需要数据格式
前两列是经纬度，第三列是品种或亚型，第四列是每个品种的数量分布
Longitude Latitude diqu subspe num
-104 39 West_Europe Bos_taurus 10
-3 56 West_Europe Bos_taurus 30
-3 51 West_Europe Bos_taurus 20
2 -44 Central_South_Europe Bos_taurus 26
2 45 Central_South_Europe Bos_taurus 22
3 51 West_Europe Bos_taurus 20
5 43 Central_South_Europe Bos_taurus 20
7 45 Central_South_Europe Bos_taurus 8
7 46 Central_South_Europe Bos_taurus 30
12 49 Central_South_Europe Bos_taurus 23
29 1 Africa Bos_taurus_Bos_indicus 17
36 3 Africa Bos_taurus_Bos_indicus 5
44 36 The_Middle_East_Northwest_China Bos_taurus_Bos_indicus 8
70 -20 India_Paksitan Bos_indicus 10
71 30 India_Paksitan Bos_indicus 4
75 32 India_Paksitan Bos_indicus 20
77 28 India_Paksitan Bos_indicus 5
80 43 Northwest_China Bos_taurus 11
87 43 Northwest_China Bos_taurus 30
90 38 Northwest_China Bos_taurus 5
画图
library(ggplot2)
library(ggthemes)
mymap <- read.table("经纬度.txt", sep = "\t", header =T)
world <- map_data("world")
my_fill = c("Africa"="#984EA3","India_Paksitan"="#F781BF","South_China"="#E41A1C",
"Central_South_Europe"="#FFFF33","Northeast_Asia"="#FF7F00",
"Northwest_China"="#98F5FF","Tibet"="#377EB8","West_Europe"="#4DAF4A",
"North_Central_China"="#000000","The_Middle_East_Northwest_China"="#000000")
my_shape = c("Bos_taurus"=23,"Bos_indicus"=21,"Bos_taurus_Bos_indicus"=19)
p1 <- ggplot(world, aes(long, lat)) +
geom_map(map=world, aes(map_id=region), fill="#DEDEDE", color=NA) +
xlim(-105, 135)+ ylim(-50, 60)+
coord_quickmap()
p2 <- p1 + geom_point(data=mymap, color='black',
aes(x = Longitude, y = Latitude,
size=num, shape=subspe, fill=diqu))+
scale_fill_manual(values = my_fill)+
scale_shape_manual(values = my_shape)+
theme_map()+
theme(legend.position=c(0,-0.1),legend.justification=c(0,0), # 图例位置
legend.background=element_blank(), # 去除图例背景
legend.title=element_blank(), # 去除图例标题
legend.text = element_text(size=10), # 图例文本大小
legend.key=element_rect(color=NA, fill=NA))+ # 去除图例形状周围的背景
# 修改图例形状、大小
guides(fill=guide_legend(override.aes=list(size=5,shape=21)),
shape = guide_legend(override.aes = list(size=5, sahpe=my_shape)))
p2
结果展示
Rplot.png
! 代码来自文章，稍作修改
Gu, S.; Qi, T.; Rohr, J. R.; Liu, X. Meta-Analysis Reveals Less Sensitivity of Non-Native Animals than Natives to Extreme Weather Worldwide. Nat Ecol Evol 2023. https://doi.org/10.1038/s41559-023-02235-1.[图片上传失败...(image-f22196-1705367599700)]
