### zi_census_api_key() example

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_census_api_key.html

Example usage of zi_census_api_key() with a sample key and install = TRUE. The example is wrapped in if (FALSE) to prevent execution in non-interactive sessions.

```r
if (FALSE) { # interactive()
  zi_census_api_key(key = "111111abc", install = TRUE)
}
```

--------------------------------

### zi_label() examples with UDS, USPS, and custom dictionary

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_label.html

Complete example block from the documentation. It creates sample data, then shows commented-out calls for UDS and USPS crosswalks, and an active example using a custom dictionary (`mo_label`) with output.

```r
# create sample data
df <- data.frame(
  id = c(1:3),
  zip5 = c("63005", "63139", "63636"),
  zip3 = c("630", "631", "636")
)

if (FALSE) { # interactive()
# UDS crosswalk
zi_label(df, input_var = zip5, label_source = "UDS", vintage = 2022)

# USPS crosswalk
zi_label(df, input_var = zip3, label_source = "USPS", type = "zip3",
  vintage = 202408)
}
# custom dictionary
## load sample ZIP3 label data to simulate custom dictionary
mo_label <- zi_mo_usps

## label
zi_label(df, input_var = zip3, label_source = mo_label, source_var = zip3,
  type = "zip3")
#> # A tibble: 3 × 5
#>      id zip5  zip3  label_area label_state
#>   <int> <chr> <chr> <chr>      <chr>      
#> 1     1 63005 630   ST LOUIS   MO         
#> 2     2 63139 631   ST LOUIS   MO         
#> 3     3 63636 636   ST LOUIS   MO         
```

--------------------------------

### Interactive examples for zi_load_labels with UDS and USPS

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_load_labels.html

Interactive examples showing how to load zip5 labels from UDS and zip3 labels from USPS. The code is wrapped in if (FALSE) to prevent execution in non-interactive sessions. The USPS example uses vintage 202408 and the UDS example uses vintage 2022.

```r
if (FALSE) { # interactive()
  # zip5 labels via UDS
  zi_load_labels(source = "UDS", type = "zip5", vintage = 2022)

  # zip3 labels via USPS
  zi_load_labels(source = "USPS", type = "zip3", vintage = 202408)
}
```

--------------------------------

### Install development version of zippeR from GitHub

Source: https://pfizer-opensource.github.io/zippeR/index.html

Installs the development version of zippeR from GitHub using remotes::install_github(). Requires the remotes package, which can be installed first if needed.

```R
# install.packages("remotes")
remotes::install_github("pfizer-evgen/zippeR")
```

--------------------------------

### Example: zi_load_labels_list(type = "zip3")

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_load_labels_list.html

Example usage wrapped in `if (FALSE) { # interactive() }` to prevent execution during package checks. It loads the list of available label data sets for three-digit ZIP Codes.

```r
if (FALSE) { # interactive()
  zi_load_labels_list(type = "zip3")
}
```

--------------------------------

### Install zippeR from CRAN

Source: https://pfizer-opensource.github.io/zippeR/index.html

Installs the stable version of zippeR from CRAN using install.packages().

```R
install.packages("zippeR")
```

