ARCOS

https://wpinvestigative.github.io/arcos/
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ARCOS is an R wrapper package for accessing the DEA's ARCOS database, which tracks opioid pill...
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### Example usage of total_distributors_county

Source: https://wpinvestigative.github.io/arcos/reference/total_distributors_county.html

Shows a practical example of using `total_distributors_county` to get distribution data for Mingo County, WV, and then displaying the first few rows of the result. This is helpful for understanding the function's output format.

```r
# \donttest{
library(arcos)

mingo_wv <- total_distributors_county(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
# >   BUYER_STATE buyer_county             Reporter_family total_dosage_unit
# > 1          WV        MINGO      AmerisourceBergen Drug           3701200
# > 2          WV        MINGO                   Anda, Inc             82400
# > 3          WV        MINGO   Associated Pharmacies Inc              1500
# > 4          WV        MINGO       Auburn Pharmaceutical             17700
# > 5          WV        MINGO Butler Animal Health Supply               700
# > 6          WV        MINGO             Cardinal Health           3979150
# >   total_records
# > 1          4292
# > 2            89
# > 3             1
# > 4            10
# > 5             4
# > 6          5915
# }

```

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

### Example usage of total_manufacturers_county

Source: https://wpinvestigative.github.io/arcos/reference/total_manufacturers_county.html

This example demonstrates how to use the `total_manufacturers_county` function to get data for Mingo County, WV, and then displays the first few rows of the result.

```r
# \donttest{
library(arcos)

mingo_wv <- total_manufacturers_county(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   buyer_state buyer_county      combined_labeler_name total_dosage_unit
#> 1          WV        MINGO                 AAI Pharma             11100
#> 2          WV        MINGO                AbbVie Inc.             95100
#> 3          WV        MINGO       Actavis Pharma, Inc.          31142920
#> 4          WV        MINGO             Allergan, Inc.               200
#> 5          WV        MINGO              Alvogen, Inc.             42100
#> 6          WV        MINGO Amneal Pharmaceuticals LLC            213600
#>   total_records
#> 1            31
#> 2           399
#> 3         23127
#> 4             2
#> 5           147
#> 6           794
# }
```

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

### Install and Load Required R Packages

Source: https://wpinvestigative.github.io/arcos/articles/county-analysis.html

Installs and loads necessary R packages for data analysis and visualization. Ensure these packages are installed before running the analysis.

```r
# Uncomment and run the lines below to see if you have the packages required already installed
# packages <- c("tidyverse", "jsonlite", "knitr", "geofacet", "scales", "forcats")
# if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
#   install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org") # }


# These are all the packages you'll need to run everything below 

library(arcos)
library(knitr)
library(tigris)
library(viridis)
library(dplyr)
library(ggplot2)
library(scales)
library(forcats)
```

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

### Example usage of not_pharmacies

Source: https://wpinvestigative.github.io/arcos/reference/not_pharmacies.html

Demonstrates how to use the `not_pharmacies` function to get a list of misidentified pharmacies and view the first few results.

```r
# \donttest{
library(arcos)

np <- not_pharmacies(key="WaPo")

head(np)
# }
```

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

### Example Usage of pharm_tracts

Source: https://wpinvestigative.github.io/arcos/reference/pharm_tracts.html

Shows a practical example of using the `pharm_tracts` function to get pharmacy data for Mingo County, WV, and then displays the first few rows of the result.

```r
# \donttest{
library(arcos)

mingo_wv <- pharm_tracts(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)

```

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

### Call total_distributors_state function

Source: https://wpinvestigative.github.io/arcos/reference/total_distributors_state.html

This is a basic example of how to call the `total_distributors_state` function with a state and key. It shows the minimal arguments required.

```r
total_distributors_state(state = "WV", key = "WaPo")
```

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

### Load and display total distributors by state data

Source: https://wpinvestigative.github.io/arcos/reference/total_distributors_state.html

This example demonstrates how to load the `arcos` library, call the `total_distributors_state` function to get data for West Virginia, and then display the first few rows of the resulting data frame.

```r
# \donttest{
library(arcos)

wv <- total_distributors_state(state="WV", key="WaPo")

head(wv)
#>   buyer_state buyer_county        Reporter_family total_dosage_unit
#> 1          WV       CABELL AmerisourceBergen Drug          27564760
#> 2          WV      KANAWHA               Rite Aid          18987950
#> 3          WV      KANAWHA        Cardinal Health          18418060
#> 4          WV     HARRISON        Cardinal Health          15477110
#> 5          WV      HANCOCK        Cardinal Health          12907450
#> 6          WV        LOGAN   McKesson Corporation          12548310
#>   total_records
#> 1         51557
#> 2         29772
#> 3         52962
#> 4         36515
#> 5         31477
#> 6         15677
# }

