diff --git a/DESCRIPTION b/DESCRIPTION
index 2504f8eb30d51ac90cff6992317483df73342c49..6fe4f960de6ae721ada65964f0fcd689a4d653f4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -20,8 +20,10 @@ Imports:
data.table,
jsonlite
Suggests:
+ ggplot2,
knitr,
rmarkdown,
- tinytest
+ tinytest,
+ xts
VignetteBuilder: knitr
RoxygenNote: 7.1.1
diff --git a/NAMESPACE b/NAMESPACE
index 64bf2c01c514e9c8d5bc5d1c23866a983a61bfb8..22472dee279a00e2b8320a225f374642eb16084e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -7,7 +7,9 @@ export(rdb_datasets)
export(rdb_dimensions)
export(rdb_last_updates)
export(rdb_providers)
+export(rdb_rename_xts)
export(rdb_series)
+export(rdb_to_xts)
import(curl)
import(data.table)
import(jsonlite)
diff --git a/NEWS.md b/NEWS.md
index 2948e66814cb6b969ff7dae1e2dc32bed5ec3a15..22a38a15782038688576c576f2cc323fccdbe1fd 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,8 @@
* New badge in README.
* Correction of .gitlab-ci.yml with pkg-config.
* Correction of `rdb()` examples in the doc and README.
+* Simplification of README.
+* New fonctions `rdb_to_xts()` and `rdb_rename_xts()` that were in README.
# rdbnomics 0.6.2
diff --git a/R/rdb.R b/R/rdb.R
index 378b37910a830e200b2b1d14667b16143f05a094..618f560c0e7a9ca380734cd18762a9967589259e 100644
--- a/R/rdb.R
+++ b/R/rdb.R
@@ -174,7 +174,7 @@
#' ## Apply filter(s) to the series
#' # One filter
#' df1 <- rdb(
-#' ids = c("IMF/WEO/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"),
+#' ids = c("IMF/WEO/ABW.BCA.us_dollars", "IMF/WEO/ABW.BCA_NGDPD.pcent_gdp"),
#' filters = list(
#' code = "interpolate",
#' parameters = list(frequency = "daily", method = "spline")
@@ -183,7 +183,7 @@
#'
#' # Two filters
#' df1 <- rdb(
-#' ids = c("IMF/WEO/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"),
+#' ids = c("IMF/WEO/ABW.BCA.us_dollars", "IMF/WEO/ABW.BCA_NGDPD.pcent_gdp"),
#' filters = list(
#' list(
#' code = "interpolate",
diff --git a/R/rdb_rename_xts.R b/R/rdb_rename_xts.R
new file mode 100644
index 0000000000000000000000000000000000000000..04c2d6f3c8f5e7c63f6c100ef0152bd4f749a0e4
--- /dev/null
+++ b/R/rdb_rename_xts.R
@@ -0,0 +1,46 @@
+#' Rename the xts object columns
+#'
+#' In the \code{xts} object returned by the function \code{rdb_to_xts}, the
+#' series codes are used as column names. If you prefer the series names
+#' (or apply a function to them), the function \code{rdb_rename_xts} is here for
+#' that.
+#'
+#' @param x \code{xts} object. The \code{xts} object returned by the function
+#' \code{rdb_to_xts}.
+#' @param fun function (default NULL). The function to apply to the column names.
+#' @param ... Arguments for the function \code{fun}.
+#' @return A \code{xts} object.
+#' @examples
+#' \dontrun{
+#' library(xts)
+#' library(data.table)
+#' library(rdbnomics)
+#'
+#' df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
+#' df <- rdb_to_xts(df)
+#' rdb_rename_xts(df)
+#' }
+#' @seealso \code{\link{rdb}}, \code{\link{rdb_to_xts}}
+#' @author Sebastien Galais
+#' @export
+rdb_rename_xts <- function(x, fun = NULL, ...) {
+ code <- paste(
+ " xts_ok <- try(utils::packageVersion('xts'), silent = TRUE)",
+ " if (inherits(xts_ok, 'try-error')) {",
+ " stop(",
+ " 'Please install the package xts to use rdb_to_xts().',",
+ " call. = FALSE",
+ " )",
+ " }",
+ " nm <- xts::xtsAttributes(x)$codename",
+ " cols <- nm$series_name[match(names(x), nm$series_code)]",
+ " if (is.null(fun)) {",
+ " names(x) <- cols",
+ " } else {",
+ " names(x) <- sapply(X = cols, FUN = fun, ..., USE.NAMES = FALSE)",
+ " }",
+ " x",
+ sep = "\n"
+ )
+ eval(parse(text = code))
+}
diff --git a/R/rdb_to_xts.R b/R/rdb_to_xts.R
new file mode 100644
index 0000000000000000000000000000000000000000..7d80e6bf25e4aa3c5a8091b3d31c56e88d94acc0
--- /dev/null
+++ b/R/rdb_to_xts.R
@@ -0,0 +1,94 @@
+#' Transform the data.table object into a xts object
+#'
+#' For some analysis, it is more convenient to have a \code{xts} object
+#' instead of a \code{data.table} object.
