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README.md
In IMFData: R Interface for International Monetary Fund(IMF) Data API
IMFData
IMFData is an R package to access IMF (Internation Monetary of Fund) data . It has three main goals:
- Findout available datasets in the API.
- Findout the dataset datastructure and code to use to make a query.
- Query through the API.
Installation
Right now, you can install
-
from CRAN with
r
install.packages('IMFData')
-
the latest development version from github with
r
devtools::install_github('mingjerli/IMFData')
Loading the package
library(IMFData)
How to use IMFData
If you don't know anything about IMF data API, the following four steps is a good way to start.
Findout available dataset in IMF data.
availableDB <- DataflowMethod()
availableDB$DatabaseID
#> [1] "FSIRE" "FAS" "IFS" "MCDREO"
#> [5] "FSIBS" "FSI" "DOT" "FSIREM"
#> [9] "CDIS" "GFS01M" "GFS01" "BOP"
#> [13] "BOPAGG" "CPIS" "APDREO" "FM"
#> [17] "AFRREO" "MCDREO201410" "FM201410" "AFRREO201410"
#> [21] "MCDREO201501" "APDREO201410" "COMMP" "COMMPP"
#> [25] "WoRLD" "GFSR" "GFSSSUC" "GFSCOFOG"
#> [29] "GFSFALCS" "GFSIBS" "GFSMAB" "GFSE"
#> [33] "PGI" "WHDREO201504" "WCED" "WHDREO"
#> [37] "FM201504" "APDREO201504" "MCDREO201505" "AFRREO201504"
#> [41] "CDISARCHIVE" "ICSD" "HPDD" "COFR"
#> [45] "CPI" "IRFCL" "COFER" "FM201510"
#> [49] "RAFIT2AGG" "MCDREO201510" "WHDREO201510" "APDREO201510"
#> [53] "AFRREO201510" "GFSYR2014" "GFSYSSUC2014" "GFSYCOFOG2014"
#> [57] "GFSYIBS2014" "GFSYMAB2014" "GFSYFALCS2014" "GFSYE2014"
#> [61] "GFSIBS2015" "GFSR2015" "GFSFALCS2015" "GFSSSUC2015"
#> [65] "GFSMAB2015" "GFSCOFOG2015" "GFSE2015" "BOPSDMXUSD"
availableDB$DatabaseText
#> [1] "Financial Soundness Indicators (FSI), Reporting Entities"
#> [2] "Financial Access Survey (FAS)"
#> [3] "International Financial Statistics (IFS)"
#> [4] "Middle East and Central Asia Regional Economic Outlook (MCDREO)"
#> [5] "Financial Soundness Indicators (FSI), Balance Sheets"
#> [6] "Financial Soundness Indicators (FSI)"
#> [7] "Direction of Trade Statistics (DOTS)"
#> [8] "Financial Soundness Indicators (FSI), Reporting Entities - Multidimensional"
#> [9] "Coordinated Direct Investment Survey (CDIS)"
#> [10] "Government Finance Statistics (GFS 2001) - Multidimensional"
#> [11] "Government Finance Statistics (GFS 2001)"
#> [12] "Balance of Payments (BOP)"
#> [13] "Balance of Payments (BOP), World and Regional Aggregates"
#> [14] "Coordinated Portfolio Investment Survey (CPIS)"
#> [15] "Asia and Pacific Regional Economic Outlook (APDREO)"
#> [16] "Fiscal Monitor (FM)"
#> [17] "Sub-Saharan Africa Regional Economic Outlook (AFRREO)"
#> [18] "MCD Regional Economic Outlook October 2014"
#> [19] "Fiscal Monitor (FM) October 2014"
#> [20] "Sub-Saharan Africa Regional Economic Outlook (AFRREO) October 2014"
#> [21] "MCD Regional Economic Outlook January 2015"
#> [22] "Asia and Pacific Regional Economic Outlook (APDREO) October 2014"
#> [23] "Primary Commodity Prices"
#> [24] "Primary Commodity Prices Projections"
#> [25] "World Revenue Longitudinal Data (WoRLD)"
#> [26] "Government Finance Statistics (GFS), Revenue"
#> [27] "Government Finance Statistics (GFS), Statement of Sources and Uses of Cash"
#> [28] "Government Finance Statistics (GFS), Expenditure by Function of Government (COFOG)"
#> [29] "Government Finance Statistics (GFS), Financial Assets and Liabilities by Counterpart Sector"
#> [30] "Government Finance Statistics (GFS), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities)"
#> [31] "Government Finance Statistics (GFS), Main Aggregates and Balances"
#> [32] "Government Finance Statistics (GFS), Expense"
#> [33] "Principal Global Indicators (PGI)"
#> [34] "Western Hemisphere Regional Economic Outlook (WHDREO) April 2015"
#> [35] "World Commodity Exporters (WCED)"
#> [36] "Western Hemisphere Regional Economic Outlook (WHDREO)"
#> [37] "Fiscal Monitor (FM) April 2015"
#> [38] "Asia and Pacific Regional Economic Outlook (APDREO) April 2015"
#> [39] "MCD Regional Economic Outlook May 2015"
#> [40] "Sub-Saharan Africa Regional Economic Outlook (AFRREO) April 2015"
#> [41] "Coordinated Direct Investment Survey (CDIS) - Archive"
#> [42] "Investment and Capital Stock (ICSD)"
#> [43] "Historical Public Debt (HPDD)"
#> [44] "Coverage of Fiscal Reporting (COFR)"
#> [45] "Consumer Price Index (CPI)"