--------------------------------

### zi_crosswalk examples with UDS, HUD, and custom dictionary

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_crosswalk.html

Example usage of `zi_crosswalk` with UDS, HUD, and a custom dictionary. The UDS and HUD calls are wrapped in `if (FALSE)` because they require interactive sessions or an API key. The custom dictionary example uses `zi_mo_hud` and `zi_prep_hud` to prepare the crosswalk, then calls `zi_crosswalk` with `zip_source = mo_xwalk`. The output shows the resulting tibble with `source_geoid` appended.

```r
# create sample data
df <- data.frame(id = c(1:3), zip5 = c("63005", "63139", "63636"))

if (FALSE) { # interactive()
# UDS crosswalk
zi_crosswalk(df, input_var = zip5, zip_source = "UDS", year = 2022)
}
if (FALSE) { # nzchar(Sys.getenv("hud_key"))
# HUD crosswalk
zi_crosswalk(df, input_var = zip5, zip_source = "HUD", year = 2023,
  qtr = 1, target = "COUNTY", query = "MO", by = "residential",
  return_max = TRUE, key = Sys.getenv("hud_key"))
}
# custom dictionary
## load sample crosswalk data to simulate custom dictionary
mo_xwalk <- zi_mo_hud

# prep crosswalk
# when a ZIP Code crosses county boundaries, the portion with the largest
# number of residential addresses will be returned
mo_xwalk <- zi_prep_hud(mo_xwalk, by = "residential", return_max = TRUE)

## crosswalk
zi_crosswalk(df, input_var = zip5, zip_source = mo_xwalk, source_var = zip5,
  source_result = geoid)
#> # A tibble: 3 × 3
#>      id zip5  source_geoid
#>   <int> <chr> <chr>       
#> 1     1 63005 29189       
#> 2     2 63139 29510       
#> 3     3 63636 29093       
```

--------------------------------

### Inspect structure and first rows of zi_mo_zcta3

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_mo_zcta3.html

The examples show how to inspect the dataset using `utils::str()` and `utils::head()`, with the output displayed for each.

```R
utils::str(zi_mo_zcta3)
#> Classes ‘sf’ and 'data.frame':	31 obs. of  2 variables:
#>  $ ZCTA3   : chr  "501" "516" "525" "526" ...
#>  $ geometry:sfc_MULTIPOLYGON of length 31; first list element: List of 21
#>   ..$ :List of 1
#>   .. ..$ : num [1:7, 1:2] -92.9 -92.9 -92.9 -92.9 -92.9 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:38, 1:2] -93.7 -93.7 -93.6 -93.6 -93.6 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:8, 1:2] -93.7 -93.7 -93.7 -93.7 -93.7 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:27, 1:2] -93.2 -93.2 -93.2 -93.2 -93.2 ...
#>   ..$ :List of 2
#>   .. ..$ : num [1:51, 1:2] -92.4 -92.4 -92.4 -92.4 -92.4 ...
#>   .. ..$ : num [1:9, 1:2] -92.3 -92.3 -92.3 -92.3 -92.3 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:9, 1:2] -95 -95 -95 -95 -95 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:284, 1:2] -93.4 -93.4 -93.4 -93.4 -93.3 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:352, 1:2] -94.3 -94.3 -94.3 -94.3 -94.3 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:17, 1:2] -93.7 -93.7 -93.7 -93.7 -93.6 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:7, 1:2] -93.4 -93.4 -93.4 -93.4 -93.4 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:95, 1:2] -93.4 -93.4 -93.4 -93.4 -93.4 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:144, 1:2] -93.8 -93.8 -93.7 -93.7 -93.7 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:347, 1:2] -94.7 -94.7 -94.8 -94.8 -94.9 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:746, 1:2] -93.9 -93.9 -93.9 -93.9 -93.9 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:206, 1:2] -94.3 -94.3 -94.3 -94.3 -94.3 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:10, 1:2] -93.3 -93.3 -93.3 -93.3 -93.3 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:234, 1:2] -93.5 -93.5 -93.5 -93.5 -93.5 ...
#>   ..$ :List of 3
#>   .. ..$ : num [1:579, 1:2] -93.1 -93.1 -93.1 -93.1 -93.1 ...
#>   .. ..$ : num [1:5, 1:2] -92.9 -92.9 -92.9 -92.9 -92.9 ...
#>   .. ..$ : num [1:6, 1:2] -92.8 -92.8 -92.8 -92.8 -92.8 ...
#>   ..$ :List of 2
#>   .. ..$ : num [1:1521, 1:2] -93.3 -93.3 -93.2 -93.2 -93.2 ...
#>   .. ..$ : num [1:10, 1:2] -93.2 -93.2 -93.2 -93.2 -93.2 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:9, 1:2] -93.1 -93.1 -93.1 -93.1 -93.1 ...
#>   ..$ :List of 1
#>   .. ..$ : num [1:363, 1:2] -93.7 -93.7 -93.6 -93.6 -93.6 ...
#>   ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
#>  - attr(*, "sf_column")= chr "geometry"
#>  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA
#>   ..- attr(*, "names")= chr "ZCTA3"
```

```R
utils::head(zi_mo_zcta3)
#> Simple feature collection with 6 features and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -95.88891 ymin: 37.93668 xmax: -88.97182 ymax: 42.69899
#> Geodetic CRS:  NAD83
#>   ZCTA3                       geometry
#> 1   501 MULTIPOLYGON (((-92.9414 41...
#> 2   516 MULTIPOLYGON (((-95.36826 4...
#> 3   525 MULTIPOLYGON (((-91.91263 4...
#> 4   526 MULTIPOLYGON (((-91.43297 4...
#> 5   620 MULTIPOLYGON (((-90.61378 3...
#> 6   630 MULTIPOLYGON (((-90.32484 3...
```

