Dev generals pkg review (#125)

* All comments for general changes have been implemented. * Automatic readme update --------- Co-authored-by: GitHub Action <[email protected]>
epiverse-trace · Jun 12, 2024 · ff9c60f · ff9c60f
1 parent 2c01cb2
commit ff9c60f
Show file tree

Hide file tree

Showing 10 changed files with 82 additions and 72 deletions.
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -5,6 +5,7 @@
 ^codecov\.yml$
 ^_pkgdown\.yml$
 ^docs$
+^pkgdown$
 ^\.lintr$
 ^.*\.Rproj$
 ^\.Rproj\.user$

diff --git a/.lintr b/.lintr
@@ -1,20 +1,38 @@
-linters: linters_with_tags(
-    tags = NULL, # include all linters
+linters: all_linters(
+    packages = c("lintr", "etdev"),
     object_name_linter = NULL,
-    undesirable_function_linter = NULL,
     implicit_integer_linter = NULL,
     extraction_operator_linter = NULL,
     todo_comment_linter = NULL,
+    library_call_linter = NULL,
+    undesirable_function_linter(
+      modify_defaults(
+        default_undesirable_functions,
+        citEntry = "use the more modern bibentry() function",
+        library = NULL # too many false positive in too many files
+      )
+    ),
     function_argument_linter = NULL,
     indentation_linter = NULL, # unstable as of lintr 3.1.0
     # Use minimum R declared in DESCRIPTION or fall back to current R version.
     # Install etdev package from https://github.com/epiverse-trace/etdev
     backport_linter(if (length(x <- etdev::extract_min_r_version())) x else getRversion())
   )
 exclusions: list(
-    "tests/testthat.R" = list(unused_import_linter = Inf),
+    "tests/testthat.R" = list(
+      unused_import_linter = Inf
+    ),
+    "tests" = list(
+      undesirable_function_linter = Inf
+    ),
     "data-raw" = list(
       missing_package_linter = Inf,
       namespace_linter = Inf
-    )
-  )
+    ),
+    # RcppExports.R is auto-generated and will not pass many linters. In
+    # particular, it can create very long lines.
+    "R/RcppExports.R",
+    # R/stanmodels.R is auto-generated and will not pass many linters. In
+    # particular, it uses `sapply()`.
+    "R/stanmodels.R"
+  )
diff --git a/LICENSE b/LICENSE
@@ -1,2 +1,2 @@
-YEAR: 2023
+YEAR: 2024
 COPYRIGHT HOLDER: ColOpenData authors
diff --git a/NEWS.md b/NEWS.md
@@ -1,5 +1,11 @@
-# 0.1.0
+# 0.2.0
 
-* This project now includes a
-   [`NEWS.md`](https://r-pkgs.org/other-markdown.html#sec-news) file to inform
-   users about changes and new features.
+This project now includes a [`NEWS.md`](https://r-pkgs.org/other-markdown.html#sec-news) file to inform users about changes and new features.
+
+## New Features
+
+-   **Data Categories:** ColOpenData now includes four data categories: demographic, geospatial, climate, and population projections.
+
+-   **Data Merging:** Users can now merge information from demographic and geospatial data.
+
+-   **Auxiliary Functions:** Added auxiliary functions to retrieve information using DIVIPOLA codification.
diff --git a/R/dev-utils.R b/R/dev-utils.R
@@ -1,7 +1,9 @@
 # This unexported function adds a custom item to `usethis::use_release_issue()`
-release_bullets <- function() { # nocov start
+release_bullets <- function() {
   c(
     "Run `goodpractice::gp()`",
-    "Review [WORDLIST](https://docs.cran.dev/spelling#wordlist)"
+    "Review [WORDLIST](https://docs.cran.dev/spelling#wordlist)",
+    "Check if `# nolint` comments are still needed with recent lintr releases",
+    "All contributors to this release are acknowledged in some way"
   )
-} # nocov end
+}
diff --git a/README.Rmd b/README.Rmd
@@ -3,11 +3,14 @@ output: github_document
 ---
 
 <!-- README.md is generated from README.Rmd. Please edit that file. -->
+
 <!-- The code to render this README is stored in .github/workflows/render-readme.yaml -->
+
 <!-- Variables marked with double curly braces will be transformed beforehand: -->
+
 <!-- `packagename` is extracted from the DESCRIPTION file -->
-<!-- `gh_repo` is extracted via a special environment variable in GitHub Actions -->
 
