rdb: Download DBnomics data.

In rdbnomics: Download DBnomics Data

Description

rdb downloads data series from DBnomics using shortcuts like ids, dimensions, mask, query or using an api_link.

Usage

rdb(
  provider_code = NULL,
  dataset_code = NULL,
  ids = NULL,
  dimensions = NULL,
  mask = NULL,
  query = NULL,
  api_link = NULL,
  filters = getOption("rdbnomics.filters"),
  use_readLines = getOption("rdbnomics.use_readLines"),
  curl_config = getOption("rdbnomics.curl_config"),
  verbose = getOption("rdbnomics.verbose_warning"),
  ...
)

Arguments

provider_code	Character string (default NULL). DBnomics code of the provider.
dataset_code	Character string (default NULL). DBnomics code of the dataset.
ids	Character string (default NULL). DBnomics code of one or several series.
dimensions	List or character string (single quoted) (default NULL). DBnomics code of one or several dimensions in the specified provider and dataset. If it is a named list, then the function toJSON (from the package jsonlite) is applied to generate the json object.
mask	Character string (default NULL). DBnomics code of one or several masks in the specified provider and dataset.
query	Character string (default NULL). A query to filter/select series from a provider's dataset.
api_link	Character string. DBnomics API link of the search. It should starts with http:// or https://.
filters	List (default NULL). This argument must be a named list for one filter because the function toJSON of the package jsonlite is used before sending the request to the server. For multiple filters, you have to provide a list of valid filters (see examples). A valid filter is a named list with an element code which is a character string, and an element parameters which is a named list with elements frequency and method or a NULL.
use_readLines	Logical (default FALSE). If TRUE, then the data are requested and read with the base function readLines i.e. through the default R internet connection. This can be used to get round the error Could not resolve host: api.db.nomics.world.
curl_config	Named list (default NULL). If not NULL, it is used to configure a proxy connection. This configuration is passed to the function curl_fetch_memory of the package curl. A temporary curl_handle object is created internally with arguments equal to the provided list in curl_config. For curl_fetch_memory arguments see curl_fetch. For available curl options see curl_options, names(curl_options()) and libcurl.
verbose	Logical (default FALSE). Show warnings of the function.
...	Arguments to be passed to the internal function .rdb.

Details

This function gives you access to hundreds of millions data series from DBnomics API (documentation about the API can be found here). The code of each series is given on the DBnomics website.

In the event that only the argument ids is provided (and those in the ellipsis ...), the argument name can be dropped. The character string vector is directly passed to ids.
If only the argument api_link is provided (and those in the ellipsis ...), then the argument name can be dropped. The character string vector is directly passed to api_link.
In the same way, if only provider_code, dataset_code and mask are provided then the arguments names can be dropped. The last character string is automatically passed to mask.

Value

A data.table.

Author(s)

Sebastien Galais

Examples

## 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")
    )
  )
)

## End(Not run)

rdbnomics documentation built on Oct. 26, 2020, 1:06 a.m.