```

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

### Retrieve and display monthly pill data for a county

Source: https://wpinvestigative.github.io/arcos/reference/combined_buyer_monthly.html

This example demonstrates how to use the `combined_buyer_monthly` function to get monthly pill data for a specific county ('Mingo', 'WV') in a given year (2012). It then displays the first few rows of the resulting data frame.

```r
# \donttest{
library(arcos)

mingo_wv <- combined_buyer_monthly(county = "Mingo", state="WV", year=2012, key="WaPo")

head(mingo_wv)
#>   BUYER_DEA_NO   BUYER_BUS_ACT BUYER_COUNTY BUYER_STATE year month DOSAGE_UNIT
#> 1    AA8151728 RETAIL PHARMACY        MINGO          WV 2012    01       18800
#> 2    AA8151728 RETAIL PHARMACY        MINGO          WV 2012    02       20700
#> 3    AA8151728 RETAIL PHARMACY        MINGO          WV 2012    03       16100
#> 4    AA8151728 RETAIL PHARMACY        MINGO          WV 2012    04       23100
#> 5    AA8151728 RETAIL PHARMACY        MINGO          WV 2012    05       19400
#> 6    AA8151728 RETAIL PHARMACY        MINGO          WV 2012    06       17400
# }

```

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

### Retrieve and display total manufacturers data for a state

Source: https://wpinvestigative.github.io/arcos/reference/total_manufacturers_state.html

This example demonstrates how to use the `total_manufacturers_state` function to get data for West Virginia and then display the first few rows of the result using `head()`.

```r
library(arcos)

wv <- total_manufacturers_state(state="WV", key="WaPo")

head(wv)
```

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

### Load arcos and Get Pharmacy Addresses

Source: https://wpinvestigative.github.io/arcos/reference/buyer_addresses.html

This example shows how to load the `arcos` library and then use the `buyer_addresses` function to retrieve pharmacy data for Mingo County, WV. It also displays the first few rows of the resulting data frame.

```r
library(arcos)

mingo_wv <- buyer_addresses(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
```

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

### Install Development Version from GitHub

Source: https://wpinvestigative.github.io/arcos/index.html

Installs the development version of the arcos package from GitHub. Requires the devtools package.

```r
# install.packages("devtools")
devtools::install_github('wpinvestigative/arcos')
```

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

### Example of total_pharmacies_county usage

Source: https://wpinvestigative.github.io/arcos/reference/total_pharmacies_county.html

This example demonstrates how to call the `total_pharmacies_county` function and then view the first few rows of the resulting data frame, which includes pharmacy details and total dispensed pills.

```r
# \donttest{
library(arcos)

mingo_wv <- total_pharmacies_county(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   buyer_state buyer_county buyer_dea_no               buyer_name buyer_city
#> 1          WV        MINGO    BS7437064               STROSNIDER     KERMIT
#> 2          WV        MINGO    BH6954401  HURLEY DRUG COMPANY INC WILLIAMSON
#> 3          WV        MINGO    FT0251227 TUG VALLEY PHARMACY, LLC WILLIAMSON
#> 4          WV        MINGO    FR0261684       RIVERSIDE PHARMACY    GILBERT
#> 5          WV        MINGO    AA8151728      ADKINS PHARMACY INC    GILBERT
#> 6          WV        MINGO    FG0153863         GILBERT PHARMACY    GILBERT
#>   total_dosage_unit total_records
#> 1          13168350          7691
#> 2           8890370         11138
#> 3           8827860          5390
#> 4           1780680          2249
#> 5           1576200          2491
#> 6           1403720          3138
# }

```

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

### Retrieve and display summarized buyer details

Source: https://wpinvestigative.github.io/arcos/reference/buyer_details.html

This example demonstrates how to use the `buyer_details` function to fetch data for a specific county and state, then display the first few rows of the result. Ensure the 'arcos' library is loaded.

```r
# \donttest{
library(arcos)

mingo_wv <- buyer_details(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   BUYER_DEA_NO   BUYER_BUS_ACT                   BUYER_NAME
#> 1    BH6954401 RETAIL PHARMACY      HURLEY DRUG COMPANY INC
#> 2    BS7437064 RETAIL PHARMACY                   STROSNIDER
#> 3    FS1092787 RETAIL PHARMACY         SAV-RITE PHARMACY #2
#> 4    FT0251227 RETAIL PHARMACY     TUG VALLEY PHARMACY, LLC
#> 5    BM2933263 RETAIL PHARMACY MATEWAN FAMILY PHARMACY, INC
#> 6    FR0261684 RETAIL PHARMACY           RIVERSIDE PHARMACY
#>            BUYER_ADDRESS1 BUYER_CITY BUYER_STATE BUYER_ZIP BUYER_COUNTY
#> 1        210 LOGAN STREET WILLIAMSON          WV     25661        MINGO
#> 2              PO BOX 600     KERMIT          WV     25674        MINGO
#> 3 ROUTE 52 STONECOAL ROAD     KERMIT          WV     25674        MINGO
#> 4              PO BOX 538 WILLIAMSON          WV     25661        MINGO
#> 5        RT. 49, MAIN ST.    MATEWAN          WV     25678        MINGO
#> 6 22 LARRY JOE HARLESS DR    GILBERT          WV     25621        MINGO
#>       BUYER_ADDL_CO_INFO    BUYER_ADDRESS2
#> 1                   <NA>              <NA>
#> 2 DBA: SAV-RITE PHARMACY 50 LINCOLN STREET
#> 3        STROSNIDER DRUG      P.O. BOX 660
#> 4                   <NA>      54 W 2ND AVE
#> 5                   <NA>              <NA>
#> 6                   <NA>              <NA>
# }
```

