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ggplot2可视化美国各行业平均薪资
本节我们通过TidyTuesday 2021年第21周的数据集来分析美国各行业的薪资范围，后台回复2021-5-22获取本文代码及数据，扫描文末二维码加入交流群更多内容为您而准备
加载R包
library(tidyverse)
library(scales)
library(glue)
library(ggbump)
# install.packages("ggbump")
library(showtext)
导入数据
salary_industry <- read.delim("salary_industry.txt")
salary_gender <- read.delim("salary_gender.txt")
summary_industry <- read.delim("summary_industry.txt")
summary_education <- read.delim("summary_education.txt")
定义颜色
woman_clr <- "#38D1B8"
man_clr <- "#DBBE61"
update_geom_defaults("text",list(family="robotocondensed"))
数据可视化
final <- ggplot(data=salary_industry)+
geom_segment(aes(x=-0.5,xend=-2,y=index_ind,yend=index_ind),
color="grey50",linetype="13")+
geom_label(aes(x=-0.5,y=index_ind,label=industry),
hjust=1,color="grey90",label.size=0,
fill="grey20",size=4.5,family="robotocondensed")+
geom_text(aes(x=2.8,y=index_edu+10,label=education),
hjust=0,color="grey90",size=4.5)+
geom_text(aes(x=1.15,y=index+5,label=salary),
color="grey90",family="oswald",size=4.5)+
geom_sigmoid(aes(x=-0.4,xend=0.8,y=index_ind,
yend=index+5,group=factor(group)),color="grey40")+
geom_sigmoid(aes(x=1.5,xend=2.7,y=index+5,yend=index_edu+10,
group=factor(group2)),color="grey40")+
geom_sigmoid(data=summary_industry,
aes(x=-0.4,xend=0.8,y=index_ind,
yend=index+5,group=factor(group),
color=avg_salary),size=1,inherit.aes=FALSE)+
geom_point(data=summary_industry,
aes(x=-0.4,y=index_ind,color=avg_salary),
size=2,inherit.aes=FALSE)+
geom_point(data=summary_industry,
aes(x=0.8,y=index+5,color=avg_salary),
size=2,inherit.aes=FALSE)+
geom_sigmoid(data=summary_education,
aes(x=1.5,xend=2.7,y=index+5,yend=index_edu+10,
group=factor(group),color=avg_salary),size=1,inherit.aes=FALSE)+
geom_point(data=summary_education,
aes(x=1.5,y=index+5,color=avg_salary),size=2,inherit.aes=FALSE)+
geom_point(data=summary_education,
aes(x=2.7,y=index_edu+10,color=avg_salary),size=2,inherit.aes=FALSE)+
geom_segment(data=filter(salary_gender,gap_direction=="Woman"),
aes(x=-2,xend=-2-gender_scale,y=index_ind-0.1,
yend=index_ind-0.1),color=woman_clr,size=4)+
geom_segment(data=filter(salary_gender,gap_direction=="Man"),
aes(x=-2,xend=-2+gender_scale,y=index_ind-0.1,
yend=index_ind-0.1),color=man_clr,size=4)+
geom_text(data=filter(salary_gender,gap_direction=="Woman"),
aes(x=-2,y=index_ind,label=dollar(gender_gap,accuracy=1)),
nudge_y=0.3,color=woman_clr,hjust=1.1)+
geom_text(data=filter(salary_gender,gap_direction=="Man"),
aes(x=-2,y=index_ind,label=dollar(gender_gap,accuracy=1)),
nudge_y=0.3,color=man_clr,hjust=-0.1)+
annotate("text",x=-2.2,y=26,label="Women",color=woman_clr,size=5)+
annotate("text",x=-1.8,y=26,label="Men",color=man_clr,size=5)+
annotate("text",x=-2,y=27,label="Averagegendergap",color="grey80",
size=5,fontface="bold")+
scale_x_continuous(limits=c(-2.2,3.5))+
scale_color_viridis_c(label=dollar_format())+
guides(color=guide_colorbar(title.position="top",
title.hjust=0.5,
barwidth=15))+
labs(title="SalaryoverviewintheUSbyindustry,educationandgendergap",
color="Averageannualsalary")+
theme_void()+
theme(plot.background=element_rect(fill="grey20",color=NA),
plot.title=element_text(family="oswald",size=20,color="grey80",
hjust=0.5,face="bold",margin=margin(10,0,0,0)),
legend.position=c(0.8,0.13),
legend.direction="horizontal",
legend.title=element_text(size=12,color="grey80"),
legend.text=element_text(size=10,color="grey80"))
保存图片
ggsave(final,file="final.pdf",dpi=320,width=17,height=10,units="in")
ggsave(final,file="final.png",dpi=320,width=17,height=10,units="in")