The councilverse is a set of packages that work in tandem to assist
the NYCC’s data team in doing their work. This package is designed to
make it easy to install and load multiple ‘councilverse’ packages in a
single step.
You can install the released version of councilverse from GitHub
remotes::install_github("newyorkcitycouncil/councilverse")Note that the order of loading the libraries is important. Make sure to
load councilverse last.
library(tidyverse)
# load last
library(councilverse)For a demo of the 2 largest packages (councildown and councilcount)
available in councilverse, see vignettes/councilverse.Rmd
First load the councilverse package as above.
The councildown package implements style guide compliant defaults for
R Markdown documents, ggplot2 plots, and leaflet maps. For more
information, please visit the package directly:
https://github.com/NewYorkCityCouncil/councildown/ . The
councilverse vignette goes through an example using most of
councildown’s available functions.
The councilcount package allows easy access to ACS population data
across various geographic boundaries. For the boundaries that are not
native to the ACS, such as council districts, an estimate is provided.
For more information, please visit the package directly:
https://github.com/NewYorkCityCouncil/councilcount/ . The
councilverse vignette goes through an example using most of
councilcount’s available functions.
The following functions are standalone functions in councilverse used
in more niche situations.
encodeGraphic() helps convert images to raw HTML. This is useful for
adding extra graphics to any HTML element in visualizations.
gt_table() similarly converts gt outputs to raw HTML.
An example of both these functions being used can be found in the NYCHA Scrape repo: https://newyorkcitycouncil.github.io/NYCHA_Scrape/visualization/heat_outage_scatter.html
file_name_generator() creates file names using accepted guidelines
detailed in the function’s documentation.
# All fields used
file_name_generator(order = "01", description = "acs_poverty", time_granularity = "daily", disaggregation_categories = c("cd", "race"), date_year = 2018, file_extension = ".R",... = "example")
#> [1] "01_acs-poverty_daily_by-cd-race_2018_example.R"unzip_sf() loads in shapefiles that come originally as zip files.
url <- "https://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nyct2020_22a.zip"
# unzip the zip file
zip <- unzip_sf(url)
# assign to variable with read_csv or other read functions
sf <- sf::read_sf(zip)