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

### Install Required R Packages

Source: https://wpinvestigative.github.io/arcos/articles/per-capita-pharmacies.html

Installs R packages if they are not already present on the system. This is a prerequisite for running the subsequent analysis.

```r
# Uncomment and run the lines below to see if you have the packages required already installed
# packages <- c("dplyr", "jsonlite", "knitr", "geofacet", "scales")
# if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
#   install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org") # }

library(arcos)
library(dplyr)
library(lubridate)
library(data.table)
library(formattable)
library(vroom)
library(stringr)
library(scales)
library(knitr)
```

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

### Retrieve and display annual buyer data

Source: https://wpinvestigative.github.io/arcos/reference/combined_buyer_annual.html

This example demonstrates how to use the `combined_buyer_annual` function to get data for Mingo County, WV, and then displays the first few rows of the resulting data frame using `head()`.

```R
# \donttest{
library(arcos)

mingo_wv <- combined_buyer_annual(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   BUYER_DEA_NO   BUYER_BUS_ACT BUYER_COUNTY BUYER_STATE year DOSAGE_UNIT
#> 1    AA8151728 RETAIL PHARMACY        MINGO          WV 2006      279800
#> 2    AA8151728 RETAIL PHARMACY        MINGO          WV 2007      252400
#> 3    AA8151728 RETAIL PHARMACY        MINGO          WV 2008      219400
#> 4    AA8151728 RETAIL PHARMACY        MINGO          WV 2009      192000
#> 5    AA8151728 RETAIL PHARMACY        MINGO          WV 2010      181600
#> 6    AA8151728 RETAIL PHARMACY        MINGO          WV 2011      211800
# }
```

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

### Load Required R Packages

Source: https://wpinvestigative.github.io/arcos/articles/annual-maps.html

Installs and loads necessary R packages for data manipulation, plotting, and API interaction. Ensure these packages are installed before running the script.

```r
# Uncomment and run the lines below to see if you have the packages required already installed
# packages <- c("dplyr", "ggplot2", "jsonlite", "knitr", "geofacet", "scales")
# if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
#   install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org") # }

# These are all the packages you'll need to run everything below 

library(dplyr)
library(ggplot2)
library(arcos)
library(jsonlite)
library(knitr)
library(geofacet)
library(scales)
```

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

### Load and display county population data

Source: https://wpinvestigative.github.io/arcos/reference/county_population.html

This example demonstrates how to load the `arcos` library, call the `county_population` function to get data for Mingo County, WV, and then display the first few rows of the resulting data frame.

```r
# \donttest{
library(arcos)

mingo_wv <- county_population(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   BUYER_COUNTY BUYER_STATE countyfips STATE COUNTY county_name
#> 1        MINGO          WV      54059    54     59       Mingo
#> 2        MINGO          WV      54059    54     59       Mingo
#> 3        MINGO          WV      54059    54     59       Mingo
#> 4        MINGO          WV      54059    54     59       Mingo
#> 5        MINGO          WV      54059    54     59       Mingo
#> 6        MINGO          WV      54059    54     59       Mingo
#>                          NAME   variable year population
#> 1 Mingo County, West Virginia B01003_001 2006      27054
#> 2 Mingo County, West Virginia B01003_001 2007      27145
#> 3 Mingo County, West Virginia B01003_001 2008      26850
#> 4 Mingo County, West Virginia B01003_001 2009      26562
#> 5 Mingo County, West Virginia B01003_001 2010      26956
#> 6 Mingo County, West Virginia B01003_001 2011      26859
# }
```

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

### Install Latest Stable Release from CRAN

Source: https://wpinvestigative.github.io/arcos/index.html

Installs the latest stable version of the arcos package from CRAN.

```r
install.packages("arcos")
```

