library(tidycensus)
library(tidyverse)
library(hutils)
library(gt)

# Grab Public Use Microdata for Albany County, with 
# PINCP = Total Personal Income see View(pums_variables) 
# puma areas can be found with tigris and mapview for an interactive
# map of the puma areas: mapview::mapview(tigris::pumas('ny'))
aci <- get_pums(variables = 'PINCP', state = "NY",puma = c('02001','02002'))

# next use hutil's weight2rows to expand out the dataframe so that 
# eached weighted observation has one row per observation
# then filter to only include persons with salary income, > $2
# extract out only the .$PINCP variable, then calculate quantile for
# 0, 5, 10, ... 90, 95, 99 percent
aci %>% 
  weight2rows('PWGTP') %>% 
  filter(PINCP > 2) %>%
  .$PINCP %>%
  quantile(c(seq(0,0.95,0.05),.99)) -> y

# create a tibble with the quantile ranges 
# (what percentage am i)
# and calculated quantile values (how much income)
df2 <- tibble(
  x=100-c(seq(0,0.95,0.05),.99)*100, 
  y=y
)

# Zero out the 100% value, to overcome 
# how the Census stores $1, $2, etc.
df2[1,2] <- 0

# Create the table with gt
df2 %>%
  select( Salary = y, `Top Percent` = x) %>%
  gt() %>%
  fmt_currency(1, decimals = 0) %>%
  fmt_percent(2, scale_values = F, decimals = 1) %>%
  opt_stylize() %>%
  opt_css('body { text-align: center} ') %>%
  cols_align('center') %>%
  tab_header('What percent is a salary in Albany County?',
             'Includes only persons with a yearly income of more then $1 a year.') %>%
  tab_footnote(html('Andy Arthur, 1/22/23.<br /><em>Data Source:</em> 2016-2020 Public Use Microdata, <br />Total person\'s income,  NY PUMA 02001 and 02002.'))