Week 4 Discussion
Bella Starlin
2023-02-16
Question 2
Initial Setup
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.4 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(bea.R)## Loading required package: data.table
##
## Attaching package: 'data.table'
##
## The following objects are masked from 'package:dplyr':
##
## between, first, last
##
## The following object is masked from 'package:purrr':
##
## transpose
##
## Creating a generic function for 'toJSON' from package 'jsonlite' in package 'googleVis'
## Note: As of February 2018, beaGet() requires 'TableName' for NIPA and NIUnderlyingDetail data instead of 'TableID.' See https://github.us-bea/bea.R for details.beaKey <- "080D210A-54D6-4EF8-ABE5-DC1995EAF7EC"Using BEA APIs
beaSets(beaKey = beaKey)## No encoding supplied: defaulting to UTF-8.## $Dataset
## DatasetName DatasetDescription
## 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 GDPbyIndustry GDP by Industry
## 10 Regional Regional data sets
## 11 UnderlyingGDPbyIndustry Underlying GDP by Industry
## 12 APIDatasetMetaData Metadata about other API datasets
##
## attr(,"params")
## ParameterName ParameterValue
## 1 USERID 080D210A-54D6-4EF8-ABE5-DC1995EAF7EC
## 2 METHOD GETDATASETLIST
## 3 RESULTFORMAT JSONbeaSearch("GDP", beaKey = beaKey)## Creating first-time local copy of metadata for all datasets - only done once.## Datasets will be updated only if timestamps indicate metadata obsolete in future searches,## and only obsolete metadata sets will be updated (it's faster this way).## ## No encoding supplied: defaulting to UTF-8.## Warning in beaSearch("GDP", beaKey = beaKey): Regional metadata is missing from
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/beaR/data and
## may be locked for updating on the BEA API; searching national metadata only.## SeriesCode RowNumber LineDescription
## 1: A191RO 10 Gross domestic product (GDP)
## 2: PE000009 330 Average of GDP and GDI
## 3: A191RL 10 Gross domestic product (GDP)
## 4: PB000009 130 Average of GDP and GDI
## 5: PA000009 200 Average of GDP and GDI, current dollars
## 6: A191RC 10 Gross domestic product (GDP)
## 7: LA000009 290 Average of GDP and GDI
## 8: SB000008 360 Statistical discrepancy as a percentage of GDP
## 9: A191RX 10 Gross domestic product (GDP)
## 10: LB000009 130 Average of GDP and GDI
## 11: A191RL 20 Gross domestic product (GDP)
## 12: PB000009 40 Average of GDP and GDI
## 13: A191RC 20 Gross domestic product (GDP)
## 14: LA000009 40 Average of GDP and GDI
## 15: A191RX 20 Gross domestic product (GDP)
## 16: LB000009 40 Average of GDP and GDI
## 17: A191RX 10 Gross domestic product (GDP)
## 18: BB00RL 120 GDP less final sales of domestic computers
## 19: BB00RX 20 GDP less final sales of domestic computers
## 20: A191RX 20 Gross domestic product (GDP)
## 21: SWGXSL 120 GDP less final sales of software
## 22: SWGXSX 30 GDP less final sales of software
## 23: A191RL 110 GDP
## 24: A191RO 10 Gross domestic product (GDP)
## 25: PE000009 330 Average of GDP and GDI
## 26: A191RL 10 Gross domestic product (GDP)
## 27: PB000009 130 Average of GDP and GDI
## 28: PA000009 200 Average of GDP and GDI, current dollars
## 29: A191RC 10 Gross domestic product (GDP)
## 30: LA000009 290 Average of GDP and GDI
## 31: SB000008 360 Statistical discrepancy as a percentage of GDP
## 32: A191RX 10 Gross domestic product (GDP)
## 33: LB000009 130 Average of GDP and GDI
## 34: A191RL 20 Gross domestic product (GDP)
## 35: PB000009 40 Average of GDP and GDI
## 36: A191RC 20 Gross domestic product (GDP)
## 37: LA000009 40 Average of GDP and GDI
## 38: A191RX 20 Gross domestic product (GDP)
## 39: LB000009 40 Average of GDP and GDI
## 40: A191RX 10 Gross domestic product (GDP)
## 41: BB00RL 120 GDP less final sales of domestic computers
## 42: BB00RX 20 GDP less final sales of domestic computers
## 43: A191RX 20 Gross domestic product (GDP)
## 44: SWGXSL 120 GDP less final sales of software
## 45: SWGXSX 30 GDP less final sales of software
## 46: A191RL 110 GDP
## SeriesCode RowNumber LineDescription
## LineNumber ParentLineNumber Tier Path TableID DatasetName
