Merge branch 'develop'

EnergyEconomyDecoupling · Dec 23, 2023 · 685e464 · 685e464
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: MWTools
 Title: Functions For Calculation of Muscle Work Within the PSUT Framework
-Version: 0.2.6
-Date: 2023-12-21
+Version: 0.2.7
+Date: 2023-12-23
 Authors@R: c(person(given = "Zeke",
                     family = "Marshall",
                     role = c("aut", "cre"),

diff --git a/NEWS.md b/NEWS.md
@@ -6,8 +6,14 @@ output: html_document
 Cite all releases with doi [10.5281/zenodo.7584858](https://doi.org/10.5281/zenodo.7584858), 
 which always resolves to the latest release.
 
+# MWTools 0.2.7 (2023-12-23) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.<>.svg)](https://doi.org/10.5281/zenodo.<>)
 
-# MWTools 0.2.6 (2023-12-21)
+* Fixing vignette.
+* No new tests.
+    * Still at 453 tests, all passing.
+    * Test coverage remains at 100%.
+
+# MWTools 0.2.6 (2023-12-21) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10420270.svg)](https://doi.org/10.5281/zenodo.10420270)
 
 * Added a statement of need to the README file.
 * Added a vignette, moving some example code 

diff --git a/R/calc_amw_functions.R b/R/calc_amw_functions.R
@@ -93,7 +93,7 @@ tidy_fao_live_animals <- function(.df,
                        values_from = dplyr::all_of(value_fao_col),
                        names_from = dplyr::all_of(item_fao_col)) %>%
     dplyr::mutate("{buffaloes}" := .data[[cattle_and_buffaloes]] - .data[[cattle]], .keep = "unused") |>
-    tidyr::pivot_longer(cols = buffaloes,
+    tidyr::pivot_longer(cols = dplyr::all_of(buffaloes),
                         names_to = item_fao_col,
                         values_to = value_fao_col)
 

diff --git a/R/calc_hmw_functions.R b/R/calc_hmw_functions.R
@@ -29,12 +29,14 @@ prepareRawILOData <- function(ilo_working_hours_data, ilo_employment_data){
   # Mean weekly hours actually worked per employed person by sex and economic activity:
   # HOW_TEMP_SEX_ECO_NB_A
   working_hours <- ilo_working_hours_data |>
-    dplyr::select("ref_area", "sex.label", "classif1.label", "time", "obs_value") |>
+    # dplyr::select("ref_area", "sex.label", "classif1.label", "time", "obs_value") |>
+    dplyr::select(dplyr::all_of(c("ref_area", "sex.label", "classif1.label", "time", "obs_value"))) |>
     magrittr::set_colnames(c(country_code_col, sex_ilo_col, sector_col, year_col, yearly_working_hours_ilo_col))
 
   # Employment by sex and economic activity (thousands): EMP_TEMP_SEX_ECO_NB_A
   employment <- ilo_employment_data |>
-    dplyr::select("ref_area", "sex.label", "classif1.label", "time", "obs_value") |>
+    # dplyr::select("ref_area", "sex.label", "classif1.label", "time", "obs_value") |>
+    dplyr::select(dplyr::all_of(c("ref_area", "sex.label", "classif1.label", "time", "obs_value"))) |>
     magrittr::set_colnames(c(country_code_col, sex_ilo_col, sector_col, year_col, employed_persons_ilo_col))
 
