- Part I: {ggnorthcarolina}
- Part II: Building functionality
- Step 0. Prepare reference datasets.
- Write functions w/ ‘recipe’ substeps: 1. write compute function; 2. define ggproto; 3. write geom_*; 4. test.
traditional readme, but at first aspirational)
ggnorthcarolina allows you to create informative county maps straight from a flat tabular data file. i.e. a file that has the county id in column as well as characteristics about the counties to be represented by fill color for example.
If you would like to use ggnorthcarolina as a template, click “Use this template” ->
You can install the development version of ggnorthcarolina from GitHub with:
# install.packages("devtools")
devtools::install_github("EvaMaeRey/ggnorthcarolina")North Carolina county characteristics data, and wanting to map that data, but not having experience with boundary files or just not wanting to think about joins to use a geom_sf() layer.
library(ggplot2)
library(ggnorthcarolina)
northcarolina_county_flat |>
ggplot() +
aes(fips = fips) +
geom_county()
last_plot() +
aes(fill = BIR74)
#library(patchwork)
# A + BAnd here is the input dataset, which is indeed just a tabular, flat dataset. It has no boundary information
northcarolina_county_flat %>% head()By declaring the aesthetic fips, the geom_ function joins the flat file to boundary data and an SF layer is plotted.
Furthermore, we also make labeling these polygons easy:
northcarolina_county_flat %>%
ggplot() +
aes(fips = fips,
fill = SID74,
label = paste0(county_name, "\n", SID74)) +
geom_county() +
geom_county_label()
ggwipe::last_plot_wipe_last() +
geom_county_label(
lineheight = .8,
size = 2,
check_overlap = TRUE,
color = "oldlace")The second-order goal of ggnorthcarolina is to serve as a model and template for other ggplot-based geography-specific convenience mapping packages. Because of this, and because the package is generally new and could use other sets of eyes on pretty much every decision, I’m using a literate programming paradigm to write a narrative for this package.
A prerequisite to embarking on the following journey is that you have geographic data that you’d like to connect up to a flat file for mapping within ggplot2. In our case, for convenience, we use nc.shp provided in the sf package. You’ll see that file read in as an sf object later with the following code.
Such a shape file can live in the data-raw folder, or can be accessed from another package:
st_read(system.file("shape/nc.shp", package="sf"))
This doesn’t show all the pain that you will actually be in if you want to create a choropleth. Because we are working with an object that already has geometries as a list-column. If you were working with a flat file (which is the imagined )
library(tidyverse)
sf::st_read(system.file("shape/nc.shp", package="sf")) |>
dplyr::select(FIPS, NAME, geometry) ->
id_and_boundaries
#> Reading layer `nc' from data source
#> `/Library/Frameworks/R.framework/Versions/4.2/Resources/library/sf/shape/nc.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
ggnc::nc_flat |>
dplyr::left_join(id_and_boundaries, by = "FIPS") |>
ggplot() +
geom_sf(aes(geometry = geometry)) + # why am I doing aes here. Surprisingly this didn't work
aes(fill = 1000* SID74 / BIR74)
The functions that you create in the R folder will use data that is
prepared in the ./data-raw/DATASET.R file. Let’s have a look at the
contents of that file to get a sense of the preparation. Functions in
the {ggnc} package will help you prepare the reference data that is
required. Keep an eye out for ggnc::create_geometries_reference() and
ggnc::prepare_polygon_labeling_data().
