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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(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))
pic <- grid::rasterGrob(img, interpolate = TRUE)

pal <- c("#FF8C00", "#A034F0", "#159090")

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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)

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library(tidyverse)
library(systemfonts)

## data
## via https://projects.oregonlive.com/weather/temps/
df <- 
  readr::read_csv("https://projects.oregonlive.com/weather/pdx_temps.csv") %>% 
  mutate(yday = lubridate::yday(date),
         year = lubridate::year(date),
         decade = year %/% 10 * 10,

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set.seed(2021)

data <- tibble(
  group = factor(c(rep("Group 1", 100), rep("Group 2", 250), rep("Group 3", 25))),
  value = c(
    ## Group 1
    seq(0, 20, length.out = 100),
    ## Group 2
    c(rep(0, 5), rnorm(30, 2, .1), rnorm(90, 5.4, .1), rnorm(90, 14.6, .1), rnorm(30, 18, .1), rep(20, 5)),
    ## Group 3

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#' Download data to draw Berlin template map
#' @examples download_data_berlin()
download_data_berlin <- function() {
  
  ## SETUP ---------------------------------------------------------------------
  ## output dir for raw geo files
  dir <- here::here("data-raw", "geo-raw")
  if (!dir.exists(dir)) dir.create(dir, showWarnings = TRUE, recursive = TRUE)
  
  ## DISTRICTS -----------------------------------------------------------------