ggplot2怎么實(shí)現(xiàn)發(fā)散性正負(fù)圖

library(ggplot2)# 使用mtcars數(shù)據(jù)集data("mtcars")  # 保留car name ，新建一列mtcars$car_name <- rownames(mtcars) # 對(duì)mpg進(jìn)行標(biāo)準(zhǔn)化處理 mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # 按照0未閾值 ，分上 下mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above")  mtcars <- mtcars[order(mtcars$mpg_z), ]  # 為展示美觀，數(shù)據(jù)排序# 改為因子，能夠保持原順序mtcars$car_name <- factor(mtcars$car_name, levels = mtcars$car_name)

ggplot(mtcars, aes(x=car_name, y=mpg_z, label=mpg_z)) +   geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +  scale_fill_manual(name="Mileage",                     labels = c("Above Average", "Below Average"),                     values = c("above"="#00ba38", "below"="#f8766d")) +   labs(subtitle="Normalised mileage from 'mtcars'",        title= "Diverging Bars") +   coord_flip() + theme_bw()

ggplot(mtcars, aes(x=car_name, y=mpg_z, label=mpg_z)) +   geom_point(stat='identity', color="orange",size=4)  +  geom_segment(aes(y = 0,                    x =car_name,                    yend = mpg_z,                    xend =car_name),                color = "grey")  +  labs(title="Diverging Lollipop Chart") +   ylim(-2.5, 2.5) +  coord_flip() + theme_bw()

ggplot(mtcars, aes(x=car_name, y=mpg_z, label=mpg_z)) +   geom_point(stat='identity', aes(col=mpg_type), size=6)  +  scale_color_manual(name="Mileage",                     labels = c("Above Average", "Below Average"),                     values = c("above"="#00ba38", "below"="#f8766d")) +   geom_text(color="white", size=2) +  labs(title="Diverging Dot Plot",       subtitle="Normalized mileage from 'mtcars': Dotplot") +   ylim(-2.5, 2.5) +  coord_flip() + theme_bw()