The OECD package allows the user to download data from the OECD’s API
in a dynamic and reproducible way.
The package can be installed from either CRAN or Github (development version):
# from CRAN
install.packages("OECD")
# from Github
library(devtools)
install_github("expersso/OECD")
library(OECD)The best way to use the package is to use the OECD Data Explorer to both browse available datasets and filter specific datasets.
In this example we will use data National Accounts at a Glance Chapter 1: GDP:
After filtering the data using the in-browser data explorer, click the “Developer API” button as seen in the screenshot below.
We extract the first string (respresenting the dataset as a whole) and the second string (representing the filter we’ve applied):
dataset <- "OECD.SDD.NAD,DSD_NAAG@DF_NAAG_I,1.0"
filter <- "A.USA+EU.B1GQ_R_POP+B1GQ_R_GR.USD_PPP_PS+PC."We then use the get_dataset function to retrieve the data:
df <- get_dataset(dataset, filter)
head(df) ACCOUNTING_ENTRY ADJUSTMENT CHAPTER CONF_STATUS CONSOLIDATION
1 B N NAAG_I F _Z
2 B N NAAG_I F _Z
3 B N NAAG_I F _Z
4 B N NAAG_I F _Z
5 B N NAAG_I F _Z
6 B N NAAG_I F _Z
COUNTERPART_AREA COUNTERPART_SECTOR CURRENCY DECIMALS FREQ INSTR_ASSET
1 D S1 _Z 2 A _Z
2 D S1 _Z 2 A _Z
3 D S1 _Z 2 A _Z
4 D S1 _Z 2 A _Z
5 D S1 _Z 2 A _Z
6 D S1 _Z 2 A _Z
MEASURE OBS_STATUS ObsValue PRICE_BASE REF_AREA REF_YEAR_PRICE
1 B1GQ_R_GR A 1.76202213801051 L EU <NA>
2 B1GQ_R_GR A 2.65890962083748 L EU <NA>
3 B1GQ_R_GR A 3.00387127627695 L EU <NA>
4 B1GQ_R_GR A 2.94990334327997 L EU <NA>
5 B1GQ_R_GR A 3.86436536858204 L EU <NA>
6 B1GQ_R_GR A 2.13343923659157 L EU <NA>
SECTOR TIME_PERIOD TRANSACTION TRANSFORMATION UNIT_MEASURE UNIT_MULT
1 S1 1996 B1GQ G1 PC 0
2 S1 1997 B1GQ G1 PC 0
3 S1 1998 B1GQ G1 PC 0
4 S1 1999 B1GQ G1 PC 0
5 S1 2000 B1GQ G1 PC 0
6 S1 2001 B1GQ G1 PC 0
We select the relevant variables:
df <- df |>
subset(select = c(REF_AREA, MEASURE, UNIT_MEASURE, TIME_PERIOD, ObsValue)) |>
transform(
ObsValue = as.numeric(ObsValue),
TIME_PERIOD = as.numeric(TIME_PERIOD)
)
names(df) <- tolower(names(df))
head(df) ref_area measure unit_measure time_period obsvalue
1 EU B1GQ_R_GR PC 1996 1.762022
2 EU B1GQ_R_GR PC 1997 2.658910
3 EU B1GQ_R_GR PC 1998 3.003871
4 EU B1GQ_R_GR PC 1999 2.949903
5 EU B1GQ_R_GR PC 2000 3.864365
6 EU B1GQ_R_GR PC 2001 2.133439
It’s not immediately clear what the values of the variables measure
and unit_measure represent, so we fetch a data dictionary and join in
to the dataset:
data_structure <- get_data_structure(dataset)
str(data_structure, max.level = 1)List of 26
$ VAR_DESC :'data.frame': 32 obs. of 2 variables:
$ CL_ACCOUNTING_ENTRY:'data.frame': 11 obs. of 2 variables:
$ CL_ACTIVITY_ISIC4 :'data.frame': 958 obs. of 2 variables:
$ CL_ADJUSTMENT :'data.frame': 17 obs. of 2 variables:
$ CL_AREA :'data.frame': 469 obs. of 2 variables:
$ CL_COICOP_99 :'data.frame': 500 obs. of 2 variables:
$ CL_CURRENCY :'data.frame': 238 obs. of 2 variables:
$ CL_PRICES :'data.frame': 14 obs. of 2 variables:
$ CL_PRODUCT_CPA2008 :'data.frame': 616 obs. of 2 variables:
$ CL_SECTOR :'data.frame': 213 obs. of 2 variables:
