Author: Sébastien Galais, Thomas Brand

# DBnomics R client

#### Sébastien Galais1, Thomas Brand2

# 1 DBnomics: the world’s economic database

Explore all the economic data from different providers (national and international statistical institutes, central banks, etc.), for free, following the link db.nomics.world
(N.B.: in the examples, data have already been retrieved on april 6th 2020).

# 2 Fetch time series by `ids`

First, let’s assume that we know which series we want to download. A series identifier (`ids`) is defined by three values, formatted like this: `provider_code`/`dataset_code`/`series_code`.

## 2.1 Fetch one series from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

    library(data.table)
    library(rdbnomics)

    df <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")
    df <- df[!is.na(value)]

In such `data.table`, you will always find at least ten columns:

- `provider_code`
- `dataset_code`
- `dataset_name`
- `series_code`
- `series_name`
- `original_period` (character string)
- `period` (date of the first day of `original_period`)
- `original_value` (character string)
- `value`
- `@frequency` (harmonized frequency generated by DBnomics)

The other columns depend on the provider and on the dataset. They always come in pairs (for the code and the name). In the data.frame `df`, you have:

- `unit` (code) and `Unit` (name)
- `geo` (code) and `Country` (name)
- `freq` (code) and `Frequency` (name)

    ##    provider_code dataset_code        dataset_name         series_code
    ## 1:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 2:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 3:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 4:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 5:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 6:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ##            series_name original_period     period original_value value
    ## 1: Annually – (Perc...            1998 1998-01-01           10.4  10.4
    ## 2: Annually – (Perc...            1999 1999-01-01            9.7   9.7
    ## 3: Annually – (Perc...            2000 2000-01-01            8.9   8.9
    ## 4: Annually – (Perc...            2001 2001-01-01            8.3   8.3
    ## 5: Annually – (Perc...            2002 2002-01-01            8.6   8.6
    ## 6: Annually – (Perc...            2003 2003-01-01            9.1   9.1
    ##    @frequency                unit                Unit  geo   Country freq
    ## 1:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 2:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 3:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 4:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 5:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 6:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ##    Frequency          indexed_at
    ## 1:  Annually 2019-11-08 16:33:50
    ## 2:  Annually 2019-11-08 16:33:50
    ## 3:  Annually 2019-11-08 16:33:50
    ## 4:  Annually 2019-11-08 16:33:50
    ## 5:  Annually 2019-11-08 16:33:50
    ## 6:  Annually 2019-11-08 16:33:50

In the event that you only use the argument `ids`, you can drop it and run:

    df <- rdb("AMECO/ZUTN/EA19.1.0.0.0.ZUTN")

## 2.2 Fetch two series from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

    df <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "AMECO/ZUTN/DNK.1.0.0.0.ZUTN"))
    df <- df[!is.na(value)]

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 2:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 3:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 4:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 5:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 6:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ##            series_name original_period     period original_value value
    ## 1: Annually – (Perc...            1960 1960-01-01              2   2.0
    ## 2: Annually – (Perc...            1961 1961-01-01            1.6   1.6
    ## 3: Annually – (Perc...            1962 1962-01-01            1.4   1.4
    ## 4: Annually – (Perc...            1963 1963-01-01              2   2.0
    ## 5: Annually – (Perc...            1964 1964-01-01            1.2   1.2
    ## 6: Annually – (Perc...            1965 1965-01-01            0.9   0.9
    ##    @frequency                unit                Unit geo Country freq
    ## 1:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 2:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 3:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 4:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 5:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 6:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ##    Frequency          indexed_at
    ## 1:  Annually 2019-11-08 16:33:50
    ## 2:  Annually 2019-11-08 16:33:50
    ## 3:  Annually 2019-11-08 16:33:50
    ## 4:  Annually 2019-11-08 16:33:50
    ## 5:  Annually 2019-11-08 16:33:50
    ## 6:  Annually 2019-11-08 16:33:50

## 2.3 Fetch two series from different datasets of different providers

    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)]