   # Convert Employed persons [1000 persons] to employed persons [persons] and

diff --git a/README.Rmd b/README.Rmd
@@ -58,55 +58,6 @@ You can install this package from [GitHub](https://github.com/) with:
 devtools::install_github("EnergyEconomyDecoupling/MWTools")
 ```
 
-<!-- ## Examples -->
-
-<!-- ### Animal Muscle Work -->
-
-<!-- Raw data for the estimation of animal muscle work is obtained from the Food and Agriculture Organisation of the United Nations Statistical Database (FAOSTAT), via the `R` package `FAOSTAT`. The `MWTools` package provides a wrapper function for downloading data for the number of live animals, `down_fao_live_animals`, which utilises the function `FAOSTAT::get_faostat_bulk()`. Once downloaded `MWTools` provides a number of functions for the calculation of the number of working animals by species, country, year and in agriculture, transport, and in total. -->
-
-<!-- The helper function `calc_amw_pfu` returns a tidy data frame containing data for the primary, final, and useful energy by species, country, year and in agriculture, transport, and in total. -->
-
-<!-- ```{r get_example_data} -->
-<!-- library(MWTools) -->
-
-<!-- fao_fp <- PFUSetup::get_abs_paths(version = "v1.2")$fao_data_path -->
-
-<!-- fao_raw_rds <- readr::read_rds(file = fao_fp) -->
-
-<!-- amw_pfu_data <- calc_amw_pfu(.df = fao_raw_rds, -->
-<!--                              concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path, -->
-<!--                              amw_analysis_data_path = PFUSetup::get_abs_paths()$amw_analysis_data_path) -->
-
-<!-- head(amw_pfu_data) -->
-
-<!-- ``` -->
-
-<!-- ### Human Muscle Work -->
-
-<!-- Raw data for the estimation of human muscle work is obtained from the International Labor Organisation (ILO), via the `R` package `Rilostat`. The `MWTools` package includes bundled ILO data for the number of employed persons by sector and mean number of working hours by sector. -->
-
-<!-- Using data for the number of employed persons and mean yearly working hours the primary, final, and useful energy associated with human muscle work can be estimated using the helper function `MWTools::calc_hmw_pfu`. -->
-
-<!-- ``` {r hmw_pfu} -->
-<!-- library(MWTools) -->
-
-<!-- ilo_working_hours_data <- readr::read_rds(file = PFUSetup::get_abs_paths(version = "v1.2")$ilo_working_hours_data_path) -->
-<!-- ilo_employment_data <- readr::read_rds(file = PFUSetup::get_abs_paths(version = "v1.2")$ilo_employment_data_path) -->
-
-<!-- preparedILOData <- prepareRawILOData(ilo_working_hours_data = ilo_working_hours_data, -->
-<!--                                      ilo_employment_data = ilo_employment_data) -->
-
-<!-- hmw_pfu_data <- calc_hmw_pfu(.df = preparedILOData, -->
-<!--                              concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path, -->
-<!--                              hmw_analysis_data_path = PFUSetup::get_abs_paths()$hmw_analysis_data_path) -->
-
-<!-- head(hmw_pfu_data) -->
-<!-- ``` -->
-
-<!-- ### PSUT matrices -->
-
-<!-- PFU data can be converted to PSUT matrices using `prep_psut()`. -->
-
 
 ## More Information
 

diff --git a/README.md b/README.md
@@ -44,36 +44,6 @@ You can install this package from [GitHub](https://github.com/) with:
 devtools::install_github("EnergyEconomyDecoupling/MWTools")
 ```
 
-<!-- ## Examples -->
-<!-- ### Animal Muscle Work -->
-<!-- Raw data for the estimation of animal muscle work is obtained from the Food and Agriculture Organisation of the United Nations Statistical Database (FAOSTAT), via the `R` package `FAOSTAT`. The `MWTools` package provides a wrapper function for downloading data for the number of live animals, `down_fao_live_animals`, which utilises the function `FAOSTAT::get_faostat_bulk()`. Once downloaded `MWTools` provides a number of functions for the calculation of the number of working animals by species, country, year and in agriculture, transport, and in total. -->
-<!-- The helper function `calc_amw_pfu` returns a tidy data frame containing data for the primary, final, and useful energy by species, country, year and in agriculture, transport, and in total. -->
-<!-- ```{r get_example_data} -->
-<!-- library(MWTools) -->
-<!-- fao_fp <- PFUSetup::get_abs_paths(version = "v1.2")$fao_data_path -->
-<!-- fao_raw_rds <- readr::read_rds(file = fao_fp) -->
-<!-- amw_pfu_data <- calc_amw_pfu(.df = fao_raw_rds, -->
-<!--                              concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path, -->
-<!--                              amw_analysis_data_path = PFUSetup::get_abs_paths()$amw_analysis_data_path) -->
-<!-- head(amw_pfu_data) -->
-<!-- ``` -->
-<!-- ### Human Muscle Work -->
-<!-- Raw data for the estimation of human muscle work is obtained from the International Labor Organisation (ILO), via the `R` package `Rilostat`. The `MWTools` package includes bundled ILO data for the number of employed persons by sector and mean number of working hours by sector. -->
-<!-- Using data for the number of employed persons and mean yearly working hours the primary, final, and useful energy associated with human muscle work can be estimated using the helper function `MWTools::calc_hmw_pfu`. -->
-<!-- ``` {r hmw_pfu} -->
-<!-- library(MWTools) -->
-<!-- ilo_working_hours_data <- readr::read_rds(file = PFUSetup::get_abs_paths(version = "v1.2")$ilo_working_hours_data_path) -->
-<!-- ilo_employment_data <- readr::read_rds(file = PFUSetup::get_abs_paths(version = "v1.2")$ilo_employment_data_path) -->
-<!-- preparedILOData <- prepareRawILOData(ilo_working_hours_data = ilo_working_hours_data, -->
-<!--                                      ilo_employment_data = ilo_employment_data) -->
-<!-- hmw_pfu_data <- calc_hmw_pfu(.df = preparedILOData, -->
-<!--                              concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path, -->
-<!--                              hmw_analysis_data_path = PFUSetup::get_abs_paths()$hmw_analysis_data_path) -->
-<!-- head(hmw_pfu_data) -->
-<!-- ``` -->
-<!-- ### PSUT matrices -->
-<!-- PFU data can be converted to PSUT matrices using `prep_psut()`. -->
-
 ## More Information
 
