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本节来复现nature communications上一篇文章中地图的绘制方法，下面来进行复现过程，由于未找到作者提供的数据信息，小编自己构建了绘图文件因此结果与原图有出入仅供参考。整张图均使用R代码进行绘制

Land-use diversity predicts regional bird taxonomic and functional richness worldwide

论文原图

复现图

加载R包

# install.packages("rnaturalearthdata")

# install.packages("tidygeocoder")
library(tidygeocoder)
library(tidyverse)
library(sf)
library(camcorder)
library(scico)
library(rnaturalearth)
library(terra)
library(tidyterra)
library(geodata)
library(cowplot)
library(ggsci)

绘制点图

df1 <- read_tsv("data.xls") %>% filter(type=="Taxonomic richness") %>% 
  select(5:9) %>% 
  group_by(REALM) %>% 
  slice_head(n=1)

df1$REALM <- factor(df$REALM,levels = c("Nearctic","Palearctic","Indomalayan","Neotropic","Afrotropic","Australasia"))  

plot1 <- df1 %>% ggplot(aes(y = fct_rev(REALM))) +
  theme_bw()+
  geom_errorbarh(aes(xmin=Lower_ci,xmax=Upper_ci),height=0.1) +
  geom_point(aes(x=visregFit,color=REALM),fill="black",size=3,show.legend = F) +
  labs(x="Taxonomic richness",y=NULL)+
  scale_color_npg()+
  theme(axis.ticks.y= element_blank(),
        axis.title.y= element_blank(),
        axis.title.x = element_text(color="black",size=8,face="bold"),
        axis.text.y=element_text(color="black",size=8,face="bold"),
        axis.text.x=element_text(color="black",size=8,face="bold"))


df2 <- read_tsv("data.xls") %>%
  filter(type=="Functional richness") %>% 
  select(5:9) %>% 
  group_by(REALM) %>% 
  slice_head(n=1)

df2$REALM <- factor(df$REALM,levels = c("Nearctic","Palearctic","Indomalayan","Neotropic","Afrotropic","Australasia"))  

plot2 <- df2 %>% ggplot(aes(y = fct_rev(REALM))) +
  theme_bw()+
  geom_errorbarh(aes(xmin=Lower_ci,xmax=Upper_ci),height=0.1) +
  geom_point(aes(x=visregFit,color=REALM),fill="black",size=3,show.legend = F) +
  labs(x="Functional richness",y=NULL)+
  scale_color_npg()+
  theme(axis.ticks.y= element_blank(),
        axis.title.y= element_blank(),
        axis.title.x = element_text(color="black",size=8,face="bold"),
        axis.text.y=element_text(color="black",size=8,face="bold"),
        axis.text.x=element_text(color="black",size=8,face="bold"))

好了本节介绍到此结束，非常简单的一个案例喜欢的观众老爷欢迎分享转发,更多精彩案例欢迎关注我的公粽号R语言数据分析指南