ggnc is available on git hub as shown:
remotes::install_github("EvaMaeRey/ggnc")## code to prepare `DATASET` dataset goes here
###### 00. Read in boundaries shape file data ###########
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
northcarolina_county_sf <- st_read(system.file("shape/nc.shp", package="sf")) |>
dplyr::rename(county_name = NAME,
fips = FIPS)
#> Reading layer `nc' from data source
#> `/Library/Frameworks/R.framework/Versions/4.2/Resources/library/sf/shape/nc.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS: NAD27
####### 0. create and save flat file for examples, if desired ####
northcarolina_county_sf %>%
sf::st_drop_geometry() ->
northcarolina_county_flat
usethis::use_data(northcarolina_county_flat, overwrite = TRUE)
#> ✔ Setting active project to '/Users/evangelinereynolds/Google
#> Drive/r_packages/ggnorthcarolina'
#> ✔ Saving 'northcarolina_county_flat' to 'data/northcarolina_county_flat.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')
#### 1, create boundaries reference dataframe w xmin, ymin, xmax and ymax and save
northcarolina_county_geo_reference <- northcarolina_county_sf |>
ggnc::create_geometries_reference(
id_cols = c(county_name, fips))
usethis::use_data(northcarolina_county_geo_reference, overwrite = TRUE)
#> ✔ Saving 'northcarolina_county_geo_reference' to 'data/northcarolina_county_geo_reference.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')
############### 2. create polygon centers and labels reference data frame
# county centers for labeling polygons
northcarolina_county_centers <- northcarolina_county_sf |>
ggnc::prepare_polygon_labeling_data(id_cols = c(county_name, fips))
#> Warning in st_point_on_surface.sfc(sf::st_zm(dplyr::pull(data_sf, geometry))):
#> st_point_on_surface may not give correct results for longitude/latitude data
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
#> `.name_repair` is omitted as of tibble 2.0.0.
#> ℹ Using compatibility `.name_repair`.
#> ℹ The deprecated feature was likely used in the ggnc package.
#> Please report the issue to the authors.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
usethis::use_data(northcarolina_county_centers, overwrite = TRUE)
#> ✔ Saving 'northcarolina_county_centers' to 'data/northcarolina_county_centers.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')
####### 3. create line data
tigris::primary_secondary_roads("NC") -> northcarolina_roads
#> Retrieving data for the year 2021
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usethis::use_data(northcarolina_roads, overwrite = TRUE)
#> ✔ Saving 'northcarolina_roads' to 'data/northcarolina_roads.rda'
#> • Document your data (see 'https://r-pkgs.org/data.html')Here are a few rows of each dataset that’s created
northcarolina_county_flat %>% head()
#> AREA PERIMETER CNTY_ CNTY_ID county_name fips FIPSNO CRESS_ID BIR74 SID74
#> 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091 1
#> 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487 0
#> 3 0.143 1.630 1828 1828 Surry 37171 37171 86 3188 5
#> 4 0.070 2.968 1831 1831 Currituck 37053 37053 27 508 1
#> 5 0.153 2.206 1832 1832 Northampton 37131 37131 66 1421 9
#> 6 0.097 1.670 1833 1833 Hertford 37091 37091 46 1452 7
#> NWBIR74 BIR79 SID79 NWBIR79
#> 1 10 1364 0 19
#> 2 10 542 3 12
#> 3 208 3616 6 260
#> 4 123 830 2 145
#> 5 1066 1606 3 1197
#> 6 954 1838 5 1237
northcarolina_county_geo_reference %>% head()
#> county_name fips xmin ymin xmax ymax
#> 1 Ashe 37009 -81.74107 36.23436 -81.23989 36.58965
#> 2 Alleghany 37005 -81.34754 36.36536 -80.90344 36.57286
#> 3 Surry 37171 -80.96577 36.23388 -80.43531 36.56521
#> 4 Currituck 37053 -76.33025 36.07282 -75.77316 36.55716
#> 5 Northampton 37131 -77.90121 36.16277 -77.07531 36.55629
#> 6 Hertford 37091 -77.21767 36.23024 -76.70750 36.55629
#> geometry
#> 1 MULTIPOLYGON (((-81.47276 3...
#> 2 MULTIPOLYGON (((-81.23989 3...
#> 3 MULTIPOLYGON (((-80.45634 3...
#> 4 MULTIPOLYGON (((-76.00897 3...
#> 5 MULTIPOLYGON (((-77.21767 3...
#> 6 MULTIPOLYGON (((-76.74506 3...
northcarolina_county_centers %>% head()
#> x y county_name fips
#> 1 -81.49496 36.42112 Ashe 37009
#> 2 -81.13241 36.47396 Alleghany 37005
#> 3 -80.69280 36.38828 Surry 37171
#> 4 -75.93852 36.30697 Currituck 37053
#> 5 -77.36988 36.35211 Northampton 37131
#> 6 -77.04217 36.39709 Hertford 37091Now you’ll also want to document that data. Minimal documentation is just to quote the object that should be included in your package.
But northcarolina_county_sf has template text to show you how to
document this more correctly (I haven’t change out the WHO example I got
elsewhere.)