 Find more information, including vignettes and function documentation,

diff --git a/inst/CITATION b/inst/CITATION
@@ -4,12 +4,12 @@ citEntry(entry = "Manual",
   title        = "MWTools: Functions For Calculation of Muscle Work Within the PSUT Framework",
   author       = personList(as.person("Zeke Marshall"), as.person("Matthew Kuperus Heun")),
   year         = "2023",
-  note         = "R package version 0.2.6",
+  note         = "R package version 0.2.7",
   url          = "https://doi.org/10.5281/zenodo.7584858",
 
   textVersion  =
   paste("Zeke Marshall and Matthew Kuperus Heun (2023).",
         "MWTools: Functions For Calculation of Muscle Work Within the PSUT Framework.",
-        "R package version 0.2.6.",
+        "R package version 0.2.7.",
         "https://doi.org/10.5281/zenodo.7584858")
 )
diff --git a/vignettes/MWTools.Rmd b/vignettes/MWTools.Rmd
@@ -1,9 +1,9 @@
 ---
-title: "IEATools"
+title: "MWTools"
 output: rmarkdown::html_vignette
 bibliography: References.bib
 vignette: >
-  %\VignetteIndexEntry{IEATools}
+  %\VignetteIndexEntry{MWTools}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -35,75 +35,82 @@ Computational tools are needed to gather and shape relevant data.
 The examples below show how to use `MWTools` to address
 the needs described above.
 
-
 ## Examples
 
 ### Animal Muscle Work
 
-Raw data for the estimation of animal muscle work
-is obtained from the Food and Agriculture Organisation 
-of the United Nations Statistical Database (FAOSTAT), 
-via the `R` package `FAOSTAT`.
-The `MWTools` package provides a wrapper function 
-for downloading data for the number of live animals,
-`down_fao_live_animals()`, which utilises the function `FAOSTAT::get_faostat_bulk()`. 
-Once downloaded, `MWTools` provides a number of functions
-for the calculation of the number of working animals 
-by species, country, year and in agriculture, transport, and in total.
-
-The helper function `calc_amw_pfu()` returns a tidy data frame
-containing data for the primary, final, and useful energy 
-by species, country, year and in agriculture, transport, and in total.
-
-```{r get_example_data}
-library(MWTools)
+Raw data for the estimation of animal muscle work is obtained from the Food and 
+Agriculture Organisation of the United Nations Statistical Database (FAOSTAT), 
+via the `R` package `FAOSTAT` as follows.
 
-fao_fp <- PFUSetup::get_abs_paths(version = "v1.2")$fao_data_path
+```{r down_amw_example, eval = FALSE}
+raw_amw_data <- FAOSTAT::get_faostat_bulk(code = "QCL",  data_folder = tempdir())
+```
 
-fao_raw_rds <- readr::read_rds(file = fao_fp)
+The helper function `calc_amw_pfu` returns a tidy data frame containing data for
+the primary, final, and useful energy by species, country, year and in
+the agriculture, transport, and total sectors.
 