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

### Get Pharmacy CBSA Data for a Specific GEOID

Source: https://wpinvestigative.github.io/arcos/reference/pharm_cbsa.html

This example shows how to use the `pharm_cbsa` function to retrieve pharmacy data for a given GEOID. It requires the `arcos` library to be loaded and an API key for the Washington Post data. The output is then displayed using `head()`.

```r
# \donttest{
library(arcos)

mingo_wv <- pharm_cbsa(geoid="26580", key="WaPo")

head(mingo_wv)
#>
```

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

### Load and display pharmacy pill data for West Virginia

Source: https://wpinvestigative.github.io/arcos/reference/total_pharmacies_state.html

This example demonstrates how to load the pharmacy pill data for West Virginia using the `total_pharmacies_state` function and then display the first few rows of the resulting data frame.

```r
# \donttest{
library(arcos)

wv <- total_pharmacies_state(state="WV", key="WaPo")

head(wv)
#>
```

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

### Retrieve and Display Summarized County Annual Data

Source: https://wpinvestigative.github.io/arcos/reference/summarized_county_annual.html

This example demonstrates how to load the `arcos` library, call the `summarized_county_annual` function to get data for Mingo County, WV, and then display the first few rows of the resulting data frame using `head()`.

```r
# \donttest{
library(arcos)

mingo_wv <- summarized_county_annual(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   BUYER_COUNTY BUYER_STATE year count DOSAGE_UNIT countyfips
#> 1        MINGO          WV 2006  4192     4691980      54059
#> 2        MINGO          WV 2007  5389     7511830      54059
#> 3        MINGO          WV 2008  7496     9795100      54059
#> 4        MINGO          WV 2009  7227     7464630      54059
#> 5        MINGO          WV 2010  4466     3162950      54059
#> 6        MINGO          WV 2011  4243     2863950      54059
# }
```

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

### Load and display pharmacy county data

Source: https://wpinvestigative.github.io/arcos/reference/pharm_counties.html

Shows how to load the `arcos` library, call the `pharm_counties` function to get data for Mingo County, WV, and then display the first few rows of the resulting data frame.

```r
# \donttest{
library(arcos)

mingo_wv <- pharm_counties(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   BUYER_DEA_NO BUYER_COUNTY BUYER_STATE STATEFP COUNTYFP county_fips
#> 1    BM2933263        MINGO          WV      54      059       54059
#> 2    AM2373051        MINGO          WV      54      059       54059
#> 3    BM2373051        MINGO          WV      54      059       54059
#> 4    FM2252372        MINGO          WV      54      099       54099
#> 5    FS1092787        MINGO          WV      54      099       54099
#> 6    BH6954401        MINGO          WV      54      059       54059
# }
```

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

### Retrieve and inspect buyer list

Source: https://wpinvestigative.github.io/arcos/reference/buyer_list.html

Shows how to load the arcos library, call the `buyer_list` function with a key, and then display the first few rows of the resulting data frame.

```r
# \donttest{
library(arcos)

bl <- buyer_list(key="WaPo")

head(bl)
# >                 BUYER_BUS_ACT total_bus
# > 1              ANALYTICAL LAB       988
# > 2 AUTOMATED DISPENSING SYSTEM        10
# > 3              CANINE HANDLER        21
# > 4       CENTRAL FILL PHARMACY       143
# > 5           CHAIN HOSP/CLINIC       113
# > 6              CHAIN PHARMACY     47945
# }

```

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

### Call buyer_list function

Source: https://wpinvestigative.github.io/arcos/reference/buyer_list.html

Demonstrates how to call the `buyer_list` function with an API key. This is the primary way to retrieve the list of business types.

```r
buyer_list(key = "WaPo")
```

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

### Call buyer_details function

Source: https://wpinvestigative.github.io/arcos/reference/buyer_details.html

This snippet shows a basic call to the `buyer_details` function with required arguments: county, state, and key.

```r
buyer_details(county = "Mingo", state = "WV", key = "WaPo")
```

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

### Call total_pharmacies_state with state and key

Source: https://wpinvestigative.github.io/arcos/reference/total_pharmacies_state.html

This snippet shows the basic usage of the `total_pharmacies_state` function, specifying the state and the required API key.

```r
total_pharmacies_state(state = "WV", key = "WaPo")
```

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

### Get Pharmacy Lat/Lon by County and State

Source: https://wpinvestigative.github.io/arcos/reference/pharm_latlon.html

Use this function to get latitude and longitude for pharmacies within a specific county and state. Ensure you have the necessary API key.

```r
pharm_latlon(county = "Mingo", state = "WV", key = "WaPo")
```

```r
# \donttest{
library(arcos)

mingo_wv <- pharm_latlon(county = "Mingo", state="WV", key="WaPo")

head(mingo_wv)
#>   BUYER_DEA_NO BUYER_STATE BUYER_COUNTY     lat      lon
#> 1    FG0153863          WV        MINGO 37.6114 -81.8634
#> 2    FR0261684          WV        MINGO 37.6114 -81.8634
#> 3    AA8151728          WV        MINGO 37.6026 -81.8702
#> 4    BR0780521          WV        MINGO 37.6125 -81.8713
#> 5    BM2933263          WV        MINGO 37.6219 -82.1631
#> 6    AM2373051          WV        MINGO 37.6234 -82.1676
# }
```