+#'
+#' @param x \code{data.table}. The \code{data.table} returned by the \code{rdb}
+#' function.
+#' @param needed_columns Vector of character strings (default
+#' \code{c("period", "series_code", "series_name", "value")}). Vector of column
+#' names which are needed to transform the \code{data.table} into a \code{xts}
+#' object.
+#' @param series_columns Vector of character strings (default
+#' \code{c("series_code", "series_name")}). Vector of series column
+#' names.
+#' @return A \code{xts} object.
+#' @examples
+#' \dontrun{
+#' library(xts)
+#' library(data.table)
+#' library(rdbnomics)
+#'
+#' df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
+#' rdb_to_xts(df)
+#' }
+#' @seealso \code{\link{rdb}}, \code{\link{rdb_rename_xts}}
+#' @author Sebastien Galais
+#' @export
+rdb_to_xts <- function(
+ x,
+ needed_columns = c("period", "series_code", "series_name", "value"),
+ series_columns = c("series_code", "series_name")
+) {
+ code <- paste(
+ " xts_ok <- try(utils::packageVersion('xts'), silent = TRUE)",
+ " if (inherits(xts_ok, 'try-error')) {",
+ " stop(",
+ " 'Please install the package xts to use rdb_to_xts().',",
+ " call. = FALSE",
+ " )",
+ " }",
+ " if (is.null(x)) {",
+ " return(NULL)",
+ " }",
+ " all_cols <- length(setdiff(needed_columns, colnames(x))) != 0",
+ " if (all_cols) {",
+ " stop(",
+ " paste0(",
+ " 'To export as a xts object, some columns are missing. Needed columns ',",
+ " 'are \u0022', paste0(needed_columns, collapse = '\u0022, \u0022'),",
+ " '\u0022'",
+ " ),",
+ " call. = FALSE",
+ " )",
+ " }",
+ " x <- x[, .SD, .SDcols = needed_columns]",
+ " data.table::setcolorder(x, needed_columns)",
+ " attr_names <- NULL",
+ " if (!is.null(series_columns)) {",
+ " attr_names <- unique(x[, .SD, .SDcols = series_columns])",
+ " }",
+ " if (nrow(x) > 0) {",
+ " x <- data.table::dcast.data.table(",
+ " x, period ~ series_code,",
+ " value.var = 'value'",
+ " )",
+ " } else {",
+ " orig <- Sys.Date() - as.numeric(Sys.Date())",
+ " x <- data.table(",
+ " period = as.Date(numeric(), origin = orig),",
+ " no_code = numeric()",
+ " )",
+ " }",
+ " if (!inherits(x[[1]], 'Date')) {",
+ " stop(",
+ " paste0(",
+ " 'The first needed column ', needed_columns[1], ' is not of class ',",
+ " 'Date which is a problem for data.table::as.xts.data.table().'",
+ " ),",
+ " '\n',",
+ " paste0(",
+ " 'Please check with packageVersion(\u0022rdbnomics\u0022) that your version is ',",
+ " 'greater or equal to 0.5.2. Otherwise update rdbnomics or contact ',",
+ " 's915.stem@gmail.com.'",
+ " ),",
+ " call. = FALSE",
+ " )",
+ " }",
+ " x <- data.table::as.xts.data.table(x)",
+ " xts::xtsAttributes(x) <- list(codename = attr_names)",
+ " x",
+ sep = "\n"
+ )
+ eval(parse(text = code))
+}
diff --git a/R/sysdata.rda b/R/sysdata.rda
index 8ef293f2913bdd7889e9a058dfb3c0dee61f092a..1d3c1c78fd28f654223b25759eb5b0692a24ee1c 100644
Binary files a/R/sysdata.rda and b/R/sysdata.rda differ
diff --git a/README.md b/README.md
index 1fbed6e1d0d3f25021f82c90f70aa042129778a4..3c60f1509d8a7a8a5713e91737cc548c7042954c 100644
--- a/README.md
+++ b/README.md
@@ -27,384 +27,28 @@ remotes::install_github("dbnomics/rdbnomics", build_vignettes = TRUE, force = TR
library(rdbnomics)
```
-After installation, a vignette is available to the user:
-```r
-vignette("rdbnomics")
-```
-
-## Examples
-### Fetch time series by `ids`:
-```r
-# Fetch one series from dataset 'Unemployment rate' (ZUTN) of AMECO provider:
-df1 <- rdb(ids = "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"))
-```
-
-In the event that you only use the argument `ids`, you can drop it and run:
-```r
-df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
-```
-
