Maëlle Salmon maelle

Currently, only data within the last 90 days is available via the OpenAQ API. However, there is much more data available on OpenAQ and a variety of different access mechanisms. Note also that there is work under way to bring back to the API a mechanism to access the data older than 90 days, details here.

If you're looking to query across all the data or even easily export the data (or a subset of it), the easiest way to do that currently is using a service like Amazon Athena. I'll provide some directions on how to do that below, but at a high level, this will let you make any query of the entire dataset that you'd like (written in SQL). I'll also provide some sample queries so you can see what's possible.

On to the directions!

You will need to create an AWS account if you don't currently have one, you can start this process at htt

Have a Code of Conduct and a demonstrable commitment to diversity
- The Code of Conduct must be prominently displayed
- Sponsors and other third parties must adhere to the Code of Conduct
Provide a mechanism for low income attendees to get reduced prices and support where the event charges more than a day's income
Attendees encounter only non-defaulted opt-ins to marketing and future contact, especially from third parties
The data entrusted to the event is handled with due care and consideration
Diverse attendees' needs are considered and taken into consideration. Things like (but not limited to) large print agendas, gender neutral bathrooms, quiet rooms, family rooms, and prayer rooms are implemented to ensure a pleasant experience for all attendee
Volunteers and organisers should receive reduced or free entry to the event
New speakers are encouraged and offered extra support
The speaker selection process is performed in a way that reduces possible sources of bias

cran_db <- tools::CRAN_package_db()

plot(table(cran_db$License))

cran_db_t <- tibble::tibble(license = cran_db$License)

Eurostat, World Bank and others: https://ikashnitsky.github.io/2017/data-acquisition-two/
Star Wars data: in dplyr, http://dplyr.tidyverse.org/reference/starwars.html
Baby names data: babynames package, https://cran.r-project.org/web/packages/babynames/index.html
Movies data: https://cran.r-project.org/web/packages/ggplot2movies/index.html ggplot2movies package
Game of Thrones screen time: https://github.com/Preetish/GoT_screen_time
Open Bike Data: https://github.com/ropensci/bikedata
Tons of data through 538: https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html
Public health data England: fingertipsR, https://cran.r-project.org/web/packages/fingertipsR/
Financial data via Quandl: https://www.quandl.com/tools/r
Cyclones: https://github.com/ropensci/rrricanesdat

	#' libraries needed
	library(tidyverse)
	library(jsonlite)
	library(tidytext)
	library(quanteda)
	#' import zenodo https://zenodo.org/record/1065507#.WsNkb9NubFw
	zenodo <-
	readr::read_tsv("data/zenodo-all-metadata-records_2017-01-17.tsv") %>%
	dplyr::select(title, doi)
	# import figshare https://figshare.com/articles/Figshare_Public_Metadata_until_02_12_2014/1320834

	library(tidyverse)
	library(gh)
	library(lubridate)

	issues <- gh("/repos/ropensci/onboarding/issues?state=all&labels=package", .limit=1000)

	edits = map_df(issues,
	~data_frame(url = .$html_url,
	editor = .$assignee$login %\|\|% NA_character_,
	opened = as.Date(.$created_at))) %>%

	library(desc)
	library(httr)
	library(purrr)
	library(stringi)
	library(methods)
	library(dplyr)
	library(tidyr)
	pkgs <- rownames(available.packages())

	if(!dir.exists("DESCRIPTIONS")) dir.create("DESCRIPTIONS")

	library(magick)
	library(reshape2)
	library(dplyr)
	library(tidygraph)
	library(particles)
	library(animation)

	plot_fun <- function(sim) {
	df <- as_tibble(sim)
	plot(df$x, df$y, col = df$color, pch = '.', axes = FALSE, xlim = c(-100, 317), ylim = c(-268, 100), xlab = NA, ylab = NA)

	## GOAL:
	## re-create a figure similar to Fig. 2 in Wilson et al. (2018),
	## Nature 554: 183-188. Available from:
	## https://www.nature.com/articles/nature25479#s1
	##
	## combines a boxplot (or violin) with the raw data, by splitting each
	## category location in two (box on left, raw data on right)


	# initial set-up ----------------------------------------------------------

	library(V8)
	library(xml2)
	library(httr)
	library(rvest)
	library(stringi)
	library(tidyverse)

	get_page <- function(num=1, seed=Sys.Date()) {

	GET(