check fixes

geomarker-io · Mar 14, 2024 · 19e8e56 · 19e8e56
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commit 19e8e56
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Showing 7 changed files with 28 additions and 52 deletions.
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -12,3 +12,4 @@ training_data.rds
 ^pkgdown$
 ^CODE_OF_CONDUCT\.md$
 ^README\.Rmd$
+data-raw
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -42,6 +42,7 @@ Suggests:
     scales,
     testthat (>= 3.0.0),
     tidync,
+    tigris,
     viridis
 Config/testthat/edition: 3
 Config/testthat/parallel: true

diff --git a/R/helper.R b/R/helper.R
@@ -7,8 +7,7 @@
 }
 
 utils::globalVariables(c(
-  "s2",
-  "NAME",
+  "s2", "NAME",
   "Sample Duration", "Observation Percent",
   "State Code", "County Code", "Site Num",
   "Latitude", "Longitude", "Arithmetic Mean", "Date Local",
@@ -17,16 +16,11 @@ utils::globalVariables(c(
   "SO4SMASS", "merra_dust", "merra_oc", "merra_oc",
   "merra_bc", "merra_ss", "merra_so4", "value",
   "d", "pollutant code", "site longitude", "site latitude", "total_emissions",
-  "dist_to_point",
   "air.2m", "hpbl", "acpcp", "rhum.2m",
   "vis", "pres.sfc", "uwnd.10m", "vwnd.10m",
-  "urban_imperviousness",
   "merra_pm25",
   "plume_smoke", ".rowid",
-  "nei_point_id2w", "census_tract_id_2010",
-  "predictions", "variance.estimates",
-  "aadt_total", "aadt_total_m", "aadt_truck", "aadt_truck_m",
-  "AADT", "AADT_COMBINATION", "AADT_SINGLE_UNIT", "Shape", "s2_centroid"
+  "predictions", "variance.estimates"
 ))
 
 #' Get the closest years to a vector of dates

diff --git a/README.Rmd b/README.Rmd
@@ -16,8 +16,6 @@ knitr::opts_chunk$set(
 # Air Pollution Prediction Commons
 
 <!-- badges: start -->
-[![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
 [![R-CMD-check](https://github.com/geomarker-io/appc/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/geomarker-io/appc/actions/workflows/R-CMD-check.yaml)
 [![CRAN status](https://www.r-pkg.org/badges/version/appc)](https://CRAN.R-project.org/package=appc)
 [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
@@ -28,7 +26,7 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 The goal of the appc package is to provide daily, high resolution, near real-time, model-based ambient air pollution exposure assessments.
 This is achieved by training a generalized random forest on several geomarkers to predict daily average EPA AQS concentrations from 2017 until the present at exact locations across the contiguous United States (see `vignette("cv-model-performance")` for more details).
 The appc package contains functions for generating geomarker predictors and the ambient air pollution concentrations.
-Predictor geomarkers include weather and atmospheric information, traffic on primary roadways, urban imperviousness, wildfire smoke, industrial emissions, elevation, spatiotemporal indicators, and satellite-based aerosol diagnostics data.
+Predictor geomarkers include weather and atmospheric information, wildfire smoke plumes, elevation, and satellite-based aerosol diagnostics products.
 Source files included with the package train and evaluate models that can be updated with any release to use more recent AQS measurements and/or geomarker predictors.
 
 ## Installing
@@ -74,12 +72,10 @@ Spatiotemporal geomarkers are used for predicting air pollution concentrations,
 |-----------|---------------|
 | 🌦 weather & atmospheric conditions | `get_narr_data()` |
 | 🛰 satellite-based aerosol diagnostics | `get_merra_data()` |
-| 🚍 traffic densities | `get_traffic_summary()` |
-| 🏙 urban imperviousness | `get_urban_imperviousness()` |
 | 🔥 wildfire smoke | `get_hms_smoke_data()` |
-| 🏭 industrial emissions | `get_nei_point_summary()` |
 | 🗻 elevation | `get_elevation_summary()` |
-| 🔗 census tract identifier | `get_census_tract_id()` |
+
+Currently, `get_urban_imperviousness()`, `get_traffic()`, and `get_nei_point_summary()` are stashed in the `/inst` folder and not integrated into this package.
 