-### Fetch time series by `mask` :
-```r
-# Fetch one series from dataset 'Balance of Payments' (BOP) of IMF:
-df1 <- rdb("IMF", "BOP", mask = "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")
-```
-
-In the event that you only use the arguments `provider_code`, `dataset_code` and `mask`, you can drop the name `mask` and run:
-```r
-df4 <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")
-```
-
-### Fetch time series by `dimensions`:
-```r
-# 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:
-df3 <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS")))
-# or
-df3 <- rdb("WB", "DB", dimensions = '{"country":["DZ","PE"],"indicator":["ENF.CONT.COEN.COST.ZS","IC.REG.COST.PC.FE.ZS"]}')
-```
-
-### Fetch time series with a `query`:
-```r
-# Fetch one series from dataset 'WEO by countries' (WEO) of IMF provider:
-df1 <- rdb("IMF", "WEO", query = "France current account balance percent")
-
-# Fetch series from dataset 'WEO by countries' (WEO) of IMF provider:
-df2 <- rdb("IMF", "WEO", query = "current account balance percent")
-```
-
-### Fetch one series from the dataset 'Doing Business' of WB provider with the link:
-```r
-df1 <- rdb(api_link = "https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")
-```
-
-In the event that you only use the argument `api_link`, you can drop the name and run:
-```r
-df1 <- rdb("https://api.db.nomics.world/v22/series/WB/DB?dimensions=%7B%22country%22%3A%5B%22FR%22%2C%22IT%22%2C%22ES%22%5D%7D&q=IC.REG.PROC.FE.NO&observations=1&format=json&align_periods=1&offset=0&facets=0")
-```
-
-## Fetch the available datasets of a provider
-```r
-# Example with the IMF datasets:
-df_datasets <- rdb_datasets(provider_code = "IMF")
-
-# Example with the IMF and BDF datasets:
-df_datasets <- rdb_datasets(provider_code = c("IMF", "BDF"))
-```
-
-In the event that you only request the datasets for one provider, if you define
-`simplify = TRUE`, then the result will be a `data.table` not a named list.
-```r
-df_datasets <- rdb_datasets(provider_code = "IMF", simplify = TRUE)
-```
-
-## Fetch the possible dimensions of available datasets of a provider
-```r
-# Example for the dataset WEO of the IMF:
-df_dimensions <- rdb_dimensions(provider_code = "IMF", dataset_code = "WEO")
-```
-
-In the event that you only request the dimensions for one dataset for one
-provider, if you define `simplify = TRUE`, then the result will be a named list
-`data.table` not a nested named list.
-```r
-df_dimensions <- rdb_dimensions(provider_code = "IMF", dataset_code = "WEO", simplify = TRUE)
-```
-
-## Fetch the number of series of available datasets of a provider
-```r
-# Example for the dataset WEOAGG of the IMF:
-df_series <- rdb_series(provider_code = "IMF", dataset_code = "WEOAGG")
-
-# With dimensions
-df_series <- rdb_series("IMF", "WEO", dimensions = list(`weo-country` = "AGO"))
-df_series <- rdb_series("IMF", "WEO", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE)
-
-# With a query
-df_series <- rdb_series("IMF", "WEO", query = "ARE")
-df_series <- rdb_series("IMF", c("WEO", "WEOAGG"), query = "NGDP_RPCH")
-```
-
-:warning: We ask the user to use this function parsimoniously because there are a huge amount
-of series per dataset. Please only fetch for one dataset if you need it or
-visit the website [https://db.nomics.world](https://db.nomics.world).
-
-## Proxy configuration or connection error `Could not resolve host`
-When using the function `rdb`, you may come across the following error:
-```r
-Error in open.connection(con, "rb") :
- Could not resolve host: api.db.nomics.world
-```
-To get round this situation, you have two possibilities:
-
-1. configure **curl** to use a specific and authorized proxy.