--------------------------------

### zi_convert examples: adding a new column and overwriting

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_convert.html

Two examples from the function documentation. The first creates a data frame with a zip5 column and converts it to a new zip3 column, showing the resulting tibble. The second creates a data frame with a zip column and converts it in place, overwriting the original column, also showing the output.

```r
# add new column
## create sample data
df <- data.frame(id = c(1:3), zip5 = c("63005", "63139", "63636"))

## convert ZIP Codes to ZIP3, creating a new column
zi_convert(.data = df, input_var = zip5, output_var = zip3)
#> # A tibble: 3 × 3
#>      id zip5  zip3 
#>   <int> <chr> <chr>
#> 1     1 63005 630  
#> 2     2 63139 631  
#> 3     3 63636 636  

# overwrite existing column
## create sample data
df <- data.frame(id = c(1:3), zip = c("63005", "63139", "63636"))

## convert ZIP Codes to ZIP3, creating a new column
zi_convert(.data = df, input_var = zip)
#> # A tibble: 3 × 2
#>      id zip  
#>   <int> <chr>
#> 1     1 630  
#> 2     2 631  
#> 3     3 636  


```

--------------------------------

### zi_get_demographics() example

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_get_demographics.html

Example usage wrapped in if (FALSE) for interactive sessions. Downloads all ZCTAs, then limits output to a subset of ZCTAs in Missouri using the intersects method.

```r
if (FALSE) { # interactive()
  # download all ZCTAs
  zi_get_demographics(year = 2012, variables = "B01003_001", survey = "acs5")

  # limit output to subset of ZCTAs
  ## download all ZCTAs in Missouri, intersects method
  mo20 <- zi_get_geometry(year = 2020, state = "MO", method = "intersect")

  ## download demographic data
  zi_get_demographics(year = 2012, variables = "B01003_001", survey = "acs5",
      zcta = mo20$GEOID)
}
```

--------------------------------

### Example: Preparing HUD crosswalk data

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_prep_hud.html

Loads sample HUD crosswalk data for Missouri, then calls `zi_prep_hud()` with `by = "residential"` and `return_max = TRUE`. The output shows a tibble with columns zip5, geoid, state, state_fips, and ratio. The comment notes that the sample data can be replicated with `zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1, target = "COUNTY", query = "MO")`.

```r
# load sample crosswalk data
mo_xwalk <- zi_mo_hud

  # the above data can be replicated with the following code:
  # zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1,
  #   target = "COUNTY", query = "MO")

# prep crosswalk
# when a ZIP Code crosses county boundaries, the portion with the largest
# number of residential addresses will be returned
zi_prep_hud(mo_xwalk, by = "residential", return_max = TRUE)
#> # A tibble: 1,127 × 5
#>    zip5  geoid state state_fips ratio
#>    <chr> <chr> <chr> <chr>      <dbl>
#>  1 72643 05009 MO    29         1    
#>  2 63501 29001 MO    29         1    
#>  3 63533 29001 MO    29         0.978
#>  4 63540 29001 MO    29         1    
#>  5 63546 29001 MO    29         0.630
#>  6 63559 29001 MO    29         0.950
#>  7 64421 29003 MO    29         1    
#>  8 64427 29003 MO    29         0.889
#>  9 64436 29003 MO    29         0.980
#> 10 64449 29003 MO    29         1    
#> # ℹ 1,117 more rows
```