    ##    provider_code dataset_code        dataset_name         series_code
    ## 1:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 2:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 3:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 4:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 5:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 6:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ##            series_name original_period     period original_value value
    ## 1: Annually – (Perc...            1997 1997-01-01           11.5  11.5
    ## 2: Annually – (Perc...            1998 1998-01-01             11  11.0
    ## 3: Annually – (Perc...            1999 1999-01-01           10.2  10.2
    ## 4: Annually – (Perc...            2000 2000-01-01            9.2   9.2
    ## 5: Annually – (Perc...            2001 2001-01-01            8.4   8.4
    ## 6: Annually – (Perc...            2002 2002-01-01            8.6   8.6
    ##    @frequency                unit Unit  geo Country freq Frequency age
    ## 1:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
    ## 2:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
    ## 3:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
    ## 4:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
    ## 5:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
    ## 6:     annual percentage-of-ac...   NA ea19      NA    a  Annually  NA
    ##    Age class FREQ Geopolitical entity (reporting)          indexed_at
    ## 1:        NA   NA                              NA 2020-05-07 08:46:14
    ## 2:        NA   NA                              NA 2020-05-07 08:46:14
    ## 3:        NA   NA                              NA 2020-05-07 08:46:14
    ## 4:        NA   NA                              NA 2020-05-07 08:46:14
    ## 5:        NA   NA                              NA 2020-05-07 08:46:14
    ## 6:        NA   NA                              NA 2020-05-07 08:46:14
    ##    observations_attributes s_adj Seasonal adjustment sex Sex Unit of measure
    ## 1:                            NA                  NA  NA  NA              NA
    ## 2:                            NA                  NA  NA  NA              NA
    ## 3:                            NA                  NA  NA  NA              NA
    ## 4:                            NA                  NA  NA  NA              NA
    ## 5:                            NA                  NA  NA  NA              NA
    ## 6:                            NA                  NA  NA  NA              NA

# 3 Fetch time series by `mask`

The code mask notation is a very concise way to select one or many time series at once.

## 3.1 Fetch one series from dataset ‘Balance of Payments’ (BOP) of IMF

    df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR")
    df <- df[!is.na(value)]

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 2:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 3:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 4:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 5:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 6:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ##            series_name original_period     period original_value     value
    ## 1: Annual – France ...            1999 1999-01-01      47672.847 47672.847
    ## 2: Annual – France ...            2000 2000-01-01      17550.669 17550.669
    ## 3: Annual – France ...            2001 2001-01-01      23278.392 23278.392
    ## 4: Annual – France ...            2002 2002-01-01      18718.406 18718.406
    ## 5: Annual – France ...            2003 2003-01-01      14195.493 14195.493
    ## 6: Annual – France ...            2004 2004-01-01       7520.754  7520.754
    ##    @frequency FREQ Frequency          indexed_at           Indicator
    ## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ##      INDICATOR observations_attributes REF_AREA Reference Area
    ## 1: BCA_BP6_EUR          OFFICIAL_BPM,K       FR         France
    ## 2: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 3: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 4: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 5: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 6: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France

In the event that you only use the arguments `provider_code`, `dataset_code` and `mask`, you can drop the name `mask` and run:

    df <- rdb("IMF", "BOP", "A.FR.BCA_BP6_EUR")

## 3.2 Fetch two series from dataset ‘Balance of Payments’ (BOP) of IMF

You just have to add a `+` between two different values of a dimension.

    df <- rdb("IMF", "BOP", mask = "A.FR+ES.BCA_BP6_EUR")
    df <- df[!is.na(value)]

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
    ## 2:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
    ## 3:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
    ## 4:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
    ## 5:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
    ## 6:           IMF          BOP Balance of Payme... A.ES.BCA_BP6_EUR
    ##            series_name original_period     period original_value  value
    ## 1: Annual – Spain –...            1999 1999-01-01         -19295 -19295
    ## 2: Annual – Spain –...            2000 2000-01-01         -27921 -27921
    ## 3: Annual – Spain –...            2001 2001-01-01         -30672 -30672
    ## 4: Annual – Spain –...            2002 2002-01-01         -27959 -27959
    ## 5: Annual – Spain –...            2003 2003-01-01         -31153 -31153
    ## 6: Annual – Spain –...            2004 2004-01-01         -47116 -47116
    ##    @frequency FREQ Frequency          indexed_at           Indicator
    ## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ##      INDICATOR observations_attributes REF_AREA Reference Area
    ## 1: BCA_BP6_EUR          OFFICIAL_BPM,K       ES          Spain
    ## 2: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
    ## 3: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
    ## 4: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
    ## 5: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain
    ## 6: BCA_BP6_EUR           OFFICIAL_BPM,       ES          Spain

## 3.3 Fetch all series along one dimension from dataset ‘Balance of Payments’ (BOP) of IMF

    df <- rdb("IMF", "BOP", mask = "A..BCA_BP6_EUR")
    df <- df[!is.na(value)]
    df <- df[order(-period, REF_AREA)]
    df <- head(df, 100)