$ CL_TRANSFORMATION :'data.frame': 59 obs. of 2 variables:
$ CL_UNIT_MEASURE :'data.frame': 867 obs. of 2 variables:
$ CL_CHAPTER :'data.frame': 11 obs. of 2 variables:
$ CL_CONSOLIDAT :'data.frame': 7 obs. of 2 variables:
$ CL_INSTR_ASSET :'data.frame': 184 obs. of 2 variables:
$ CL_MATURITY :'data.frame': 70 obs. of 2 variables:
$ CL_MEASURE_NA_DASH :'data.frame': 224 obs. of 2 variables:
$ CL_PENS_FUNDTYPE :'data.frame': 23 obs. of 2 variables:
$ CL_TABLEID :'data.frame': 80 obs. of 2 variables:
$ CL_TRANSACTION :'data.frame': 308 obs. of 2 variables:
$ CL_VALUATION :'data.frame': 19 obs. of 2 variables:
$ CL_CONF_STATUS :'data.frame': 11 obs. of 2 variables:
$ CL_DECIMALS :'data.frame': 16 obs. of 2 variables:
$ CL_FREQ :'data.frame': 34 obs. of 2 variables:
$ CL_OBS_STATUS :'data.frame': 20 obs. of 4 variables:
$ CL_UNIT_MULT :'data.frame': 31 obs. of 4 variables:
names(data_structure$CL_MEASURE_NA_DASH) <- c("measure", "measure_lbl")
names(data_structure$CL_UNIT_MEASURE) <- c("unit_measure", "unit_measure_lbl")
df <- df |>
merge(data_structure$CL_MEASURE_NA_DASH, by = "measure") |>
merge(data_structure$CL_UNIT_MEASURE, by = "unit_measure")
head(df) unit_measure measure ref_area time_period obsvalue
1 PC B1GQ_R_GR EU 1996 1.762022
2 PC B1GQ_R_GR EU 1997 2.658910
3 PC B1GQ_R_GR EU 1998 3.003871
4 PC B1GQ_R_GR EU 1999 2.949903
5 PC B1GQ_R_GR EU 2000 3.864365
6 PC B1GQ_R_GR EU 2001 2.133439
measure_lbl unit_measure_lbl
1 Real gross domestic product growth rate Percentage change
2 Real gross domestic product growth rate Percentage change
3 Real gross domestic product growth rate Percentage change
4 Real gross domestic product growth rate Percentage change
5 Real gross domestic product growth rate Percentage change
6 Real gross domestic product growth rate Percentage change
The get_data_structure function returns a list of dataframes with
human-readable values for variable names and values. The first data
frame contains the variable names and shows the dimensions of a dataset:
data_structure$VAR_DESC id description
1 FREQ Frequency of observation
2 REF_AREA Reference area
3 MEASURE Measure
4 EXPENDITURE Expenditure
5 ACTIVITY Economic activity
6 UNIT_MEASURE Unit of measure
7 CHAPTER Chapter
8 TIME_PERIOD Time period
9 OBS_VALUE Observation value
10 ADJUSTMENT Adjustment
11 COUNTERPART_AREA Counterpart area
12 SECTOR Institutional sector
13 COUNTERPART_SECTOR Counterpart institutional sector
14 CONSOLIDATION Consolidation status
15 ACCOUNTING_ENTRY Accounting entry
16 TRANSACTION Transaction
17 INSTR_ASSET Instruments and assets
18 MATURITY Original and residual maturity
19 PRODUCT Product
20 PENSION_FUNDTYPE Pension fund type
21 CURRENCY_DENOM Currency of denomination
22 VALUATION Valuation
23 PRICE_BASE Price base
24 TRANSFORMATION Transformation
25 TABLE_IDENTIFIER Table identifier
26 REF_YEAR_PRICE Price reference year
27 BASE_PER Base period
28 CONF_STATUS Confidentiality status
29 DECIMALS Decimals
30 OBS_STATUS Observation status
31 UNIT_MULT Unit multiplier
32 CURRENCY Currency
This package is in no way officially related to or endorsed by the OECD.