Submit border patrol data to Tidy Tuesday

data/curated/border-patrol/cbp_resp.md

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+|variable               |class     |description                           |
+|:----------------------|:---------|:-------------------------------------|
+|fiscal_year            |double    |The fiscal year the encounter took place |
+|month_grouping         |character |Allows for comparisons between completed FY months vs. those remaining |
+|month_abbv             |character |The month the encounter took place (abbreviated) |
+|component              |character |Who in CBP was involved in the encounter |
+|land_border_region     |character |The border region in which the encounter occurred; border regions are defined by each component Nationwide numbers are calculated by adding together Northern Land Border, Southwest Land Border, and Other regions  |
+|area_of_responsibility |character |The field office or sector where the encounter occurred |
+|aor_abbv               |character |The field office or sector where the encounter occurred (abbreviated) |
+|demographic            |character |Categories under which individuals were encountered based on factors such as age, admissibility, and relationship |
+|citizenship            |character |Citizenship of the individual encountered |
+|title_of_authority     |character |The authority under which the noncitizen was processed |
+|encounter_type         |character |The category of encounter based on Title of Authority and component (Title 8 for USBP = Apprehensions; Title 8 for OFO = Inadmissibles; Title 42 = Expulsions) |
+|encounter_count        |double    |The number of individuals encountered |

data/curated/border-patrol/cbp_state.md

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+|variable           |class     |description                           |
+|:------------------|:---------|:-------------------------------------|
+|fiscal_year        |double    |The fiscal year the encounter took place |
+|month_grouping     |character |Allows for comparisons between completed FY months vs. those remaining |
+|month_abbv         |character |The month the encounter took place (abbreviated) |
+|land_border_region |character |The border region in which the encounter occurred; border regions are defined by each component Nationwide numbers are calculated by adding together Northern Land Border, Southwest Land Border, and Other regions |
+|state              |character |State of the encounter |
+|demographic        |character |Categories under which individuals were encountered based on factors such as age, admissibility, and relationship |
+|citizenship        |character |Citizenship of the individual encountered |
+|title_of_authority |character |The authority under which the noncitizen was processed |
+|encounter_count    |double    |The number of individuals encountered |

data/curated/border-patrol/cleaning.R

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+cbp_resp <- bind_rows(
+  read_csv("https://www.cbp.gov/sites/default/files/assets/documents/2023-Nov/nationwide-encounters-fy20-fy23-aor.csv"),
+  read_csv("https://www.cbp.gov/sites/default/files/2024-10/nationwide-encounters-fy21-fy24-aor.csv")
+) |>
+  janitor::clean_names() |>
+  unique()
+
+cbp_state <- bind_rows(
+  read_csv("https://www.cbp.gov/sites/default/files/assets/documents/2023-Nov/nationwide-encounters-fy20-fy23-state.csv"),
+  read_csv("https://www.cbp.gov/sites/default/files/2024-10/nationwide-encounters-fy21-fy24-state.csv")
+) |>
+  janitor::clean_names() |>
+  unique()