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:           IMF          BOP Balance of Payme... A.DE.BCA_BP6_EUR
    ## 2:           IMF          BOP Balance of Payme... A.EE.BCA_BP6_EUR
    ## 3:           IMF          BOP Balance of Payme... A.LU.BCA_BP6_EUR
    ## 4:           IMF          BOP Balance of Payme... A.LV.BCA_BP6_EUR
    ## 5:           IMF          BOP Balance of Payme... A.PT.BCA_BP6_EUR
    ## 6:           IMF          BOP Balance of Payme... A.RS.BCA_BP6_EUR
    ##            series_name original_period     period original_value      value
    ## 1: Annual – Germany...            2019 2019-01-01         245532 245532.000
    ## 2: Annual – Estonia...            2019 2019-01-01        615.256    615.256
    ## 3: Annual – Luxembo...            2019 2019-01-01     2846.74554   2846.746
    ## 4: Annual – Latvia ...            2019 2019-01-01           -163   -163.000
    ## 5: Annual – Portuga...            2019 2019-01-01           -181   -181.000
    ## 6: Annual – Serbia ...            2019 2019-01-01    -3159.86552  -3159.866
    ##    @frequency FREQ Frequency          indexed_at           Indicator
    ## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ##      INDICATOR observations_attributes REF_AREA Reference Area
    ## 1: BCA_BP6_EUR           OFFICIAL_BPM,       DE        Germany
    ## 2: BCA_BP6_EUR           OFFICIAL_BPM,       EE        Estonia
    ## 3: BCA_BP6_EUR           OFFICIAL_BPM,       LU     Luxembourg
    ## 4: BCA_BP6_EUR           OFFICIAL_BPM,       LV         Latvia
    ## 5: BCA_BP6_EUR           OFFICIAL_BPM,       PT       Portugal
    ## 6: BCA_BP6_EUR           OFFICIAL_BPM,       RS         Serbia

## 3.4 Fetch series along multiple dimensions from dataset ‘Balance of Payments’ (BOP) of IMF

    df <- rdb("IMF", "BOP", mask = "A.FR.BCA_BP6_EUR+IA_BP6_EUR")
    df <- df[!is.na(value)]
    df <- df[order(period), head(.SD, 50), by = INDICATOR]

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 2:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 3:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 4:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 5:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ## 6:           IMF          BOP Balance of Payme... A.FR.BCA_BP6_EUR
    ##            series_name original_period     period original_value     value
    ## 1: Annual – France ...            1999 1999-01-01      47672.847 47672.847
    ## 2: Annual – France ...            2000 2000-01-01      17550.669 17550.669
    ## 3: Annual – France ...            2001 2001-01-01      23278.392 23278.392
    ## 4: Annual – France ...            2002 2002-01-01      18718.406 18718.406
    ## 5: Annual – France ...            2003 2003-01-01      14195.493 14195.493
    ## 6: Annual – France ...            2004 2004-01-01       7520.754  7520.754
    ##    @frequency FREQ Frequency          indexed_at           Indicator
    ## 1:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 2:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 3:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 4:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 5:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ## 6:     annual    A    Annual 2020-03-26 20:10:01 Current Account,...
    ##      INDICATOR observations_attributes REF_AREA Reference Area
    ## 1: BCA_BP6_EUR          OFFICIAL_BPM,K       FR         France
    ## 2: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 3: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 4: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 5: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France
    ## 6: BCA_BP6_EUR           OFFICIAL_BPM,       FR         France

# 4 Fetch time series by `dimensions`

Searching by `dimensions` is a less concise way to select time series than using the code `mask`, but it works with all the different providers. You have a “Description of series code” at the bottom of each dataset page on the DBnomics website.

## 4.1 Fetch one value of one dimension from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

    df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19"))
    df <- df[!is.na(value))]
    # or
    # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}')
    # df <- df[!is.na(value))]

    ##    provider_code dataset_code        dataset_name         series_code
    ## 1:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 2:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 3:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 4:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 5:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ## 6:         AMECO         ZUTN Unemployment rat... EA19.1.0.0.0.ZUT...
    ##            series_name original_period     period original_value value
    ## 1: Annually – (Perc...            1998 1998-01-01           10.4  10.4
    ## 2: Annually – (Perc...            1999 1999-01-01            9.7   9.7
    ## 3: Annually – (Perc...            2000 2000-01-01            8.9   8.9
    ## 4: Annually – (Perc...            2001 2001-01-01            8.3   8.3
    ## 5: Annually – (Perc...            2002 2002-01-01            8.6   8.6
    ## 6: Annually – (Perc...            2003 2003-01-01            9.1   9.1
    ##    @frequency                unit                Unit  geo   Country freq
    ## 1:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 2:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 3:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 4:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 5:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ## 6:     annual percentage-of-ac... (Percentage of a... ea19 Euro area    a
    ##    Frequency          indexed_at
    ## 1:  Annually 2019-11-08 16:33:50
    ## 2:  Annually 2019-11-08 16:33:50
    ## 3:  Annually 2019-11-08 16:33:50
    ## 4:  Annually 2019-11-08 16:33:50
    ## 5:  Annually 2019-11-08 16:33:50
    ## 6:  Annually 2019-11-08 16:33:50

## 4.2 Fetch two values of one dimension from dataset ‘Unemployment rate’ (ZUTN) of AMECO provider

    df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk")))
    df <- df[!is.na(value))]
    # or
    # df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}')
    # df <- df[!is.na(value))]