#> [46] "International Reserves and Foreign Currency Liquidity (IRFCL)"
#> [47] "Currency Composition of Official Foreign Exchange Reserves (COFER)"
#> [48] "Fiscal Monitor (FM) October 2015"
#> [49] "RA-FIT Round 2 Aggregates"
#> [50] "MCD Regional Economic Outlook (MCDREO) October 2015"
#> [51] "Western Hemisphere Regional Economic Outlook (WHDREO) October 2015"
#> [52] "Asia and Pacific Regional Economic Outlook (APDREO) October 2015"
#> [53] "Sub-Saharan Africa Regional Economic Outlook (AFRREO) October 2015"
#> [54] "Government Finance Statistics Yearbook (GFSY 2014), Revenue"
#> [55] "Government Finance Statistics Yearbook (GFSY 2014), Statement of Sources and Uses of Cash"
#> [56] "Government Finance Statistics Yearbook (GFSY 2014), Expenditure by Function of Government (COFOG)"
#> [57] "Government Finance Statistics Yearbook (GFSY 2014), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities)"
#> [58] "Government Finance Statistics Yearbook (GFSY 2014), Main Aggregates and Balances"
#> [59] "Government Finance Statistics Yearbook (GFSY 2014), Financial Assets and Liabilities by Counterpart Sector"
#> [60] "Government Finance Statistics Yearbook (GFSY 2014), Expense"
#> [61] "Government Finance Statistics Yearbook (GFSY 2015), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities)"
#> [62] "Government Finance Statistics Yearbook (GFSY 2015), Revenue"
#> [63] "Government Finance Statistics Yearbook (GFSY 2015), Financial Assets and Liabilities by Counterpart Sector"
#> [64] "Government Finance Statistics Yearbook (GFSY 2015), Statement of Sources and Uses of Cash"
#> [65] "Government Finance Statistics Yearbook (GFSY 2015), Main Aggregates and Balances"
#> [66] "Government Finance Statistics Yearbook (GFSY 2015), Expenditure by Function of Government (COFOG)"
#> [67] "Government Finance Statistics Yearbook (GFSY 2015), Expense"
#> [68] "Balance of Payments (BOP), Global SDMX (US Dollars)"
Findout how many dimension available in a given dataset. Here, we use IFS(International Financial Statistics) for example,
# Get dimension code of IFS dataset
IFS.available.codes <- DataStructureMethod("IFS")
# Available dimension code
names(IFS.available.codes)
#> [1] "CL_FREQ" "CL_AREA_IFS" "CL_INDICATOR_IFS"
# Possible code in the first dimension
IFS.available.codes[[1]]
#> CodeValue CodeText
#> 1 A Annual
#> 2 B Bi-annual
#> 3 Q Quarterly
#> 4 M Monthly
#> 5 D Daily
#> 6 W Weekly
Search possible code to use in each dimension. Here, we want to search code related to GDP in CL_INDICATOR_IFS dimension,
# Search code contains GDP
CodeSearch(IFS.available.codes, "CL_INDICATOR_IFS", "GDP")
#> CodeValue
#> 1464 GGXWDG_G01_GDP_PT
#> 1468 GGX_G01_GDP_PT
#> 1486 GGR_G01_GDP_PT
#> 1567 NGDP_F_XDC
#> 1568 NGDP_D_IX
#> 1569 NGDP_D_PC_CP_A_PT
#> 1570 NGDP_D_SA_IX
#> 1571 NGDP_AR_XDC
#> 1572 NGDP_EUR
#> 1573 NGDP_XDC
#> 1574 NGDP_SA_AR_XDC
#> 1575 NGDP_SA_EUR
#> 1576 NGDP_SA_XDC
#> 1577 NGDP_USD
#> 1578 NGDP_R_AR_XDC
#> 1579 NGDP_R_EUR
#> 1580 NGDP_R_F_XDC
#> 1581 NGDP_R_IX
#> 1582 NGDP_R_XDC
#> 1583 NGDP_R_PT
#> 1584 NGDP_R_CH_SA_AR_XDC
#> 1585 NGDP_R_SA_AR_XDC
#> 1586 NGDP_R_SA_EUR
#> 1587 NGDP_R_SA_IX
#> 1588 NGDP_R_SA_XDC
#> 1597 NGDPNPI_AR_XDC
#> 1598 NGDPNPI_EUR
#> 1599 NGDPNPI_XDC
#> 1600 NGDPNPI_SA_AR_XDC
#> 1601 NGDPNPI_SA_EUR
#> 1602 NGDPNPI_SA_XDC
#> 1603 NGDPNPI_USD
#> 2606 NSDGDP_EUR
#> 2607 NSDGDP_XDC
#> 2608 NSDGDP_SA_AR_XDC
#> 2609 NSDGDP_USD
#> 2671 All_Indicators_Excluding_NGDP_Indicators
#> CodeText
#> 1464 General Government, Gross debt position, 2001 Manual, Percent of GDP, Percent
#> 1468 General Government, Memo Item: Expenditure, 2001 Manual, Percent of GDP, Percent
#> 1486 General Government, Revenue, 2001 Manual, Percent of GDP, Percent
#> 1567 Gross Domestic Product, at Factor Cost, National Currency
#> 1568 Gross Domestic Product, Deflator, Index
#> 1569 Gross Domestic Product, Deflator, Percentage change, corresponding period previous year, Percent
#> 1570 Gross Domestic Product, Deflator, Seasonally adjusted, Index
#> 1571 Gross Domestic Product, Nominal, Annualized Rate, National Currency
#> 1572 Gross Domestic Product, Nominal, Euro
#> 1573 Gross Domestic Product, Nominal, National Currency
#> 1574 Gross Domestic Product, Nominal, Seasonally adjusted, annualized Rate, National Currency
#> 1575 Gross Domestic Product, Nominal, Seasonally Adjusted, Euros
