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# canadata

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The goal of canadata ("Canada" + "data") is to provide standardized Canadian spatial datasets for use in R. 

The majority of data comes effectively unaltered from open data sources such as OpenStreetMap (thanks to {osmdata}) and Canada's Federal OpenData platform. Where possible, links to related datasets will be added (such as what Province/Territory, or forecast zone a community is in).

All polygon layers have been smoothed as minimally as possible for file size requirements while trying to maintain spatial accuracy. As a result, more complex boundaries may be smoother than in reality causing potential edge case issues. Please [submit and issue](https://github.com/B-Nilson/canadata/issues/new) if you encounter a problem that could be resolved be less smoothing.

All point layers have been saved as non-sf objects (lat/lng columns instead of a POINT geometry), and require converting to sf before performing spatial operations (see #converting-point-layers)


## Installation

You can install the development version of canadata like so:

``` r
# install.packages("pak")
pak::pak("B-Nilson/canadata")
```

## Datasets

The primary datasets made available are:

```{r datasets}
library(canadata)

provinces_and_territories |> print()
forecast_zones |> print()
indigenous_lands |> print()
communities |> print()
indigenous_communities |> print()
gridded_2016_population |> print()
```

## Converting Point Layers

All point layers have been saved as non-sf objects (lat/lng columns instead of a POINT geometry) with a CRS of `"WGS84"`, and require converting to sf before performing spatial operations:

```{r convert_points}
library(canadata)

communities |> 
  sf::st_as_sf(coords = c("lng", "lat"), crs = "WGS84") |>
  print()

indigenous_communities |> 
  sf::st_as_sf(coords = c("lng", "lat"), crs = "WGS84") |>
  print()


gridded_2016_population |> 
  sf::st_as_sf(coords = c("lng", "lat"), crs = "WGS84") |>
  print()

```