Load Packages

library(tidyverse)
library(ggtext)
library(ggthemes)
library(ggpubr)

Download Dataset directly from the MakeOver Monday website

Source: UN Stats-https://unstats.un.org/unsd/gender/timeuse/index.html

df <- read.csv("https://query.data.world/s/tsrp74qzuipbt3fmihwm5332n5zqjl",
               header=TRUE,
               stringsAsFactors=FALSE)

Plot

dot_plot <- 
  df %>% 
  filter(Time.use == "Unpaid domestic, care and volunteer work") %>% 
  group_by(Country,Time.use,Gender) %>% 
  summarise(Hours = mean(Average.Time..hours.,na.rm = TRUE)) %>%
  pivot_wider(names_from = Gender,values_from = Hours) %>% 
  mutate(Diff = Women-Men) %>% 
  ggplot() +
  geom_point(aes(x=reorder(Country,Diff),y=Men),color="black",fill="#2c7bb6",size=7,shape=21) +
  geom_point(aes(x=reorder(Country,Diff),y=Women),color="black",fill="orange",size=7,shape=21) +
  geom_line(aes(x=Country,y=1.905,group=1),color="lightblue",linetype = "dashed",size=5) +
  geom_line(aes(x=Country,y=4.475,group=1),color="#fdbb84",linetype = "dashed",size=5) +
  coord_flip()+
  theme_calc(base_size = 22) +
  labs(title = "<strong><span style='color:#2c7bb6'>Men</strong></b> and <strong><span style='color:orange'>Women</span></strong></b>: Average amount of Hours spent unpaid domestic, care and volunteer work",
       subtitle = "Ordered from the country with the highest difference between genders to the lowest; Dashed lines are averages") + 
  theme(legend.position = "none",
        plot.title = element_markdown()) +
  xlab("")
dot_plot