#> 1576 Gross Domestic Product, Nominal, Seasonally Adjusted, National Currency
#> 1577 Gross Domestic Product, Nominal, US Dollars
#> 1578 Gross Domestic Product, Real, Annualized Rate, National Currency
#> 1579 Gross Domestic Product, Real, Euros
#> 1580 Gross Domestic Product, Real, Factor Cost, National Currency
#> 1581 Gross Domestic Product, Real, Index
#> 1582 Gross Domestic Product, Real, National Currency
#> 1583 Gross Domestic Product, Real, Percent
#> 1584 Gross Domestic Product, Real, Reference chained, seasonally adjusted, Annualized Rate, National Currency
#> 1585 Gross Domestic Product, Real, Seasonally adjusted, Annualized Rate, National Currency
#> 1586 Gross Domestic Product, Real, Seasonally Adjusted, Euros
#> 1587 Gross Domestic Product, Real, Seasonally adjusted, Index
#> 1588 Gross Domestic Product, Real, Seasonally Adjusted, National Currency
#> 1597 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Annualized Rate, National Currency
#> 1598 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Euros
#> 1599 Income Receipts from Rest of the World less Income Payments to the Rest of the World, National Currency
#> 1600 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Seasonally adjusted, annualized Rate, National Currency
#> 1601 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Seasonally adjusted, Euro
#> 1602 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Seasonally Adjusted, National Currency
#> 1603 Income Receipts from Rest of the World less Income Payments to the Rest of the World, US Dollars
#> 2606 Statistical Discrepancy in GDP, Nominal, Euros
#> 2607 Statistical Discrepancy in GDP, Nominal, National Currency
#> 2608 Statistical Discrepancy in GDP, Nominal, Seasonally adjusted, Annualized Rate, National Currency
#> 2609 Statistical Discrepancy in GDP, Nominal, US Dollars
#> 2671 All Indicators Excluding NGDP Indicators
Make API call to get data,
databaseID <- "IFS"
startdate = "2001-01-01"
enddate = "2016-12-31"
checkquery = FALSE
## Germany, Norminal GDP in Euros, Norminal GDP in National Currency
queryfilter <- list(CL_FREA = "", CL_AREA_IFS = "GR", CL_INDICATOR_IFS = c("NGDP_EUR",
"NGDP_XDC"))
GR.NGDP.query <- CompactDataMethod(databaseID, queryfilter, startdate, enddate,
checkquery)
GR.NGDP.query[, 1:5]
#> @FREQ @REF_AREA @INDICATOR @UNIT_MULT @TIME_FORMAT
#> 2 Q GR NGDP_EUR 9 P3M
#> 4 A GR NGDP_EUR 9 P1Y
GR.NGDP.query$Obs[[1]]
#> @TIME_PERIOD @OBS_VALUE @OBS_STATUS
#> 1 2001-Q1 35.2366 K
#> 2 2001-Q2 36.7264 <NA>
#> 3 2001-Q3 39.8428 <NA>
#> 4 2001-Q4 40.3881 <NA>
#> 5 2002-Q1 37.4971 <NA>
#> 6 2002-Q2 39.8739 <NA>
#> 7 2002-Q3 42.3377 <NA>
#> 8 2002-Q4 43.752 <NA>
#> 9 2003-Q1 40.8806 <NA>
#> 10 2003-Q2 43.4188 <NA>
#> 11 2003-Q3 46.6817 <NA>
#> 12 2003-Q4 47.9238 <NA>
#> 13 2004-Q1 44.8781 <NA>
#> 14 2004-Q2 47.604999892938 <NA>
#> 15 2004-Q3 50.8154 <NA>
#> 16 2004-Q4 50.4173 <NA>
#> 17 2005-Q1 45.727 <NA>
#> 18 2005-Q2 49.1768012641032 <NA>
#> 19 2005-Q3 51.7502 <NA>
#> 20 2005-Q4 52.5883 <NA>
#> 21 2006-Q1 50.0931 <NA>
#> 22 2006-Q2 53.9923 <NA>
#> 23 2006-Q3 55.9232 <NA>
#> 24 2006-Q4 57.8529 <NA>
#> 25 2007-Q1 52.8308 <NA>
#> 26 2007-Q2 58.359 <NA>
#> 27 2007-Q3 59.929 <NA>
#> 28 2007-Q4 61.5758 <NA>
#> 29 2008-Q1 55.8782 <NA>
#> 30 2008-Q2 60.746 <NA>
#> 31 2008-Q3 63.0783 <NA>
#> 32 2008-Q4 62.2879 <NA>
#> 33 2009-Q1 53.3813 <NA>
#> 34 2009-Q2 60.2148 <NA>
#> 35 2009-Q3 61.2555 <NA>
#> 36 2009-Q4 62.6826 <NA>
#> 37 2010-Q1 54.2695 <NA>
#> 38 2010-Q2 57.379 <NA>
#> 39 2010-Q3 57.6284 <NA>
#> 40 2010-Q4 56.7546 <NA>
#> 41 2011-Q1 48.8302 <NA>
#> 42 2011-Q2 53.0734 <NA>
#> 43 2011-Q3 53.7756 <NA>
#> 44 2011-Q4 51.3496 <NA>
#> 45 2012-Q1 45.0995 <NA>
#> 46 2012-Q2 48.4733 <NA>
#> 47 2012-Q3 49.7557 <NA>
#> 48 2012-Q4 47.8755 <NA>
#> 49 2013-Q1 42.1708 <NA>
#> 50 2013-Q2 45.8799 <NA>
#> 51 2013-Q3 47.5605 <NA>
#> 52 2013-Q4 44.7779 <NA>
#> 53 2014-Q1 40.4676 <NA>
#> 54 2014-Q2 44.3952 <NA>
#> 55 2014-Q3 48.0023 <NA>
#> 56 2014-Q4 44.6942 <NA>
#> 57 2015-Q1 40.4384 <NA>
#> 58 2015-Q2 44.6036 <NA>
#> 59 2015-Q3 46.7534 <NA>
#> 60 2015-Q4 44.2277 <NA>
#> 61 2016-Q1 39.9625 <NA>
#> 62 2016-Q2 45.1606 <NA>
GR.NGDP.query$Obs[[2]]
#> @TIME_PERIOD @OBS_VALUE @OBS_STATUS
#> 1 2001 151.9872 K
#> 2 2002 162.2742 <NA>
#> 3 2003 178.5709 <NA>
#> 4 2004 193.715823551657 <NA>
#> 5 2005 199.242311837475 <NA>
#> 6 2006 217.861568188497 <NA>
#> 7 2007 232.694592661749 <NA>
