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library(ggplot2)
library(treemapify)
library(ggtext)
library(prismatic)

owid_urban <- readr::read_csv("https://www.ggplot2-uncharted.com/data/hyde.csv")

treemap <- owid_urban |> 
  sf::st_drop_geometry() |> 
  filter(!is.na(continent)) |> 

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library(ggplot2)
library(patchwork)

# get photo
img <- magick::image_read("https://www.yan-holtz.com/img/team/YH_pic_square.png")

# retrieve lightness
c <- magick::image_channel(img)
img_df <- tibble::as_tibble(as.data.frame.table(drop(as.integer(c[[1]]))))
img_df <- dplyr::mutate(img_df, across(c(Var1, Var2), as.numeric))

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library(ggplot2)

value <- 7.3

ggplot() +
  # Add gap to bottom
  coord_radial(
    start = 4/3 * pi,  # 240°
    end = 2/3 * pi,    # 120°
    expand = FALSE

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## packages
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggbraid)
library(colorspace)
library(owidR)
# plus ggtext via namespace

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library(tidyverse)
library(sf)
library(terra)
library(stars)
library(ggspatial)
library(systemfonts)
library(patchwork)

register_variant(
  name = "Input Mono Light",

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library(dplyr)
library(forcats)
library(ggplot2)
library(palmerpenguins)
library(ggtext)
library(colorspace)
library(ragg)

url <- "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png"
img <- magick::image_read((url))

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library(tidyverse)

## data:
## https://www.kaggle.com/datasets/sudalairajkumar/daily-temperature-of-major-cities?resource=download
temp <- read_csv(here::here("city_temperature.csv"))

## select a few interesting city around the world
temp_selected <- 
  temp %>% 
  #filter(str_detect(City, "New York|London|Helsinki|Shanghai|Rio|Jakarta|Cairo|Canberra|Delhi|Capetown|Hong Kong|Reykjavik")) %>% 

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---
title: "Gender Pay Gap: Dumbbell Plot (EU only)" 
author:
    - name: "Cédric Scherer"
      url: www.cedricscherer.com
output: 
    distill::distill_article:
        highlight: kate
        code_folding: false  
        toc: true            

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#' Function to extract Jupiter colors as hex codes
#'
#' @param ... Character names of colors
#'
#' @examples
#' jupiter_colors()
#' jupiter_colors("petrol")
#'
#' @export
jupiter_colors <- function(...) {

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library(tidyverse)
library(systemfonts)

mpg_sum <- mpg %>% 
  ## just use 2008 data
  dplyr::filter(year == 2008) %>%
  ## turn into lumped factors with capitalized names
  dplyr::mutate(
    manufacturer = stringr::str_to_title(manufacturer),
    manufacturer = forcats::fct_lump(manufacturer, n = 10)