# NOT RUN {
## By ids
# Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider:
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
# or when no argument names are given (provider_code -> ids)
df1 <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
# Fetch two series from dataset 'Unemployment rate' (ZUTN) of AMECO provider:
df2 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"))
# Fetch two series from different datasets of different providers:
df3 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "IMF/BOP/A.FR.BCA_BP6_EUR"))
## By dimensions
# Fetch one value of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider:
df1 <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea12"))
# or
df1 <- rdb("AMECO", "ZUTN", dimensions = '{"geo":["ea12"]}')
# Fetch two values of one dimension from dataset 'Unemployment rate' (ZUTN) of AMECO provider:
df2 <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea12", "dnk")))
# or
df2 <- rdb("AMECO", "ZUTN", dimensions = '{"geo":["ea12","dnk"]}')
# Fetch several values of several dimensions from dataset 'Doing business' (DB) of World Bank:
dim <- list(
country = c("DZ", "PE"),
indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS")
)
df3 <- rdb("WB", "DB", dimensions = dim)
# or
dim <- paste0(
'{"country":["DZ","PE"],',
'"indicator":["ENF.CONT.COEN.COST.ZS","IC.REG.COST.PC.FE.ZS"]}'
)
df3 <- rdb("WB", "DB", dimensions = dim)
## By mask
# Fetch one series from dataset 'Balance of Payments' (BOP) of IMF:
df1 <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR")
# or when no argument names are given except provider_code and dataset_code (ids -> mask)
df1 <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")
# Fetch two series from dataset 'Balance of Payments' (BOP) of IMF:
df2 <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
# Fetch all series along one dimension from dataset 'Balance of Payments' (BOP) of IMF:
df3 <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR")
# Fetch series along multiple dimensions from dataset 'Balance of Payments' (BOP) of IMF:
df4 <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR")
## By query
# Fetch one series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) from IMF :
df1 <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent")
# Fetch series from dataset 'WEO by countries (2019-10 release)' (WEO:2019-10) from IMF :
df2 <- rdb("IMF", "WEO:2019-10", query = "current account balance percent")
## By api_link
# Fetch two series from different datasets of different providers :
df1 <- rdb(
api_link = paste0(
"https://api.db.nomics.world/v22/",
"series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX"
)
)
# Fetch one series from the dataset 'Doing Business' of WB provider :
df2 <- rdb(
api_link = paste0(
"https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22",
"indicator%22%3A%5B%22IC.REG.PROC.FE.NO%22%5D%7D&q=Doing%20Business",
"&observations=1&format=json&align_periods=1&offset=0&facets=0"
)
)
# or when no argument names are given (provider_code -> api_link)
df1 <- rdb(
paste0(
"https://api.db.nomics.world/v22/",
"series?observations=1&series_ids=AMECO/ZUTN/EA19.1.0.0.0.ZUTN,IMF/CPI/A.AT.PCPIT_IX"
)
)
## Use a specific proxy to fetch the data
# Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider :
h <- list(
proxy = "<proxy>",
proxyport = <port>,
proxyusername = "<username>",
proxypassword = "<password>"
)
options(rdbnomics.curl_config = h)
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
# or to use once
options(rdbnomics.curl_config = NULL)
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h)
## Use R default connection to avoid a proxy failure (in some cases)
# Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider :
options(rdbnomics.use_readLines = TRUE)
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
# or to use once
df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE)
## Apply filter(s) to the series
# One filter
df1 <- rdb(
ids = c("IMF/WEO:2019-10/ABW.BCA.us_dollars", "IMF/WEO:2019-10/ABW.BCA_NGDPD.pcent_gdp"),
filters = list(
code = "interpolate",
parameters = list(frequency = "daily", method = "spline")
)
)
# Two filters
df1 <- rdb(
ids = c("IMF/WEO:2019-10/ABW.BCA.us_dollars", "IMF/WEO:2019-10/ABW.BCA_NGDPD.pcent_gdp"),
filters = list(
list(
code = "interpolate",
parameters = list(frequency = "quarterly", method = "spline")
),
list(
code = "aggregate",
parameters = list(frequency = "annual", method = "average")
)
)
)
# }
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