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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 -----------------------------------------------------------------

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library(tidyverse)
library(igraph)
library(ggraph)

df <- readRDS("data.Rds")

graph <- graph_from_data_frame(df)

sorting <- c(25, 24, 1, 2, 3, 4, 5, 6, 7, 8, 26, 27, c(9:23))

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library(tidyverse)
library(gganimate)
library(systemfonts)

df_mac_raw <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-12-22/big-mac.csv')

df_mac <- 
  df_mac_raw %>% 
  mutate(year = lubridate::year(date)) %>% 
  dplyr::select(date, year, iso_a3, currency_code, name, local_price, 

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#### 1. Sign up at GitHub.com ################################################

## If you do not have a GitHub account, sign up here:
## https://github.com/join

# ----------------------------------------------------------------------------

#### 2. Install git ##########################################################

## If you do not have git installed, please do so: 

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library(shiny)
library(tmap)
library(tidyverse)
library(fullPage)

data(World)
world_vars <- setdiff(names(World), c("iso_a3", "name", "sovereignt", "geometry"))

ui <- pagePiling(
    sections.color = c('#ebebeb', '#f1f1f1'),

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library(shiny)
library(tmap)

data(World)
world_vars <- setdiff(names(World), c("iso_a3", "name", "sovereignt", "geometry"))


ui <- fluidPage(
  tmapOutput("map"),
  selectInput("var", "Variable", world_vars)

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library(tidyverse)
library(patchwork)

df <- tibble(x = 1, y = 1)

g <- ggplot(df, aes(x, y)) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  coord_cartesian(clip = "off") +
  theme_void() +

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library(tidyverse)
library(lubridate)

locations <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-23/locations.csv')

movement <- locations %>% 
  filter(study_site != "Hart Ranges") %>% 
  mutate(
    season = fct_rev(season), # reverse seasons
    longitude = round(longitude, 2), # round long, lat to reduce number of points