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 2:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 3:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 4:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 5:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 6:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ##            series_name original_period     period original_value value
    ## 1: Annually – (Perc...            1960 1960-01-01              2   2.0
    ## 2: Annually – (Perc...            1961 1961-01-01            1.6   1.6
    ## 3: Annually – (Perc...            1962 1962-01-01            1.4   1.4
    ## 4: Annually – (Perc...            1963 1963-01-01              2   2.0
    ## 5: Annually – (Perc...            1964 1964-01-01            1.2   1.2
    ## 6: Annually – (Perc...            1965 1965-01-01            0.9   0.9
    ##    @frequency                unit                Unit geo Country freq
    ## 1:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 2:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 3:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 4:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 5:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 6:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ##    Frequency          indexed_at
    ## 1:  Annually 2019-11-08 16:33:50
    ## 2:  Annually 2019-11-08 16:33:50
    ## 3:  Annually 2019-11-08 16:33:50
    ## 4:  Annually 2019-11-08 16:33:50
    ## 5:  Annually 2019-11-08 16:33:50
    ## 6:  Annually 2019-11-08 16:33:50

## 4.3 Fetch several values of several dimensions from dataset ‘Doing business’ (DB) of World Bank

    df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS")))
    df <- df[!is.na(value))]
    # or
    # df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}')
    # df <- df[!is.na(value))]

    ##    provider_code dataset_code   dataset_name         series_code
    ## 1:            WB           DB Doing Business IC.REG.COST.PC.F...
    ## 2:            WB           DB Doing Business IC.REG.COST.PC.F...
    ## 3:            WB           DB Doing Business IC.REG.COST.PC.F...
    ## 4:            WB           DB Doing Business IC.REG.COST.PC.F...
    ## 5:            WB           DB Doing Business IC.REG.COST.PC.F...
    ## 6:            WB           DB Doing Business IC.REG.COST.PC.F...
    ##            series_name original_period     period original_value value
    ## 1: Cost of business...            2003 2003-01-01           16.3  16.3
    ## 2: Cost of business...            2004 2004-01-01           14.6  14.6
    ## 3: Cost of business...            2005 2005-01-01           12.4  12.4
    ## 4: Cost of business...            2006 2006-01-01           13.4  13.4
    ## 5: Cost of business...            2007 2007-01-01           13.2  13.2
    ## 6: Cost of business...            2008 2008-01-01           10.8  10.8
    ##    @frequency country Country          indexed_at           indicator
    ## 1:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
    ## 2:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
    ## 3:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
    ## 4:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
    ## 5:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
    ## 6:     annual      DZ Algeria 2020-03-11 14:09:09 IC.REG.COST.PC.F...
    ##              Indicator observations_attributes
    ## 1: Cost of business...              ATTRIBUTE,
    ## 2: Cost of business...              ATTRIBUTE,
    ## 3: Cost of business...              ATTRIBUTE,
    ## 4: Cost of business...              ATTRIBUTE,
    ## 5: Cost of business...              ATTRIBUTE,
    ## 6: Cost of business...              ATTRIBUTE,

# 5 Fetch time series with a `query`

The query is a Google-like search that will filter/select time series from a provider’s dataset.

## 5.1 Fetch one series from dataset ‘WEO by countries (2019-10 release)’ (WEO:2019-10) of IMF

    df <- rdb("IMF", "WEO:2019-10", query = "France current account balance percent")
    df <- df[!is.na(value))]

    ##    provider_code dataset_code        dataset_name         series_code
    ## 1:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
    ## 2:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
    ## 3:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
    ## 4:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
    ## 5:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
    ## 6:           IMF  WEO:2019-10 WEO by countries... FRA.BCA_NGDPD.pc...
    ##            series_name original_period     period original_value  value
    ## 1: France – Current...            1980 1980-01-01         -0.595 -0.595
    ## 2: France – Current...            1981 1981-01-01         -0.771 -0.771
    ## 3: France – Current...            1982 1982-01-01         -2.062 -2.062
    ## 4: France – Current...            1983 1983-01-01         -0.838 -0.838
    ## 5: France – Current...            1984 1984-01-01         -0.142 -0.142
    ## 6: France – Current...            1985 1985-01-01         -0.063 -0.063
    ##    @frequency      unit           Unit          indexed_at weo-country
    ## 1:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
    ## 2:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
    ## 3:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
    ## 4:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
    ## 5:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
    ## 6:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         FRA
    ##    weo-subject WEO Country         WEO Subject
    ## 1:   BCA_NGDPD      France Current account ...
    ## 2:   BCA_NGDPD      France Current account ...
    ## 3:   BCA_NGDPD      France Current account ...
    ## 4:   BCA_NGDPD      France Current account ...
    ## 5:   BCA_NGDPD      France Current account ...
    ## 6:   BCA_NGDPD      France Current account ...

