Frank Hänel

# Loading packages
library(tidyverse)

library(tidytuesdayR)
library(showtext)

library(glue)
library(ggtext)
library(ggchicklet)

# Loading and filtering data
wages <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-05/wages.csv')

# Filter the data
df <- wages %>%
  filter(facet == "demographics: college or more" & year > 2012)
df2 <- wages %>%
  filter(facet == "demographics: less than college" & year > 2012)

# Loading fonts and colors
font_add_google("Poppins", "poppins")
font_add('fa-reg', 'c:/Users/info/OneDrive/Dokumente/fonts/Font Awesome 6 Free-Regular-400.otf')
font_add('fa-brands', 'c:/Users/info/OneDrive/Dokumente/fonts/Font Awesome 6 Brands-Regular-400.otf')
font_add('fa-solid', 'c:/Users/info/OneDrive/Dokumente/fonts/Font Awesome 6 Free-Solid-900.otf')
showtext_auto()
bg <- "white"
col1 <- thematic::okabe_ito()[1]
col2 <- thematic::okabe_ito()[2]
col3 <- thematic::okabe_ito()[4]
col4 <- thematic::okabe_ito()[3]
grey1 <- "lightgrey"
grey2 <- "darkgrey"

# text creation
twitter <- glue("<span style='color:{col4};font-family:fa-brands;'>&#xf099;</span>")
mastodon <- glue("<span style='color:{col4};font-family:fa-brands;'>&#xf4f6;</span>")
link <- glue("<span style='color:{col4};font-family:fa-solid;'>&#xf0c1;</span>")
data <- glue("<span style='color:{col4};font-family:fa-solid;'>&#xf1c0;</span>")
quote <- glue("<span style='color:{col4};font-family:fa-solid;'>&#xf10d;</span>")
space <- glue("<span style='color:{bg}'>-</span>")
space2 <- glue("<span style='color:{bg}'>--</span>") # can't believe I'm doing this
union <- glue("<span style='color:{col1}'><b>Union Members</b></span>")
nonunion <- glue("<span style='color:{col2}'><b>Non Union Workers</b></span>")
wage <- glue("<span style='color:{col3}'><b>overall wage</b></span>")
less <- glue("<span style='color:{grey2}'><b>less than college</b></span>")
more <- glue("<span style='color:{grey1}'><b>college or more</b></span>")

t <- glue("<b>Wages of {union} and {nonunion} by Educational Background<br>from 2013 to 2022</b>")
s <- glue("Educational Background: {less} | {more} ({wage})")
cap <- glue("{twitter}{space2}@web_design_fh{space2} 
	{space2}{mastodon}{space2}@frankhaenel @fosstodon.org{space2}
	{space2}{link}{space}{space2}www.frankhaenel.de<br>
	{data}{space2}Union{space}Membership,{space}Coverage,{space}and{space}Earnings{space}from{space}the{space}CPS{space}by{space}Barry{space}Hirsch{space}(Georgia{space}State{space}University),David{space}Macpherson{space}(Trinity{space}University),{space}and{space}William{space}Even{space}(Miami{space}University)<br>
	{quote}{space2}Macpherson,{space}David{space}A.{space}and{space}Hirsch,{space}Barry{space}T..{space}2023.{space}“{space}Five{space}decades{space}of{space}CPS{space}wages,{space}methods,{space}and{space}union-nonunion{space}wage{space}gaps{space}at{space}Unionstats.com.”<br>{space2}{space2}Industrial{space}Relations:{space}A{space}Journal{space}of{space}Economy{space}and{space}Society{space}00:{space}1–9.")

# Define bar_height
bar_height <- 0.2

# Create the plot
ggplot(data = df) +   ggchicklet:::geom_rrect(
     aes(
         xmin = union_wage,
         xmax = nonunion_wage,
         ymin = year - bar_height,
         ymax = year + bar_height,
         ),color=grey1,fill=grey1,
     # Use relative npc unit (values between 0 and 1)
     # This ensures that radius is not too large for your canvas
     r = unit(0, 'npc')
 ) +
 ggchicklet:::geom_rrect(data=df2,
     aes(
         xmin = union_wage,
         xmax = nonunion_wage,
         ymin = year - bar_height,
         ymax = year + bar_height,
     ),color= grey2,fill=grey2,
     # Use relative npc unit (values between 0 and 1)
     # This ensures that radius is not too large for your canvas
     r = unit(0, 'npc')
 ) +
 geom_point(data = df, aes(x = union_wage,y = year),color=col1,size = 6) +
 geom_point(data = df2, aes(x = union_wage,y = year),color=col1,size = 6) +
 geom_point(data = df, aes(x = nonunion_wage,y = year),color=col2,size = 6) +
 geom_point(data = df2, aes(x = nonunion_wage,y = year),color=col2,size = 6) +
 geom_point(data = df, aes(x = wage,y = year),color=col3,size = 3) +
 geom_point(data = df2, aes(x = wage,y = year),color=col3,size = 3) +
 labs(title = t, subtitle = s, caption = cap, x = "Mean hourly earnings in dollars", y = "Year") +
 theme_minimal() +
 theme(plot.margin = margin(10, 10, 10, 10),
 plot.title = element_markdown(size = 18, hjust = 0, lineheight = 1.3, family = "poppins"),
 plot.subtitle = element_markdown(size = 15, hjust = 0, lineheight = 1.3, family = "poppins"),
 plot.caption = element_markdown(size = 9, hjust = 0, lineheight = 1.3, color = grey2, family = "poppins"),
 axis.title = element_markdown(size = 8, color = grey2, family = "poppins"),
 axis.text = element_markdown(size = 8, color = grey2, family = "poppins")) +
 ylim(2012, 2023)