--------------------------------

### zi_get_geometry examples for ZCTA downloads

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_get_geometry.html

These examples show various calls to zi_get_geometry for downloading ZCTA geometry data. They cover downloading all ZCTAs for 2020 with and without territories, selecting states with the intersect method, selecting a single county, and downloading 2018 ZCTAs with territories. The code is wrapped in an if (FALSE) block, indicating it is for interactive use.

```R
if (FALSE) { # interactive()
  # five-digit ZCTAs
  ## download all ZCTAs for 2020 including territories
  zi_get_geometry(year = 2020, territory = c("AS", "GU", "MP", "PR", "VI"),
      shift_geo = TRUE)

  ## download all ZCTAs for 2020 excluding territories
  zi_get_geometry(year = 2020, shift_geo = TRUE)

  ## download all ZCTAs in a selection of states, intersects method
  zi_get_geometry(year = 2020, state = c("IA", "IL", "MO"), method = "intersect")

  ## download all ZCTAs in a single county - St. Louis City, MO
  zi_get_geometry(year = 2020, state = "MO", county = "29510",
      method = "intersect")

  # three-digit ZCTAs
  ## download all ZCTAs for 2018 including territories
  zi_get_geometry(year = 2018, territory = c("AS", "GU", "MP", "PR", "VI"),
      shift_geo = TRUE)
}

```

--------------------------------

### zi_aggregate examples

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_aggregate.html

Example code for aggregating demographic data to three-digit ZCTAs. The example loads sample data, aggregates a single variable, and then aggregates multiple variables with wide output. The code is wrapped in an interactive() check.

```r
if (FALSE) { # interactive()
# load sample demographic data
mo22_demos <- zi_mo_pop

  # the above data can be replicated with the following code:
  # zi_get_demographics(year = 2022, variables = c("B01003_001", "B19013_001"),
  #   survey = "acs5")

# load sample geometric data
mo22_zcta3 <- zi_mo_zcta3

  # the above data can be replicated with the following code:
  # zi_get_geometry(year = 2022, style = "zcta3", state = "MO",
  #   method = "intersect")

# aggregate a single variable
zi_aggregate(mo22_demos, year = 2020, extensive = "B01003_001", survey = "acs5",
  zcta = mo22_zcta3$ZCTA3)

# aggregate multiple variables, outputting wide data
zi_aggregate(mo22_demos, year = 2020,
  extensive = "B01003_001", intensive = "B19013_001", survey = "acs5",
  zcta = mo22_zcta3$ZCTA3, output = "wide")
}
```

--------------------------------

### Example Tibble Output

Source: https://pfizer-opensource.github.io/zippeR/articles/converting-zips.html

A sample tibble showing the structure of ZIP code data with id, ZIP, and source_zcta columns.

```text
# A tibble: 3 × 3                                                                                                                                                                                      
     id ZIP   source_zcta 
  <int> <chr> <chr>
1     1 63139 63139
2     2 63108 63108
3     3 00501 11742
```

--------------------------------

### Example: zi_list_zctas for Missouri with intersect and centroid methods

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_list_zctas.html

Example usage of zi_list_zctas for Missouri with both methods. The intersect method returns ZCTAs that border the state, while the centroid method returns only ZCTAs whose centroids lie within the state. The code is wrapped in if (FALSE) for interactive use only.

```r
if (FALSE) { # interactive()
# Missouri ZCTAs, intersect method
## return list
mo_zctas <- zi_list_zctas(year = 2021, state = "MO", method = "intersect")

## preview ZCTAs
mo_zctas[1:10]

# Missouri ZCTAs, centroid method
## return list
mo_zctas <- zi_list_zctas(year = 2021, state = "MO", method = "centroid")

## preview ZCTAs
mo_zctas[1:10]
}
```