+After installation, a vignette is available to the user with:
-2. use the default R internet connection i.e. the Internet Explorer proxy defined in *internet2.dll*.
-
-### Configure **curl** to use a specific and authorized proxy
-In **rdbnomics**, by default the function `curl_fetch_memory` (of the package **curl**) is used to fetch the data. If a specific proxy must be used, it is possible to define it permanently with the package option `rdbnomics.curl_config` or on the fly through the argument `curl_config`. Because the object is a named list, its elements are passed to the connection (the `curl_handle` object created internally with `new_handle()`) with `handle_setopt()` before using `curl_fetch_memory`.
-
-To see the available parameters, run `names(curl_options())` in *R* or visit the website <a href="https://curl.haxx.se/libcurl/c/curl_easy_setopt.html" target="_blank">https://curl.haxx.se/libcurl/c/curl_easy_setopt.html</a>. Once they are chosen, you define the curl object as follows:
-```r
-h <- list(
- proxy = "<proxy>",
- proxyport = <port>,
- proxyusername = "<username>",
- proxypassword = "<password>"
-)
-```
-
-#### Set the connection up for a session
-The curl connection can be set up for a session by modifying the following package option:
-```r
-options(rdbnomics.curl_config = h)
-```
-When fetching the data, the following command is executed:
-```r
-hndl <- curl::new_handle()
-curl::handle_setopt(hndl, .list = getOption("rdbnomics.curl_config"))
-curl::curl_fetch_memory(url = <...>, handle = hndl)
-```
-After configuration, just use the standard functions of **rdbnomics** e.g.:
-```r
-df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
-```
-This option of the package can be disabled with:
-```r
-options(rdbnomics.curl = NULL)
-```
-
-#### Use the connection only for a function call
-If a complete configuration is not needed but just an "on the fly" execution, then use the argument `curl_config` of the function `rdb`:
-```r
-df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h)
-```
-
-### Use the default R internet connection
-To retrieve the data with the default R internet connection, **rdbnomics** will use the base function `readLines`.
-
-#### Set the connection up for a session
-To activate this feature for a session, you need to enable an option of the package:
-```r
-options(rdbnomics.use_readLines = TRUE)
-```
-And then use the standard function as follows:
-```r
-df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
-```
-This configuration can be disabled with:
-```r
-options(rdbnomics.use_readLines = FALSE)
-```
-
-#### Use the connection only for a function call
-If you just want to do it once, you may use the argument `use_readLines` of the function `rdb`:
-```r
-df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE)
-```
-
-## Transform time series with filters
-The **rdbnomics** package can interact with the *Time Series Editor* of DBnomics to transform time series by applying filters to them.
-Available filters are listed on the filters page [https://editor.nomics.world/filters](https://editor.nomics.world/filters).
-
-Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation:
+* many examples,
+* how to configure a proxy,
+* how to use default R internet connection,
+* transform time series with filters,
+* ...
+Just follow the link <a href="https://cran.r-project.org/web/packages/rdbnomics/vignettes/rdbnomics.html" target="_blank">rdbnomics</a> or run:
```r
-filters <- list(
- code = "interpolate",
- parameters = list(frequency = "monthly", method = "spline")
-)
-
-df <- rdb(
- ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
- filters = filters
-)
-```
-
-If you want to apply more than one filter, the `filters` argument will be a list of valid filters:
-
-```r
-filters <- list(
- list(
- code = "interpolate",
- parameters = list(frequency = "monthly", method = "spline")
- ),
- list(
- code = "aggregate",
- parameters = list(frequency = "bi-annual", method = "end_of_period")
- )
-)
-
-df <- rdb(
- ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"),
- filters = filters
-)
-```
-
-The `data.table` columns change a little bit when filters are used. There are two new columns:
-
-- `period_middle_day`: the middle day of `original_period` (can be useful when you compare graphically interpolated series and original ones).
-- `filtered` (boolean): `TRUE` if the series is filtered, `FALSE` otherwise.
-
-The content of two columns are modified:
-
-- `series_code`: same as before for original series, but the suffix `_filtered` is added for filtered series.
-- `series_name`: same as before for original series, but the suffix ` (filtered)` is added for filtered series.
