High low – Unlocking the Power of Data Visualization with R

library(tidyverse)
tuesdata <- tidytuesdayR::tt_load(2023, week = 07)

age_gaps <- tuesdata$age_gaps
age_gaps%>%head

age_gaps%>%names

age_gaps%>%dim

library(geomtextpath)

age_gaps%>%
  group_by(movie_name)%>%
  mutate(avg=mean(age_difference))%>%
  pivot_longer(cols = c("character_1_gender","character_2_gender"),names_to = "type",values_to = "gender") %>%
  mutate(type=ifelse(type=="character_1_gender","First Character Gender","Second Character Gender")) %>%
  ggplot(aes(avg,fill=gender))+
  geom_histogram()+
  facet_wrap(~type)+
  tvthemes::scale_fill_brooklyn99(reverse=FALSE)+
  labs(title="Hollywood Age Gaps",
       subtitle="Age difference - Avg values",
       caption="\nDataSource: #TidyTuesday 2023 Week7 Hollywood Age Gaps\nDataViz: Federica Gazzelloni #30DayChartChallenge 2023 Day9 - high/low\n",x="",y="Count")+
  tvthemes::theme_brooklyn99(text.font="Roboto Condensed")+
  theme(panel.grid = element_line(linetype="dashed",linewidth = 0.2),
        strip.text = element_text(face="bold"))+
    annotate(
    "textsegment",
    x=60,xend=20,y=150,yend=70, 
    label = "high/low", arrow = arrow(length = unit(5,units = "pt"))
  )

ggsave("w7_HIAG.png",
       width = 7,height = 5)