The goal of OSO is to provide a straightforward way of reading data into R from the Bureau of Economic Analysis (BEA) and improving discovery of available data sets.
Installation
You can install the development version of OSO from GitHub with:
#install.packages("remotes")
remotes::install_github("aberuiz/OSO")
#>
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> checking for file ‘/private/var/folders/gh/fbq3g2jj4h9b09knhwdpmxpc0000gn/T/RtmpZzEYtC/remotesb77157cbaa4a/aberuiz-OSO-0c932ec/DESCRIPTION’ ... ✔ checking for file ‘/private/var/folders/gh/fbq3g2jj4h9b09knhwdpmxpc0000gn/T/RtmpZzEYtC/remotesb77157cbaa4a/aberuiz-OSO-0c932ec/DESCRIPTION’
#> ─ preparing ‘OSO’:
#> checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
#> ─ checking for LF line-endings in source and make files and shell scripts
#> ─ checking for empty or unneeded directories
#> Omitted ‘LazyData’ from DESCRIPTION
#> ─ building ‘OSO_0.1.0.tar.gz’
#>
#>
library(OSO)API Registration
Every function in OSO does require an API key from BEA. You can register for a key on the BEA Website.
For saving your API Key into the environment you can use setbeaKey. To permanently store your BEA API Key in .Renviron use the argument ‘install = TRUE’.
setbeaKey(APIkey = "<Your 36 character API Key>", install = TRUE)Once you have set up your key OSO will automatically check for your key in the stored environment using getbeaKey when you make a request.
Use getbeaKey to check what BEA API Key you have stored. You can overwrite an existing key using the argument ‘overwrite = TRUE’ in setbeaKey.
Available Datasets
Use beaDatasetList to view all datasets available through the BEA API.
Disclaimer as of this writing, not all datasets can be accessed through OSO. Look below for a list of future development.
beaDatasetList()
#> # A tibble: 13 × 2
#> DatasetName DatasetDescription
#> <chr> <chr>
#> 1 NIPA Standard NIPA tables
#> 2 NIUnderlyingDetail Standard NI underlying detail tables
#> 3 MNE Multinational Enterprises
#> 4 FixedAssets Standard Fixed Assets tables
#> 5 ITA International Transactions Accounts
#> 6 IIP International Investment Position
#> 7 InputOutput Input-Output Data
#> 8 IntlServTrade International Services Trade
#> 9 IntlServSTA International Services Supplied Through Affiliates
#> 10 GDPbyIndustry GDP by Industry
#> 11 Regional Regional data sets
#> 12 UnderlyingGDPbyIndustry Underlying GDP by Industry
#> 13 APIDatasetMetaData Metadata about other API datasetsFinding Parameters & Values
In this example, we’ll focus on accessing the ‘Regional’ dataset.
Before making a call using beaRegional you may want to look at the parameters necessary to make a request.
Using beaParamList we can get the list of parameters we can use for the ‘Regional’ dataset.
beaParamList(
DatasetName = "Regional"
)
#> # A tibble: 4 × 6
#> ParameterName ParameterDataType ParameterDescription ParameterIsRequiredF…¹
#> <chr> <chr> <chr> <chr>
#> 1 GeoFips string Comma-delimited list o… 1
#> 2 LineCode integer Line code for a statis… 1
#> 3 TableName string Regional income or pro… 1
#> 4 Year string Comma-delimted list of… 0
#> # ℹ abbreviated name: ¹ParameterIsRequiredFlag
#> # ℹ 2 more variables: MultipleAcceptedFlag <chr>, ParameterDefaultValue <chr>From here, you are returned all parameters you can use for this dataset.
To get all available Values for each of the above parameters you can use beaParamValues.
