gistfile1.txt

### Earnings Dumbbell Chart

library(ggplot2)
library(tidyr)
library(dplyr)
library(scales)

schoolearnings <- read.csv("schoolearnings.csv", stringsAsFactors=FALSE)

df <- schoolearnings %>% gather(gender,value,2:3)
men <- df %>% filter(gender=="Men") %>% arrange(desc(value)) %>% .$School
df$School <- factor(df$School, levels=rev(men))
schoolearnings$School <- factor(schoolearnings$School, levels=rev(men))


## Nice dotchart
ggplot() + 
  geom_segment(data=schoolearnings, aes(x=Women, xend=Men, y=School, yend=School), color="gray77",lwd=1)+
  geom_point(data=df, aes(value, School, group=gender,color=gender), size=5) +
  scale_color_manual(values=c("thistle4", "tomato4"))+
  scale_x_continuous(breaks = seq(60,160,20), labels=c("$60k", "$80k","$100k","$120k","$140k","$160k")) +
  ggtitle("Gender earnings disparity - 10 years after enrollment") +
  xlab("") + ylab("") +
  theme(
    plot.title = element_text(hjust=0,vjust=1, size=rel(2.3)),
    panel.background = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(color="gray70",linetype = c("28")),
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),
    plot.background  = element_blank(),
    text = element_text(color="gray20", size=10),
    axis.text = element_text(size=rel(1.0)),
    axis.text.x = element_text(color="gray20",size=rel(1.8)),
    axis.text.y = element_text(color="gray20", size=rel(1.6)),
    axis.title.x = element_text(size=rel(1.5), vjust=0),
    axis.title.y = element_text(size=rel(1.5), vjust=1),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    legend.position = "none"
  )

schoolearnings.csv

The data may be +/- a thousand $ per data point as I estimated them from a different figure !

The data may be +/- a thousand $ per data point as I estimated them from a different figure !