## 1: 1 0 1 T10111 NIPA
## 2: 32 0 32 T10111 NIPA
## 3: 1 0 1 T10701 NIPA
## 4: 12 0 12 T10701 NIPA
## 5: 19 0 19 T10701 NIPA
## 6: 1 0 1 T10705 NIPA
## 7: 27 0 27 T10705 NIPA
## 8: 34 0 34 T10705 NIPA
## 9: 1 0 1 T10706 NIPA
## 10: 12 0 12 T10706 NIPA
## 11: 1 0 1 T11701 NIPA
## 12: 3 0 3 T11701 NIPA
## 13: 1 0 1 T11705 NIPA
## 14: 3 0 3 T11705 NIPA
## 15: 1 0 1 T11706 NIPA
## 16: 3 0 3 T11706 NIPA
## 17: 1 0 1 U90200 NIPA
## 18: 11 0 11 U90200 NIPA
## 19: 2 0 2 U90200 NIPA
## 20: 1 0 1 U90300 NIPA
## 21: 10 0 10 U90300 NIPA
## 22: 2 0 2 U90300 NIPA
## 23: 9 0 9 U90300 NIPA
## 24: 1 0 1 T10111 NIUnderlyingDetail
## 25: 32 0 32 T10111 NIUnderlyingDetail
## 26: 1 0 1 T10701 NIUnderlyingDetail
## 27: 12 0 12 T10701 NIUnderlyingDetail
## 28: 19 0 19 T10701 NIUnderlyingDetail
## 29: 1 0 1 T10705 NIUnderlyingDetail
## 30: 27 0 27 T10705 NIUnderlyingDetail
## 31: 34 0 34 T10705 NIUnderlyingDetail
## 32: 1 0 1 T10706 NIUnderlyingDetail
## 33: 12 0 12 T10706 NIUnderlyingDetail
## 34: 1 0 1 T11701 NIUnderlyingDetail
## 35: 3 0 3 T11701 NIUnderlyingDetail
## 36: 1 0 1 T11705 NIUnderlyingDetail
## 37: 3 0 3 T11705 NIUnderlyingDetail
## 38: 1 0 1 T11706 NIUnderlyingDetail
## 39: 3 0 3 T11706 NIUnderlyingDetail
## 40: 1 0 1 U90200 NIUnderlyingDetail
## 41: 11 0 11 U90200 NIUnderlyingDetail
## 42: 2 0 2 U90200 NIUnderlyingDetail
## 43: 1 0 1 U90300 NIUnderlyingDetail
## 44: 10 0 10 U90300 NIUnderlyingDetail
## 45: 2 0 2 U90300 NIUnderlyingDetail
## 46: 9 0 9 U90300 NIUnderlyingDetail
## LineNumber ParentLineNumber Tier Path TableID DatasetName
## TableName
## 1: Table 1.1.11. Real Gross Domestic Product: Percent Change From Quarter One Year Ago
## 2: Table 1.1.11. Real Gross Domestic Product: Percent Change From Quarter One Year Ago
## 3: Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product
## 4: Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product
## 5: Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product
## 6: Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income
## 7: Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income
## 8: Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income
## 9: Table 1.7.6. Relation of Real Gross Domestic Product, Real Gross National Product, and Real Net National Product, Chained Dollars
## 10: Table 1.7.6. Relation of Real Gross Domestic Product, Real Gross National Product, and Real Net National Product, Chained Dollars
## 11: Table 1.17.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates
## 12: Table 1.17.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates
## 13: Table 1.17.5. Gross Domestic Product, Gross Domestic Income, and Other Major NIPA Aggregates
## 14: Table 1.17.5. Gross Domestic Product, Gross Domestic Income, and Other Major NIPA Aggregates
## 15: Table 1.17.6. Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates, Chained Dollars
## 16: Table 1.17.6. Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates, Chained Dollars
## 17: Table 9.2U. Final Sales of Domestic Computers
## 18: Table 9.2U. Final Sales of Domestic Computers
## 19: Table 9.2U. Final Sales of Domestic Computers
## 20: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 21: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 22: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 23: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 24: Table 1.1.11. Real Gross Domestic Product: Percent Change From Quarter One Year Ago
## 25: Table 1.1.11. Real Gross Domestic Product: Percent Change From Quarter One Year Ago
## 26: Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product
## 27: Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product
## 28: Table 1.7.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross National Product, and Real Net National Product
## 29: Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income
## 30: Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income
## 31: Table 1.7.5. Relation of Gross Domestic Product, Gross National Product, Net National Product, National Income, and Personal Income
## 32: Table 1.7.6. Relation of Real Gross Domestic Product, Real Gross National Product, and Real Net National Product, Chained Dollars
## 33: Table 1.7.6. Relation of Real Gross Domestic Product, Real Gross National Product, and Real Net National Product, Chained Dollars
## 34: Table 1.17.