#' World Health Organization TB data
#'
#' A subset of data from the World Health Organization Global Tuberculosis
#' Report ...
#'
#' @format ## `who`
#' A data frame with 7,240 rows and 60 columns:
#' \describe{
#' \item{country}{Country name}
#' \item{iso2, iso3}{2 & 3 letter ISO country codes}
#' \item{year}{Year}
#' ...
#' }
#' @source <https://www.who.int/teams/global-tuberculosis-programme/data>
"northcarolina_county_sf"
#> [1] "northcarolina_county_sf"
#' World Health Organization TB data
#'
#' A subset of data from the World Health Organization Global Tuberculosis
#' Report ...
#'
#' @format ## `who`
#' A data frame with 7,240 rows and 60 columns:
#' \describe{
#' \item{country}{Country name}
#' \item{iso2, iso3}{2 & 3 letter ISO country codes}
#' \item{year}{Year}
#' ...
#' }
#' @source <https://www.who.int/teams/global-tuberculosis-programme/data>
"northcarolina_county_flat"
#> [1] "northcarolina_county_flat"
#' World Health Organization TB data
#'
#' A subset of data from the World Health Organization Global Tuberculosis
#' Report ...
#'
#' @format ## `who`
#' A data frame with 7,240 rows and 60 columns:
#' \describe{
#' \item{country}{Country name}
#' \item{iso2, iso3}{2 & 3 letter ISO country codes}
#' \item{year}{Year}
#' ...
#' }
#' @source <https://www.who.int/teams/global-tuberculosis-programme/data>
"northcarolina_county_centers"
#> [1] "northcarolina_county_centers"
#' World Health Organization TB data
#'
#' A subset of data from the World Health Organization Global Tuberculosis
#' Report ...
#'
#' @format ## `who`
#' A data frame with 7,240 rows and 60 columns:
#' \describe{
#' \item{country}{Country name}
#' \item{iso2, iso3}{2 & 3 letter ISO country codes}
#' \item{year}{Year}
#' ...
#' }
#' @source <https://www.who.int/teams/global-tuberculosis-programme/data>
"northcarolina_county_geo_reference"
#> [1] "northcarolina_county_geo_reference"
#' World Health Organization TB data
#'
#' A subset of data from the World Health Organization Global Tuberculosis
#' Report ...
#'
#' @format ## `who`
#' A data frame with 7,240 rows and 60 columns:
#' \describe{
#' \item{country}{Country name}
#' \item{iso2, iso3}{2 & 3 letter ISO country codes}
#' \item{year}{Year}
#' ...
#' }
#' @source <https://www.who.int/teams/global-tuberculosis-programme/data>
"northcarolina_roads"
#> [1] "northcarolina_roads"Write functions w/ ‘recipe’ substeps: 1. write compute function; 2. define ggproto; 3. write geom_*; 4. test.
################# Step 1. Compute panel function ###########
#' Title
#'
#' @param data
#' @param scales
#' @param keep_county
#'
#' @return
#' @export
#'
#' @examples
#' library(dplyr)
#' #northcarolina_county_flat |> rename(fips = FIPS) |> compute_county_northcarolina() |> head()
#' #northcarolina_county_flat |> rename(fips = FIPS) |> compute_county_northcarolina(keep_county = "Ashe")
compute_county_northcarolina <- function(data, scales, keep_county = NULL, drop_county = NULL){
reference_filtered <- northcarolina_county_geo_reference
#
if(!is.null(keep_county)){
keep_county %>% tolower() -> keep_county
reference_filtered %>%
dplyr::filter(.data$county_name %>%
tolower() %in%
keep_county) ->
reference_filtered
}
if(!is.null(drop_county)){
drop_county %>% tolower() -> drop_county
reference_filtered %>%
dplyr::filter(!(.data$county_name %>%
tolower() %in%
drop_county)) ->
reference_filtered
}
#
# # to prevent overjoining
# reference_filtered %>%
# dplyr::select("fips", # id columns
# "geometry",
# "xmin","xmax",
# "ymin", "ymax") ->
# reference_filtered
data %>%
dplyr::inner_join(reference_filtered) #%>% # , by = join_by(fips)
# dplyr::mutate(group = -1) %>%
# dplyr::select(-fips) #%>%
# sf::st_as_sf() %>%
# sf::st_transform(crs = 5070)
}
