Henryjean

library(tidyverse)
library(gt)

# create data
df <- data.frame(
              stringsAsFactors = FALSE,
                              player = c("Evan Mobley",
                                         "Sandro Mamukelashvili","Charles Bassey","Luka Garza",
                                         "Moses Wright","Neeimisa Queta",
                                         "Isaiah Jackson","Day'Ron Sharpe"),

Torvaney

#' Helper functions for styling with ggtext
#'
#' @param text The text which should be styled
#' @param ... Style options. For example `color="red"`
#' @param tag HTML tag to apply to the text. Defaults to `span`
#'
#' @export
tagged_text <- function(text, ..., tag = "span") {
  glue::glue("<{tag} style='{parse_style_options(...)}'>{text}</{tag}>")
}

OTStats

# Levante UD Rolling xG Plot
# Owen Thompson
# 2022-01-18

# -- Load libraries
library(tidyverse)
library(ggtext)

# -- Resources ----------------------------------.
# Adjust Transparency (alpha) of stat_smooth lines, not just transparency of Confidence Interval

BenjaminWolfe

# See `curl.md`. Here is the R equivalent.

library(httr)
library(jsonlite)

make_page <- function(url, headers, data) {
  response <- POST(
    url = url,
    body = toJSON(data, auto_unbox = TRUE),
    config = add_headers(.headers = headers)

andrewbtran

# code from https://github.com/gkaramanis/aRtist/blob/main/genuary/2021/2021-3/2021-3.R

library(ggplot2)
library(dplyr)
library(magick)
library(ggridges)

# Read in image and convert to grayscale
img <- image_read(here::here("MRlWzfMH_400x400.jpg")) %>%
  image_convert(colorspace = "gray")

favstats

library(tidyverse)

## this r code is taking from here: https://github.com/mkearney/pkguse
read_r_files <- function(...) {
    dirs <- unlist(list(...))
    r <- unlist(lapply(dirs, list.files,
                       pattern = "\\.(R|Rmd|Rmarkdown|rmd|r|Rhistory)$",
                       recursive = TRUE,
                       full.names = TRUE,
                       all.files = TRUE))

meghall06

# to create the plots in this Twitter thread: https://twitter.com/MeghanMHall/status/1560411406935138305

library(tidyverse)
library(ggrepel)
library(scales)

df1 <- txhousing %>%
  filter(city %in% c("Houston","Austin")) %>%
  group_by(year, city) %>%
  summarize(avg = mean(median, na.rm = TRUE)) %>%

jeroen

# https://twitter.com/henrikbengtsson/status/1583520708470067201

library(shiny)
library(sortable)
library(pdftools)
library(magick)

# The file to manipulate
pdffile <- tempfile(fileext = '.pdf')
download.file('https://www.uscis.gov/sites/default/files/document/forms/i-9-paper-version.pdf', pdffile)

koenvo

Cronjob and docker?

Some applications consist of multiple services. In those cases it can be useful to use a tool like docker-compose. With docker-compose you can define all your services within a single docker-compose yaml file, and add the file to your repository. This way you keep all services definition at a single place, with version control.

But what if you want to run a cronjob?

Cron on the host

One solution is to add a call to docker-compose to the crontab in the host system. In this example we would like to run a script called cleanup-old-files.py.

veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture