| # Calculate expected points for a FantasyPL draft, head-to-head league. | |
| # | |
| # Calculate expected points (xPts) and expected rank (xRank) for each | |
| # team in a head-to-head draft league as the average points (i.e. 0, 1, 3) | |
| # expected against all teams in that league each gameweek, not just the actual | |
| # opponent. | |
| # | |
| # @param leagueid: integer | |
| library(dplyr) |
Here's some fake data showing how shot logs might look for a soccer match.
library(tibble)
library(dplyr)
df <- tibble::tibble(
shot_id = seq.int(1, 8),
minute = c(7, 13, 25, 30, 41, 44, 58, 78), ## doesn't matter, just for illustrative purposes
| library(tidycensus) | |
| library(tidyverse) | |
| options(tigris_use_cache = TRUE) | |
| state_names <- c(state.name, "District of Columbia") | |
| names(state_names) <- state_names | |
| tictoc::tic() | |
| age_maps <- map(state_names, ~{ | |
| #=============================================================================== | |
| # 2022-12-25 -- ik-q | |
| # World cups -- was Qatar that exceptional? | |
| # Ilya Kashnitsky, [email protected], @ikashnitsky | |
| #=============================================================================== | |
| library(tidyverse) | |
| library(magrittr) | |
| library(wdman) | |
| library(RSelenium) |
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.
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?
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.
| # 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) |
| # 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)) %>% |
| 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)) |
| # 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") |