+<!-- `gh_repo` is extracted via a special environment variable in GitHub Actions -->
 
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
@@ -18,87 +21,62 @@ knitr::opts_chunk$set(
 )
 ```
 
-# ColOpenData <img src="man/figures/logo.svg" align="right" width="200" />
+# {{ packagename }} <img src="man/figures/logo.svg" align="right" width="200"/>
 
 <!-- badges: start -->
 
-[![License:
-MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/license/mit/)
-[![R-CMD-check](https://github.com/epiverse-trace/ColOpenData/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/epiverse-trace/ColOpenData/actions/workflows/R-CMD-check.yaml)
-[![Codecov test
-coverage](https://codecov.io/gh/epiverse-trace/ColOpenData/branch/main/graph/badge.svg)](https://app.codecov.io/gh/epiverse-trace/ColOpenData?branch=main)
-[![lifecycle-experimental](https://raw.githubusercontent.com/reconverse/reconverse.github.io/master/images/badge-experimental.svg)](https://www.reconverse.org/lifecycle.html#experimental)
-[![CRAN status](https://www.r-pkg.org/badges/version/ColOpenData)](https://CRAN.R-project.org/package=ColOpenData)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/license/mit/) [![R-CMD-check](https://github.com/epiverse-trace/ColOpenData/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/epiverse-trace/ColOpenData/actions/workflows/R-CMD-check.yaml) [![Codecov test coverage](https://codecov.io/gh/epiverse-trace/ColOpenData/branch/main/graph/badge.svg)](https://app.codecov.io/gh/epiverse-trace/ColOpenData?branch=main) [![lifecycle-experimental](https://raw.githubusercontent.com/reconverse/reconverse.github.io/master/images/badge-experimental.svg)](https://www.reconverse.org/lifecycle.html#experimental) [![CRAN status](https://www.r-pkg.org/badges/version/ColOpenData)](https://CRAN.R-project.org/package=ColOpenData)
+
 <!-- badges: end -->
 
 **ColOpenData** is a package designed to access curated and wrangled Colombian demographic, geospatial and climate data, retrieved from various open Colombian data sources. The package addresses the challenge of scattered Colombian data across multiple web sources by providing functions that enable users to select and load desired datasets without the need for extensive data acquisition processes. Additionally, the tidy data structure offered for demographic and climate data facilitates analysis and visualization.
 
-ColOpenData is developed at [Universidad de Los
-Andes](https://uniandes.edu.co/) as part of the [Epiverse-TRACE
-program](https://data.org/initiatives/epiverse/).
+ColOpenData is developed at [Universidad de Los Andes](https://uniandes.edu.co/) as part of the [Epiverse-TRACE program](https://data.org/initiatives/epiverse/).
 
 ## Installation
 
-You can install the development version of ColOpenData from
-[GitHub](https://github.com/) with:
+You can install the development version of ColOpenData from [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("pak")
 pak::pak("epiverse-trace/ColOpenData")
 ```
+
 ## Quick Overview
 
-**ColOpenData** contains data from two public data sources: The National 
-Administrative Department of Statistics 
-[(DANE)](https://www.dane.gov.co/index.php/en/), and the Institute of Hydrology,
-Meteorology and Environmental Studies [(IDEAM)]( http://www.ideam.gov.co/). 
-The available data is divided in four categories: 
+**ColOpenData** contains data from two public data sources: The National Administrative Department of Statistics [(DANE)](https://www.dane.gov.co/index.php/en/), and the Institute of Hydrology, Meteorology and Environmental Studies [(IDEAM)](http://www.ideam.gov.co/). The available data is divided in four categories:
 
-- **Demographic:** Demographic and Socioeconomic data presents information from 
-the National Population and Dwelling Census (CNPV) of 2018. The CNPV data corresponds to the most recent census available to
-date and the information is presented as an answer to three questions: 
-How many are we?, Where are we? and How do  we live? Further information can be consulted
-[here](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018).
+-   **Demographic:** Demographic and Socioeconomic data presents information from the National Population and Dwelling Census (CNPV) of 2018. The CNPV data corresponds to the most recent census available to date and the information is presented as an answer to three questions: How many are we?, Where are we? and How do we live? Further information can be consulted at the [CNPV results](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018).
 
