library(ISLR2)
library(tidyverse)
library(ggdist)
library(distributional)
library(extrafont)
# loadfonts()
p <- Wage %>%
mutate(education=gsub("\\d. ","",education)) %>% #count(year)
group_by(education)%>%
mutate(mean=mean(wage),
sd=sd(wage)) %>%
ungroup() %>% # pull(mean)%>%summary
select(education,mean,sd) %>%
distinct()%>%
ggplot(aes(y=fct_reorder(education,mean),
xdist = dist_normal(mean, sd),
layout = "weave",
fill = stat(x < 111.70))) +
stat_dots(position = "dodge", color = "grey70")+
geom_vline(xintercept = 111.70, alpha = 0.25) +
scale_x_continuous(breaks = c(20,60,90,112,140,180,220)) +
tvthemes::scale_fill_hilda()+
labs(x="Wage values from 2003 to 2009",
y="",color="Race",fill="wage < avg",
title="Wage distribution vs education 2003-2009",
subtitle="Normalized values",
caption="#30DayChartChallenge 2022 #day9 - Distribution/Statistics - v2\nDataSource: {ISLR2} Wage dataset | DataViz: Federica Gazzelloni")+
tvthemes::theme_avatar()+
theme(text = element_text(family="Chelsea Market"),
legend.background = element_blank(),
legend.box.background = element_blank(),
legend.key = element_blank(),
legend.key.width = unit(0.5,units="cm"),
legend.direction = "horizontal",
legend.position = c(0.8,0.1))
ggsave("day9_statistics_v2.png",
dpi=320,
width = 9,
height = 6)
library(patchwork)
p/p
ggsave("poster.pdf",
dpi=320,
height =841 , width = 594,
units = "mm")