#> 8 2008 241.990389906673 <NA>
#> 9 2009 237.534181456484 <NA>
#> 10 2010 226.031447205434 <NA>
#> 11 2011 207.028875339588 <NA>
#> 12 2012 191.203907934554 <NA>
#> 13 2013 180.389043118584 <NA>
#> 14 2014 177.55942109271 <NA>
#> 15 2015 176.022666208548 <NA>
## Quarterly, US, NGDP_SA_AR_XDC
queryfilter <- list(CL_FREA = "Q", CL_AREA_IFS = "US", CL_INDICATOR_IFS = "NGDP_SA_AR_XDC")
Q.US.NGDP.query <- CompactDataMethod(databaseID, queryfilter, startdate, enddate,
checkquery)
Q.US.NGDP.query[, 1:5]
#> @FREQ @REF_AREA @INDICATOR @UNIT_MULT @TIME_FORMAT
#> 1 Q US NGDP_SA_AR_XDC 9 P3M
Q.US.NGDP.query$Obs[[1]]
#> @TIME_PERIOD @OBS_VALUE
#> 1 2001-Q1 10508.1
#> 2 2001-Q2 10638.4
#> 3 2001-Q3 10639.5
#> 4 2001-Q4 10701.3
#> 5 2002-Q1 10834.4
#> 6 2002-Q2 10934.8
#> 7 2002-Q3 11037.1
#> 8 2002-Q4 11103.8
#> 9 2003-Q1 11230.1
#> 10 2003-Q2 11370.7
#> 11 2003-Q3 11625.1
#> 12 2003-Q4 11816.8
#> 13 2004-Q1 11988.4
#> 14 2004-Q2 12181.4
#> 15 2004-Q3 12367.7
#> 16 2004-Q4 12562.2
#> 17 2005-Q1 12813.7
#> 18 2005-Q2 12974.1
#> 19 2005-Q3 13205.4
#> 20 2005-Q4 13381.6
#> 21 2006-Q1 13648.9
#> 22 2006-Q2 13799.8
#> 23 2006-Q3 13908.5
#> 24 2006-Q4 14066.4
#> 25 2007-Q1 14233.2
#> 26 2007-Q2 14422.3
#> 27 2007-Q3 14569.7
#> 28 2007-Q4 14685.3
#> 29 2008-Q1 14668.4
#> 30 2008-Q2 14813
#> 31 2008-Q3 14843
#> 32 2008-Q4 14549.9
#> 33 2009-Q1 14383.9
#> 34 2009-Q2 14340.4
#> 35 2009-Q3 14384.1
#> 36 2009-Q4 14566.5
#> 37 2010-Q1 14681.1
#> 38 2010-Q2 14888.6
#> 39 2010-Q3 15057.7
#> 40 2010-Q4 15230.2
#> 41 2011-Q1 15238.4
#> 42 2011-Q2 15460.9
#> 43 2011-Q3 15587.1
#> 44 2011-Q4 15785.3
#> 45 2012-Q1 15973.9
#> 46 2012-Q2 16121.9
#> 47 2012-Q3 16227.9
#> 48 2012-Q4 16297.3
#> 49 2013-Q1 16475.4
#> 50 2013-Q2 16541.4
#> 51 2013-Q3 16749.3
#> 52 2013-Q4 16999.9
#> 53 2014-Q1 17025.2
#> 54 2014-Q2 17285.6
#> 55 2014-Q3 17569.4
#> 56 2014-Q4 17692.2
#> 57 2015-Q1 17783.6
#> 58 2015-Q2 17998.3
#> 59 2015-Q3 18141.9
#> 60 2015-Q4 18222.8
#> 61 2016-Q1 18281.6
#> 62 2016-Q2 18437.6
## See exact API call to the data source
CompactDataMethod(databaseID, queryfilter, startdate, enddate, checkquery, verbose = TRUE)$Obs[[1]]
#>
#> making API call:
#> http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/IFS/Q.US.NGDP_SA_AR_XDC?startPeriod=2001-01-01&endPeriod=2016-12-31
#> @TIME_PERIOD @OBS_VALUE
#> 1 2001-Q1 10508.1
#> 2 2001-Q2 10638.4
#> 3 2001-Q3 10639.5
#> 4 2001-Q4 10701.3
#> 5 2002-Q1 10834.4
#> 6 2002-Q2 10934.8
#> 7 2002-Q3 11037.1
#> 8 2002-Q4 11103.8
#> 9 2003-Q1 11230.1
#> 10 2003-Q2 11370.7
#> 11 2003-Q3 11625.1
#> 12 2003-Q4 11816.8
#> 13 2004-Q1 11988.4
#> 14 2004-Q2 12181.4
#> 15 2004-Q3 12367.7
#> 16 2004-Q4 12562.2
#> 17 2005-Q1 12813.7
#> 18 2005-Q2 12974.1
#> 19 2005-Q3 13205.4
#> 20 2005-Q4 13381.6
#> 21 2006-Q1 13648.9
#> 22 2006-Q2 13799.8
#> 23 2006-Q3 13908.5
#> 24 2006-Q4 14066.4
#> 25 2007-Q1 14233.2
#> 26 2007-Q2 14422.3
#> 27 2007-Q3 14569.7
#> 28 2007-Q4 14685.3
#> 29 2008-Q1 14668.4
#> 30 2008-Q2 14813
#> 31 2008-Q3 14843
#> 32 2008-Q4 14549.9
#> 33 2009-Q1 14383.9
#> 34 2009-Q2 14340.4
#> 35 2009-Q3 14384.1
#> 36 2009-Q4 14566.5
#> 37 2010-Q1 14681.1
#> 38 2010-Q2 14888.6
#> 39 2010-Q3 15057.7
#> 40 2010-Q4 15230.2
#> 41 2011-Q1 15238.4
#> 42 2011-Q2 15460.9
#> 43 2011-Q3 15587.1
#> 44 2011-Q4 15785.3
#> 45 2012-Q1 15973.9
#> 46 2012-Q2 16121.9
#> 47 2012-Q3 16227.9
#> 48 2012-Q4 16297.3
#> 49 2013-Q1 16475.4
#> 50 2013-Q2 16541.4
#> 51 2013-Q3 16749.3
#> 52 2013-Q4 16999.9
#> 53 2014-Q1 17025.2
#> 54 2014-Q2 17285.6
#> 55 2014-Q3 17569.4
#> 56 2014-Q4 17692.2
#> 57 2015-Q1 17783.6
#> 58 2015-Q2 17998.3
#> 59 2015-Q3 18141.9
#> 60 2015-Q4 18222.8
#> 61 2016-Q1 18281.6
#> 62 2016-Q2 18437.6
## Return a simple data frame
head(CompactDataMethod(databaseID, queryfilter, startdate, enddate, checkquery,
tidy = TRUE))
#> @TIME_PERIOD @OBS_VALUE @FREQ @REF_AREA @INDICATOR @UNIT_MULT
#> 1 2001-Q1 10508.1 Q US NGDP_SA_AR_XDC 9
#> 2 2001-Q2 10638.4 Q US NGDP_SA_AR_XDC 9
#> 3 2001-Q3 10639.5 Q US NGDP_SA_AR_XDC 9
#> 4 2001-Q4 10701.3 Q US NGDP_SA_AR_XDC 9
#> 5 2002-Q1 10834.4 Q US NGDP_SA_AR_XDC 9
#> 6 2002-Q2 10934.8 Q US NGDP_SA_AR_XDC 9
#> @TIME_FORMAT
#> 1 P3M
#> 2 P3M
#> 3 P3M
#> 4 P3M
#> 5 P3M
#> 6 P3M
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IMFData
IMFData is an R package to access IMF (Internation Monetary of Fund) data . It has three main goals:
- Findout available datasets in the API.
