Discussion 4
Yixuan Huang
2023-02-13
rm(list=ls()) # remove all objects from environment cat(“) # Clear the console
library(bea.R)## 载入需要的程辑包：data.table## 从package 'googleVis'里的程辑包'jsonlite'为'toJSON'建立新的泛型函数## 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.library(ggplot2)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2
## ──## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.4 ✔ forcats 1.0.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::between() masks data.table::between()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks data.table::first()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks data.table::last()
## ✖ purrr::transpose() masks data.table::transpose()library(dplyr)
beaKey <- "414F3C21-8AAE-42F7-B4A0-56CB9AEA89E3"
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 414F3C21-8AAE-42F7-B4A0-56CB9AEA89E3
## 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
## D:/Study/R-4.2.2/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', ...))
## apiCallbea10 <- beaGet(list("UserID" = beaKey,
"Method" = "GetData",
"datasetname" = "NIPA",
"TableName" = "T10705",
"Frequency" = "A",
"Year" = "X"
))## No encoding supplied: defaulting to UTF-8.colnames(bea10) <- sub("DataValue_"," ", colnames(bea10))
realGDP <- pivot_longer(data = bea10,
cols = 8:101,
names_to = "year",
values_to = "value"
)
realGDP <- realGDP[-c(95:3196), ]
realGDP## # A tibble: 94 × 9
## TableName SeriesCode LineNumber LineDe…¹ METRI…² CL_UNIT UNIT_…³ year value
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 104556
## 2 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 92160
## 3 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 77391
## 4 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 59522
## 5 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 57154
## 6 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 66800
## 7 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 74241
## 8 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 84830
## 9 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 93003
## 10 T10705 A191RC 1 Gross d… Curren… Level 6 " 19… 87352
## # … with 84 more rows, and abbreviated variable names ¹LineDescription,
## # ²METRIC_NAME, ³UNIT_MULTRealGDPplot <- ggplot(realGDP,
aes( x = year, y = value),
na.rm = TRUE) +
geom_point(color = "#FFC0CB", size = 0.75) +
labs(x = "YEAR", y = "GDP(CurrentDollars)") +
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5))
RealGDPplot