## 5.2 Fetch series from dataset ‘WEO by countries (2019-10 release)’ (WEO:2019-10) of IMF

    df <- rdb("IMF", "WEO:2019-10", query = "current account balance percent")
    df <- df[!is.na(value))]

    ##    provider_code dataset_code        dataset_name         series_code
    ## 1:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
    ## 2:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
    ## 3:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
    ## 4:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
    ## 5:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
    ## 6:           IMF  WEO:2019-10 WEO by countries... DEU.BCA_NGDPD.pc...
    ##            series_name original_period     period original_value  value
    ## 1: Germany – Curren...            1980 1980-01-01         -1.782 -1.782
    ## 2: Germany – Curren...            1981 1981-01-01         -0.684 -0.684
    ## 3: Germany – Curren...            1982 1982-01-01          0.866  0.866
    ## 4: Germany – Curren...            1983 1983-01-01          0.666  0.666
    ## 5: Germany – Curren...            1984 1984-01-01          1.423  1.423
    ## 6: Germany – Curren...            1985 1985-01-01          2.662  2.662
    ##    @frequency      unit           Unit          indexed_at weo-country
    ## 1:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
    ## 2:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
    ## 3:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
    ## 4:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
    ## 5:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
    ## 6:     annual pcent_gdp Percent of GDP 2020-10-14 14:39:56         DEU
    ##    weo-subject WEO Country         WEO Subject
    ## 1:   BCA_NGDPD     Germany Current account ...
    ## 2:   BCA_NGDPD     Germany Current account ...
    ## 3:   BCA_NGDPD     Germany Current account ...
    ## 4:   BCA_NGDPD     Germany Current account ...
    ## 5:   BCA_NGDPD     Germany Current account ...
    ## 6:   BCA_NGDPD     Germany Current account ...

# 6 Fetch time series found on the web site

When you don’t know the codes of the dimensions, provider, dataset or series, you can:

- go to the page of a dataset on DBnomics website, for example Doing Business,

- select some dimensions by using the input widgets of the left column,

- click on “Copy API link” in the menu of the “Download” button,

- use the `rdb(api_link = ...)` function such as below.

    df <- 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")
    df <- df[!is.na(value))]

    ##    provider_code dataset_code   dataset_name         series_code
    ## 1:            WB           DB Doing Business IC.REG.PROC.FE.N...
    ## 2:            WB           DB Doing Business IC.REG.PROC.FE.N...
    ## 3:            WB           DB Doing Business IC.REG.PROC.FE.N...
    ## 4:            WB           DB Doing Business IC.REG.PROC.FE.N...
    ## 5:            WB           DB Doing Business IC.REG.PROC.FE.N...
    ## 6:            WB           DB Doing Business IC.REG.PROC.FE.N...
    ##            series_name original_period     period original_value value
    ## 1: Starting a busin...            2004 2004-01-01              8     8
    ## 2: Starting a busin...            2004 2004-01-01             10    10
    ## 3: Starting a busin...            2004 2004-01-01             10    10
    ## 4: Starting a busin...            2005 2005-01-01              5     5
    ## 5: Starting a busin...            2005 2005-01-01             10    10
    ## 6: Starting a busin...            2005 2005-01-01             10    10
    ##    @frequency country Country          indexed_at           indicator
    ## 1:     annual      FR  France 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
    ## 2:     annual      IT   Italy 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
    ## 3:     annual      ES   Spain 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
    ## 4:     annual      FR  France 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
    ## 5:     annual      IT   Italy 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
    ## 6:     annual      ES   Spain 2020-03-11 14:09:09 IC.REG.PROC.FE.N...
    ##              Indicator observations_attributes
    ## 1: Starting a busin...              ATTRIBUTE,
    ## 2: Starting a busin...              ATTRIBUTE,
    ## 3: Starting a busin...              ATTRIBUTE,
    ## 4: Starting a busin...              ATTRIBUTE,
    ## 5: Starting a busin...              ATTRIBUTE,
    ## 6: Starting a busin...              ATTRIBUTE,

In the event that you only use the argument `api_link`, you can drop the name and run:

    df <- 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")

# 7 Fetch time series from the cart

On the cart page of the DBnomics website, click on “Copy API link” and copy-paste it as an argument of the `rdb(api_link = ...)` function. Please note that when you update your cart, you have to copy this link again, because the link itself contains the ids of the series in the cart.

    df <- rdb(api_link = "https://api.db.nomics.world/v22/series?observations=1&series_ids=BOE/6008/RPMTDDC,BOE/6231/RPMTBVE")
    df <- df[!is.na(value))]