 ## Developing
 
@@ -96,12 +92,9 @@ Available recipes:
     dl_geomarker_data      # download all geomarker ahead of time, if not already cached
     docker_test            # run tests without cached release files
     docker_tool            # build docker image preloaded with {appc} and data
+    make_training_data     # make training data for GRF
     release_hms_smoke_data # install smoke data from source and upload to github release
     release_merra_data     # upload merra data to github release
     release_model          # upload grf model and training data to current github release
-    release_nei_data       # install nei data from source and upload to github release
-    release_smoke_data     # install smoke data from source and upload to github release
-    release_traffic_data   # install traffic data from source and upload to github release
-    release_urban_imperviousness_data # install nlcd urban imperviousness data from source and upload to github release
     train_model            # train grf model and render report
 ```
diff --git a/README.md b/README.md
@@ -5,8 +5,6 @@
 
 <!-- badges: start -->
 
-[![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
 [![R-CMD-check](https://github.com/geomarker-io/appc/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/geomarker-io/appc/actions/workflows/R-CMD-check.yaml)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/appc)](https://CRAN.R-project.org/package=appc)
@@ -24,12 +22,10 @@ until the present at exact locations across the contiguous United States
 (see `vignette("cv-model-performance")` for more details). The appc
 package contains functions for generating geomarker predictors and the
 ambient air pollution concentrations. Predictor geomarkers include
-weather and atmospheric information, traffic on primary roadways, urban
-imperviousness, wildfire smoke, industrial emissions, elevation,
-spatiotemporal indicators, and satellite-based aerosol diagnostics data.
-Source files included with the package train and evaluate models that
-can be updated with any release to use more recent AQS measurements
-and/or geomarker predictors.
+weather and atmospheric information, wildfire smoke plumes, elevation,
+and satellite-based aerosol diagnostics products. Source files included
+with the package train and evaluate models that can be updated with any
+release to use more recent AQS measurements and/or geomarker predictors.
 
 ## Installing
 
@@ -52,25 +48,25 @@ appc::predict_pm25(
   dates = list(as.Date(c("2023-05-18", "2023-11-06")), as.Date(c("2023-06-22", "2023-08-15")))
 )
 #> ℹ (down)loading random forest model
-#> ✔ (down)loading random forest model [8.3s]
+#> ✔ (down)loading random forest model [8.2s]
 #> 
 #> ℹ checking that s2 locations are within the contiguous united states
-#> ✔ checking that s2 locations are within the contiguous united states [54ms]
+#> ✔ checking that s2 locations are within the contiguous united states [55ms]
 #> 
 #> ℹ adding coordinates
 #> ✔ adding coordinates [1.3s]
 #> 
 #> ℹ adding elevation
-#> ✔ adding elevation [1.4s]
+#> ✔ adding elevation [1.3s]
 #> 
 #> ℹ adding HMS smoke data
-#> ✔ adding HMS smoke data [986ms]
+#> ✔ adding HMS smoke data [967ms]
 #> 
 #> ℹ adding NARR
 #> ✔ adding NARR [3.1s]
 #> 
 #> ℹ adding MERRA
-#> ✔ adding MERRA [556ms]
+#> ✔ adding MERRA [569ms]
 #> 
 #> ℹ adding time components
 #> ✔ adding time components [24ms]
@@ -123,16 +119,16 @@ Spatiotemporal geomarkers are used for predicting air pollution
 concentrations, but also serve as exposures or confounding exposures
 themselves. View information and options about each geomarker:
 
-| geomarker                             | appc function                |
-|---------------------------------------|------------------------------|
-| 🌦 weather & atmospheric conditions    | `get_narr_data()`            |
-| 🛰 satellite-based aerosol diagnostics | `get_merra_data()`           |
-| 🚍 traffic densities                  | `get_traffic_summary()`      |
-| 🏙 urban imperviousness                | `get_urban_imperviousness()` |
-| 🔥 wildfire smoke                     | `get_hms_smoke_data()`       |
-| 🏭 industrial emissions               | `get_nei_point_summary()`    |
-| 🗻 elevation                          | `get_elevation_summary()`    |
-| 🔗 census tract identifier            | `get_census_tract_id()`      |
+| geomarker                             | appc function             |
+|---------------------------------------|---------------------------|
+| 🌦 weather & atmospheric conditions    | `get_narr_data()`         |
+| 🛰 satellite-based aerosol diagnostics | `get_merra_data()`        |
+| 🔥 wildfire smoke                     | `get_hms_smoke_data()`    |
+| 🗻 elevation                          | `get_elevation_summary()` |
+
+Currently, `get_urban_imperviousness()`, `get_traffic()`, and
+`get_nei_point_summary()` are stashed in the `/inst` folder and not
+integrated into this package.
 
 ## Developing
 
@@ -154,12 +150,9 @@ Available recipes:
     dl_geomarker_data      # download all geomarker ahead of time, if not already cached
     docker_test            # run tests without cached release files
     docker_tool            # build docker image preloaded with {appc} and data
+    make_training_data     # make training data for GRF
     release_hms_smoke_data # install smoke data from source and upload to github release
     release_merra_data     # upload merra data to github release
     release_model          # upload grf model and training data to current github release
-    release_nei_data       # install nei data from source and upload to github release
-    release_smoke_data     # install smoke data from source and upload to github release
-    release_traffic_data   # install traffic data from source and upload to github release
-    release_urban_imperviousness_data # install nlcd urban imperviousness data from source and upload to github release
     train_model            # train grf model and render report
 ```
diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -6,9 +6,6 @@ reference:
   contents:
   - get_elevation_summary
   - get_narr_data
-  - get_nei_point_summary
-  - get_urban_imperviousness
-  - get_traffic_summary
   - get_merra_data
   - get_hms_smoke_data
 - title: Air pollution assessment

diff --git a/justfile b/justfile
@@ -20,12 +20,9 @@ dl_geomarker_data:
   #!/usr/bin/env Rscript
   library(appc)
   install_elevation_data()
-  install_traffic()
   tidyr::expand_grid(narr_var = c("air.2m", "hpbl", "acpcp", "rhum.2m", "vis", "pres.sfc", "uwnd.10m", "vwnd.10m"),
                      narr_year = as.character(2017:2023)) |>
     purrr::pmap_chr(install_narr_data)
-  purrr::map_chr(c("2017", "2020"), install_nei_point_data)
-  purrr::map_chr(c("2016", "2019", "2021"), install_urban_imperviousness)
   install_hms_smoke_data()
   purrr::map_chr(as.character(2017:2023), install_merra_data)