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates
## 35: Table 1.17.1. Percent Change From Preceding Period in Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates
## 36: Table 1.17.5. Gross Domestic Product, Gross Domestic Income, and Other Major NIPA Aggregates
## 37: Table 1.17.5. Gross Domestic Product, Gross Domestic Income, and Other Major NIPA Aggregates
## 38: Table 1.17.6. Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates, Chained Dollars
## 39: Table 1.17.6. Real Gross Domestic Product, Real Gross Domestic Income, and Other Major NIPA Aggregates, Chained Dollars
## 40: Table 9.2U. Final Sales of Domestic Computers
## 41: Table 9.2U. Final Sales of Domestic Computers
## 42: Table 9.2U. Final Sales of Domestic Computers
## 43: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 44: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 45: Table 9.3U. Gross Domestic Product and Final Sales of Software
## 46: Table 9.3U. Gross Domestic Product and Final Sales of Software
## TableName
## ReleaseDate NextReleaseDate MetaDataUpdated Account
## 1: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 2: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 3: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 4: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 5: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 6: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 7: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 8: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 9: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 10: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 11: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 12: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 13: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 14: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 15: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 16: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 17: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 18: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 19: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:29.923 National
## 20: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:29.923 National
## 21: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:29.923 National
## 22: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:29.923 National
## 23: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:29.923 National
## 24: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 25: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 26: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 27: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 28: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 29: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 30: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 31: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 32: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 33: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 34: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 35: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 36: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 37: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 38: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 39: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 40: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 41: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 42: Feb 28 2019 8:30AM Mar 28 2019 8:30AM 2019-03-06T10:13:32.840 National
## 43: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 44: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 45: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 46: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## ReleaseDate NextReleaseDate MetaDataUpdated Account