    ##    provider_code dataset_code        dataset_name series_code
    ## 1:           BOE         6008 Industrial analy...     RPMTDDC
    ## 2:           BOE         6231 Industrial analy...     RPMTBVE
    ## 3:           BOE         6008 Industrial analy...     RPMTDDC
    ## 4:           BOE         6231 Industrial analy...     RPMTBVE
    ## 5:           BOE         6008 Industrial analy...     RPMTDDC
    ## 6:           BOE         6231 Industrial analy...     RPMTBVE
    ##            series_name original_period     period original_value value
    ## 1: Monthly amounts ...         2009-07 2009-07-01          10150 10150
    ## 2: Monthly amounts ...         2009-07 2009-07-01          11066 11066
    ## 3: Monthly amounts ...         2009-08 2009-08-01          10400 10400
    ## 4: Monthly amounts ...         2009-08 2009-08-01          11260 11260
    ## 5: Monthly amounts ...         2009-09 2009-09-01          11595 11595
    ## 6: Monthly amounts ...         2009-09 2009-09-01          11208 11208
    ##    @frequency freq    Freq          indexed_at Instrument Currency
    ## 1:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
    ## 2:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
    ## 3:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
    ## 4:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
    ## 5:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
    ## 6:    monthly    M Monthly 2020-03-31 08:03:49            Sterling
    ##    INSTRUMENT_CURRENCY         Instruments INSTRUMENTS           Of Sector
    ## 1:                   £ Deposits and rep...       LDPRP Education provid...
    ## 2:                   £     Lending - total      ALENDG Education provid...
    ## 3:                   £ Deposits and rep...       LDPRP Education provid...
    ## 4:                   £     Lending - total      ALENDG Education provid...
    ## 5:                   £ Deposits and rep...       LDPRP Education provid...
    ## 6:                   £     Lending - total      ALENDG Education provid...
    ##    OF_SECTOR Output In OUTPUT_IN Seasonal Adjustment SEASONAL_ADJUSTMENT
    ## 1:        ED  Sterling         £ Not seasonally a...                   U
    ## 2:        ED  Sterling         £ Not seasonally a...                   U
    ## 3:        ED  Sterling         £ Not seasonally a...                   U
    ## 4:        ED  Sterling         £ Not seasonally a...                   U
    ## 5:        ED  Sterling         £ Not seasonally a...                   U
    ## 6:        ED  Sterling         £ Not seasonally a...                   U
    ##                 Sector SECTOR                Type TYPE
    ## 1: UK resident mone...   BKBS Amounts outstand...    L
    ## 2: UK resident mone...   BKBS Amounts outstand...    L
    ## 3: UK resident mone...   BKBS Amounts outstand...    L
    ## 4: UK resident mone...   BKBS Amounts outstand...    L
    ## 5: UK resident mone...   BKBS Amounts outstand...    L
    ## 6: UK resident mone...   BKBS Amounts outstand...    L

# 8 Fetch the available datasets of a provider

When fetching series from DBnomics, you need to give a provider and a dataset before specifying correct dimensions. With the function `rdb_datasets`, you can download the list of the available datasets for a provider.
For example, to fetch the IMF datasets, you have to use:

    rdb_datasets(provider_code = "IMF")

The result is a named list (its name is IMF) with one element which is a `data.table`:

    ## List of 1
    ##  $ IMF:Classes 'data.table' and 'data.frame':    17 obs. of  2 variables:
    ##   ..$ code: chr [1:17] "BOP" "BOPAGG" "COMMP" "COMMPP" ...
    ##   ..$ name: chr [1:17] "Balance of Payments (BOP)" "Balance of Payments (BOP), World and Regional Aggregates" "Primary Commodity Prices" "Primary Commodity Prices Projections" ...
    ##   ..- attr(*, ".internal.selfref")=<externalptr>

With the same function, if you want to fetch the available datasets for multiple providers, you can give a vector of providers and get a named list.

    rdb_datasets(provider_code = c("IMF", "BDF"))

    ## List of 2
    ##  $ IMF:Classes 'data.table' and 'data.frame':    17 obs. of  2 variables:
    ##   ..$ code: chr [1:17] "BOP" "BOPAGG" "COMMP" "COMMPP" ...
    ##   ..$ name: chr [1:17] "Balance of Payments (BOP)" "Balance of Payments (BOP), World and Regional Aggregates" "Primary Commodity Prices" "Primary Commodity Prices Projections" ...
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ BDF:Classes 'data.table' and 'data.frame':    45 obs. of  2 variables:
    ##   ..$ code: chr [1:45] "AME" "BLS" "BPM6" "BSI" ...
    ##   ..$ name: chr [1:45] "Macro Economy - Euro Area" "Bank Lending Survey" "Balance of payments- 6th manual" "Monetary Statistics - Euro Area" ...
    ##   ..- attr(*, ".internal.selfref")=<externalptr>

    ## Number of datasets for IMF : 17
    ## Number of datasets for BDF : 45

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.

    rdb_datasets(provider_code = "IMF", simplify = TRUE)

    ##      code                name
    ## 1:    BOP Balance of Payme...
    ## 2: BOPAGG Balance of Payme...
    ## 3:  COMMP Primary Commodit...
    ## 4: COMMPP Primary Commodit...
    ## 5:    CPI Consumer Price I...
    ## 6:   CPIS Coordinated Port...

