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(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

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')

arvi1000

library(babynames)
library(tidyverse)

bab <- babynames %>% filter(sex=="F" & year >= 1945)

bab$rgx_match <- as.character(NA)
for(i in c('Amy', 'Beth', 'Meg', 'Jo')) {
  rgx <- paste0('^', i, '.*$')
  bab$rgx_match <- coalesce(
    ifelse(grepl(rgx, bab$name), rgx, NA),

arvi1000

library(data.table)
library(tidyverse)
library(ggrepel)

"Model, reach, drop, grams
Zipp sl88, 88, 130, 275
Fizik r3 snake, 85, 140, 245
RH Maes P, 115, 125, 298
VO course, 115, 125, 395
Ritchey NeoClassic,  73, 128, 260