-- **Geospatial:** This data is retrieved from the National Geostatistical Framework (MGN), which includes maps and a summarized version of the 2018 census, aggregated to spatial geometries. The data is available at different aggregation levels including: Blocks, Urban and Rural Sections, Urban and Rural Sectors, Urban Areas, Municipalities and Departments. More information is available [here](https://www.dane.gov.co/index.php/actualidad-dane/5454-el-dane-actualizo-el-marco-geoestadistico-nacional-a-2018).
+-   **Geospatial:** This data is retrieved from the National Geostatistical Framework (MGN), which includes maps and a summarized version of the 2018 census, aggregated to spatial geometries. The data is available at different aggregation levels including: Blocks, Urban and Rural Sections, Urban and Rural Sectors, Urban Areas, Municipalities and Departments. More information is available at [MGN update news](https://www.dane.gov.co/index.php/actualidad-dane/5454-el-dane-actualizo-el-marco-geoestadistico-nacional-a-2018).
 
-- **Climate:** Climate data is recovered from backup information provided by IDEAM, containing historical data from the first station in the country until May 31st 2023. This backup includes temperature, precipitation, sunshine duration, wind direction, among others. More information can be found [here](http://www.ideam.gov.co/).
+-   **Climate:** Climate data is recovered from backup information provided by IDEAM, containing historical data from the first station in the country until May 31st 2023. This backup includes temperature, precipitation, sunshine duration, wind direction, among others. More information can be found at [IDEAM website](http://www.ideam.gov.co/).
 
-- **Population projections:**  Population Projections data 
-contains the population projections and back projections from 1950 to 2070, considering the post COVID-19 update, which was calculated
-based on the results of CNPV of 2018. Further information can be consulted
-[here](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion).
+-   **Population projections:** Population Projections data contains the population projections and back projections from 1950 to 2070, considering the post COVID-19 update, which was calculated based on the results of CNPV of 2018. Further information can be consulted at [DANE website](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion).
 
 Documentation and vignettes are available for the modules in the [user vignettes](https://epiverse-trace.github.io/ColOpenData/).
 
 ### Related R Packages
 
 Similar R packages are offered for international communities, allowing the user to download census, geospatial and climate data.
 
-- [cancensus](https://mountainmath.github.io/cancensus/): Canada
-- [censobr](https://ipeagit.github.io/censobr/): Brazil
-- [tidycensus](https://walker-data.com/tidycensus/): US
-- [geofi](https://ropengov.github.io/geofi/): Finland
-- [climate](https://bczernecki.github.io/climate/)
+-   [cancensus](https://mountainmath.github.io/cancensus/): Canada
+-   [censobr](https://ipeagit.github.io/censobr/): Brazil
+-   [tidycensus](https://walker-data.com/tidycensus/): US
+-   [geofi](https://ropengov.github.io/geofi/): Finland
+-   [climate](https://bczernecki.github.io/climate/)
 
 ### Lifecycle
 
-This package is currently *experimental*, as defined by the [RECON software
-lifecycle](https://www.reconverse.org/lifecycle.html). Therefore, this is 
-a functional draft and can be tested outside of the development team. However, 
-it still may change over time.
+This package is currently *experimental*, as defined by the [RECON software lifecycle](https://www.reconverse.org/lifecycle.html). Therefore, this is a functional draft and can be tested outside of the development team. However, it still may change over time.
 
 ### Contributions
 
-Contributions are welcome via [pull
-requests](https://github.com/ColOpenData/pulls).
+Contributions are welcome via [pull requests](https://github.com/ColOpenData/pulls).
 
