使用ggvoronoi按因子着色时,手动设置Voronio图的颜色
早上,下午 或者晚上使用ggvoronoi按因子着色时,手动设置Voronio图的颜色,r,ggplot2,colors,voronoi,R,Ggplot2,Colors,Voronoi,早上,下午 或者晚上 # Reproducible data df <- quakes[1:20, 1:2] df$years <- as.factor(rep(c("2000","2020"), each=10)) df$cluster <- as.factor(c("1","1","1","1","1","1","2","2","2","2", "2","2","2","2","2","3","3","3","3","3
# Reproducible data
df <- quakes[1:20, 1:2]
df$years <- as.factor(rep(c("2000","2020"), each=10))
df$cluster <- as.factor(c("1","1","1","1","1","1","2","2","2","2",
"2","2","2","2","2","3","3","3","3","3"))
#可复制数据
df您可以定义一个向量,将颜色赋予“cluster”变量的每个值,然后将它们传递到scale\u fill\u manual
函数的参数values=
,如下所示:
库(ggplot2)
图书馆(ggvoronoi)
图书馆(dplyr)
对于(以df$年为单位的i){
#
颜色=c(“1”表示“绿色”,“2”表示“蓝色”,“3”表示“红色”)
单年%
过滤器(年=i)
#
#
plot很成功。非常感谢。我经常把自己和ggplot颜色选项混淆。不客气;)别担心,你不是唯一一个;)
years <- levels(df$years)
library(dplyr)
library(ggplot2)
library(ggvoronoi)
for(i in years){
#
single_year <- df %>%
filter(years == i)
#
#
plot <- ggplot(single_year,
aes(x=lat,
y=long)) +
#
geom_voronoi(aes(fill=(cluster))) +
#
stat_voronoi(geom="path" )+
#
geom_point() +
#
labs(title = paste(i))
#
#
ggsave(paste0(i,".jpeg"), plot = last_plot(), # Watch out for the SAVE!!!
device = 'jpeg')
#
}