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image: rocker/tidyverse:latest

pipelines:
  default:
    - step:
        script:
          - cd /opt/atlassian/pipelines/agent/build
          - Rscript -e 'devtools::install_deps(".", dependencies=TRUE, upgrade=FALSE)'
          - Rscript -e 'devtools::build()'
          - Rscript -e 'devtools::install(upgrade_dependencies = FALSE)'

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# get all people you follow on Mastodon and which lists you've assigned them to. Inspired by Jon Udell doing this with Steampipe, his blog post: https://blog.jonudell.net/2022/12/22/lists-and-people-on-mastodon/

library(rtoot) # need the dev version, and you need to have gotten and stored a user token
library(dplyr) 
library(purrr)
library(magrittr)
library(glue)

# If you don't know your Mastodon account ID
my_display_name <- "YOUR DISPLAY NAME HERE" 

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get_forecast_data <- function(city, forecast_url) {
  req<-httr::GET(forecast_url)
  json <- httr::content(req, as = "text")
  weather_data <- jsonlite::fromJSON(json)
  forecast_df <- weather_data$properties$periods
  forecast_df <- forecast_df %>%
    dplyr::mutate(
      City = city,
      Temp = if_else(isDaytime == TRUE, "High", "Low"),
      Date = as.Date(substr(startTime,1,10)),

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# Function to set all vector items to blank except for every nth item

everyn <- function(myvec, n){
  myvec <- sort(unique(myvec))
  for(i in 1:length(myvec)) {
    if( i %% n != 1) {
      myvec[i] <- ""
    }
  }
  return(myvec)

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library(rvest)
library(rio)
library(dplyr)
library(xml2)

# If your spreadsheet is named "data.xlsx" and the column with submitter names is named "submitting_lab"
data <- rio::import("data.xlsx") %>%
  dplyr::pull(submitting_lab) %>%
  unique()

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snippet mygis_dt_merge_districts_2_precincts
	${1:my_geography} <- as.data.table(${1:my_geography})
	${2:my_new_geography} <- fram2[, .(geometry = st_union(geometry)), by = ${3:larger_district}]
	${2:my_new_geography} <- sf::st_sf(${2:my_new_geography})
	# test plot
	# ggplot(${2:my_new_geography}, aes(geometry=geometry)) +
	# geom_sf()

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#' Description
#' This file runs a live election-night forecast based on The Economist's pre-election forecasting model
#' available at projects.economist.com/us-2020-forecast/president.
#' It is resampling model based on https://pkremp.github.io/update_prob.html.
#' This script does not input any real election results! You will have to enter your picks/constraints manually (scroll to the bottom of the script).
#' 
#' Licence
#' This software is published by *[The Economist](https://www.economist.com)* under the [MIT licence](https://opensource.org/licenses/MIT). The data generated by *The Economist* are available under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
#' The licences include only the data and the software authored by *The Economist*, and do not cover any *Economist* content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of *Economist* content can be fou

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library(reactable)
library(dplyr)

red_pal <- function(x) rgb(colorRamp(c("#FFCDD2FF", "#C62828FF"))(x), maxColorValue = 255)
blue_pal <- function(x) rgb(colorRamp(c("#BBDEFBFF", "#1565C0FF"))(x), maxColorValue = 255)

mtcars %>%
  select(cyl, mpg) %>%
  reactable(
    pagination = FALSE,