-
-## Transform the `data.table` object into a `xts` object
-For some analysis, it is more convenient to have a `xts` object instead of a `data.table` object. To transform
-it, you can use the following functions:
-```r
-library(xts)
-library(data.table)
-library(rdbnomics)
-
-to_xts <- function(
- x,
- needed_columns = c("period", "series_code", "series_name", "value"),
- series_columns = c("series_code", "series_name")
-) {
- if (is.null(x)) {
- return(NULL)
- }
-
- all_cols <- length(setdiff(needed_columns, colnames(x))) != 0
- if (all_cols) {
- stop(
- paste0(
- "To export as a xts object, some columns are missing. Needed columns ",
- "are \u0022", paste0(needed_columns, collapse = "\u0022, \u0022"),
- "\u0022"
- ),
- call. = FALSE
- )
- }
-
- x <- x[, .SD, .SDcols = needed_columns]
- data.table::setcolorder(x, needed_columns)
-
- attr_names <- NULL
- if (!is.null(series_columns)) {
- attr_names <- unique(x[, .SD, .SDcols = series_columns])
- }
-
- if (nrow(x) > 0) {
- x <- data.table::dcast.data.table(
- x, period ~ series_code,
- value.var = "value"
- )
- } else {
- orig <- Sys.Date() - as.numeric(Sys.Date())
- x <- data.table(
- period = as.Date(numeric(), origin = orig),
- no_code = numeric()
- )
- }
-
- if (!inherits(x[[1]], "Date")) {
- stop(
- paste0(
- "The first needed column '", needed_columns[1], "' is not of class ",
- "'Date' which is a problem for data.table::as.xts.data.table()."
- ),
- "\n",
- paste0(
- "Please check with packageVersion(\"rdbnomics\") that your version is ",
- "greater or equal to '0.5.2'. Otherwise update rdbnomics or contact ",
- "s915.stem@gmail.com."
- ),
- call. = FALSE
- )
- }
-
- x <- data.table::as.xts.data.table(x)
- xts::xtsAttributes(x) <- list(codename = attr_names)
-
- x
-}
-
-rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
-#> ... original_value period provider_code REF_AREA Reference Area series_code ...
-#> 1: NA 1940-01-01 IMF ES Spain A.ES.BCA_BP6_EUR
-#> 2: NA 1941-01-01 IMF ES Spain A.ES.BCA_BP6_EUR
-#> --- ... ... ... ... ... ...
-#> 159: -15136.8 2018-01-01 IMF FR France A.FR.BCA_BP6_EUR
-#> 160: NA 2019-01-01 IMF FR France A.FR.BCA_BP6_EUR
-
-to_xts(rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR"))
-#> A.ES.BCA_BP6_EUR A.FR.BCA_BP6_EUR
-#> 1940-01-01 NA NA
-#> 1941-01-01 NA NA
-#> 1942-01-01 NA NA
-#> ... ... ...
-#> 2017-01-01 31086 -16397.700
-#> 2018-01-01 23283 -15136.800
-#> 2019-01-01 NA NA
+vignette("rdbnomics")
```
-
-In the `xts` object, the series codes are used as column names. If you prefer
-the series names (or apply a function to them), you can utilize the function:
+A lot of examples are available in the functions' documentations:
```r
-library(magrittr)
-
-rdb_rename_xts <- function(x, fun = NULL, ...) {
- nm <- xts::xtsAttributes(x)$codename
- cols <- nm$series_name[match(names(x), nm$series_code)]
- if (is.null(fun)) {
- names(x) <- cols
- } else {
- names(x) <- sapply(X = cols, FUN = fun, ..., USE.NAMES = FALSE)
- }
- x
-}
-
-rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") %>%
- to_xts() %>%
- rdb_rename_xts()
-#> Annual – Spain – Current Account, Total, Net, Euros Annual – France – Current Account, Total, Net, Euros
-#> 1940-01-01 NA NA
-#> 1941-01-01 NA NA
-#> 1942-01-01 NA NA
-#> ... ... ...
-#> 2017-01-01 31086 -16397.700
-#> 2018-01-01 23283 -15136.800
-#> 2019-01-01 NA NA
-
-rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR") %>%
- to_xts() %>%
- rdb_rename_xts(stringr::word, start = 3)
-#> Spain France
-#> 1940-01-01 NA NA
-#> 1941-01-01 NA NA
-#> 1942-01-01 NA NA
-#> ... ... ...