The extent of datasets gathered by DBnomics can be appreciate by using the function with the argument `provider_code` set to `NULL`:

    options(rdbnomics.progress_bar_datasets = TRUE)
    rdb_datasets()
    options(rdbnomics.progress_bar_datasets = FALSE)

    ##    Provider Number of datasets
    ## 1:     AFDB                  1
    ## 2:    AMECO                481
    ## 3:      BCB                 85
    ## 4:    BCEAO                 28
    ## 5:      BDF                 45
    ## 6:      BEA                555

# 9 Fetch the possible dimensions of available datasets of a provider

When fetching series from DBnomics, it can be interesting and especially useful to specify dimensions for a particular dataset to download only the series you want to analyse. With the function `rdb_dimensions`, you can download these dimensions and their meanings.
For example, for the dataset WEO:2019-10 of the IMF, you may use:

    rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10")

The result is a nested named list (its names are IMF, WEO:2019-10 and the dimensions names) with a `data.table` at the end of each branch:

    ## Number of dimensions for IMF/WEO:2019-10 : 3

    ##                   unit                Unit
    ## 1:                 idx               Index
    ## 2: national_currenc... National currenc...
    ## 3: national_currenc... National currenc...
    ## 4:               pcent             Percent
    ## 5:        pcent_change      Percent change
    ## 6:           pcent_gdp      Percent of GDP

    ##    weo-country         WEO Country
    ## 1:         ABW               Aruba
    ## 2:         AFG         Afghanistan
    ## 3:         AGO              Angola
    ## 4:         ALB             Albania
    ## 5:         ARE United Arab Emir...
    ## 6:         ARG           Argentina

    ##    weo-subject         WEO Subject
    ## 1:         BCA Current account ...
    ## 2:   BCA_NGDPD Current account ...
    ## 3:     FLIBOR6 Six-month London...
    ## 4:         GGR General governme...
    ## 5:    GGR_NGDP General governme...
    ## 6:        GGSB General governme...

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.

    rdb_dimensions(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)

    ## List of 3
    ##  $ unit       :Classes 'data.table' and 'data.frame':    12 obs. of  2 variables:
    ##   ..$ unit: chr [1:12] "idx" "national_currency" "national_currency_per_current_international_dollar" "pcent" ...
    ##   ..$ Unit: chr [1:12] "Index" "National currency" "National currency per current international dollar" "Percent" ...
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ weo-country:Classes 'data.table' and 'data.frame':    194 obs. of  2 variables:
    ##   ..$ weo-country: chr [1:194] "ABW" "AFG" "AGO" "ALB" ...
    ##   ..$ WEO Country: chr [1:194] "Aruba" "Afghanistan" "Angola" "Albania" ...
    ##   ..- attr(*, ".internal.selfref")=<externalptr>
    ##  $ weo-subject:Classes 'data.table' and 'data.frame':    45 obs. of  2 variables:
    ##   ..$ weo-subject: chr [1:45] "BCA" "BCA_NGDPD" "FLIBOR6" "GGR" ...
    ##   ..$ WEO Subject: chr [1:45] "Current account balance" "Current account balance" "Six-month London interbank offered rate (LIBOR)" "General government revenue" ...
    ##   ..- attr(*, ".internal.selfref")=<externalptr>

You can measure the vast extent of datasets gathered by DBnomics by downloading all the possible dimensions. To do this, you have to set the arguments `provider_code` and `dataset_code` to `NULL`.
⚠ It’s relatively long to run and heavy to show so we display the first 100.

    options(rdbnomics.progress_bar_datasets = TRUE)
    rdb_dimensions()
    options(rdbnomics.progress_bar_datasets = FALSE)

    ##    Provider Dataset Dimension Number of codes
    ## 1:     AFDB bbkawjf   country             109
    ## 2:     AFDB bbkawjf frequency               1
    ## 3:     AFDB bbkawjf indicator            1279
    ## 4:     AFDB bbkawjf     scale               1
    ## 5:     AFDB bbkawjf     units             169
    ## 6:    AMECO    AAGE      freq               1

# 10 Fetch the series codes and names of available datasets of a provider

You can download the list of series, and especially their codes, of a dataset’s provider by using the function `rdb_series`. The result is a nested named list with a `data.table` at the end of each branch. If you define `simplify = TRUE`, then the result will be a `data.table` not a nested named list.
For example, for the IMF provider and the dataset WEO:2019-10, the command is (only first 100):

    rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", simplify = TRUE)

    ##            series_code         series_name
    ## 1: ABW.BCA.us_dolla... Aruba – Current ...
    ## 2: ABW.BCA_NGDPD.pc... Aruba – Current ...
    ## 3: ABW.FLIBOR6.pcen... Aruba – Six-mont...
    ## 4: ABW.GGR.national... Aruba – General ...
    ## 5: ABW.GGR_NGDP.pce... Aruba – General ...
    ## 6: ABW.GGSB.nationa... Aruba – General ...