--------------------------------

### zi_validate() examples with successful and failed validation

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_validate.html

Examples showing successful validation of five-digit ZIPs, validation of a data frame column, and failed validation with verbose output. The verbose output shows a tibble with validation criteria and results.

```r
# sample five-digit ZIPs
zips <- c("63088", "63108", "63139")

# successful validation
zi_validate(zips)
#> [1] TRUE

# sample five-digit ZIPs in data frame
zips <- data.frame(id = c(1:3), ZIP = c("63139", "63108", "00501"), stringsAsFactors = FALSE)

# successful validation
zi_validate(zips$ZIP)
#> [1] TRUE

# sample five-digit ZIPs with character
zips <- c("63088", "63108", "zip")

# failed validation
zi_validate(zips)
#> [1] FALSE
zi_validate(zips, verbose = TRUE)
#> # A tibble: 4 × 2
#>   condition                                   result
#>   <chr>                                       <lgl> 
#> 1 Input is a character vector?                TRUE  
#> 2 All input values have 5 characters?         FALSE 
#> 3 No input values are over 5 characters long? TRUE  
#> 4 All input values are numeric?               FALSE 
```

--------------------------------

### zi_census_api_key() usage

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_census_api_key.html

Shows the function signature with arguments key, overwrite, and install. The key is a character scalar obtained from the Census API signup page. overwrite and install are logical scalars with defaults FALSE.

```r
zi_census_api_key(key, overwrite = FALSE, install = FALSE)
```

--------------------------------

### Load UDS Mapper crosswalks

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_load_crosswalk.html

Example of loading a UDS Mapper crosswalk file for a specific year.

```R
if (FALSE) { # interactive()
 # former UDS mapper crosswalks
 zi_load_crosswalk(zip_source = "UDS", year = 2020)
}
```

--------------------------------

### Load HUD crosswalks

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_load_crosswalk.html

Examples of loading HUD crosswalk files for various targets including CBSA, County, and Tract, requiring a HUD API key.

```R
if (FALSE) { # nzchar(Sys.getenv("hud_key"))
 # HUD crosswalks
 ## ZIP Code to CBSA crosswalk for all ZIP Codes
 zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1, target = "CBSA",
     query = "all", key = Sys.getenv("hud_key"))

 ## ZIP Code to County crosswalk for all ZIP Codes in Missouri
 zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1, target = "COUNTY",
     query = "MO", key = Sys.getenv("hud_key"))

 ## ZIP Code to Tract crosswalk for ZIP Code 63139 in St. Louis City
 zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1, target = "TRACT",
     query = 63139, key = Sys.getenv("hud_key"))
}
```

--------------------------------

### Get Missouri ZCTA geometry with centroid method and includes

Source: https://pfizer-opensource.github.io/zippeR/articles/demographic-geometric-zcta-data.html

Downloads ZCTA geometry for Missouri using the centroid method and adds specific ZCTAs whose centroids fall outside the state. The `includes` argument is required because those ZCTAs lie in Iowa.

```r
geo12 <- zi_get_geometry(year = 2012, state = "MO", method = "centroid", 
    includes = c("51640", "52542", "52573", "52626"))
```

