题（legend.position='bottom'，
legend.direction=‘水平’）
#>警告：已删除包含缺失值的158行（geom_spoke）。


这取决于您的数据帧是否有序，并不是无限灵活的，但如果它能以最小的努力为您绘制出一个漂亮的图，那么它可能毫无用处

一种更稳健的方法是制作一个子集数据帧，供
geom_spoke
使用，例如，选择
Lon
和
Lat
的每一个其他值，这里使用不同值向量上的循环子集：

库（dplyr）
风箭头%
过滤器（Lon%在%排序中（唯一（Lon））[c（真、假）]，
%排序中的Lat%（唯一（Lat））[c（真、假）]）
ggplot（wind.dt，
aes（x=Lon，
y=纬度，
填充=平均风，
角度=风向，
半径=比例：：重新缩放（平均风，c（.2，.8）））+
geom_光栅（）+
geom_spoke（数据=wind.arrows，#这是绘图代码中唯一的区别
箭头=箭头（长度=单位（.05，'英寸'））+
刻度填充蒸馏器（调色板=“RdYlGn”）+
坐标等于（展开=0）+
主题（legend.position='bottom'，
legend.direction=‘水平’）


这种方法使得获取（和缩放）网格相当容易，但获取菱形图案需要更多的逻辑：

wind.arrows%
过滤器（（Lon%在%sort中）（唯一（Lon））[c（真、假）]&
Lat%在%排序中（唯一（Lat））[c（真、假）]|
（Lon%以%排序（唯一（Lon））[c（假、真）]&
%排序中的Lat%（唯一（Lat））[c（假，真）]）

回答得很好，谢谢。此外，如何通过每5个网格跳过一次来绘制箭头？ie在第一个网格单元和第5和第10网格单元上绘制箭头，依此类推。@LilyNature使用一对选项进行了更新。这里似乎没有正确绘制方向，例如，第一个网格单元的值为81.7度。箭头似乎没有显示81.7度。应该改变什么才能使其正确方向。哎呀，文档没有明确说明，但是看看这个例子，看起来角度应该是弧度，而不是度，所以在
aes
中设置
angle=wind\u dir/180*pi
，或者预先转换，所有东西都或多或少地指向上（角度从正x轴测量）。谢谢@alistaire，为了获得正确的方向（参考气象惯例），我们必须从270减去方向，并使用角度=风向/180*pi。