AI代码解释 dat<-with(density(df$x),data.frame(x,y))dat dat1<-dat[dat$x<(-2),]dat2<-dat[dat$x>2,]ggplot()+geom_density(data=df,aes(x=x),fill="red")+geom_area(data=dat1,aes(x=x,y=y),fill="blue")+geom_area(data=dat2,aes(x=x,y=y),fill="blue")+theme_bw() ima...
Set the aestheticpatternto your choice of pattern e.gpattern = 'stripe', and set other options usingpattern_*aesthetics df<-data.frame(level=c("a","b","c",'d'),outcome=c(2.3,1.9,3.2,1)) ggplot(df)+geom_col_pattern( aes(level,outcome,pattern_fill=level),pattern='stripe',fill='...
AI代码解释 >library(ggplot2)>ls("package:ggplot2",pattern="^geom_.+")[1]"geom_abline""geom_area""geom_bar""geom_bin2d""geom_blank"[6]"geom_boxplot""geom_col""geom_contour""geom_count""geom_crossbar"[11]"geom_curve""geom_density""geom_density_2d""geom_density2d""geom_dotplot"...
> library(ggplot2) > ls("package:ggplot2",pattern="stat_.+") [1] "stat_bin" "stat_bin_2d" "stat_bin_hex" "stat_bin2d" "stat_binhex" [6] "stat_boxplot" "stat_contour" "stat_count" "stat_density" "stat_density_2d" [11] "stat_density2d" "stat_ecdf" "stat_ellipse" "st...
scale_shape_*函数用于调整形状。 ls("package:ggplot2", pattern="^scale_shape_.+") #[1] "scale_shape_binned" "scale_shape_continuous" "scale_shape_discrete" #[4] "scale_shape_identity" "scale_shape_manual" "scale_shape_ordinal"
> ls("package:ggplot2",pattern="stat_.+") [1] "stat_bin" "stat_bin_2d" "stat_bin_hex" "stat_bin2d" "stat_binhex" [6] "stat_boxplot" "stat_contour" "stat_count" "stat_density" "stat_density_2d" [11] "stat_density2d" "stat_ecdf" "stat_ellipse" "stat_function" "stat_id...
ggplot(test,aes(cars,freq))+geom_bar(stat="identity",fill = "green")+coord_polar()+theme(legend.position="none") 众筹用户画像 library(RMySQL) library(ggplot2) library(reshape) # 文件操作 # list.files(pattern=("\\.pdf$")) # file.remove( list.files(pattern=("\\.pdf$"))) ...
You can see the traffic increase in air passengers over the years along with the repetitive seasonal patterns in traffic. Whereas Nottingham does not show an increase in overal temperatures over the years, but they definitely follow a seasonal pattern. library(ggplot2) library(forecast) theme_set...
ls('package:ggplot2', pattern='^stat_.+')## [1] 'stat_bin' 'stat_bin_2d' 'stat_bin_hex' ## [4] 'stat_bin2d' 'stat_binhex' 'stat_boxplot' ## [7] 'stat_contour' 'stat_count' 'stat_density' ## [10] 'stat_density_2d' 'stat_density2d' 'stat_ecdf' ## [13] 'stat_...
rename_with( ~ paste(.x, "value", sep = "_"), !ends_with("se") & !reliability ) %>% pivot_longer( -reliability, names_to = c("group", "key", ".value"), names_pattern = "^(.*)_(.*)_(.*)$" ) %>% ggplot(aes(x = reorder(key, -value), y = value, fill = grou...