unify targets; fix #51

panukatan · Sep 1, 2024 · 0074dc6 · 0074dc6
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diff --git a/.github/workflows/deploy-targets-releases.yml b/.github/workflows/deploy-targets-releases.yml
@@ -40,13 +40,13 @@ jobs:
 
       - name: Run workflows
         run: |
-          targets::tar_make(script = "_targets_climate.R")
-          targets::tar_make(script = "_targets_cyclones.R")
-          targets::tar_make(script = "_targets_dam.R")
+          targets::tar_make(climate_download_files)
+          targets::tar_make(cyclone_reports_download_files)
+          targets::tar_make(dam_level_data_raw_csv)
         shell: Rscript {0}
 
       - name: Create weekly data release
         run: |
-          targets::tar_make(script = "_targets_releases.R")
+          targets::tar_make(paglaom_weekly_release)
         shell: Rscript {0}
 
diff --git a/README.Rmd b/README.Rmd
@@ -110,117 +110,79 @@ to install all R package dependencies.
 
 Currently, the project has workflows that curate the following datasets:
 
-1. Tropical cyclones data for various cyclones entering the Philippine area of responsibility since 2017
+1. Tropical cyclones data for various cyclones entering the Philippine area of responsibility since 2017;
 
-```{r cyclones_workflow, echo = FALSE, message = FALSE, results = 'asis'}
-cat(
-  "```mermaid",
-  targets::tar_mermaid(
-    targets_only = TRUE, color = FALSE, legend = FALSE, 
-    script = "_targets_cyclones.R"
-  ),
-  "```",
-  sep = "\n"
-)
-```
-
-
-2. Daily heat index data from various data collection points in the Philippines
-
-```{r heat_workflow, echo = FALSE, message = FALSE, results = 'asis'}
-cat(
-  "```mermaid",
-  targets::tar_mermaid(
-    targets_only = TRUE, color = FALSE, legend = FALSE, 
-    script = "_targets_heat.R"
-  ),
-  "```",
-  sep = "\n"
-)
-```
+2. Daily heat index data from various data collection points in the Philippines;
 
+3. Climatological extremes and normals data over time; and,
 
-3. Climatological extremes and normals data over time
+4. Daily dam water level data.
 
-```{r climate_workflow, echo = FALSE, message = FALSE, results = 'asis'}
-cat(
-  "```mermaid",
-  targets::tar_mermaid(
-    targets_only = TRUE, color = FALSE, legend = FALSE, 
-    script = "_targets_climate.R"
-  ),
-  "```",
-  sep = "\n"
-)
-```
+The following diagram illustrates these workflows
 
-
-4. Daily dam water level data
-
-```{r dam_workflow, echo = FALSE, message = FALSE, results = 'asis'}
+```{r overall_workflow, echo = FALSE, message = FALSE, results = 'asis'}
 cat(
   "```mermaid",
   targets::tar_mermaid(
-    targets_only = TRUE, color = FALSE, legend = FALSE, 
-    script = "_targets_dam.R"
+    targets_only = TRUE, 
+    names = dplyr::starts_with(c("climate", "cyclone", "dam", "heat")),
+    color = FALSE, legend = FALSE
   ),
   "```",
   sep = "\n"
 )
 ```
 
-
 To run any of these workflows, run the following command on the R console:
 
 ```R
-targets::tar_make(script = "NAME_OF_TARGET_SCRIPT_R_FILE")
+targets::tar_make(dplyr::starts_with("PREFIX"))
 ```
 
-replacing `"NAME_OF_TARGET_SCRIPT_R_FILE"` with the name of the target script file for the specific workflow. For example, to run the cyclones workflow from R console:
+replacing `"PREFIX"` with the keyword for the type of data. For example, to run the cyclones workflow from R console:
 
 ```R
-targets::tar_make(script = "_targets_cyclones.R")
+targets::tar_make(dplyr::starts_with("cyclone"))
 ```
 
 or from the command line/terminal as follows:
 
 ```Bash
-Rscript -e "targets::tar_make(script = "_targets_cyclones.R)"
+Rscript -e "targets::tar_make(dplyr::starts_with('cyclone'))"
 ```
 
 Running specific components of a workflow involves specifying a target name or target names of the components you want to run. You should be able to run a full workflow path by just specifying the name of the last target in the workflow sequence. For example, the following will run the entire cyclones data workflow (as an alternative to what is shown above):
 
 ```R
-targets::tar_make(cyclones_peak_data_csv, script = "_targets_cyclones.R")
+targets::tar_make(cyclones_peak_data_csv)
 ```
 
 or from the command line/terminal as follows:
 