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

### Download and Join County Shapefiles

Source: https://wpinvestigative.github.io/arcos/articles/county-analysis.html

Downloads US Census shapefiles for counties in a specified state using the `tigris` package and joins this spatial data with the ARCOS prescription data. This prepares the data for mapping.

```r
## Set the option for shapefiles to load with sf
options(tigris_class = "sf")

## Function to download county shapefiles in West Virginia
wv_shape <- counties(state="WV", cb=T)

## Join the county dosage data we pulled
wv <- left_join(wv, wv_shape, by=c("countyfips"="GEOID"))
```

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

### Call total_distributors_county function

Source: https://wpinvestigative.github.io/arcos/reference/total_distributors_county.html

Demonstrates how to call the `total_distributors_county` function with specified county, state, and API key. This is useful for fetching raw distribution data for a particular region.

```r
total_distributors_county(county = "Mingo", state = "WV", key = "WaPo")
```

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

### Get Drug List

Source: https://wpinvestigative.github.io/arcos/reference/drug_list.html

Retrieves a list of all drugs available in the ARCOS database. Requires an API key for successful query.

```r
drug_list(key = "WaPo")
```

```r
# \donttest{
library(arcos)

np <- drug_list(key="WaPo")

np
#>
```

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

### Get Reporter Addresses by County and State

Source: https://wpinvestigative.github.io/arcos/reference/reporter_addresses.html

Fetches DEA designated addresses for reporters within a specified county and state. Requires an API key for the query.

```r
reporter_addresses(county = "Mingo", state = "WV", key = "WaPo")
```

```r
# \donttest{
library(arcos)

mingo_wv <- reporter_addresses(county = "Harris", state="TX", key="WaPo")

head(mingo_wv)
```

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

### buyer_details

Source: https://wpinvestigative.github.io/arcos/reference/buyer_details.html

Fetches monthly summarized pill totals for a specific county within a state. Requires an API key for authentication.

```APIDOC
## buyer_details

### Description
Retrieves monthly summarized pill totals for a specific county within a given state. This function requires an API key to be provided for successful query execution.

### Arguments

* **county** (string) - Required - Filter the data to only this county (e.g. 'Mingo').
* **state** (string) - Required - Filter the data to county within this state (e.g. 'WV').
* **key** (string) - Required - Key needed to make query successful.

### Examples

```R
# Load the library
library(arcos)

# Fetch data for Mingo county, WV
mingo_wv <- buyer_details(county = "Mingo", state="WV", key="WaPo")

# Display the first few rows of the result
head(mingo_wv)
```
```

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

### Get County Population Data

Source: https://wpinvestigative.github.io/arcos/articles/per-capita-pharmacies.html

Fetches population data for counties within a specified state using the `county_population` function. The `kable` function is used to display the head of the resulting data frame.

```r
population <- county_population(state="WV", key="WaPo")

kable(head(population))
```

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

### Create Interactive Map with Leaflet

Source: https://wpinvestigative.github.io/arcos/articles/individual-drug-mapping.html

Generates an interactive map displaying methadone treatment facilities and pharmacies. Uses CartoDB.DarkMatter tiles, sets the view, and adds circle markers with popups and a legend.

```r
m <- leaflet(buyers) %>%
  addProviderTiles(providers$CartoDB.DarkMatter) %>%
  setView(-71.091112, 42.338827, zoom = 12) %>%
  addCircleMarkers(~lon, ~lat,
                   popup=paste0(buyers$BUYER_NAME, "<br />",
                                buyers$total, " total methadone"),
                   weight = 3,
                   radius=sqrt(buyers$total)/30, 
                   color=~cof(type),
                   stroke = FALSE, 
                   fillOpacity = 0.3)%>% 
  addLegend("bottomright", 
            colors= c("#ffa500", "#13ED3F"), 
            labels=c("Detox", "Pharmacy"), 
            title="Buyer type")
m
```

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

### Prepare Data for Mapping

Source: https://wpinvestigative.github.io/arcos/articles/individual-drug-mapping.html

Filters out any entries without longitude data, arranges the dataframe by total methadone amount in descending order, and sets up color factors for different buyer types.

```r
buyers <- buyers %>%
  filter(!is.na(lon))

buyers <- buyers %>%
  arrange(desc(total))

cof <- colorFactor(c("#ffa500", "#13ED3F"), domain=c("PHARMACY", "DETOX/MAINTENANCE"))
```

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

### raw_data()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Provides access to raw ARCOS data. Warning: This is a very large file (130+ GB) and may not fit into memory. A download link is available if interested.

```APIDOC
## raw_data()

### Description
Raw ARCOS data (Warning 130+ GB file) Warning: May not fit on your memory.. If interested in the raw data, you can download via the link below.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### buyer_details()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Retrieves monthly summarized pill totals aggregated by county.