Like the function `rdb()`, you can add features to `rdb_series()`. You can ask for the series with specific `dimensions`:

    rdb_series(provider_code = "IMF", dataset_code = "WEO:2019-10", dimensions = list(`weo-subject` = "NGDP_RPCH"), simplify = TRUE)

or with a `query`:

    rdb_series(provider_code = "IMF", dataset_code = c("WEO:2019-10", "WEOAGG:2019-10"), query = "NGDP_RPCH")

⚠ 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.
For example, for the IMF provider, the number of series is (only first 5):

    ##    Dataset Number of series
    ## 1:    CPIS          2903710
    ## 2:    CDIS           772274
    ## 3:  GFSIBS           681984
    ## 4:     BOP           535322
    ## 5:     DOT           459980

# 11 Proxy configuration or connection error `Could not resolve host`

When using the function `rdb`, you may come across the following error:

    Error in open.connection(con, "rb") :
      Could not resolve host: api.db.nomics.world

To get round this situation, you have two options:

1. configure curl to use a specific and authorized proxy.

2. use the default R internet connection i.e. the Internet Explorer proxy defined in internet2.dll.

## 11.1 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 https://curl.haxx.se/libcurl/c/curl_easy_setopt.html. Once they are chosen, you define the curl object as follows:

    h <- list(
      proxy = "<proxy>",
      proxyport = <port>,
      proxyusername = "<username>",
      proxypassword = "<password>"
    )

### 11.1.1 Set the connection up for a session

The curl connection can be set up for a session by modifying the following package option:

    options(rdbnomics.curl_config = h)

When fetching the data, the following command is executed:

    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.:

    df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")

This option of the package can be disabled with:

    options(rdbnomics.curl = NULL)

### 11.1.2 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`:

    df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", curl_config = h)

## 11.2 Use the default R internet connection

To retrieve the data with the default R internet connection, rdbnomics will use the base function `readLines`.

### 11.2.1 Set the connection up for a session

To activate this feature for a session, you need to enable an option of the package:

    options(rdbnomics.use_readLines = TRUE)

And then use the standard function as follows:

    df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN")

This configuration can be disabled with:

    options(rdbnomics.use_readLines = FALSE)

### 11.2.2 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`:

    df1 <- rdb(ids = "AMECO/ZUTN/EA19.1.0.0.0.ZUTN", use_readLines = TRUE)

# 12 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.

Here is an example of how to proceed to interpolate two annual time series with a monthly frequency, using a spline interpolation:

    filters <- list(
      code = "interpolate",
      parameters = list(frequency = "monthly", method = "spline")
    )

The request is then:

    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:

    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.

    ##    provider_code dataset_code        dataset_name      series_code
    ## 1:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 2:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 3:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 4:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 5:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ## 6:         AMECO         ZUTN Unemployment rat... DNK.1.0.0.0.ZUTN
    ##            series_name original_period     period original_value value
    ## 1: Annually – (Perc...            1960 1960-01-01              2   2.0
    ## 2: Annually – (Perc...            1961 1961-01-01            1.6   1.6
    ## 3: Annually – (Perc...            1962 1962-01-01            1.4   1.4
    ## 4: Annually – (Perc...            1963 1963-01-01              2   2.0
    ## 5: Annually – (Perc...            1964 1964-01-01            1.2   1.2
    ## 6: Annually – (Perc...            1965 1965-01-01            0.9   0.9
    ##    @frequency                unit                Unit geo Country freq
    ## 1:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 2:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 3:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 4:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 5:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ## 6:     annual percentage-of-ac... (Percentage of a... dnk Denmark    a
    ##    Frequency filtered          indexed_at period_middle_day
    ## 1:  Annually    FALSE 2019-11-08 16:33:50              <NA>
    ## 2:  Annually    FALSE 2019-11-08 16:33:50              <NA>
    ## 3:  Annually    FALSE 2019-11-08 16:33:50              <NA>
    ## 4:  Annually    FALSE 2019-11-08 16:33:50              <NA>
    ## 5:  Annually    FALSE 2019-11-08 16:33:50              <NA>
    ## 6:  Annually    FALSE 2019-11-08 16:33:50              <NA>

1. Banque de France, https://github.com/s915↩︎

2. CEPREMAP↩︎