beaParamValues(
DatasetName = "Regional",
ParameterName = "TableName"
)
#> # A tibble: 94 × 2
#> Key Desc
#> <chr> <chr>
#> 1 CAEMP25N Total full-time and part-time employment by NAICS industry
#> 2 CAEMP25S Total full-time and part-time employment by SIC industry
#> 3 CAGDP1 County and MSA gross domestic product (GDP) summary
#> 4 CAGDP11 Contributions to percent change in real GDP
#> 5 CAGDP2 Gross domestic product (GDP) by county and metropolitan area
#> 6 CAGDP8 Chain-type quantity indexes for real GDP by county and metropolitan…
#> 7 CAGDP9 Real GDP by county and metropolitan area
#> 8 CAINC1 County and MSA personal income summary: personal income, population…
#> 9 CAINC30 Economic profile
#> 10 CAINC35 Personal current transfer receipts
#> # ℹ 84 more rowsFor this example we’ll look at Real GDP which is Table Value : “CAGDP9”
You can get all available linecodes for the ‘Regional’ dataset you can run beaParamValues and enter ‘linecode’ for the ParameterName. However, an easier command is beaParamValuesFiltered which allows you to view linecodes only for your table of interest: “CAGDP9”
beaParamValuesFiltered(
DatasetName = "Regional",
TargetParameter = "linecode",
TableName = "CAGDP9"
)
#> # A tibble: 34 × 2
#> Key Desc
#> <chr> <chr>
#> 1 1 [CAGDP9] Real GDP: All industry total
#> 2 10 [CAGDP9] Real GDP: Utilities (22)
#> 3 11 [CAGDP9] Real GDP: Construction (23)
#> 4 12 [CAGDP9] Real GDP: Manufacturing (31-33)
#> 5 13 [CAGDP9] Real GDP: Durable goods manufacturing (321,327-339)
#> 6 2 [CAGDP9] Real GDP: Private industries
#> 7 25 [CAGDP9] Real GDP: Nondurable goods manufacturing (311-316,322-326)
#> 8 3 [CAGDP9] Real GDP: Agriculture, forestry, fishing and hunting (11)
#> 9 34 [CAGDP9] Real GDP: Wholesale trade (42)
#> 10 35 [CAGDP9] Real GDP: Retail trade (44-45)
#> # ℹ 24 more rowsMaking the Request
Now we are ready to make a call using beaRegional for Real GDP in Construction for the entire United States using geoFips code ‘00000’.
beaRegional(
TableName = "CAGDP9",
LineCode = 11,
GeoFips = "00000",
Year = 2022
)
#> Real GDP: Construction
#> [1] "For the All industry total and Government and government enterprises, the difference between the United States and Metropolitan and Nonmetropolitan portions reflects overseas activity, economic activity taking place outside the borders of the United States by the military and associated federal civilian support staff."
#> [2] "Metropolitan Areas are defined (geographically delineated) by the Office of Management and Budget (OMB) bulletin no. 20-01 issued March 6, 2020."
#> [3] "Last updated: December 7, 2023 -- new statistics for 2022, revised statistics for 2017-2021."
#> # A tibble: 1 × 8
#> Code GeoFips GeoName TimePeriod CL_UNIT UNIT_MULT Real_GDP_Construction
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 CAGDP9-11 00000 United S… 2022 Thousa… 3 827768000
#> # ℹ 1 more variable: NoteRef <chr>Data Notes For datasets that include notes they will be provided in the console. See the above request for an example.
Multiple Values
For ‘Regional’ datasets you are able to request multiple returns for parameters ‘GeoFips’ and ‘Year’. You must insert values in a comma-delimited string. For requesting all available years you can also insert ‘ALL’.
beaRegional(
TableName = "CAGDP9",
LineCode = 11,
GeoFips = "00000, 48000",
Year = "ALL"
)
#> Real GDP: Construction
#> [1] "For the All industry total and Government and government enterprises, the difference between the United States and Metropolitan and Nonmetropolitan portions reflects overseas activity, economic activity taking place outside the borders of the United States by the military and associated federal civilian support staff."
#> [2] "Metropolitan Areas are defined (geographically delineated) by the Office of Management and Budget (OMB) bulletin no. 20-01 issued March 6, 2020."
#> [3] "Last updated: December 7, 2023 -- new statistics for 2022, revised statistics for 2017-2021."
#> # A tibble: 12 × 8
#> Code GeoFips GeoName TimePeriod CL_UNIT UNIT_MULT Real_GDP_Construction
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 CAGDP9-11 00000 United … 2017 Thousa… 3 840220000
#> 2 CAGDP9-11 00000 United … 2018 Thousa… 3 863755000
#> 3 CAGDP9-11 00000 United … 2019 Thousa… 3 882046000
#> 4 CAGDP9-11 00000 United … 2020 Thousa… 3 856487000
#> 5 CAGDP9-11 00000 United … 2021 Thousa… 3 888104000
#> 6 CAGDP9-11 00000 United … 2022 Thousa… 3 827768000
#> 7 CAGDP9-11 48000 Texas 2017 Thousa… 3 88478080
#> 8 CAGDP9-11 48000 Texas 2018 Thousa… 3 87963012
#> 9 CAGDP9-11 48000 Texas 2019 Thousa… 3 90383450
#> 10 CAGDP9-11 48000 Texas 2020 Thousa… 3 87536636
#> 11 CAGDP9-11 48000 Texas 2021 Thousa… 3 88865961
#> 12 CAGDP9-11 48000 Texas 2022 Thousa… 3 83697711
#> # ℹ 1 more variable: NoteRef <chr>