```APIDOC
## buyer_details()

### Description
Get monthly summarized pill totals by county.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### Get Total Pharmacies by State

Source: https://wpinvestigative.github.io/arcos/articles/per-capita-pharmacies.html

Retrieves a list of all pharmacies within a specified state, along with their total oxycodone and hydrocodone pill orders. The `kable` function is used to display the head of the resulting data frame.

```r
west_virginia <- total_pharmacies_state(state="WV", key="WaPo")

kable(head(west_virginia))
```

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

### Call pharm_counties function

Source: https://wpinvestigative.github.io/arcos/reference/pharm_counties.html

Demonstrates how to call the `pharm_counties` function with specified county, state, and API key. This is the primary way to initiate a query for pharmacy county data.

```r
pharm_counties(county = "Mingo", state = "WV", key = "WaPo")
```

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

### county_raw()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Downloads raw prescription data for a specified county into R, identified by state and county names.

```APIDOC
## county_raw()

### Description
Download raw prescription data for specified county (by state and county names) into R.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### Call combined_buyer_monthly function

Source: https://wpinvestigative.github.io/arcos/reference/combined_buyer_monthly.html

This snippet shows how to call the `combined_buyer_monthly` function with specific county, state, year, and key parameters. It's used to initiate the data retrieval process.

```r
combined_buyer_monthly(
  county = "Mingo",
  state = "WV",
  year = 2012,
  key = "WaPo"
)
```

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

### Retrieve and Inspect County Data

Source: https://wpinvestigative.github.io/arcos/reference/county_list.html

Shows how to call the `county_list` function and then display the first few rows of the resulting dataframe. Ensure the 'arcos' library is loaded before use.

```r
# \donttest{
library(arcos)

counties <- county_list(key="WaPo")

head(counties)
# >   BUYER_COUNTY BUYER_STATE countyfips
# > 1      AUTAUGA          AL      01001
# > 2      BALDWIN          AL      01003
# > 3      BARBOUR          AL      01005
# > 4         BIBB          AL      01007
# > 5       BLOUNT          AL      01009
# > 6      BULLOCK          AL      01011
# }

```

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

### pharmacy_raw()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Downloads raw prescription data for a specified pharmacy, identified by BUYER_DEA_NO, into R.

```APIDOC
## pharmacy_raw()

### Description
Download raw prescription data for specified pharmacy (by BUYER_DEA_NO) into R.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### Call buyer_addresses Function

Source: https://wpinvestigative.github.io/arcos/reference/buyer_addresses.html

This snippet demonstrates how to call the `buyer_addresses` function with specified county, state, and API key. It's used to fetch pharmacy address data.

```r
buyer_addresses(county = "Mingo", state = "WV", key = "WaPo")
```

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

### Call summarized_county_monthly function

Source: https://wpinvestigative.github.io/arcos/reference/summarized_county_monthly.html

Demonstrates a basic call to the summarized_county_monthly function with specified county, state, and API key.

```r
summarized_county_monthly(county = "Mingo", state = "WV", key = "WaPo")
```

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

### drug_county_biz()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Downloads raw prescription data for individual drugs and business types, aggregated by county, into R.

```APIDOC
## drug_county_biz()

### Description
Download raw prescription data for individual drugs and business type by county into R.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### Download Drug Prescription Data by County

Source: https://wpinvestigative.github.io/arcos/reference/drug_county_biz.html

Use the `drug_county_biz` function to download prescription data for a specific drug, county, state, and business type. Requires an API key.

```r
drug_county_biz(
  drug = "Fentanyl",
  county = "Laurens",
  state = "GA",
  buyer_bus_act = "CHAIN PHARMACY",
  key = "WaPo"
)
```

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

### combined_buyer_monthly

Source: https://wpinvestigative.github.io/arcos/reference/combined_buyer_monthly.html

Fetches annual total pills for each buyer (pharmacy, etc.) in a specified county and state for a given year. Requires an API key for authentication.

```APIDOC
## combined_buyer_monthly

### Description
Retrieves the annual total number of pills dispensed by each buyer (e.g., pharmacy) within a specific county and state for a given year. This function is useful for analyzing drug dispensing patterns at a granular level.

### Arguments

*   **county** (string) - Required - Filters the data to include only the specified county (e.g., 'Mingo').
*   **state** (string) - Required - Filters the data to include only the specified state (e.g., 'WV').
*   **year** (integer) - Required - Filters the data to the specified year (e.g., 2012).
*   **key** (string) - Required - An API key necessary to authenticate and successfully make the query.