-amw_pfu_data <- calc_amw_pfu(.df = fao_raw_rds,
-                             concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path,
-                             amw_analysis_data_path = PFUSetup::get_abs_paths()$amw_analysis_data_path)
+```{r calc_amw_example}
+test_amw_data <- read.csv(file = MWTools::amw_test_data_path())
 
-head(amw_pfu_data)
+amw_pfu_data <- MWTools::calc_amw_pfu(.df = test_amw_data,
+                                      concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path,
+                                      amw_analysis_data_path = PFUSetup::get_abs_paths()$amw_analysis_data_path)
 ```
 
+```{r amw_example_glimpse, echo=FALSE}
+tibble::glimpse(amw_pfu_data)
+```
 
 ### Human Muscle Work
 
-Raw data for the estimation of human muscle work
-is obtained from the International Labor Organisation (ILO), 
-via the `R` package `Rilostat`.
-The `MWTools` package includes bundled ILO data for the number of employed persons by sector and mean number of working hours by sector.
+Raw data for the estimation of human muscle work is obtained from the 
+International Labor Organisation (ILO), via the `R` package `Rilostat` as 
+follows: 
 
-Using data for the number of employed persons and 
-mean yearly working hours,
-the primary, final, and useful energy associated 
-with human muscle work can be estimated 
-using the helper function `calc_hmw_pfu()`.
+```{r down_hmw_example, eval = FALSE}
+ilo_working_hours_data <- Rilostat::get_ilostat(id = MWTools::ilo_codes$working_hours_code,
+                                                quiet = TRUE) |>
+  Rilostat::label_ilostat(code = c("ref_area"))
 
-``` {r hmw_pfu}
-library(MWTools)
+ilo_employment_data <- Rilostat::get_ilostat(id = MWTools::ilo_codes$employment_code,
+                                             quiet = TRUE) |>
+  Rilostat::label_ilostat(code = c("ref_area"))
+```
 
-ilo_working_hours_data <- readr::read_rds(file = PFUSetup::get_abs_paths(version = "v1.2")$ilo_working_hours_data_path)
-ilo_employment_data <- readr::read_rds(file = PFUSetup::get_abs_paths(version = "v1.2")$ilo_employment_data_path)
+Using data for the number of employed persons and mean yearly working hours the 
+primary, final, and useful energy associated with human muscle work can be 
+estimated using the helper function `MWTools::calc_hmw_pfu` as below.
 
-preparedILOData <- prepareRawILOData(ilo_working_hours_data = ilo_working_hours_data,
-                                     ilo_employment_data = ilo_employment_data)
+``` {r calc_hmw_example}
+ilo_working_hours_data <- read.csv(file = MWTools::ilo_working_hours_test_data_path())
+ilo_employment_data <- read.csv(file = MWTools::ilo_employment_test_data_path())
 
-hmw_pfu_data <- calc_hmw_pfu(.df = preparedILOData,
-                             concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path,
-                             hmw_analysis_data_path = PFUSetup::get_abs_paths()$hmw_analysis_data_path)
+preparedILOData <- MWTools::prepareRawILOData(ilo_working_hours_data = ilo_working_hours_data,
+                                              ilo_employment_data = ilo_employment_data)
 
-head(hmw_pfu_data)
+hmw_pfu_data <- MWTools::calc_hmw_pfu(.df = preparedILOData,
+                                      concordance_path = PFUSetup::get_abs_paths()$mw_concordance_path,
+                                      hmw_analysis_data_path = PFUSetup::get_abs_paths()$hmw_analysis_data_path)
 ```
 
+```{r hmw_example_glimpse, echo=FALSE}
+tibble::glimpse(hmw_pfu_data)
+```
 
 ### PSUT matrices
 
 PFU data can be converted to PSUT matrices using `prep_psut()`.
 
+```{r prep_psut_example}
+mw_psut <- MWTools::prep_psut(.hmw_df = hmw_pfu_data,
+                              .amw_df = amw_pfu_data)
+```
+
+```{r psut_example_glimpse, echo=FALSE}
+tibble::glimpse(mw_psut)
+```
 
 ## Conclusion