arvi1000

# Process all JPEG files in the current dir and apply timestamp, using imagemagick
import glob
import re
import os

jpgs = glob.glob('*.jpeg')
for j in jpgs:
	newfile = re.sub('\\.jpeg$', '_stamp.jpeg', j)
	im_command = f'convert {j} -undercolor Black -fill white -pointsize 100 -auto-orient -gravity NorthWest -annotate 0 "%[EXIF:DateTimeOriginal]" {newfile}'
	print(f'Processing {j}')

arvi1000

library(tidycensus)
library(tidyverse)

# find the variables we care about
acs5_2019 <- tidycensus::load_variables(year = 2019, dataset = 'acs5', cache = T)

# ok here they are
my_vars <- paste0("B08006_", sprintf('%03d', 1:17))
# select just the most important top level responses
# (e.g. no public transportation break out)

arvi1000

options(tigris_use_cache = TRUE)

library(tigris)
library(sf)

# get shapefiles (coarse resolution)
sd_sf <- school_districts(state = 'CA', cb = T)
zip_sf <- zctas(cb=T, state='CA')

# which zips overlap OUSD?

arvi1000

library(tidycensus)
library(tidyverse)
census_api_key(Sys.getenv('CENSUS_API_KEY'))
vars <- load_variables(2017, "acs5", cache = TRUE)
il <- get_acs(geography = "county",
              variables = "B00001_001", 
              state = "IL", geometry = TRUE)
ggplot(il) + 
  geom_sf(aes(fill=estimate/1000), size=0.4, color='black') +
  scale_fill_viridis_c() +

arvi1000

library(lubridate)
library(tidyverse)
# data ----
full_dat <- data.frame(
  dt = mdy(c('9/19/16', '2/27/17', '4/9/18', '9/17/18', '2/25/19', '8/5/2019',
             '1/13/2020', '6/22/2020')),
  num = c(2188,1833,1302,1171,1067,988, 892, 831)
)
obs <- nrow(full_dat)
holdout_days <- 2

arvi1000

library(tidyverse)

streak_len <- 7
ref_denom <- 276

tibble(x=1:streak_len,
       y=(1/6)^x) %>%
  ggplot(aes(x=x+1, y=y)) +
  geom_point() +
  geom_line() +

arvi1000

just to rename gist

arvi1000

library(tidyverse)

# data ----
"JonPear6  10:21 AM
1 8 9

plättyjö:plattyjo:  10:22 AM
3 1 8

swirving  10:22 AM

arvi1000

# survey of crank beauty
# https://forms.gle/CNqHkmeQa6n1cJxL7

library(tidyverse)
library(data.table)
library(patchwork)

# data  ----
"DA 7400	DA 7700	DA 7800	DA 7900	DA 9000	DA 9100
8	7	6	2	5	4

arvi1000

library(ggplot2)
library(data.table)
dat <- fread(
'name, delegates
"BUTTIGIEG", 22
"SANDERS", 21
"WARREN", 8 
"KLOBUCHAR", 7
"BIDEN", 6
"Still\nremaining", 3915')