data/curated/border-patrol/instructions.md

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+## Prepare the dataset
+
+These instructions are for preparing a dataset using the R programming language.
+We hope to provide instructions for other programming languages eventually.
+
+If you have not yet set up your computer for submitting a dataset, please see the full instructions at <https://github.com/rfordatascience/tidytuesday/blob/master/.github/pr_instructions.md>.
+
+1.  `cleaning.R`: Modify the `cleaning.R` file to get and clean the data.
+    -   Write the code to download and clean the data in `cleaning.R`.
+    -   If you're getting the data from a github repo, remember to use the 'raw' version of the URL.
+    -   This script should result in one or more data.frames, with descriptive variable names (eg `players` and `teams`, not `df1` and `df2`).
+
+2.  `saving.R`: Use`saving.R` to save your datasets. This process creates both the `.csv` file(s) and the data dictionary template file(s) for your datasets. **Don't save the CSV files using a separate process because we also need the data dictionaries.**
+    -   Run the first line of `saving.R` to create the functions we'll use to save your dataset.
+    -   Provide the name of your directory as `dir_name`.
+    -   Use `ttsave()` for each dataset you created in `cleaning.R`, substituting the name for the dataset for `YOUR_DATASET_DF`.
+
+3.  `{dataset}.md`: Edit the `{dataset}.md` files to describe your datasets (where `{dataset}` is the name of the dataset). These files are created by `saving.R`. There should be one file for each of your datasets. You most likely only need to edit the "description" column to provide a description of each variable.
+
+4.  `intro.md`: Edit the `intro.md` file to describe your dataset. You don't need to add a `# Title` at the top; this is just a paragraph or two to introduce the week.
+
+5.  Find at least one image for your dataset. These often come from the article about your dataset. If you can't find an image, create an example data visualization, and save the images in your folder as `png` files.
+
+6.  `meta.yaml`: Edit `meta.yaml` to provide information about your dataset and how we can credit you. You can delete lines from the `credit` block that do not apply to you.
+
+### Submit your pull request with the data
+
+1.  Commit the changes with this folder to your branch. In RStudio, you can do this on the "Git" tab (the "Commit" button).
+
+2.  Submit a pull request to <https://github.com/rfordatascience/tidytuesday>. In R, you can do this with `usethis::pr_push()`, and then follow the instructions in your browser.

data/curated/border-patrol/intro.md

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+DESCRIPTION
+
+This week we're exploring [U.S. Customs and Border Protection (CBP) encounter data](https://www.cbp.gov/document/stats/nationwide-encounters):
+
+> Encounter data includes U.S. Border Patrol Title 8 apprehensions, Office of Field Operations Title 8 inadmissibles, and all Title 42 expulsions for fiscal years 2020 to date. Data is available for the Northern Land Border, Southwest Land Border, and Nationwide (i.e., air, land, and sea modes of transportation) encounters. Data is extracted from live CBP systems and data sources. Statistical information is subject to change due to corrections, systems changes, change in data definition, additional information, or encounters pending final review. Final statistics are available at the conclusion of each fiscal year.
+
+Thank you to [Tony Galván](https://www.linkedin.com/in/anthony-raul-galvan/) for curating this dataset and providing a [blog post](https://posit.cloud/content/9130042) that explores the data in more detail.
+
+QUESTION?
+
+-   How has the implementation (and potential end) of Title 42 affected migration and enforcement trends compared to Title 8 actions?
+-   What are the key differences in migration patterns and enforcement activity between the Northern and Southwest Land Borders?
+-   Are there seasonal or year-over-year trends in encounters that can help predict future migration patterns?

data/curated/border-patrol/meta.yaml

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+title: U.S. Customs and Border Protection (CBP) encounter data
+article:
+  title: U.S. Border Patrol Encounters
+  url: https://posit.cloud/content/9130042
+data_source:
+  title: U.S. Customs and Border Protection
+  url: https://www.cbp.gov/
+images:
+# Please include at least one image, and up to three images
+- file: inadmissible-aliens-plot.png
+  alt: >
+    A bar plot of monthly inadmissible CHNV (citizens of Cuba, Haiti, Nicaragua, and Venezuela) aliens encountered by U.S. Border Patrol agents from 2020 to 2024.
+credit:
+# We want to thank you for curating this dataset! If you do not want a 
+# particular type of credit, please delete the related line.
+  post: Tony Galván
+  linkedin: https://www.linkedin.com/in/anthony-raul-galvan
+  twitter: @GDataScience1
+  github: https://github.com/gdatascience

data/curated/border-patrol/saving.R

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+# Run this
+source("data/curated/curation_scripts.R")
+
+# Fill in the name of the folder you created in "curated", then run this.
+dir_name <- "border-patrol"
+
+# Run this for each of your datasets, replacing YOUR_DATASET_DF with the name of
+# a data.frame from cleaning.R.
+ttsave(cbp_resp, dir_name = dir_name)
+ttsave(cbp_state, dir_name = dir_name)