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)
Unless you know the exact code of the series you're looking for, the best way to start is by downloading a dataframe with all the available datasets and their descriptions, and then run searches on it. The search string can be a regular expression and is case-insensitive by default.
dataset_list <- get_datasets()
search_dataset("unemployment", data = dataset_list)
id title
AVD_DUR Average duration of unemployment
AEO2012_CH6_FIG19 Figure 19: The trade off between vulnerable employment...
AEO2012_CH6_FIG29 Figure 29: Youth employment and unemployment by education...
AEO2012_CH6_FIG4 Figure 4: Youth and adult unemployment
DUR_I Incidence of unemployment by duration
DUR_D Unemployment by duration
In the following we'll explore the DUR_D data set, which contains data on
the duration of unemployment.
dataset <- "DUR_D"
Before downloading the series we are interested in, it is often prudent to look at the data structure, to see what type of breakdowns the data set offers:
dstruc <- get_data_structure(dataset)
str(dstruc, max.level = 1)
## List of 12
## $ VAR_DESC :'data.frame': 12 obs. of 2 variables:
## $ COUNTRY :'data.frame': 53 obs. of 2 variables:
## $ TIME :'data.frame': 51 obs. of 2 variables:
## $ SEX :'data.frame': 3 obs. of 2 variables:
## $ AGE :'data.frame': 7 obs. of 2 variables:
## $ DURATION :'data.frame': 8 obs. of 2 variables:
## $ FREQUENCY :'data.frame': 1 obs. of 2 variables:
## $ OBS_STATUS :'data.frame': 15 obs. of 2 variables:
## $ UNIT :'data.frame': 316 obs. of 2 variables:
## $ POWERCODE :'data.frame': 32 obs. of 2 variables:
## $ REFERENCEPERIOD:'data.frame': 92 obs. of 2 variables:
## $ TIME_FORMAT :'data.frame': 5 obs. of 2 variables:
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:
dstruc$VAR_DESC
## id description
## 1 COUNTRY Country
## 2 TIME Time
## 3 SEX Sex
## 4 AGE Age
## 5 DURATION Duration
## 6 FREQUENCY Frequency
## 7 OBS_VALUE Observation Value
## 8 TIME_FORMAT Time Format
## 9 OBS_STATUS Observation Status
## 10 UNIT Unit
## 11 POWERCODE Unit multiplier
## 12 REFERENCEPERIOD Reference period
It is often easiest not to specify any filters at this point, but rather
download the entire dataset and then filter it with native R functions.
However, sometimes the dataset is very large, so filtering it before download
will cut down on download time. To illustrate, let's find out the available
filters for the variables SEX and AGE:
dstruc$SEX
## id label
## 1 MW All persons
## 2 MEN Men
## 3 WOMEN Women
dstruc$AGE
## id label
## 1 1519 15 to 19
## 2 1524 15 to 24
## 3 2024 20 to 24
## 4 2554 25 to 54
## 5 5564 55 to 64
## 6 6599 65+
## 7 900000 Total
Let's say we're only interested in the duration of unemployment of men aged 20
to 24 in Germany and France. We provide these filters in the form of a list to
the filter argument of the get_dataset function:
filter_list <- list(c("DEU", "FRA"), "MW", "2024")
df <- get_dataset(dataset = dataset, filter = filter_list)
head(df)
## COUNTRY SEX AGE DURATION FREQUENCY TIME_FORMAT obsTime obsValue
## 1 FRA MW 2024 UN1 A P1Y 1975 30.97
## 2 FRA MW 2024 UN1 A P1Y 1976 27.16
## 3 FRA MW 2024 UN1 A P1Y 1977 29.21
## 4 FRA MW 2024 UN1 A P1Y 1978 26.52
## 5 FRA MW 2024 UN1 A P1Y 1979 32.02
## 6 FRA MW 2024 UN1 A P1Y 1980 31.93
Let's say we're only interested in long-term unemployment. We can then first
look at the variable DURATION to find the different levels, then go back to
our list of variable descriptions to learn what they mean:
unique(df$DURATION)
## [1] "UN1" "UN2" "UN3" "UN4" "UN5" "UN" "UND" "UNK"
dstruc$DURATION
## id label
## 1 UN1 < 1 month
## 2 UN2 > 1 month and < 3 months
## 3 UN3 > 3 month and < 6 months
## 4 UN4 > 6 month and < 1 year
## 5 UN5 1 year and over
## 6 UN Total
## 7 UND Total Declared
## 8 UNK Unknown
We could of course merge the two data structures, but working with the mnemonic labels usually saves you quite a bit of typing in the long run.
We can now subset to only those unemployed for a year or more, and finally produce a plot.
df_plot <- df[df$DURATION == "UN5", ]
df_plot$obsTime <- as.numeric(df_plot$obsTime)
library(ggplot2)
qplot(data = df_plot, x = obsTime, y = obsValue, color = COUNTRY, geom = "line") +
labs(x = NULL, y = "Persons, thousands", color = NULL,
title = "Number of long-term unemployed men, aged 20-24")
If we want more in-depth information about a dataset (e.g. methodology, exact
definitions of variables, etc), browse_metadata opens up a web
browser with the metadata for the requested series.
browse_metadata(dataset)
If one does not know exactly what data one is looking for, or if a data set
contains e.g. a large number of breakdowns, it is often easier to first explore
the data on the OECD stats website and then use the
oecd package to make the data acquisition programmatic and reproducible. The
workflow would then be as follows:
filter argument of the get_dataset
function.df <- get_dataset("REGION_INNOVATION",
"1+2.BEL+BE1+BE2+BE3+BEZZ.PCT_BIOTECH.ALL+AVG.2013+2014+2015")
head(df)
## # A tibble: 6 x 7
## TL REG_ID VAR POS TIME POWERCODE obsValue
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 1 BEL PCT_BIOTECH ALL 2013 0 107.
## 2 1 BEL PCT_BIOTECH ALL 2014 0 111.
## 3 1 BEL PCT_BIOTECH ALL 2015 0 127.
## 4 2 BE1 PCT_BIOTECH ALL 2013 0 6.73
## 5 2 BE1 PCT_BIOTECH ALL 2014 0 13.7
## 6 2 BE1 PCT_BIOTECH ALL 2015 0 9.97
This package is in no way officially related to or endorsed by the OECD.