- Findout the dataset datastructure and code to use to make a query.
- Query through the API.
Installation
Right now, you can install
-
from CRAN with
r install.packages('IMFData') -
the latest development version from github with
r devtools::install_github('mingjerli/IMFData')
Loading the package
library(IMFData)
How to use IMFData
If you don't know anything about IMF data API, the following four steps is a good way to start.
Findout available dataset in IMF data.
availableDB <- DataflowMethod()
availableDB$DatabaseID
#> [1] "FSIRE" "FAS" "IFS" "MCDREO"
#> [5] "FSIBS" "FSI" "DOT" "FSIREM"
#> [9] "CDIS" "GFS01M" "GFS01" "BOP"
#> [13] "BOPAGG" "CPIS" "APDREO" "FM"
#> [17] "AFRREO" "MCDREO201410" "FM201410" "AFRREO201410"
#> [21] "MCDREO201501" "APDREO201410" "COMMP" "COMMPP"
#> [25] "WoRLD" "GFSR" "GFSSSUC" "GFSCOFOG"
#> [29] "GFSFALCS" "GFSIBS" "GFSMAB" "GFSE"
#> [33] "PGI" "WHDREO201504" "WCED" "WHDREO"
#> [37] "FM201504" "APDREO201504" "MCDREO201505" "AFRREO201504"
#> [41] "CDISARCHIVE" "ICSD" "HPDD" "COFR"
#> [45] "CPI" "IRFCL" "COFER" "FM201510"
#> [49] "RAFIT2AGG" "MCDREO201510" "WHDREO201510" "APDREO201510"
#> [53] "AFRREO201510" "GFSYR2014" "GFSYSSUC2014" "GFSYCOFOG2014"
#> [57] "GFSYIBS2014" "GFSYMAB2014" "GFSYFALCS2014" "GFSYE2014"
#> [61] "GFSIBS2015" "GFSR2015" "GFSFALCS2015" "GFSSSUC2015"
#> [65] "GFSMAB2015" "GFSCOFOG2015" "GFSE2015" "BOPSDMXUSD"
availableDB$DatabaseText
#> [1] "Financial Soundness Indicators (FSI), Reporting Entities"
#> [2] "Financial Access Survey (FAS)"
#> [3] "International Financial Statistics (IFS)"
#> [4] "Middle East and Central Asia Regional Economic Outlook (MCDREO)"
#> [5] "Financial Soundness Indicators (FSI), Balance Sheets"
#> [6] "Financial Soundness Indicators (FSI)"
#> [7] "Direction of Trade Statistics (DOTS)"
#> [8] "Financial Soundness Indicators (FSI), Reporting Entities - Multidimensional"
#> [9] "Coordinated Direct Investment Survey (CDIS)"
#> [10] "Government Finance Statistics (GFS 2001) - Multidimensional"
#> [11] "Government Finance Statistics (GFS 2001)"
#> [12] "Balance of Payments (BOP)"
#> [13] "Balance of Payments (BOP), World and Regional Aggregates"
#> [14] "Coordinated Portfolio Investment Survey (CPIS)"
#> [15] "Asia and Pacific Regional Economic Outlook (APDREO)"
#> [16] "Fiscal Monitor (FM)"
#> [17] "Sub-Saharan Africa Regional Economic Outlook (AFRREO)"
#> [18] "MCD Regional Economic Outlook October 2014"
#> [19] "Fiscal Monitor (FM) October 2014"
#> [20] "Sub-Saharan Africa Regional Economic Outlook (AFRREO) October 2014"
#> [21] "MCD Regional Economic Outlook January 2015"
#> [22] "Asia and Pacific Regional Economic Outlook (APDREO) October 2014"
#> [23] "Primary Commodity Prices"
#> [24] "Primary Commodity Prices Projections"
#> [25] "World Revenue Longitudinal Data (WoRLD)"
#> [26] "Government Finance Statistics (GFS), Revenue"
#> [27] "Government Finance Statistics (GFS), Statement of Sources and Uses of Cash"
#> [28] "Government Finance Statistics (GFS), Expenditure by Function of Government (COFOG)"
#> [29] "Government Finance Statistics (GFS), Financial Assets and Liabilities by Counterpart Sector"
#> [30] "Government Finance Statistics (GFS), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities)"
#> [31] "Government Finance Statistics (GFS), Main Aggregates and Balances"
#> [32] "Government Finance Statistics (GFS), Expense"
#> [33] "Principal Global Indicators (PGI)"
#> [34] "Western Hemisphere Regional Economic Outlook (WHDREO) April 2015"
#> [35] "World Commodity Exporters (WCED)"
#> [36] "Western Hemisphere Regional Economic Outlook (WHDREO)"
#> [37] "Fiscal Monitor (FM) April 2015"
#> [38] "Asia and Pacific Regional Economic Outlook (APDREO) April 2015"
#> [39] "MCD Regional Economic Outlook May 2015"
#> [40] "Sub-Saharan Africa Regional Economic Outlook (AFRREO) April 2015"
#> [41] "Coordinated Direct Investment Survey (CDIS) - Archive"
#> [42] "Investment and Capital Stock (ICSD)"
#> [43] "Historical Public Debt (HPDD)"
#> [44] "Coverage of Fiscal Reporting (COFR)"
#> [45] "Consumer Price Index (CPI)"
#> [46] "International Reserves and Foreign Currency Liquidity (IRFCL)"
#> [47] "Currency Composition of Official Foreign Exchange Reserves (COFER)"
#> [48] "Fiscal Monitor (FM) October 2015"