### Example

```R
library(arcos)

mingo_wv <- combined_buyer_monthly(county = "Mingo", state="WV", year=2012, key="WaPo")

head(mingo_wv)
```

### See Also

*   [Washington Post DEA Pain Pill Database Resources](https://www.washingtonpost.com/graphics/2019/investigations/dea-pain-pill-database/#download-resources)
*   Other summarized functions: `buyer_details()`, `combined_buyer_annual()`, `summarized_county_annual()`, `summarized_county_monthly()`, `total_distributors_county()`, `total_distributors_state()`, `total_manufacturers_county()`, `total_manufacturers_state()`, `total_pharmacies_county()`, `total_pharmacies_state()`
```

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

### state_population

Source: https://wpinvestigative.github.io/arcos/reference/state_population.html

Fetches annual population data for a given state between 2006 and 2014. Requires a valid API key.

```APIDOC
## Function: state_population

### Description
Retrieves annual population data for a specified state within the years 2006 to 2014. This function is useful for demographic analysis and historical population tracking.

### Arguments

*   **state** (string) - Required - The identifier for the state for which to retrieve population data (e.g., 'WV').
*   **key** (string) - Required - An API key necessary to authenticate the request and ensure successful data retrieval.

### Example

```R
# Load the arcos library
library(arcos)

# Get population data for West Virginia
wv_population <- state_population(state = "WV", key = "WaPo")

# Display the first few rows of the data
head(wv_population)
```

### Response Example

```
  BUYER_STATE year population
1          WV 2009    1811403
2          WV 2010    1840802
3          WV 2011    1846372
4          WV 2012    1850481
5          WV 2006    1827912
6          WV 2007    1834052
```
```

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

### Manually Geolocation Detox/Maintenance Facilities

Source: https://wpinvestigative.github.io/arcos/articles/individual-drug-mapping.html

Creates a dataframe with manually entered latitude and longitude for detox and maintenance facilities using tribble.

```r
detox <- tribble(
  ~BUYER_DEA_NO, ~lat,  ~lon,
  "PV0130562",  42.366429,  -71.058752,
  "PA0203024",  42.327501,  -71.083288,
  "RB0192574",  42.363672,  -71.05972,
  "RC0304395",  42.319285,  -71.052785,
  "RH0102549",  42.333502,  -71.066541,
  "RH0345783",  42.333502,  -71.066541,
  "RC0252558",  42.333435,  -71.073025,
  "RB0307480",  42.334507,  -71.0741,
  "RC0441751",  42.300033,  -71.101911,
  "RD0284581",  42.319239,  -71.096921,
  "RR0198336",  42.300033,  -71.101911,
  "RC0463327",  42.333138,  -71.070542
)
```

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

### Download Raw Pharmacy Data

Source: https://wpinvestigative.github.io/arcos/reference/pharmacy_raw.html

Call the pharmacy_raw function with the BUYER_DEA_NO and API key to download data for a specific pharmacy. Ensure you have the necessary key for the query.

```r
pharmacy_raw(buyer_dea_no = "AB0454176", key = "WaPo")
```

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

### Call total_manufacturers_state function

Source: https://wpinvestigative.github.io/arcos/reference/total_manufacturers_state.html

This snippet shows how to call the `total_manufacturers_state` function with a state and API key. It is used to initiate the data retrieval process.

```r
total_manufacturers_state(state = "WV", key = "WaPo")
```

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

### Join Pharmacy Data with Population Data

Source: https://wpinvestigative.github.io/arcos/articles/per-capita-pharmacies.html

Merges pharmacy dispensing data with county population data using a left join. This step is crucial for subsequent per capita calculations.

```r
wv_joined <- left_join(west_virginia, population)
#> Joining with `by = join_by(buyer_state, buyer_county)`

kable(head(wv_joined))
```

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

### Download Raw Data for a Specific County

Source: https://wpinvestigative.github.io/arcos/reference/county_raw_fips.html

Use this function to download raw prescription data for a single county. Ensure you have a valid API key and the correct FIPS code for the desired county.

```r
county_raw_fips(fips = "01001", key = "WaPo")
```

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

### Basic Usage of county_list

Source: https://wpinvestigative.github.io/arcos/reference/county_list.html

Demonstrates the basic call to the `county_list` function with the required API key. This function retrieves a dataframe containing county names, state abbreviations, and FIPS codes.

```r
county_list(key = "WaPo")
```

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

### Format Pharmacy Data Table

Source: https://wpinvestigative.github.io/arcos/articles/per-capita-pharmacies.html

Formats a data frame to display pharmacy information, applying specific formatting to columns like 'Pharmacy', 'Pills', and 'Pills per person'. Use this to create a visually appealing and informative table.