## apiCall
## 1: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10111', ...))
## 2: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10111', ...))
## 3: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10701', ...))
## 4: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10701', ...))
## 5: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10701', ...))
## 6: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10705', ...))
## 7: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10705', ...))
## 8: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10705', ...))
## 9: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10706', ...))
## 10: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10706', ...))
## 11: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T11701', ...))
## 12: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T11701', ...))
## 13: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T11705', ...))
## 14: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T11705', ...))
## 15: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T11706', ...))
## 16: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T11706', ...))
## 17: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90200', ...))
## 18: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90200', ...))
## 19: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90200', ...))
## 20: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90300', ...))
## 21: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90300', ...))
## 22: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90300', ...))
## 23: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'U90300', ...))
## 24: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10111', ...))
## 25: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10111', ...))
## 26: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10701', ...))
## 27: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10701', ...))
## 28: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10701', ...))
## 29: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10705', ...))
## 30: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10705', ...))
## 31: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10705', ...))
## 32: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10706', ...))
## 33: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T10706', ...))
## 34: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T11701', ...))
## 35: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T11701', ...))
## 36: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T11705', ...))
## 37: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T11705', ...))
## 38: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T11706', ...))
## 39: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'T11706', ...))
## 40: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90200', ...))
## 41: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90200', ...))
## 42: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90200', ...))
## 43: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90300', ...))
## 44: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90300', ...))
## 45: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90300', ...))
## 46: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90300', ...))
## apiCallbeadata <- beaGet(list('UserID' = beaKey ,
'Method' = 'GetData',
'DatasetName' = 'NIPA',
'TableName' = 'T10705',
'Frequency' = 'A',
'Year' = 'X'
))## No encoding supplied: defaulting to UTF-8.head(beadata)## TableName SeriesCode LineNumber
