欢迎关注R语言数据分析指南
本节来介绍如何使用ggplot2包绘制配对条形图,下面通过一个小案例进行展示
安装并加载R包
package.list=c("tidyverse","ggtext","ggnewscale","scico")
for (package in package.list) {
if (!require(package,character.only=T, quietly=T)) {
install.packages(package)
library(package, character.only=T)
}
}
加载数据
big_dave <- readr::read_csv('big_dave.csv')
times <- readr::read_csv('times.csv')
数据清洗
df1 <- big_dave %>%
filter(!is.na(definition)) %>%
mutate(definition = str_to_lower(definition)) %>%
count(definition, sort=T) %>%
mutate(pct=n/sum(n)) %>%
mutate(g=1,grp="Big Dave's") %>% slice(1:20)
df2 <- times %>%
filter(!is.na(definition)) %>%
mutate(definition = str_to_lower(definition)) %>%
count(definition, sort=T) %>%
mutate(pct=n/sum(n)) %>%
mutate(g=2,grp="Times") %>% slice(1:20)
数据合并
df = rbind(df1,df2) %>% group_by(grp) %>%
mutate(rank=rank(desc(n),ties.method = 'first')) %>%
ungroup()
筛选部分数据
selected = df %>% select(definition, g) %>%
count(definition) %>%filter(n==2) %>% pull(definition)
数据可视化
df %>% ggplot(aes(x=g, y=rank)) +
geom_text(aes(label=definition, hjust=ifelse(g==1,1,0))) +
geom_line(data=df %>% filter(definition %in% selected),aes(group=definition)) +
geom_segment(data=df %>% filter(g==2),
aes(x=g+.8, xend=g+.8+pct*600, y=rank, yend=rank, color=pct), size=5) +
geom_segment(data=df %>% filter(g==1),
aes(x=g-.8, xend=g-.8-pct*600, y=rank, yend=rank, color=pct), size=5) +
scico::scale_color_scico(palette="acton", direction=-1) +
ggnewscale::new_scale_color() +
geom_text(data=df %>%
filter(g==2),aes(x=g+0.85,y=rank,color=I(ifelse(pct>0.0015,"white","black")),
abel=scales::percent(pct, accuracy = .01)),size=3, hjust=0) +
ggnewscale::new_scale_color() +
geom_text(data=df %>% filter(g==1),
aes(x=g-0.85, y=rank, color=I(ifelse(pct>0.0014,"white","black")),
label=scales::percent(pct, accuracy = .01)), size=3, hjust=1) +
annotate(geom="text",y=-.3,x=0.68, label="Big Dave's",size=4.3, fontface="bold") +
annotate(geom="text",y=-.3,x=2.2, label="Times",size=4.3, fontface="bold") +
scale_y_reverse() +
scale_x_continuous(limits=c(-1.5,4.5), expand=c(0,0)) +
theme_void()+
theme(legend.position = "none",plot.margin=margin(.1,.1,.1,.1,unit="cm"))
数据获取
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