 ```Bash
-Rscript -e "targets::tar_make(cyclones_peak_data_csv, script = "_targets_cyclones.R")"
+Rscript -e "targets::tar_make(cyclones_peak_data_csv)"
 ```
 
 The target `cyclones_peak_data_csv` is the last target of the cyclones data workflow. Hence, to be able to produce the `cyclones_peak_data_csv` target requires running this series of linked targets.
 
 If you would like to run a set of interrelated but not fully linked targets, you will need to specify more than one target name. For this, you can use `tidyselect` approaches to name targets to be run. For example:
 
 ```R
-targets::tar_make(dplyr::starts_with("cyclone"), script = "_targets_cyclones.R")
+targets::tar_make(dplyr::starts_with(c("cyclone", "dam"))
 ```
 
-will run all targets in the cyclones data workflow whose names start with *"cyclone"*.
+will run all targets in the cyclones and dam levels data workflow.
 
 The project also has a workflow for weekly GitHub release of the various raw datasets.
 
 ```{r release_workflow, echo = FALSE, message = FALSE, results = 'asis'}
 cat(
   "```mermaid",
   targets::tar_mermaid(
-    targets_only = TRUE, color = FALSE, legend = FALSE, 
-    script = "_targets_releases.R"
+    targets_only = TRUE, names = dplyr::starts_with("paglaom_weekly"),
+    color = FALSE, legend = FALSE
   ),
   "```",
-  
   sep = "\n"
 )
 ```

diff --git a/README.md b/README.md
@@ -177,95 +177,51 @@ to install all R package dependencies.
 Currently, the project has workflows that curate the following datasets:
 
 1.  Tropical cyclones data for various cyclones entering the Philippine
-    area of responsibility since 2017
+    area of responsibility since 2017;
 
-<!-- end list -->
+2.  Daily heat index data from various data collection points in the
+    Philippines;
 
-``` mermaid
-graph LR
-  style Graph fill:#FFFFFF00,stroke:#000000;
-  subgraph Graph
-    direction LR
-    x1cf596d0c4f824b5["cyclone_reports_download_files"]:::outdated --> x721677a433d907a0["cyclones_peak_data"]:::outdated
-    x6f87cfcc96bb274d(["cyclone_reports_links"]):::outdated --> x1cf596d0c4f824b5["cyclone_reports_download_files"]:::outdated
-    x721677a433d907a0["cyclones_peak_data"]:::outdated --> xdc5659ed1b9a196a(["cyclones_peak_data_csv"]):::outdated
-  end
-```
+3.  Climatological extremes and normals data over time; and,
 
-2.  Daily heat index data from various data collection points in the
-    Philippines
+4.  Daily dam water level data.
 
-<!-- end list -->
+The following diagram illustrates these workflows
 
 ``` mermaid
 graph LR
   style Graph fill:#FFFFFF00,stroke:#000000;
   subgraph Graph
     direction LR
-    x567709ab5f0adc71(["heat_index_pubfiles_url"]):::outdated --> x56bd7c118ed46a38(["heat_index_links"]):::outdated
     x56bd7c118ed46a38(["heat_index_links"]):::outdated --> x113a83dcec46090f(["heat_index_links_urls"]):::outdated
+    x567709ab5f0adc71(["heat_index_pubfiles_url"]):::uptodate --> x56bd7c118ed46a38(["heat_index_links"]):::outdated
+    xb48a3b157c96bffd(["climate_pubfiles_url"]):::uptodate --> xa37a01adfb45bd68(["climate_directory_urls"]):::uptodate
+    x26b861c7a0a21b52["climate_pdf_urls"]:::uptodate --> xc044beb81380bb4a["climate_download_files"]:::uptodate
     xc432bd4e21a7b9fa(["heat_index_links_dates"]):::outdated --> x4f749438c4164b8e["heat_index_download_files"]:::outdated
     x113a83dcec46090f(["heat_index_links_urls"]):::outdated --> x4f749438c4164b8e["heat_index_download_files"]:::outdated
     x56bd7c118ed46a38(["heat_index_links"]):::outdated --> xc432bd4e21a7b9fa(["heat_index_links_dates"]):::outdated
-  end
-```
-
-3.  Climatological extremes and normals data over time
-
-<!-- end list -->
-
-``` mermaid
-graph LR
-  style Graph fill:#FFFFFF00,stroke:#000000;
-  subgraph Graph
-    direction LR
-    xb48a3b157c96bffd(["climate_pubfiles_url"]):::uptodate --> xa37a01adfb45bd68(["climate_directory_urls"]):::uptodate
-    xc044beb81380bb4a["climate_download_files"]:::uptodate --> x84b857e732ff3c29(["pagasa_weather_stations"]):::outdated
     xa37a01adfb45bd68(["climate_directory_urls"]):::uptodate --> x26b861c7a0a21b52["climate_pdf_urls"]:::uptodate
-    x26b861c7a0a21b52["climate_pdf_urls"]:::uptodate --> xc044beb81380bb4a["climate_download_files"]:::uptodate
-    xc044beb81380bb4a["climate_download_files"]:::uptodate --> xe06460aefd475ca2(["climate_data_extremes_2020"]):::outdated
-    xc044beb81380bb4a["climate_download_files"]:::uptodate --> xc83b489a1c433852(["climate_data_extremes_2021"]):::outdated
-    xc044beb81380bb4a["climate_download_files"]:::uptodate --> x9b64b30afbfc8ff9(["climate_data_extremes_2022"]):::outdated
-    xc044beb81380bb4a["climate_download_files"]:::uptodate --> xf620d5783ff15609(["climate_data_extremes_2023"]):::outdated
-    xc044beb81380bb4a["climate_download_files"]:::uptodate --> x42da7c0722c063a6(["climate_data_normals_1991_2020"]):::outdated
-  end
-```
-
-4.  Daily dam water level data
-
-<!-- end list -->
-
-``` mermaid
-graph LR
-  style Graph fill:#FFFFFF00,stroke:#000000;
-  subgraph Graph
-    direction LR
-    x0de96327cc07b160(["dam_level_data"]):::outdated --> x202d34e7af3ea1c1(["dam_level_data_raw_csv"]):::outdated
-    xd2c3c65ab78d2c70(["dam_level_data_processed"]):::uptodate --> xfa0b497de91938bb(["dam_level_data_csv"]):::uptodate
-    x7255575025352eb6(["dam_level_data_files"]):::uptodate --> xd2c3c65ab78d2c70(["dam_level_data_processed"]):::uptodate
-    xc77bb431ac7c3081(["dam_level_url"]):::uptodate --> x0de96327cc07b160(["dam_level_data"]):::outdated
   end
 ```
 
 To run any of these workflows, run the following command on the R
 console:
 