-#> 2017-01-01 31086 -16397.700
-#> 2018-01-01 23283 -15136.800
-#> 2019-01-01 NA NA
+?rdb
+?rdb_datasets
+?rdb_dimensions
+?rdb_series
+?rdb_providers
+?rdb_last_updates
+?rdb_to_xts
+?rdb_rename_xts
```
## P.S.
diff --git a/man/rdb.Rd b/man/rdb.Rd
index c6c065fa26a488ae58532e2ecda4a020b5b94d95..0d3388e49a4ce0e60212422b60a6beb354e48c00 100644
--- a/man/rdb.Rd
+++ b/man/rdb.Rd
@@ -209,7 +209,7 @@ 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/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"),
+ ids = c("IMF/WEO/ABW.BCA.us_dollars", "IMF/WEO/ABW.BCA_NGDPD.pcent_gdp"),
filters = list(
code = "interpolate",
parameters = list(frequency = "daily", method = "spline")
@@ -218,7 +218,7 @@ df1 <- rdb(
# Two filters
df1 <- rdb(
- ids = c("IMF/WEO/ABW.BCA", "IMF/WEO/ABW.BCA_NGDPD"),
+ ids = c("IMF/WEO/ABW.BCA.us_dollars", "IMF/WEO/ABW.BCA_NGDPD.pcent_gdp"),
filters = list(
list(
code = "interpolate",
diff --git a/man/rdb_rename_xts.Rd b/man/rdb_rename_xts.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..6e7423290170e3a3f54b29126c47d4761cdc1f90
--- /dev/null
+++ b/man/rdb_rename_xts.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/rdb_rename_xts.R
+\name{rdb_rename_xts}
+\alias{rdb_rename_xts}
+\title{Rename the xts object columns}
+\usage{
+rdb_rename_xts(x, fun = NULL, ...)
+}
+\arguments{
+\item{x}{\code{xts} object. The \code{xts} object returned by the function
+\code{rdb_to_xts}.}
+
+\item{fun}{function (default NULL). The function to apply to the column names.}
+
+\item{...}{Arguments for the function \code{fun}.}
+}
+\value{
+A \code{xts} object.
+}
+\description{
+In the \code{xts} object returned by the function \code{rdb_to_xts}, the
+series codes are used as column names. If you prefer the series names
+(or apply a function to them), the function \code{rdb_rename_xts} is here for
+that.
+}
+\examples{
+\dontrun{
+library(xts)
+library(data.table)
+library(rdbnomics)
+
+df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
+df <- rdb_to_xts(df)
+rdb_rename_xts(df)
+}
+}
+\seealso{
+\code{\link{rdb}}, \code{\link{rdb_to_xts}}
+}
+\author{
+Sebastien Galais
+}
diff --git a/man/rdb_to_xts.Rd b/man/rdb_to_xts.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..d0654f31427d5e38218d76354bb3fd57fbe9ec63
--- /dev/null
+++ b/man/rdb_to_xts.Rd
@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/rdb_to_xts.R
+\name{rdb_to_xts}
+\alias{rdb_to_xts}
+\title{Transform the data.table object into a xts object}
+\usage{
+rdb_to_xts(
+ x,
+ needed_columns = c("period", "series_code", "series_name", "value"),
+ series_columns = c("series_code", "series_name")
+)
+}
+\arguments{
+\item{x}{\code{data.table}. The \code{data.table} returned by the \code{rdb}
+function.}
+
+\item{needed_columns}{Vector of character strings (default
+\code{c("period", "series_code", "series_name", "value")}). Vector of column
+names which are needed to transform the \code{data.table} into a \code{xts}
+object.}
+
+\item{series_columns}{Vector of character strings (default
+\code{c("series_code", "series_name")}). Vector of series column
+names.}
+}
+\value{
+A \code{xts} object.
+}
+\description{
+For some analysis, it is more convenient to have a \code{xts} object
+instead of a \code{data.table} object.
+}
+\examples{
+\dontrun{
+library(xts)
+library(data.table)
+library(rdbnomics)
+
+df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
+rdb_to_xts(df)
+}
+}
+\seealso{
+\code{\link{rdb}}, \code{\link{rdb_rename_xts}}
+}
+\author{
+Sebastien Galais
+}
diff --git a/vignettes/rdbnomics.Rmd b/vignettes/rdbnomics.Rmd
index 15ce0dba41fe2ab93778dc9359d10803b1f58b05..354abaff571200005fe83431a9bb81177f775709 100644
--- a/vignettes/rdbnomics.Rmd
+++ b/vignettes/rdbnomics.Rmd
@@ -237,7 +237,7 @@ mtext(
## Fetch two series from different datasets of different providers
```{r, eval = FALSE}
-df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.TOTAL.PC_ACT.T.EA19"))
+df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19"))
df <- df[!is.na(value)]
```
```{r, eval = TRUE, echo = FALSE}