#> [49] "RA-FIT Round 2 Aggregates"
#> [50] "MCD Regional Economic Outlook (MCDREO) October 2015"
#> [51] "Western Hemisphere Regional Economic Outlook (WHDREO) October 2015"
#> [52] "Asia and Pacific Regional Economic Outlook (APDREO) October 2015"
#> [53] "Sub-Saharan Africa Regional Economic Outlook (AFRREO) October 2015"
#> [54] "Government Finance Statistics Yearbook (GFSY 2014), Revenue"
#> [55] "Government Finance Statistics Yearbook (GFSY 2014), Statement of Sources and Uses of Cash"
#> [56] "Government Finance Statistics Yearbook (GFSY 2014), Expenditure by Function of Government (COFOG)"
#> [57] "Government Finance Statistics Yearbook (GFSY 2014), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities)"
#> [58] "Government Finance Statistics Yearbook (GFSY 2014), Main Aggregates and Balances"
#> [59] "Government Finance Statistics Yearbook (GFSY 2014), Financial Assets and Liabilities by Counterpart Sector"
#> [60] "Government Finance Statistics Yearbook (GFSY 2014), Expense"
#> [61] "Government Finance Statistics Yearbook (GFSY 2015), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities)"
#> [62] "Government Finance Statistics Yearbook (GFSY 2015), Revenue"
#> [63] "Government Finance Statistics Yearbook (GFSY 2015), Financial Assets and Liabilities by Counterpart Sector"
#> [64] "Government Finance Statistics Yearbook (GFSY 2015), Statement of Sources and Uses of Cash"
#> [65] "Government Finance Statistics Yearbook (GFSY 2015), Main Aggregates and Balances"
#> [66] "Government Finance Statistics Yearbook (GFSY 2015), Expenditure by Function of Government (COFOG)"
#> [67] "Government Finance Statistics Yearbook (GFSY 2015), Expense"
#> [68] "Balance of Payments (BOP), Global SDMX (US Dollars)"
Findout how many dimension available in a given dataset. Here, we use IFS(International Financial Statistics) for example,
# Get dimension code of IFS dataset
IFS.available.codes <- DataStructureMethod("IFS")
# Available dimension code
names(IFS.available.codes)
#> [1] "CL_FREQ" "CL_AREA_IFS" "CL_INDICATOR_IFS"
# Possible code in the first dimension
IFS.available.codes[[1]]
#> CodeValue CodeText
#> 1 A Annual
#> 2 B Bi-annual
#> 3 Q Quarterly
#> 4 M Monthly
#> 5 D Daily
#> 6 W Weekly
Search possible code to use in each dimension. Here, we want to search code related to GDP in CL_INDICATOR_IFS dimension,
# Search code contains GDP
CodeSearch(IFS.available.codes, "CL_INDICATOR_IFS", "GDP")
#> CodeValue
#> 1464 GGXWDG_G01_GDP_PT
#> 1468 GGX_G01_GDP_PT
#> 1486 GGR_G01_GDP_PT
#> 1567 NGDP_F_XDC
#> 1568 NGDP_D_IX
#> 1569 NGDP_D_PC_CP_A_PT
#> 1570 NGDP_D_SA_IX
#> 1571 NGDP_AR_XDC
#> 1572 NGDP_EUR
#> 1573 NGDP_XDC
#> 1574 NGDP_SA_AR_XDC
#> 1575 NGDP_SA_EUR
#> 1576 NGDP_SA_XDC
#> 1577 NGDP_USD
#> 1578 NGDP_R_AR_XDC
#> 1579 NGDP_R_EUR
#> 1580 NGDP_R_F_XDC
#> 1581 NGDP_R_IX
#> 1582 NGDP_R_XDC
#> 1583 NGDP_R_PT
#> 1584 NGDP_R_CH_SA_AR_XDC
#> 1585 NGDP_R_SA_AR_XDC
#> 1586 NGDP_R_SA_EUR
#> 1587 NGDP_R_SA_IX
#> 1588 NGDP_R_SA_XDC
#> 1597 NGDPNPI_AR_XDC
#> 1598 NGDPNPI_EUR
#> 1599 NGDPNPI_XDC
#> 1600 NGDPNPI_SA_AR_XDC
#> 1601 NGDPNPI_SA_EUR
#> 1602 NGDPNPI_SA_XDC
#> 1603 NGDPNPI_USD
#> 2606 NSDGDP_EUR
#> 2607 NSDGDP_XDC
#> 2608 NSDGDP_SA_AR_XDC
#> 2609 NSDGDP_USD
#> 2671 All_Indicators_Excluding_NGDP_Indicators
#> CodeText
#> 1464 General Government, Gross debt position, 2001 Manual, Percent of GDP, Percent
#> 1468 General Government, Memo Item: Expenditure, 2001 Manual, Percent of GDP, Percent
#> 1486 General Government, Revenue, 2001 Manual, Percent of GDP, Percent
#> 1567 Gross Domestic Product, at Factor Cost, National Currency
#> 1568 Gross Domestic Product, Deflator, Index
#> 1569 Gross Domestic Product, Deflator, Percentage change, corresponding period previous year, Percent
#> 1570 Gross Domestic Product, Deflator, Seasonally adjusted, Index
#> 1571 Gross Domestic Product, Nominal, Annualized Rate, National Currency
#> 1572 Gross Domestic Product, Nominal, Euro
#> 1573 Gross Domestic Product, Nominal, National Currency
#> 1574 Gross Domestic Product, Nominal, Seasonally adjusted, annualized Rate, National Currency
#> 1575 Gross Domestic Product, Nominal, Seasonally Adjusted, Euros
#> 1576 Gross Domestic Product, Nominal, Seasonally Adjusted, National Currency
#> 1577 Gross Domestic Product, Nominal, US Dollars