--------------------------------

### zi_prep_hud(.data, by, return_max = TRUE)

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_prep_hud.html

Prepares HUD crosswalk data for use in the zi_crosswalk function. It takes the output from zi_load_crosswalk() with HUD data and returns a tibble that has been further prepared for use as a crosswalk.

```APIDOC
## zi_prep_hud(.data, by, return_max = TRUE)

### Description
Prepares HUD crosswalk data for use in the zi_crosswalk() function. The output from zi_load_crosswalk() for HUD data requires additional processing to be used in the zi_crosswalk() function.

### Usage
zi_prep_hud(.data, by, return_max = TRUE)

### Arguments
- **.data** - The output from zi_load_crosswalk() with HUD data.
- **by** - Character scalar; the column name to use for identifying the best match for a given ZIP Code. This could be either "residential", "commercial", or "total".
- **return_max** - Logical scalar; if TRUE (default), only the county with the highest proportion of the ZIP Code type will be returned. If the ZIP Code straddles two states, two records will be returned. If FALSE, all records for the ZIP Code will be returned. Where a tie exists (i.e. two counties each contain half of all addresses), the county with the lowest GEOID value will be returned.

### Value
A tibble that has been further prepared for use as a crosswalk.

### Examples
```r
# load sample crosswalk data
mo_xwalk <- zi_mo_hud

# the above data can be replicated with the following code:
# zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1,
#   target = "COUNTY", query = "MO")

# prep crosswalk
# when a ZIP Code crosses county boundaries, the portion with the largest
# number of residential addresses will be returned
zi_prep_hud(mo_xwalk, by = "residential", return_max = TRUE)
#> # A tibble: 1,127 × 5
#>    zip5  geoid state state_fips ratio
#>    <chr> <chr> <chr> <chr>      <dbl>
#>  1 72643 05009 MO    29         1    
#>  2 63501 29001 MO    29         1    
#>  3 63533 29001 MO    29         0.978
#>  4 63540 29001 MO    29         1    
#>  5 63546 29001 MO    29         0.630
#>  6 63559 29001 MO    29         0.950
#>  7 64421 29003 MO    29         1    
#>  8 64427 29003 MO    29         0.889
#>  9 64436 29003 MO    29         0.980
#> 10 64449 29003 MO    29         1    
#> # ℹ 1,117 more rows
```
```

--------------------------------

### Usage: zi_prep_hud()

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_prep_hud.html

Function signature for `zi_prep_hud()`. Arguments: `.data` (output from `zi_load_crosswalk()` with HUD data), `by` (column name for best match: "residential", "commercial", or "total"), and `return_max` (logical, default TRUE).

```r
zi_prep_hud(.data, by, return_max = TRUE)
```

--------------------------------

### Inspecting the zi_mo_hud dataset

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_mo_hud.html

Use utils::str and utils::head to examine the structure and first few rows of the dataset.

```R
utils::str(zi_mo_hud)
#> tibble [1,749 × 8] (S3: tbl_df/tbl/data.frame)
#>  $ ZIP      : chr [1:1749] "63103" "63447" "63362" "63134" ...
#>  $ GEOID    : chr [1:1749] "29510" "29111" "29113" "29189" ...
#>  $ RES_RATIO: num [1:1749] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ BUS_RATIO: num [1:1749] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ OTH_RATIO: num [1:1749] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ TOT_RATIO: num [1:1749] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ CITY     : chr [1:1749] "SAINT LOUIS" "LA BELLE" "MOSCOW MILLS" "SAINT LOUIS" ...
#>  $ STATE    : chr [1:1749] "MO" "MO" "MO" "MO" ...
utils::head(zi_mo_hud)
#> # A tibble: 6 × 8
#>   ZIP   GEOID RES_RATIO BUS_RATIO OTH_RATIO TOT_RATIO CITY            STATE
#>   <chr> <chr>     <dbl>     <dbl>     <dbl>     <dbl> <chr>           <chr>
#> 1 63103 29510         1         1         1         1 SAINT LOUIS     MO   
#> 2 63447 29111         1         1         1         1 LA BELLE        MO   
#> 3 63362 29113         1         1         1         1 MOSCOW MILLS    MO   
#> 4 63134 29189         1         1         1         1 SAINT LOUIS     MO   
#> 5 65439 29093         1         1         1         1 BIXBY           MO   
#> 6 65686 29209         1         1         1         1 KIMBERLING CITY MO
```

--------------------------------

### Get nation-wide ZCTA geometry with shift_geo

Source: https://pfizer-opensource.github.io/zippeR/articles/demographic-geometric-zcta-data.html