###### Step 2. Specify ggproto ###############
StatCountynorthcarolina <- ggplot2::ggproto(
`_class` = "StatCountynorthcarolina",
`_inherit` = ggplot2::Stat,
compute_panel = compute_county_northcarolina,
default_aes = ggplot2::aes(geometry = ggplot2::after_stat(geometry)))
########### Step 3. geom function, inherits from sf ##################
#' Title
#'
#' @param mapping
#' @param data
#' @param position
#' @param na.rm
#' @param show.legend
#' @param inherit.aes
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
geom_county <- function(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
crs = "NAD27", # "NAD27", 5070, "WGS84", "NAD83", 4326 , 3857
...) {
c(ggplot2::layer_sf(
stat = StatCountynorthcarolina, # proto object from step 2
geom = ggplot2::GeomSf, # inherit other behavior
data = data,
mapping = mapping,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = rlang::list2(na.rm = na.rm, ...)),
coord_sf(crs = crs,
default_crs = sf::st_crs(crs),
datum = crs,
default = TRUE)
)
}library(ggplot2)
northcarolina_county_flat %>%
ggplot() +
aes(fips = fips) +
geom_county()
last_plot()-> p
p$coordinates$crs <- 4326
p################# Step 1. Compute panel function ###########
#' Title
#'
#' @param data
#' @param scales
#' @param keep_county
#'
#' @return
#' @export
#'
#' @examples
compute_panel_county_centers <- function(data,
scales,
keep_county = NULL){
centers_filtered <- northcarolina_county_centers
if(!is.null(keep_county)){
keep_county %>% tolower() -> keep_county
centers_filtered %>%
dplyr::filter(.data$county_name %>%
tolower() %in%
keep_county) ->
centers_filtered}
data %>%
dplyr::inner_join(centers_filtered) %>%
dplyr::select(x, y, label)
}
###### Step 2. Specify ggproto ###############
StatCountycenters <- ggplot2::ggproto(
`_class` = "StatCountycenters",
`_inherit` = ggplot2::Stat,
# required_aes = c("label"), # for some reason this breaks things... why?
compute_panel = compute_panel_county_centers
)
########### Step 3. 'stamp' function, inherits from sf ##################
#' Title
#'
#' @param mapping
#' @param data
#' @param position
#' @param na.rm
#' @param show.legend
#' @param inherit.aes
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
geom_county_label <- function(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE, ...) {
ggplot2::layer(
stat = StatCountycenters, # proto object from Step 2
geom = ggplot2::GeomText, # inherit other behavior
data = data,
mapping = mapping,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}northcarolina_county_flat |>
dplyr::rename(fips = fips) |>
dplyr::rename(label = county_name) |>
compute_panel_county_centers() |>
head()library(ggplot2)
northcarolina_county_flat %>%
ggplot() +
aes(fips = fips, label = county_name) +
geom_county_label()
#> Joining with `by = join_by(fips)`
northcarolina_county_flat %>%
ggplot() +
aes(fips = fips, label = county_name) +
geom_county() +
geom_county_label()
#> Joining with `by = join_by(fips)`
#> Joining with `by = join_by(fips)`
northcarolina_county_flat %>%
ggplot() +
aes(fips = fips, label = SID74, fill = SID74) +
geom_county() +
geom_county_label(color = "oldlace")
#> Joining with `by = join_by(fips)`
#> Joining with `by = join_by(fips)`
northcarolina_county_flat %>%
ggplot() +
aes(fips = fips, fill = SID74,
label = paste0(county_name, "\n", SID74)) +
geom_county() +
geom_county_label(lineheight = .7,
size = 2, check_overlap= TRUE,
color = "oldlace")
#> Joining with `by = join_by(fips)`
#> Joining with `by = join_by(fips)`
#' Title
#'
#' @param data
#' @param ...
#'
#' @return
#' @export
#'
#' @examples
stamp_roads <- function(data = northcarolina_roads, fill = NULL, fips = NULL, ...){
geom_sf(data = data, aes(fill = fill, fips = fips), ...)