#> 1578 Gross Domestic Product, Real, Annualized Rate, National Currency
#> 1579 Gross Domestic Product, Real, Euros
#> 1580 Gross Domestic Product, Real, Factor Cost, National Currency
#> 1581 Gross Domestic Product, Real, Index
#> 1582 Gross Domestic Product, Real, National Currency
#> 1583 Gross Domestic Product, Real, Percent
#> 1584 Gross Domestic Product, Real, Reference chained, seasonally adjusted, Annualized Rate, National Currency
#> 1585 Gross Domestic Product, Real, Seasonally adjusted, Annualized Rate, National Currency
#> 1586 Gross Domestic Product, Real, Seasonally Adjusted, Euros
#> 1587 Gross Domestic Product, Real, Seasonally adjusted, Index
#> 1588 Gross Domestic Product, Real, Seasonally Adjusted, National Currency
#> 1597 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Annualized Rate, National Currency
#> 1598 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Euros
#> 1599 Income Receipts from Rest of the World less Income Payments to the Rest of the World, National Currency
#> 1600 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Seasonally adjusted, annualized Rate, National Currency
#> 1601 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Seasonally adjusted, Euro
#> 1602 Income Receipts from Rest of the World less Income Payments to the Rest of the World, Seasonally Adjusted, National Currency
#> 1603 Income Receipts from Rest of the World less Income Payments to the Rest of the World, US Dollars
#> 2606 Statistical Discrepancy in GDP, Nominal, Euros
#> 2607 Statistical Discrepancy in GDP, Nominal, National Currency
#> 2608 Statistical Discrepancy in GDP, Nominal, Seasonally adjusted, Annualized Rate, National Currency
#> 2609 Statistical Discrepancy in GDP, Nominal, US Dollars
#> 2671 All Indicators Excluding NGDP Indicators
Make API call to get data,
databaseID <- "IFS"
startdate = "2001-01-01"
enddate = "2016-12-31"
checkquery = FALSE
## Germany, Norminal GDP in Euros, Norminal GDP in National Currency
queryfilter <- list(CL_FREA = "", CL_AREA_IFS = "GR", CL_INDICATOR_IFS = c("NGDP_EUR",
"NGDP_XDC"))
GR.NGDP.query <- CompactDataMethod(databaseID, queryfilter, startdate, enddate,
checkquery)
GR.NGDP.query[, 1:5]
#> @FREQ @REF_AREA @INDICATOR @UNIT_MULT @TIME_FORMAT
#> 2 Q GR NGDP_EUR 9 P3M
#> 4 A GR NGDP_EUR 9 P1Y
GR.NGDP.query$Obs[[1]]
#> @TIME_PERIOD @OBS_VALUE @OBS_STATUS
#> 1 2001-Q1 35.2366 K
#> 2 2001-Q2 36.7264 <NA>
#> 3 2001-Q3 39.8428 <NA>
#> 4 2001-Q4 40.3881 <NA>
#> 5 2002-Q1 37.4971 <NA>
#> 6 2002-Q2 39.8739 <NA>
#> 7 2002-Q3 42.3377 <NA>
#> 8 2002-Q4 43.752 <NA>
#> 9 2003-Q1 40.8806 <NA>
#> 10 2003-Q2 43.4188 <NA>
#> 11 2003-Q3 46.6817 <NA>
#> 12 2003-Q4 47.9238 <NA>
#> 13 2004-Q1 44.8781 <NA>
#> 14 2004-Q2 47.604999892938 <NA>
#> 15 2004-Q3 50.8154 <NA>
#> 16 2004-Q4 50.4173 <NA>
#> 17 2005-Q1 45.727 <NA>
#> 18 2005-Q2 49.1768012641032 <NA>
#> 19 2005-Q3 51.7502 <NA>
#> 20 2005-Q4 52.5883 <NA>
#> 21 2006-Q1 50.0931 <NA>
#> 22 2006-Q2 53.9923 <NA>
#> 23 2006-Q3 55.9232 <NA>
#> 24 2006-Q4 57.8529 <NA>
#> 25 2007-Q1 52.8308 <NA>
#> 26 2007-Q2 58.359 <NA>
#> 27 2007-Q3 59.929 <NA>
#> 28 2007-Q4 61.5758 <NA>
#> 29 2008-Q1 55.8782 <NA>
#> 30 2008-Q2 60.746 <NA>
#> 31 2008-Q3 63.0783 <NA>
#> 32 2008-Q4 62.2879 <NA>
#> 33 2009-Q1 53.3813 <NA>
#> 34 2009-Q2 60.2148 <NA>
#> 35 2009-Q3 61.2555 <NA>
#> 36 2009-Q4 62.6826 <NA>
#> 37 2010-Q1 54.2695 <NA>
#> 38 2010-Q2 57.379 <NA>
#> 39 2010-Q3 57.6284 <NA>
#> 40 2010-Q4 56.7546 <NA>
#> 41 2011-Q1 48.8302 <NA>
#> 42 2011-Q2 53.0734 <NA>
#> 43 2011-Q3 53.7756 <NA>
#> 44 2011-Q4 51.3496 <NA>
#> 45 2012-Q1 45.0995 <NA>
#> 46 2012-Q2 48.4733 <NA>
#> 47 2012-Q3 49.7557 <NA>
#> 48 2012-Q4 47.8755 <NA>
#> 49 2013-Q1 42.1708 <NA>
#> 50 2013-Q2 45.8799 <NA>
#> 51 2013-Q3 47.5605 <NA>
#> 52 2013-Q4 44.7779 <NA>
#> 53 2014-Q1 40.4676 <NA>
#> 54 2014-Q2 44.3952 <NA>
#> 55 2014-Q3 48.0023 <NA>
#> 56 2014-Q4 44.6942 <NA>
#> 57 2015-Q1 40.4384 <NA>
#> 58 2015-Q2 44.6036 <NA>
#> 59 2015-Q3 46.7534 <NA>
#> 60 2015-Q4 44.2277 <NA>
#> 61 2016-Q1 39.9625 <NA>
#> 62 2016-Q2 45.1606 <NA>
GR.NGDP.query$Obs[[2]]
#> @TIME_PERIOD @OBS_VALUE @OBS_STATUS
#> 1 2001 151.9872 K
#> 2 2002 162.2742 <NA>
#> 3 2003 178.5709 <NA>
#> 4 2004 193.715823551657 <NA>
#> 5 2005 199.242311837475 <NA>
#> 6 2006 217.861568188497 <NA>
