Casey NewtonHappy 2023! You can close all your 2022 tabs now, it will be fine I promise
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| letterboxd | https://www.letterboxd.com/queermath |
renata g
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in my movies era, #rstats, gay, jewish. she/her
i finished my π year of #movies π with a lil blog post (an incredibly fun thing i will never do again) https://rgerecke.notion.site/the-year-in-movies-52ec6d18ba804b9796222ed164f690fb
the year in movies
if we had a conversation this year, it probably came up that I'm in my β¨movies eraβ¨. iβve watched a lot of movies! so obviously i have to write a year in review, which i will not publish until January so that it covers the true full spectrum of what i watched in 2022.
Steven Capsuto - Alt. ChannelsTODAY IN QUEER TV HISTORY
HOMOSEXUALS - 12/18/1979, ABC
A 1-hour prime-time ABC News documentary built around first-person narratives by a diverse range of gay women and men in different parts of the U.S.
Here, Gwendolyn Rogers tells how she felt the first time she entered a lesbian bar in 1969.
#LGBTQ #lesbian #gay #LesbianHistory #LGBTQHistory #queer #QueerHistory #MediaStudies
Clip: HOMOSEXUALS - 12/18/1979 - An ABC NEWS CLOSEUP documentary
TanHey friends, #AdventOfCode starts TONIGHT! I've organized a friendly leaderboard every year for the #rstats (and friends) community, and you can join 2022's with this code:
1032765-5d428d59
@emilhvitfeldt did a great talk on why AOC is so awesome: https://www.youtube.com/watch?v=HnHAIdqULd0
and you can see my take on the leaderboard here: https://rstats-aoc.netlify.app/
Emil Hvitfeldt - I Did 'Advent of Code' and Here Is What I Learned
where's my #spotifywrapped for #rstats package usage??
i got to give meaningful feedback on a #survey with good bones ... no better feeling!!! field good surveys, friends
was helping someone establish data management & a dashboard for department KPIs and they ghosted me for a month.......
............they have returned with a list of 107 indicators
Danielle NavarroMy favourite trick for working with huge data sets in R. If your dataset is larger than memory and the query result is also larger than memory, you can still use dplyr/arrow pipelines. Example:
library(arrow)
library(dplyr)
nyc_taxi <- open_dataset("nyc-taxi/")
nyc_taxi |>
filter(payment_type == "Credit card") |>
group_by(year, month) |>
write_dataset("nyc-taxi-credit")
Input is 1.7 billion rows (70GB), output is 500 million (15GB). Takes 3-4 mins on my laptop π
Allison McCartneyπππ πβπΆππ πππΆπΉπ π»ππ ππππ πΈπ½πΆπππ