Good evening! Cloudy and 29 degrees in Delmar. ⛅ Breezy, ️17 mph breeze from the northwest 🌬 . The current wind chill is 17. There are 8 inches of snow on the ground. ☃ ️Things will start to thaw out at tomorrow around 9 am. 🌡️
I’m surprised as anyone that we got as much snow 🌨 as we did. It’s winter though but after being so mild and snow free since mid December it was kind of a shock 🤯 for a change. I thought it was going to be more of a rain event. At least I didn’t need my umbrella for the evening commute. I was fine leaving it home.
While there was some snow on the sidewalks and a few icy patches, they weren’t bad for the evening walk 🚶. Made pasta for dinner 🍲 but I enforced the one bowl, eat only until I’m full rule and save the rest in the refrigerator. I won’t call it a diet but like everything with inflation I’m trying to tighten my belt. I don’t want to be cheap and miserable but I do want to build a good future 🔮 for myself in all aspects of life. 40 is such a critical age, to really set yourself on the right step to the future.
Dug out my truck and cleaned off the solar panel ☀ so things will get charged up hopefully starting tomorrow and I’ll be ready the next time I need to drive somewhere like the dentist on Friday or maybe sooner if I need groceries especially if I decide to go camping 🏕 for my birthday. You never know it might be fun, especially if there is snow for skiing.
Tonight will have a Waxing Crescent 🌒 Moon with 7% illuminated. The Snow ❄ Moon is on Sunday, February 5. The darkest hour is at 12:08 am, followed by dawn at 6:48 am, and sun starting to rise at 7:19 am in the east-southeast (116°) and last for 3 minutes and 14 seconds. Sunrise is 44 seconds earlier than yesterday. 🌄 The golden hour ends at 8:02 am with sun in the east-southeast (124°).Tonight will have 14 hours and 16 minutes of darkness, a decrease of 2 minutes and 3 seconds over last night.
In four weeks on February 20 the sun will be setting in the west-southwest (256°) at 5:34 pm,🌄 which is 36 minutes and 22 seconds later then tonight. That’s it? Ugh. Although at least by then the evening commute won’t be so dark. In 2021 on that day, we had partly sunny skies and temperatures between 28 and 13 degrees. Typically, you have temperatures between 37 and 19 degrees. The record high of 66 degrees was set back in 1930.
Looking ahead, Save the Pine Bush Turns 45 🦋 Next Monday, Presidents Day 👴 is in 4 weeks, 8 PM Dusk 🌆 is in 11 weeks, May 🕊 is in 14 weeks, 8:30 PM Sunset ️⛱️ is in 19 weeks, Average High is 80 🏖 is in 21 weeks, World Mosquito Day 🐞 is in 30 weeks, Labor Day 👨🏭 is in 32 weeks, September 11th 🇺🇸 is in 33 weeks, 7 PM Sunset 🌆 is in 34 weeks, Average Night Below Freezing 🌌 is in 42 weeks, Thanksgiving 🦃 is in 10 months, Cyber Monday 🛍️ is in 44 weeks, First Sunday of Advent ✝️ is in 45 weeks, Bake Cookies Day 🍪 is in 47 weeks, Festivus 🍖 is in 11 months and Christmas 🎅 is in 48 weeks.
Public health officials in New York are planning an expansion of infectious disease monitoring in wastewater in order to detect more illnesses that may be otherwise quietly spreading through a community.
The state Department of Health on Monday announced its plan through $21.6 million in funding, including a $6.6 million grant from the U.S. Centers for Disease Control and Prevention.
Under a series of pilot programs, health officials will begin testing for Influenza A, RSV, Hepatitis A, Norovirus, and antimicrobial-resistant genes. The initial monitoring programs will begin in Erie, Onondaga, Jefferson and Westchester counties.
Here is how to a create a table of percent income using R and Census Public Use Microdata and the Qauntile function.
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.'))
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