README.md

In tweed1e/canmap: Standard Canadian geography shapefiles

canmap

canmap provides easy access to standard Canadian geographic shapefiles, as well as the associated metadata that helps pick which one you’d like. It’s named after mapbox, but instead of a box it’s a can. Except mapcan was already taken on CRAN.

Installation

You can install the external development version of canmap with:

remotes::install_github("twedl/canmap")

And you can install the internal development version of canmap with:

remotes::install_gitlab("tweejes/canmap", host = "gitlab.statcan.ca")

Example

library(canmap)

default_shp <- dplyr::filter(shapefile_paths,
                  geo_code == "pr_" &
                  file_type == "digital boundary file" &
                  format == "ArcGIS (.shp)" &
                  language == "english"  &
                  ref_date == 2016 &
                  projection == "projection in Lambert conformal conic"
                )

default_shp
#> # A tibble: 1 x 10
#>   filepath size  path  ref_date geo_code geo_level file_type format projection
#>   <chr>    <chr> <chr>    <dbl> <chr>    <chr>     <chr>     <chr>  <chr>     
#> 1 lpr_000… 1.31M http…     2016 pr_      province… digital … ArcGI… projectio…
#> # … with 1 more variable: language <chr>

# then pick a shapefile and get the link:
(url <- default_shp[1, ]$path)
#> [1] "http://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/files-fichiers/2016/lpr_000a16a_e.zip"

# then you can download it yourself, or use download_geography(url)
shp_path <- download_geography(url)
#> lpr_000a16a_e.zip already downloaded, returning filepath to unzipped .shp.

# and use sf to read the shapefile.
provinces <- sf::read_sf(shp_path)

Then input into ggplot + sf:

library(ggplot2)

ggplot() +
  geom_sf(data = provinces) +
  theme_minimal() +
  labs(title = "Provinces & Territories / Digital Boundary File", 
       x = "Longitude", y = "Latitude",
       caption = paste0(".zip source name: ", default_shp$filepath, ".zip"))

Documentation

A list of useful links to clean up later:

• Geography definitions and documentation
• Root for geography downloads and documentation
• Direct link to download directory for 2016 shapefiles
• Heirarchy of geography

Parsing the filename for metadata

Suppose you’ve downloaded the geography file lpr_000a16a_e.zip. The filename defines the important geographic characteristics of the file (you can process using the code_pos dataset for code positions, or geo_info).

str(geo_info("lpr_000a16a_e"))
#> Classes 'tbl_df', 'tbl' and 'data.frame':    1 obs. of  8 variables:
#>  $ filename  : chr "lpr_000a16a_e"
#>  $ ref_date  : chr "2016"
#>  $ geo_code  : chr "pr_"
#>  $ geo_level : chr "province and territory"
#>  $ file_type : chr "digital boundary file"
#>  $ format    : chr "ArcGIS (.shp)"
#>  $ projection: chr "projection in Lambert conformal conic"
#>  $ language  : chr "english"

And each of these codes has a meaning that can be found (sometimes) in the geography guide that accompanies a downloaded file (but you can’t find out the details until after you’ve downloaded it, and is missing some information).

Your first default parameters should be:

• file_type == "a" (digital boundary file—it doesn’t look as good but it’s smaller)
• format == "a" (ArcGIS/ArcInfo®/.shp—for use with sf and other R geographic packages)
• geo_coverage == "000" (Canada—the only option AFAIK)
• projection == "g" (geographic projection/lat-long—this makes it less likely for the user to get caught up in coordinate reference systems [CRS] conversion issues)

The most important choices for the user are: year (2016 is the latest census year currently available), language (english or french) and geo_code/geo_level. A list of geo codes and geo levels are given in the code_book dataset:

dplyr::filter(code_book, code_type == "geo_level")
#> # A tibble: 34 x 3
#>    code_type code  code_desc                                        
#>    <chr>     <chr> <chr>                                            
#>  1 geo_level pr_   province and territory                           
#>  2 geo_level cd_   census division                                  
#>  3 geo_level ccs   census consolidated subdivision                  
#>  4 geo_level csd   census subdivision                               
#>  5 geo_level er_   economic region                                  
#>  6 geo_level cma   census metropolitan area and census agglomeration
#>  7 geo_level fed   federal electoral district                       
#>  8 geo_level ct_   census tract                                     
#>  9 geo_level dpl   designated place                                 
#> 10 geo_level pc_   population centre                                
#> # … with 24 more rows

Business data usually isn’t released below the economic region level (er_), while census data can go down to census tract (ct_), dissemination area (da_) or dissemination block (db_).

English and french maps are in different files, so they have different codes: lpr_000a16a_e.zip has the english province/territory maps and lpr_000a16a_f.zip has the french province/territory maps. The only difference, AFAIK, is that the guide and geography names are in french in the french version.

Notes & Other Packages

There are some other great packages to make Canadian maps!

• cancensus (R package available from CRAN + github)
• censusmapper.ca (from the same ppl as ^, but a website)
• rcanvec (R package to get v cool NTS maps, good for small scales)

And others probably! openstreetmap and osmdata and leaflet are all useful as well.

Please note that the ‘canmap’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

tweed1e/canmap documentation built on Dec. 21, 2024, 3:19 p.m.