DA_DIS#11_Merge_Data
2023-11-19
1. Set up
# key
beakey <- "073D1606-20C5-4EDB-A502-FEB84C7E2461"
# search codes
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 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 datasets
##
## attr(,"params")
## ParameterName ParameterValue
## 1 USERID 073D1606-20C5-4EDB-A502-FEB84C7E2461
## 2 METHOD GETDATASETLIST
## 3 RESULTFORMAT JSONbeaSearch('gross domestic', 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("gross domestic", beakey): Regional metadata is missing
## from
## /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/beaR/data
## and may be locked for updating on the BEA API; searching national metadata
## only.## SeriesCode RowNumber LineDescription LineNumber
## 1: A191RL 10 Gross domestic product 1
## 2: A009RL 100 Structures 10
## 3: Y033RL 110 Equipment 11
## 4: Y001RL 120 Intellectual property products 12
## 5: A011RL 130 Residential 13
## ---
## 2982: B935RY 70 Private fixed investment 5
## 2983: SWSNXY 80 Net exports 6
## 2984: SWSHXY 90 Exports 7
## 2985: SWSHMY 100 Imports 8
## 2986: SWSHGY 110 Government 9
## ParentLineNumber Tier Path TableID DatasetName
## 1: 0 1 T10101 NIPA
## 2: 0 10 T10101 NIPA
## 3: 0 11 T10101 NIPA
## 4: 0 12 T10101 NIPA
## 5: 0 13 T10101 NIPA
## ---
## 2982: 0 5 U90500 NIUnderlyingDetail
## 2983: 0 6 U90500 NIUnderlyingDetail
## 2984: 0 7 U90500 NIUnderlyingDetail
## 2985: 0 8 U90500 NIUnderlyingDetail
## 2986: 0 9 U90500 NIUnderlyingDetail
## TableName
## 1: Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product
## 2: Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product
## 3: Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product
## 4: Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product
## 5: Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product
## ---
## 2982: Table 9.5U. Contributions to Percent Change in Real Gross Domestic Product From Final Sales of Computers, Software, and Communications Equipment
## 2983: Table 9.5U. Contributions to Percent Change in Real Gross Domestic Product From Final Sales of Computers, Software, and Communications Equipment
## 2984: Table 9.5U. Contributions to Percent Change in Real Gross Domestic Product From Final Sales of Computers, Software, and Communications Equipment
## 2985: Table 9.5U. Contributions to Percent Change in Real Gross Domestic Product From Final Sales of Computers, Software, and Communications Equipment
## 2986: Table 9.5U. Contributions to Percent Change in Real Gross Domestic Product From Final Sales of Computers, Software, and Communications Equipment
## 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
## ---
## 2982: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 2983: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 2984: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 2985: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## 2986: Jul 31 2018 8:30AM Jan 1 1900 12:00AM 2019-03-06T10:13:32.840 National
## apiCall
## 1: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10101', ...))
## 2: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10101', ...))
## 3: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10101', ...))
## 4: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10101', ...))
## 5: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIPA', 'TableName' = 'T10101', ...))
## ---
## 2982: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90500', ...))
## 2983: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90500', ...))
## 2984: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90500', ...))
## 2985: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90500', ...))
## 2986: beaGet(list('UserID' = '[your_key]', 'Method' = 'GetData', 'DatasetName' = 'NIUnderlyingDetail', 'TableName' = 'U90500', ...))2. Load GDP Data
# set up
beaSpecs <- list(
"UserID" = beakey,
"Method" = "GetData",
"datasetname" = "NIPA",
"TableName" = "T10705",
"Frequency" = "A",
"Year" = "X")
bea <- beaGet(beaSpecs, asWide = FALSE)## No encoding supplied: defaulting to UTF-8.# print data options
unique(bea$LineDescription)## [1] "Gross domestic product (GDP)"
## [2] "Plus: Income receipts from the rest of the world"
## [3] "Less: Income payments to the rest of the world"
## [4] "Equals: Gross national product"
## [5] "Less: Consumption of fixed capital"
## [6] "Private"
## [7] "Domestic business"
## [8] "Capital consumption allowances"
## [9] "Less: Capital consumption adjustment"
## [10] "Households and institutions"
## [11] "Government"
## [12] "General government"
## [13] "Government enterprises"
## [14] "Equals: Net national product"
## [15] "Less: Statistical discrepancy"
## [16] "Equals: National income"
## [17] "Corporate profits with inventory valuation and capital consumption adjustments"
## [18] "Taxes on production and imports less subsidies"
## [19] "Contributions for government social insurance, domestic"
## [20] "Net interest and miscellaneous payments on assets"
## [21] "Business current transfer payments (net)"
## [22] "Current surplus of government enterprises"
## [23] "Plus: Personal income receipts on assets"
## [24] "Plus: Personal current transfer receipts"
## [25] "Equals: Personal income"
## [26] "Gross domestic income (GDI)"
## [27] "Average of GDP and GDI"
## [28] "Gross national income"
## [29] "Gross national factor income"
## [30] "Net domestic product"
## [31] "Net domestic income"
## [32] "Net national factor income"
## [33] "Net domestic purchases"
## [34] "Statistical discrepancy as a percentage of GDP"# extract data
gdp_data <- bea |>
filter( LineDescription == "Gross domestic product (GDP)")
# print data
head(gdp_data,5)## TableName SeriesCode LineNumber LineDescription TimePeriod
## 1: T10705 A191RC 1 Gross domestic product (GDP) 1929
## 2: T10705 A191RC 1 Gross domestic product (GDP) 1930
## 3: T10705 A191RC 1 Gross domestic product (GDP) 1931
## 4: T10705 A191RC 1 Gross domestic product (GDP) 1932
## 5: T10705 A191RC 1 Gross domestic product (GDP) 1933
## METRIC_NAME CL_UNIT UNIT_MULT DataValue NoteRef
## 1: Current Dollars Level 6 104556 T10705
## 2: Current Dollars Level 6 92160 T10705
## 3: Current Dollars Level 6 77391 T10705
## 4: Current Dollars Level 6 59522 T10705
## 5: Current Dollars Level 6 57154 T107053. Graph GDP
# change variable type from "chr" -> "num"
gdp_data$TimePeriod <- as.numeric(gdp_data$TimePeriod)
# plot gdp time-series data
ggplot(data = gdp_data, aes(x = TimePeriod, y = DataValue)) +
geom_line(color = "aquamarine3") +
labs(x = "Year", y = "Real GDP") +
theme_classic()
4. Explanation of Economic downturn
When: 2008 & 2020
2008 The Sub-mortgage Loan Crisis
- Cause: To increase the sales of homes and to apeal to the ideal of homeownership as part of the American Dream, the financial sector decreased the standard in which they have to approve loans used for home-purchasing activities. Hence, exposed the financial sector to greater risks. Eventually, in November, 2007. The sub-mortgage market crashed, causing the entire financial market to collapse. In the end, the U.S. government stepped in to save many of the insolvent financial firms. the effect of this collapse was so great that many countries also suffered from the effect of the financial market meltdown.
2020 The COVID-19 Economic Crisis
- Cause: a new strand of coronavirus, first discovered in Wuhan, China, spreaded across the world in January, 2020. This caused much of the globe to implement lock-downs as a way to detain or slow-down the spread of the virus. However, such an method also interrupted the global supply chain and day to day business at large. Therefore, acted under fear and doubts, the financial market entered an immediate short-term bear market where stocks across all firms’s market value decreased significantly.