#> 7 2007 232.694592661749 <NA>
#> 8 2008 241.990389906673 <NA>
#> 9 2009 237.534181456484 <NA>
#> 10 2010 226.031447205434 <NA>
#> 11 2011 207.028875339588 <NA>
#> 12 2012 191.203907934554 <NA>
#> 13 2013 180.389043118584 <NA>
#> 14 2014 177.55942109271 <NA>
#> 15 2015 176.022666208548 <NA>
## Quarterly, US, NGDP_SA_AR_XDC
queryfilter <- list(CL_FREA = "Q", CL_AREA_IFS = "US", CL_INDICATOR_IFS = "NGDP_SA_AR_XDC")
Q.US.NGDP.query <- CompactDataMethod(databaseID, queryfilter, startdate, enddate,
checkquery)
Q.US.NGDP.query[, 1:5]
#> @FREQ @REF_AREA @INDICATOR @UNIT_MULT @TIME_FORMAT
#> 1 Q US NGDP_SA_AR_XDC 9 P3M
Q.US.NGDP.query$Obs[[1]]
#> @TIME_PERIOD @OBS_VALUE
#> 1 2001-Q1 10508.1
#> 2 2001-Q2 10638.4
#> 3 2001-Q3 10639.5
#> 4 2001-Q4 10701.3
#> 5 2002-Q1 10834.4
#> 6 2002-Q2 10934.8
#> 7 2002-Q3 11037.1
#> 8 2002-Q4 11103.8
#> 9 2003-Q1 11230.1
#> 10 2003-Q2 11370.7
#> 11 2003-Q3 11625.1
#> 12 2003-Q4 11816.8
#> 13 2004-Q1 11988.4
#> 14 2004-Q2 12181.4
#> 15 2004-Q3 12367.7
#> 16 2004-Q4 12562.2
#> 17 2005-Q1 12813.7
#> 18 2005-Q2 12974.1
#> 19 2005-Q3 13205.4
#> 20 2005-Q4 13381.6
#> 21 2006-Q1 13648.9
#> 22 2006-Q2 13799.8
#> 23 2006-Q3 13908.5
#> 24 2006-Q4 14066.4
#> 25 2007-Q1 14233.2
#> 26 2007-Q2 14422.3
#> 27 2007-Q3 14569.7
#> 28 2007-Q4 14685.3
#> 29 2008-Q1 14668.4
#> 30 2008-Q2 14813
#> 31 2008-Q3 14843
#> 32 2008-Q4 14549.9
#> 33 2009-Q1 14383.9
#> 34 2009-Q2 14340.4
#> 35 2009-Q3 14384.1
#> 36 2009-Q4 14566.5
#> 37 2010-Q1 14681.1
#> 38 2010-Q2 14888.6
#> 39 2010-Q3 15057.7
#> 40 2010-Q4 15230.2
#> 41 2011-Q1 15238.4
#> 42 2011-Q2 15460.9
#> 43 2011-Q3 15587.1
#> 44 2011-Q4 15785.3
#> 45 2012-Q1 15973.9
#> 46 2012-Q2 16121.9
#> 47 2012-Q3 16227.9
#> 48 2012-Q4 16297.3
#> 49 2013-Q1 16475.4
#> 50 2013-Q2 16541.4
#> 51 2013-Q3 16749.3
#> 52 2013-Q4 16999.9
#> 53 2014-Q1 17025.2
#> 54 2014-Q2 17285.6
#> 55 2014-Q3 17569.4
#> 56 2014-Q4 17692.2
#> 57 2015-Q1 17783.6
#> 58 2015-Q2 17998.3
#> 59 2015-Q3 18141.9
#> 60 2015-Q4 18222.8
#> 61 2016-Q1 18281.6
#> 62 2016-Q2 18437.6
## See exact API call to the data source
CompactDataMethod(databaseID, queryfilter, startdate, enddate, checkquery, verbose = TRUE)$Obs[[1]]
#>
#> making API call:
#> http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/IFS/Q.US.NGDP_SA_AR_XDC?startPeriod=2001-01-01&endPeriod=2016-12-31
#> @TIME_PERIOD @OBS_VALUE
#> 1 2001-Q1 10508.1
#> 2 2001-Q2 10638.4
#> 3 2001-Q3 10639.5
#> 4 2001-Q4 10701.3
#> 5 2002-Q1 10834.4
#> 6 2002-Q2 10934.8
#> 7 2002-Q3 11037.1
#> 8 2002-Q4 11103.8
#> 9 2003-Q1 11230.1
#> 10 2003-Q2 11370.7
#> 11 2003-Q3 11625.1
#> 12 2003-Q4 11816.8
#> 13 2004-Q1 11988.4
#> 14 2004-Q2 12181.4
#> 15 2004-Q3 12367.7
#> 16 2004-Q4 12562.2
#> 17 2005-Q1 12813.7
#> 18 2005-Q2 12974.1
#> 19 2005-Q3 13205.4
#> 20 2005-Q4 13381.6
#> 21 2006-Q1 13648.9
#> 22 2006-Q2 13799.8
#> 23 2006-Q3 13908.5
#> 24 2006-Q4 14066.4
#> 25 2007-Q1 14233.2
#> 26 2007-Q2 14422.3
#> 27 2007-Q3 14569.7
#> 28 2007-Q4 14685.3
#> 29 2008-Q1 14668.4
#> 30 2008-Q2 14813
#> 31 2008-Q3 14843
#> 32 2008-Q4 14549.9
#> 33 2009-Q1 14383.9
#> 34 2009-Q2 14340.4
#> 35 2009-Q3 14384.1
#> 36 2009-Q4 14566.5
#> 37 2010-Q1 14681.1
#> 38 2010-Q2 14888.6
#> 39 2010-Q3 15057.7
#> 40 2010-Q4 15230.2
#> 41 2011-Q1 15238.4
#> 42 2011-Q2 15460.9
#> 43 2011-Q3 15587.1
#> 44 2011-Q4 15785.3
#> 45 2012-Q1 15973.9
#> 46 2012-Q2 16121.9
#> 47 2012-Q3 16227.9
#> 48 2012-Q4 16297.3
#> 49 2013-Q1 16475.4
#> 50 2013-Q2 16541.4
#> 51 2013-Q3 16749.3
#> 52 2013-Q4 16999.9
#> 53 2014-Q1 17025.2
#> 54 2014-Q2 17285.6
#> 55 2014-Q3 17569.4
#> 56 2014-Q4 17692.2
#> 57 2015-Q1 17783.6
#> 58 2015-Q2 17998.3
#> 59 2015-Q3 18141.9
#> 60 2015-Q4 18222.8
#> 61 2016-Q1 18281.6
#> 62 2016-Q2 18437.6
## Return a simple data frame
head(CompactDataMethod(databaseID, queryfilter, startdate, enddate, checkquery,
tidy = TRUE))
#> @TIME_PERIOD @OBS_VALUE @FREQ @REF_AREA @INDICATOR @UNIT_MULT
#> 1 2001-Q1 10508.1 Q US NGDP_SA_AR_XDC 9
#> 2 2001-Q2 10638.4 Q US NGDP_SA_AR_XDC 9
#> 3 2001-Q3 10639.5 Q US NGDP_SA_AR_XDC 9
#> 4 2001-Q4 10701.3 Q US NGDP_SA_AR_XDC 9
#> 5 2002-Q1 10834.4 Q US NGDP_SA_AR_XDC 9
#> 6 2002-Q2 10934.8 Q US NGDP_SA_AR_XDC 9
#> @TIME_FORMAT
#> 1 P3M
#> 2 P3M
#> 3 P3M
#> 4 P3M
#> 5 P3M
#> 6 P3M
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