```r
wv_joined %>
  formattable(align=c("l", "l", "l", "r", "r", "r"),
              list(Pharmacy = formatter("span", style = ~ style(color="grey", font.weight = "bold")),
                   Pills=color_tile(customGreen0, customGreen),
                   `Pills per person` = normalize_bar(customRed)
                   ))
```
```

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

### drug_fips_biz()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Downloads raw prescription data for individual drugs and business types, aggregated by county FIPS code, into R.

```APIDOC
## drug_fips_biz()

### Description
Download raw prescription data for individual drugs and business type by county FIPS code into R.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### county_raw_fips()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Downloads raw prescription data for a specified county into R, identified by county FIPS code.

```APIDOC
## county_raw_fips()

### Description
Download raw prescription data for specified county (by county FIPS code) into R.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### Download Drug Prescription Data by FIPS

Source: https://wpinvestigative.github.io/arcos/reference/drug_fips_biz.html

Use this function to download prescription data for a specific drug, county FIPS code, and business activity type. Requires an API key.

```r
drug_fips_biz(
  drug = "Fentanyl",
  fips = "01001",
  buyer_bus_act = "CHAIN PHARMACY",
  key = "WaPo"
)
```

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

### Find Pharmacies by County and State

Source: https://wpinvestigative.github.io/arcos/articles/suspicious-order-detection.html

Use this function to retrieve a list of pharmacies within a specified county and state. It requires a valid API key.

```r
pharm <- buyer_addresses(county = "Seminole", state="FL", key="WaPo")

glimpse(pharm)
```

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

### Download Raw Prescription Data for a County

Source: https://wpinvestigative.github.io/arcos/reference/county_raw.html

Use this function to download raw prescription data for a specific county within a given state. Requires an API key for successful queries. The data includes information from pharmacies and practitioners for Hydrocodone and Oxycodone.

```r
county_raw(county = "Mingo", state = "WV", key = "WaPo")
```

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

### Load and Inspect Pharmacy Raw Data

Source: https://wpinvestigative.github.io/arcos/reference/pharmacy_raw.html

Loads raw pharmacy transaction data for a specific buyer DEA number and key. It then displays the first few rows of the resulting data frame for initial inspection.

```R
library(arcos)

solo_pharm <- pharmacy_raw(buyer_dea_no="AB0454176", key="WaPo")

head(solo_pharm)
```

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

### Call total_pharmacies_county function

Source: https://wpinvestigative.github.io/arcos/reference/total_pharmacies_county.html

This snippet shows the basic usage of the `total_pharmacies_county` function, specifying the county, state, and API key.

```r
total_pharmacies_county(county = "Mingo", state = "WV", key = "WaPo")
```

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

### state_population()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Retrieves annual population data for states covering the years 2006 to 2014.

```APIDOC
## state_population()

### Description
Get annual population for states between 2006 and 2014.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### summarized_county_monthly()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Retrieves monthly summarized pill totals aggregated by county.

```APIDOC
## summarized_county_monthly()

### Description
Get monthly summarized pill totals by county.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### county_population()

Source: https://wpinvestigative.github.io/arcos/reference/index.html

Retrieves annual population data for counties covering the years 2006 to 2014.

```APIDOC
## county_population()

### Description
Get annual population for counties between 2006 and 2014.

### Method
N/A (Function call)

### Endpoint
N/A (Function call)

### Parameters
N/A (Function call)

### Request Example
N/A (Function call)

### Response
N/A (Function call)
```

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

### Consolidate and Summarize Methadone Orders by Buyer Type

Source: https://wpinvestigative.github.io/arcos/articles/individual-drug-mapping.html

Cleans and consolidates buyer categories, then calculates the total amount of Methadone (in grams, converted to MME) ordered by each consolidated type. It filters for purchase orders (TRANSACTION_CODE == 'S'). The results are then arranged in descending order of total amount.

```R
# Clean up and consolidate the categories

methadone <- methadone %>%
  mutate(type=case_when(
    grepl("PHARMACY", BUYER_BUS_ACT) ~ "PHARMACY",
    grepl("DETOX", BUYER_BUS_ACT) ~ "DETOX/MAINTENANCE",
    grepl("MAINT", BUYER_BUS_ACT) ~ "DETOX/MAINTENANCE",
    grepl("-VA", BUYER_BUS_ACT) ~ "VA",
    grepl("CLINIC", BUYER_BUS_ACT) ~ "CLINIC",
    TRUE ~ BUYER_BUS_ACT
  ))

# totals
methadone_summary <- methadone %>%
  # focus only on purchase orders, which is transaction code S
  filter(TRANSACTION_CODE=="S") %>%
  group_by(type) %>%
  # calculate amount
  mutate(amount=CALC_BASE_WT_IN_GM*MME_Conversion_Factor) %>%
  summarize(total=sum(amount, na.rm=T)) %>%
  arrange(desc(total))
```
Tokens:
Raw