## 1: T10705 A191RC 1
## 2: T10705 B645RC 2
## 3: T10705 A655RC 3
## 4: T10705 A001RC 4
## 5: T10705 A262RC 5
## 6: T10705 A024RC 6
## LineDescription METRIC_NAME CL_UNIT
## 1: Gross domestic product (GDP) Current Dollars Level
## 2: Plus: Income receipts from the rest of the world Current Dollars Level
## 3: Less: Income payments to the rest of the world Current Dollars Level
## 4: Equals: Gross national product Current Dollars Level
## 5: Less: Consumption of fixed capital Current Dollars Level
## 6: Private Current Dollars Level
## UNIT_MULT DataValue_1929 DataValue_1930 DataValue_1931 DataValue_1932
## 1: 6 104556 92160 77391 59522
## 2: 6 1140 1041 767 528
## 3: 6 374 332 252 164
## 4: 6 105322 92869 77906 59886
## 5: 6 10409 10217 9514 8338
## 6: 6 9411 9221 8550 7465
## DataValue_1933 DataValue_1934 DataValue_1935 DataValue_1936 DataValue_1937
## 1: 57154 66800 74241 84830 93003
## 2: 438 438 522 570 728
## 3: 138 159 181 308 359
## 4: 57454 67079 74582 85092 93372
## 5: 8012 8430 8480 8801 9767
## 6: 7042 7288 7324 7509 8371
## DataValue_1938 DataValue_1939 DataValue_1940 DataValue_1941 DataValue_1942
## 1: 87352 93437 102899 129309 165952
## 2: 640 683 610 731 708
## 3: 261 310 330 315 297
## 4: 87731 93810 103179 129725 166363
## 5: 10042 10103 10577 12062 14911
## 6: 8580 8590 8976 9950 11246
## DataValue_1943 DataValue_1944 DataValue_1945 DataValue_1946 DataValue_1947
## 1: 203084 224447 228007 227535 249616
## 2: 699 758 756 1092 1597
## 3: 368 434 491 440 487
## 4: 203415 224771 228272 228187 250726
## 5: 18031 21341 23107 25690 29121
## 6: 11539 12038 12507 14247 17735
## DataValue_1948 DataValue_1949 DataValue_1950 DataValue_1951 DataValue_1952
## 1: 274468 272475 299827 346914 367341
## 2: 2031 1944 2186 2756 2857
## 3: 575 664 746 864 872
## 4: 275924 273755 301267 348806 369326
## 5: 31326 32284 33394 37726 40606
## 6: 20820 22590 24334 27746 29544
## DataValue_1953 DataValue_1954 DataValue_1955 DataValue_1956 DataValue_1957
## 1: 389218 390549 425478 449353 474039
## 2: 2843 3039 3507 3933 4268
## 3: 944 944 1058 1137 1213
## 4: 391117 392644 427927 452149 477093
## 5: 43488 45981 48893 54127 58919
## 6: 31344 32958 34995 38811 42259
## DataValue_1958 DataValue_1959 DataValue_1960 DataValue_1961 DataValue_1962
## 1: 481229 521654 542382 562209 603922
## 2: 3869 4269 4888 5307 5940
## 3: 1241 1508 1775 1788 1847
## 4: 483858 524415 545494 565728 608015
## 5: 62454 65445 67901 70604 74100
## 6: 44910 46819 48209 49781 51795
## DataValue_1963 DataValue_1964 DataValue_1965 DataValue_1966 DataValue_1967
## 1: 637450 684460 742289 813414 859959
## 2: 6530 7239 7881 8071 8682
## 3: 2053 2319 2602 3001 3289
## 4: 641927 689380 747568 818484 865352
## 5: 78018 82390 88008 95311 103557
## 6: 54158 57276 61568 67172 73327
## DataValue_1968 DataValue_1969 DataValue_1970 DataValue_1971 DataValue_1972
## 1: 940651 1017615 1073303 1164850 1279110
## 2: 10102 11779 12833 14011 16278
## 3: 4017 5664 6440 6417 7720
## 4: 946736 1023730 1079696 1172444 1287668
## 5: 113357 124896 136839 148926 161011
## 6: 80603 89433 98260 107635 117493
## DataValue_1973 DataValue_1974 DataValue_1975 DataValue_1976 DataValue_1977
## 1: 1425376 1545243 1684904 1873412 2081826
## 2: 23541 29820 28024 32363 37200
## 3: 10922 14300 15020 15516 16908
## 4: 1437994 1560763 1697908 1890259 2102118
## 5: 178686 206894 238510 260226 289832
## 6: 131492 153159 178790 196512 221127
## DataValue_1978 DataValue_1979 DataValue_1980 DataValue_1981 DataValue_1982
## 1: 2351599 2627333 2857307 3207041 3343789
## 2: 46252 68321 79092 92024 100993
## 3: 24671 36390 44907 59089 64483
## 4: 2373180 2659264 2891492 3239976 3380299
## 5: 327196 373882 428432 487231 536963
## 6: 252115 290733 334977 381932 420392
## DataValue_1983 DataValue_1984 DataValue_1985 DataValue_1986 DataValue_1987
## 1: 3634038 4037613 4338979 4579631 4855215
## 2: 101882 121865 112662 111324 123286
## 3: 64797 85575 87297 94367 105798
## 4: 3671122 4073903 4364344 4596588 4872702
## 5: 562624 598394 640137 685295 730385
## 6: 438788 463516 496410 531572 566309
## DataValue_1988 DataValue_1989 DataValue_1990 DataValue_1991 DataValue_1992
## 1: 5236438 5641580 5963144 6158129 6520327
## 2: 152116 177699 188847 168363 152052
## 3: 129459 