}
# # have to combine... ugh...
# tigris::area_water("NC", county = 'Alleghany')
# nc_waterggplot() +
stamp_roads()
#> Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
#> : Ignoring unknown aesthetics: fips
# overthinking it below.
stamp_county <- function(data = northcarolina_county_geo_reference, fill = NULL, fips = NULL, keep_county = NULL, drop_county = NULL){
if(!is.null(keep_county)){
keep_county %>% tolower() -> keep_county
data %>%
dplyr::filter(.data$county_name %>%
tolower() %in%
keep_county) ->
data
}
if(!is.null(drop_county)){
drop_county %>% tolower() -> drop_county
data %>%
dplyr::filter(!(.data$county_name %>%
tolower() %in%
drop_county)) ->
data
}
geom_sf(data = data,
aes(geometry = geometry, fill = fill, fips = fips))
}
# ################# Step 1. Compute panel function ###########
#
# #' Title
# #'
# #' @param data
# #' @param scales
# #' @param county
# #'
# #' @return
# #' @export
# #'
# #' @examples
# #' library(dplyr)
# #' #northcarolina_county_flat |> rename(fips = FIPS) |> compute_county_northcarolina() |> head() |> str()
# #' #northcarolina_county_flat |> rename(fips = FIPS) |> compute_county_northcarolina(keep_county = "Ashe")
# compute_county_northcarolina_stamp <- function(data, scales, keep_county = NULL){
#
# reference_filtered <- northcarolina_county_geo_reference
# #
# if(!is.null(keep_county)){
#
# keep_county %>% tolower() -> keep_county
#
# reference_filtered %>%
# dplyr::filter(.data$county_name %>%
# tolower() %in%
# keep_county) ->
# reference_filtered
#
# }
#
# reference_filtered %>%
# dplyr::select("fips", "geometry", "xmin",
# "xmax", "ymin", "ymax") ->
# reference_filtered
#
#
# reference_filtered %>%
# dplyr::mutate(group = -1) %>%
# dplyr::select(-fips)
#
# }
#
# ###### Step 2. Specify ggproto ###############
#
#
# StatCountynorthcarolinastamp <- ggplot2::ggproto(`_class` = "StatCountynorthcarolinastamp",
# `_inherit` = ggplot2::Stat,
# compute_panel = compute_county_northcarolina_stamp,
# default_aes = ggplot2::aes(geometry =
# ggplot2::after_stat(geometry)))
#
#
#
# ########### Step 3. 'stamp' function, inherits from sf ##################
#
# #' Title
# #'
# #' @param mapping
# #' @param data
# #' @param position
# #' @param na.rm
# #' @param show.legend
# #' @param inherit.aes
# #' @param ...
# #'
# #' @return
# #' @export
# #'
# #' @examples
# stamp_county <- function(
# mapping = NULL,
# data = reference_full,
# position = "identity",
# na.rm = FALSE,
# show.legend = NA,
# inherit.aes = TRUE,
# crs = "NAD27", #WGS84, NAD83
# ...
# ) {
#
# c(ggplot2::layer_sf(
# stat = StatCountynorthcarolinastamp, # proto object from step 2
# geom = ggplot2::GeomSf, # inherit other behavior
# data = data,
# mapping = mapping,
# position = position,
# show.legend = show.legend,
# inherit.aes = inherit.aes,
# params = rlang::list2(na.rm = na.rm, ...)),
# coord_sf(crs = crs,
# # default_crs = sf::st_crs(crs),
# # datum = sf::st_crs(crs),
# default = TRUE)
# )
#
# }ggplot() +
stamp_county()
#> Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
#> : Ignoring unknown aesthetics: fips
ggplot() +
stamp_county(keep_county = "Alleghany")
#> Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
#> : Ignoring unknown aesthetics: fips
northcarolina_county_flat %>%
dplyr::sample_n(10) %>%
ggplot() +
stamp_county()
#> Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
#> : Ignoring unknown aesthetics: fips
last_plot() +
aes(fips = fips) +
geom_county(color = "black")
#> Joining with `by = join_by(fips)`
last_plot() +
aes(fill = SID79 / BIR79)
#> Joining with `by = join_by(fips)`
last_plot() +
stamp_roads(alpha = .2)
#> Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
#> : Ignoring unknown aesthetics: fips
#> Joining with `by = join_by(fips)`
last_plot() +
geom_county_label()
#> Joining with `by = join_by(fips)`
#> Joining with `by = join_by(fips)`
#> Warning: Computation failed in `stat_countycenters()`.
#> Caused by error in `dplyr::select()`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `label` doesn't exist.
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