Downloads nation-wide ZCTA geometry for 2010 and shifts Alaska, Hawaii, and Puerto Rico to the lower left-hand corner of the map using the `shift_geo` argument.

```r
geo10 <- zi_get_geometry(year = 2010, shift_geo = TRUE)
```

--------------------------------

### Load HUD Crosswalk Data

Source: https://pfizer-opensource.github.io/zippeR/articles/converting-zips.html

Demonstrates downloading HUD crosswalk data using zi_load_crosswalk() with specific year, quarter, target, and query parameters.

```r
> zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1, target = "COUNTY",
+                   query = c("63138", "63139"))
```

--------------------------------

### Get Gini index for ZCTAs from geometry output

Source: https://pfizer-opensource.github.io/zippeR/articles/demographic-geometric-zcta-data.html

Downloads Gini index data (table B19083) from the 2012 ACS5 survey, limiting results to the ZCTAs in the `geo12` object from a prior `zi_get_geometry()` call.

```r
gini12 <- zi_get_demographics(year = 2012, table = "B19083", survey = "acs5", zcta = geo12$GEOID)
```

--------------------------------

### Get total population from 2010 Decennial Census

Source: https://pfizer-opensource.github.io/zippeR/articles/demographic-geometric-zcta-data.html

Downloads total population for all ZCTAs from the 2010 Decennial Census using the `zi_get_demographics()` wrapper. Note that ZCTA data for the 2020 Decennial Census have not been released.

```r
pop10 <- zi_get_demographics(year = 2010, variables = "P001001", survey = "sf1")
```

--------------------------------

### Download demographic data with zi_get_demographics()

Source: https://pfizer-opensource.github.io/zippeR/articles/three-digit-zips.html

Downloads 2020 ACS5 population estimates for all five-digit ZCTAs. Do not limit the download with the `zcta` argument; all ZCTAs must be included to avoid incorrect values for ZCTAs split across jurisdictions.

```r
mo_pop20 <- zi_get_demographics(year = 2020, variables = "B01003_001", survey = "acs5")
```

--------------------------------

### Repair ZIP codes with zi_repair()

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_repair.html

Example showing how to repair a vector of ZIP codes that contains a non-numeric value. The zi_repair() function converts the invalid entry to NA and emits a warning "NAs introduced by coercion." After repair, zi_validate() returns TRUE.

```r
# sample five-digit ZIPs with character
zips <- c("63088", "63108", "zip")

# failed validation
zi_validate(zips)
#> [1] FALSE

# repair
zips <- zi_repair(zips)
#> Warning: NAs introduced by coercion.

# successful validation
zi_validate(zips)
#> [1] TRUE
```

--------------------------------

### Replicating the zi_mo_hud dataset

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_mo_hud.html

Use the zi_load_crosswalk function to replicate the data. This requires a HUD API key stored as 'hud_key' in your .Rprofile.

```R
zi_load_crosswalk(zip_source = "HUD", year = 2023,   qtr = 1, target = "COUNTY", query = "MO")
```

--------------------------------

### Pipe download and aggregate with multiple variables

Source: https://pfizer-opensource.github.io/zippeR/articles/three-digit-zips.html

Pipes the download and aggregation workflow, specifying both extensive and intensive variables. The `variables`, `table`, `extensive`, and `intensive` arguments are not validated before being passed to the Census Bureau, so incorrectly formatted names may generate cryptic errors.

```r
zi_get_demographics(year = 2020, variables = c("B01003_001", "B19083_001"), survey = "acs5") %>%
  zi_aggregate(year = 2020, extensive = "B01003_001", intensive = "B19083_001", survey = "acs5") -> demo20
```

--------------------------------

### zi_label() function signature

Source: https://pfizer-opensource.github.io/zippeR/reference/zi_label.html

Shows the usage signature for `zi_label()`, including all arguments and their defaults.

```r
zi_label(
  .data,
  input_var,
  label_source = "UDS",
  source_var,
  type = "zip5",
  include_scf = FALSE,
  vintage = 2022
)
```