152909 154155 136777 120975
## 4: 5259095 5666369 5997836 6189716 6551404
## 5: 784496 838258 888532 932393 960247
## 6: 607913 649619 688396 721456 742886
## DataValue_1993 DataValue_1994 DataValue_1995 DataValue_1996 DataValue_1997
## 1: 6858559 7287236 7639749 8073122 8577552
## 2: 155600 184543 229833 246404 280071
## 3: 124442 161577 201881 215535 256771
## 4: 6889717 7310202 7667701 8103991 8600853
## 5: 1003498 1055610 1122381 1175306 1239325
## 6: 778210 822507 880728 929111 987753
## DataValue_1998 DataValue_1999 DataValue_2000 DataValue_2001 DataValue_2002
## 1: 9062817 9631172 10250952 10581929 10929108
## 2: 286779 324646 390638 339642 335811
## 3: 269368 293742 352157 289278 290017
## 4: 9080228 9662075 10289432 10632293 10974902
## 5: 1309737 1398934 1511225 1599511 1657976
## 6: 1052165 1132208 1231511 1311709 1361815
## DataValue_2003 DataValue_2004 DataValue_2005 DataValue_2006 DataValue_2007
## 1: 11456450 12217196 13039197 13815583 14474228
## 2: 377405 464696 569269 702621 850184
## 3: 318909 387983 494469 656161 754520
## 4: 11514946 12293909 13113997 13862043 14569892
## 5: 1719081 1821828 1971024 2124124 2252806
## 6: 1411949 1497111 1622603 1751800 1852499
## DataValue_2008 DataValue_2009 DataValue_2010 DataValue_2011 DataValue_2012
## 1: 14769862 14478067 15048970 15599731 16253970
## 2: 855223 689256 759969 827868 827438
## 3: 710009 539013 554336 589936 594681
## 4: 14915076 14628309 15254603 15837664 16486727
## 5: 2358842 2371476 2390926 2474467 2575995
## 6: 1931823 1928709 1933775 1997313 2082378
## DataValue_2013 DataValue_2014 DataValue_2015 DataValue_2016 DataValue_2017
## 1: 16843196 17550687 18206023 18695106 19477337
## 2: 847233 881582 860759 893475 1031102
## 3: 616935 646356 640376 661531 738153
## 4: 17073493 17785914 18426407 18927050 19770286
## 5: 2681218 2815026 2911385 2987071 3118724
## 6: 2176569 2298472 2388479 2459936 2576755
## DataValue_2018 DataValue_2019 DataValue_2020 DataValue_2021 DataValue_2022
## 1: 20533058 21380976 21060474 23315081 25461339
## 2: 1138659 1172200 971295 1086987 NA
## 3: 848352 894150 774325 913897 NA
## 4: 20823364 21659027 21257444 23488172 NA
## 5: 3275618 3436609 3577770 3831587 4284265
## 6: 2710537 2850090 2971829 3184502 3567807beadatatwo <- beaGet(list('UserID' = beaKey ,
'Method' = 'GetData',
'DatasetName' = 'NIPA',
'TableName' = 'T10705',
'Frequency' = 'A',
'Year' = 'X'
),
asWide = FALSE)## No encoding supplied: defaulting to UTF-8.Plotting the Graph
library(ggplot2)
beadatathree <- beadata[-(2:34),]
?pivot_longer
beadatathree_long <- pivot_longer(beadatathree, cols =8:101,
names_to = "year",
values_to = "GDP"
)
beadatathree_long## # A tibble: 94 × 9
## TableName SeriesCode LineNumber LineDe…¹ METRI…² CL_UNIT UNIT_…³ year GDP
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 104556
## 2 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 92160
## 3 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 77391
## 4 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 59522
## 5 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 57154
## 6 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 66800
## 7 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 74241
## 8 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 84830
## 9 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 93003
## 10 T10705 A191RC 1 Gross d… Curren… Level 6 Data… 87352
## # … with 84 more rows, and abbreviated variable names ¹​LineDescription,
## # ²​METRIC_NAME, ³​UNIT_MULT?ggplot
plot <- ggplot(beadatathree_long, aes(x=year,y=GDP),na.rm = TRUE) +
geom_point(color = "lightblue", size = 2) +
labs(title= "US GDP Growth",
x = "Years",
y = "GDP") +
theme(axis.text.x = element_text(angle = 30, hjust = 0.5, vjust = 0.5))
plot
Conclusion
GDP dropped during Covid and the 2008 crisis because of lower economic activity overall and lower purchasing power due to people not having extra money for consumption.