 ### Code of Conduct
 
-Please note that the ColOpenData project is released with a [Contributor
-Code of
-Conduct](https://github.com/epiverse-trace/.github/blob/main/CODE_OF_CONDUCT.md).
-By contributing to this project, you agree to abide by its terms.
+Please note that the ColOpenData project is released with a [Contributor Code of Conduct](https://github.com/epiverse-trace/.github/blob/main/CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
 
 ### Funding
 

diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@
 <!-- `packagename` is extracted from the DESCRIPTION file -->
 <!-- `gh_repo` is extracted via a special environment variable in GitHub Actions -->
 
-# ColOpenData <img src="man/figures/logo.svg" align="right" width="200" />
+# ColOpenData <img src="man/figures/logo.svg" align="right" width="200"/>
 
 <!-- badges: start -->
 
@@ -17,6 +17,7 @@ coverage](https://codecov.io/gh/epiverse-trace/ColOpenData/branch/main/graph/bad
 [![lifecycle-experimental](https://raw.githubusercontent.com/reconverse/reconverse.github.io/master/images/badge-experimental.svg)](https://www.reconverse.org/lifecycle.html#experimental)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/ColOpenData)](https://CRAN.R-project.org/package=ColOpenData)
+
 <!-- badges: end -->
 
 **ColOpenData** is a package designed to access curated and wrangled
@@ -56,28 +57,30 @@ four categories:
   of 2018. The CNPV data corresponds to the most recent census available
   to date and the information is presented as an answer to three
   questions: How many are we?, Where are we? and How do we live? Further
-  information can be consulted
-  [here](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018).
+  information can be consulted at the [CNPV
+  results](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018).
 
 - **Geospatial:** This data is retrieved from the National
   Geostatistical Framework (MGN), which includes maps and a summarized
   version of the 2018 census, aggregated to spatial geometries. The data
   is available at different aggregation levels including: Blocks, Urban
   and Rural Sections, Urban and Rural Sectors, Urban Areas,
-  Municipalities and Departments. More information is available
-  [here](https://www.dane.gov.co/index.php/actualidad-dane/5454-el-dane-actualizo-el-marco-geoestadistico-nacional-a-2018).
+  Municipalities and Departments. More information is available at [MGN
+  update
+  news](https://www.dane.gov.co/index.php/actualidad-dane/5454-el-dane-actualizo-el-marco-geoestadistico-nacional-a-2018).
 
 - **Climate:** Climate data is recovered from backup information
   provided by IDEAM, containing historical data from the first station
   in the country until May 31st 2023. This backup includes temperature,
   precipitation, sunshine duration, wind direction, among others. More
-  information can be found [here](http://www.ideam.gov.co/).
+  information can be found at [IDEAM website](http://www.ideam.gov.co/).
 
 - **Population projections:** Population Projections data contains the
   population projections and back projections from 1950 to 2070,
   considering the post COVID-19 update, which was calculated based on
-  the results of CNPV of 2018. Further information can be consulted
-  [here](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion).
+  the results of CNPV of 2018. Further information can be consulted at
+  [DANE
+  website](https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion).
 
 Documentation and vignettes are available for the modules in the [user
 vignettes](https://epiverse-trace.github.io/ColOpenData/).

diff --git a/tests/testthat/test-dev-utils.R b/tests/testthat/test-dev-utils.R
@@ -0,0 +1,3 @@
+test_that("release_bullets() returns what usethis expects", {
+  expect_type(release_bullets(), "character")
+})
diff --git a/vignettes/documentation_and_dictionaries.Rmd b/vignettes/documentation_and_dictionaries.Rmd
@@ -217,12 +217,11 @@ name_to_code_mun("Boyacá", "Tunja")
 ```
 
 ::: {style="text-align: justify;"}
-These individual codes can be used to filter information in the datasets. 
+These individual codes can be used to filter information in the datasets.
 
 On the other hand, departments' and municipalities' codes can be translated to retrieve their official names using `divipola_municipality_name` and `divipola_department_name`.
 :::
 
 ```{r tunja name}
 code_to_name_mun("15001")
 ```
-
diff --git a/vignettes/geospatial_data.Rmd b/vignettes/geospatial_data.Rmd
@@ -59,9 +59,9 @@ knitr::kable(mgncnpv)
 ::: {style="text-align: justify;"}
 In this vignette you will learn:
 
-1. How to download geospatial data using **ColOpenData**
-2. How to use census data included in geospatial datasets
-3. How to visualize spatial data using **leaflet** and **ggplot2**
+1.  How to download geospatial data using **ColOpenData**
+2.  How to use census data included in geospatial datasets
+3.  How to visualize spatial data using **leaflet** and **ggplot2**
 
 We will be using geospatial data at the level of Municipality (MPIO) for the department of Tolima and we will calculate the percentage of houses with internet connection at each municipality. Later, we will build some plots using the previously mentioned approaches for dynamic and static plots.