 ``` r
-targets::tar_make(script = "NAME_OF_TARGET_SCRIPT_R_FILE")
+targets::tar_make(dplyr::starts_with("PREFIX"))
 ```
 
-replacing `"NAME_OF_TARGET_SCRIPT_R_FILE"` with the name of the target
-script file for the specific workflow. For example, to run the cyclones
-workflow from R console:
+replacing `"PREFIX"` with the keyword for the type of data. For example,
+to run the cyclones workflow from R console:
 
 ``` r
-targets::tar_make(script = "_targets_cyclones.R")
+targets::tar_make(dplyr::starts_with("cyclone"))
 ```
 
 or from the command line/terminal as follows:
 
 ``` bash
-Rscript -e "targets::tar_make(script = "_targets_cyclones.R)"
+Rscript -e "targets::tar_make(dplyr::starts_with('cyclone'))"
 ```
 
 Running specific components of a workflow involves specifying a target
@@ -276,13 +232,13 @@ entire cyclones data workflow (as an alternative to what is shown
 above):
 
 ``` r
-targets::tar_make(cyclones_peak_data_csv, script = "_targets_cyclones.R")
+targets::tar_make(cyclones_peak_data_csv)
 ```
 
 or from the command line/terminal as follows:
 
 ``` bash
-Rscript -e "targets::tar_make(cyclones_peak_data_csv, script = "_targets_cyclones.R")"
+Rscript -e "targets::tar_make(cyclones_peak_data_csv)"
 ```
 
 The target `cyclones_peak_data_csv` is the last target of the cyclones
@@ -295,11 +251,10 @@ you can use `tidyselect` approaches to name targets to be run. For
 example:
 
 ``` r
-targets::tar_make(dplyr::starts_with("cyclone"), script = "_targets_cyclones.R")
+targets::tar_make(dplyr::starts_with(c("cyclone", "dam"))
 ```
 
-will run all targets in the cyclones data workflow whose names start
-with *“cyclone”*.
+will run all targets in the cyclones and dam levels data workflow.
 
 The project also has a workflow for weekly GitHub release of the various
 raw datasets.

diff --git a/_targets_dam.R b/_targets_dam.R
@@ -52,7 +52,8 @@ output_targets <- tar_plan(
   ### Output processed dam level data as CSV ----
   tar_target(
     name = dam_level_data_csv,
-    command = dam_archive_processed(dam_level_data_processed)
+    command = dam_archive_processed(dam_level_data_processed),
+    format = "file"
   )
 )
 

diff --git a/_targets_releases.R b/_targets_releases.R
@@ -17,12 +17,7 @@ release_targets <- tar_plan(
   tar_target(
     name = paglaom_weekly_release,
     command = paglaom_upload_weekly_release(
-      climate_download_files,
-      cyclone_reports_download_files,
-      dam_level_data_files,
-      heat_index_download_files,
-      #tag = paglaom_weekly_release_tag
-      tag = "1.8.34"
+      tag = paglaom_weekly_release_tag
     )
   )
 )