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library(tidyverse)

files = c(
  fs::dir_ls("~/Downloads/Shelter1_Tension_files"),
  fs::dir_ls("~/Downloads/Shelter_2_Tension_files"))


doggies <- data.frame(file = files) |>
  mutate(data = map(files,

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library(showtext)
font_add_google("Special Elite", "elite")
showtext_auto()

library(magick)
library(tidyverse)

# first part is common punctuation
chars = c(strsplit(intToUtf8(c(33:47, 58:64, 91:96)), "")[[1]], 
          0:9, letters, LETTERS)

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tibble::tribble(
    ~soo,                       ~station_name, ~station_code,       ~Location,        ~County,
  "ANTC",                           "Antioch",          "AN",       "Antioch", "Contra Costa",
  "ASHB",                             "Ashby",          "AS",      "Berkeley",      "Alameda",
  "DBRK",                          "Berkeley",          "BK",      "Berkeley",      "Alameda",
  "NBRK",                    "North Berkeley",          "NB",      "Berkeley",      "Alameda",
  "CAST",                     "Castro Valley",          "CV", "Castro Valley",      "Alameda",
  "COLM",                             "Colma",          "CM",         "Colma",    "San Mateo",
  "CONC",                           "Concord",          "CN",       "Concord", "Contra Costa",
  "NCON",          "North Concord / Martinez",          "NC",       "Concord", "Contra Costa",

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# recreation of https://xkcd.com/2807/


library(ggplot2)
library(sf)
library("rnaturalearth")
library("rnaturalearthdata")
# library(lwgeom)

world <- ne_countries(scale = "small", returnclass = "sf") |>

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library(tidyverse)
library(lubridate)
# library(fitzRoy)



ranks <- tibble::tribble(
           ~RK, ~Team,    ~PTS,   ~chg,
            1L, "USA", 2090.03,  -1.35,
            2L, "GER", 2061.56,  -6.56,

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library(tidyverse)
library(zipcodeR)

tuesdata <- tidytuesdayR::tt_load('2022-11-08')
state_stations <- tuesdata$state_stations
station_info <- tuesdata$station_info
zipcodeR::zip_code_db -> zips

state_stations %>%
  separate(frequency, c("freq", "band"), sep = " ", convert = TRUE, remove = FALSE) %>%

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library(tidyverse)
library(readxl)
library(lubridate)
library(ggdist)


historicalweeklydata <- 
  bind_rows(
    read_excel("~/Downloads/historicalweeklydata.xls", 
                                   sheet = "Full History", range = "a7:B4000"),

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# inspired by https://twitter.com/nytimes/status/1569377877774188551
# https://www.nytimes.com/2022/09/11/nyregion/hasidic-schools-nyc-officials.html


library(tidyverse); 
library(particles); library(tidygraph); library(ggraph)

tibble(group = rep(c(1, 1.3), each = 300),
       value = rbeta(600, 2-group, group^3),
       radius = 0.01) -> a

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library(tidyverse)
permits <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/sf_permits.csv')
permits2 <- permits %>%
  mutate(lon = stringr::word(location, 2) %>% parse_number,
         lat = stringr::word(location, 3) %>% parse_number) %>%
  mutate(use = fct_lump(proposed_use, 7) %>% fct_explicit_na())


library(ggmap)
sf_basemap = get_map(location = c(lon = -122.45, lat = 37.75),

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library(httr); library(jsonlite)
library(tidyverse); library(lubridate)
library(gganimate); library(ggtext)


sf_hosp <- GET("https://data.sfgov.org/resource/nxjg-bhem.json?$limit=999999")
sf_hosp$content %>% 
  rawToChar %>% 
  fromJSON %>% 
  mutate(patientcount = as.numeric(patientcount),