The ecb package package provides an R interface to the European Central Bank’s Data Portal.
To install the development version:
library(devtools)
install_github("expersso/ecb")
The following example extracts the last twelve observations of headline and “core” HICP inflation for a number of countries available in the ICP database. See details below on how to use the filter parameter and how to find and use the EDP series keys.
library(ecb)
library(ggplot2)
key <- "ICP.M.DE+FR+ES+IT+NL+U2.N.000000+XEF000.4.ANR"
filter <- list(lastNObservations = 12, detail = "full")
hicp <- get_data(key, filter)
hicp$obstime <- convert_dates(hicp$obstime)
ggplot(hicp, aes(x = obstime, y = obsvalue, color = title)) +
geom_line() +
facet_wrap(~ref_area, ncol = 3) +
theme_bw(8) +
theme(legend.position = "bottom") +
labs(x = NULL, y = "Percent per annum\n", color = NULL,
title = "HICP - headline and core\n")
The filter option of get_data() takes a named list of key-value pairs. If left blank, it returns all data for the current version.
Available filter parameters:
startPeriod & endPeriod
YYYY for annual data (e.g. 2013)YYYY-S[1-2] for semi-annual data (e.g. 2013-S1)YYYY-Q[1-4] for quarterly data (e.g. 2013-Q1)YYYY-MM for monthly data (e.g. 2013-01)YYYY-W[01-53] for weekly data (e.g. 2013-W01)YYYY-MM-DD for daily data (e.g. 2013-01-01)updatedAfter
filter = list(updatedAfter = 2009-05-15T14:15:00+01:00)firstNObservations & lastNObservations
filter = list(firstNObservations = 12) retrieves the first 12 observations of all specified seriesdetail
full/dataonly/serieskeysonly/nodatadataonly is the defaultserieskeysonly or nodata to list series that match a certain query, without returning the actual dataserieskeys/nodata is the convenience function get_dimensions(), which returns a list of dataframes with dimensions and explanations (see extended example below).full returns both the series values and all metadata. This entails retrieving much more data than with the dataonly option.includeHistory (not currently implemented)
false (default) returns only version currently in productiontrue returns version currently in production, as well as all previous versionsSee the EDP API for more details.
The easiest way to find and learn more about EDP series key is to browse the EDP website. After finding the series one is interested in, and applying the relevant filters (frequency, geographic area, etc), one can just copy the key:
screenshotThe EDP website also has provides all the necessary metadata, so it is much easier to explore data availability (in terms of available breakdowns, time periods, etc) directly on the website than it is to do it directly through the ecb package.
The ecb package supports using wildcards in the series key, which takes the form of simply leaving the wildcard dimension empty. For example, the key ICP.M.DE.N.000000.4.ANR retrieves HICP data for Germany (DE), while leaving the third dimension empty - ICP.M..N.000000.4.ANR - retrieves the same data for all available countries and country groups.
Instead of wildcarding, one can use the + operator to specify multiple values for a dimension. For example, ICP.M.DE.N.000000+XEF000.4.ANR retrieves both headline inflation (000000) and core inflation (XEF000). Learning that e.g. XEF000 corresponds to core inflation would be done by browsing the EDP website:
To remind oneself of what different values for different dimensions mean, one can use the get_dimensions) function, which returns a list of dataframes:
dims <- get_dimensions("ICP.M.DE.N.000000+XEF000.4.ANR")
head(dims[[1]], 8)
## dim value
## 1 FREQ M
## 2 REF_AREA DE
## 3 ADJUSTMENT N
## 4 ICP_ITEM 000000
## 5 STS_INSTITUTION 4
## 6 ICP_SUFFIX ANR
## 7 TITLE HICP - Overall index"
## 8 UNIT PCCH
As a more extended example, we will retrieve data to plot the annual change in wages against the annual change in unemployment. Economic theory suggests a negative relationship between these two variables.
We start by retrieving the two series, using wildcards for the geographic area dimension:
unemp <- get_data("LFSI.M..S.UNEHRT.TOTAL0.15_74.T",
filter = list(startPeriod = "2000"))
wages <- get_data("MNA.A.N..W2.S1.S1._Z.COM_HW._Z._T._Z.IX.V.N",
filter = list(startPeriod = "2000"))
head(unemp)
## # A tibble: 6 × 9
## freq ref_area adjustment lfs_indicator lfs_breakdown age_breakdown gender
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 M AT S UNEHRT TOTAL0 15_74 T
## 2 M AT S UNEHRT TOTAL0 15_74 T
## 3 M AT S UNEHRT TOTAL0 15_74 T
## 4 M AT S UNEHRT TOTAL0 15_74 T
## 5 M AT S UNEHRT TOTAL0 15_74 T
## 6 M AT S UNEHRT TOTAL0 15_74 T
## # ℹ 2 more variables: obstime <chr>, obsvalue <dbl>
## # A tibble: 6 × 16
## freq adjustment ref_area counterpart_area ref_sector counterpart_sector
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 A N AT W2 S1 S1
## 2 A N AT W2 S1 S1
## 3 A N AT W2 S1 S1
## 4 A N AT W2 S1 S1
## 5 A N AT W2 S1 S1
## 6 A N AT W2 S1 S1
## # ℹ 10 more variables: accounting_entry <chr>, sto <chr>, instr_asset <chr>,
## # activity <chr>, expenditure <chr>, unit_measure <chr>, prices <chr>,
## # transformation <chr>, obstime <chr>, obsvalue <dbl>
To get a human-readable description of a series:
desc <- head(get_description("LFSI.M..S.UNEHRT.TOTAL0.15_74.T"), 3)
strwrap(desc, width = 80)
## [1] "Austria; European Labour Force Survey; Unemployment rate; Total; Age 15 to 74;"
## [2] "Total; Seasonally adjusted, not working day adjusted"
## [3] "EU28 (fixed composition) as of 1 July 2013; European Labour Force Survey;"
## [4] "Unemployment rate; Total; Age 15 to 74; Total; Seasonally adjusted, not working"
## [5] "day adjusted"
## [6] "EU27 (fixed composition) as of 31 January 2020 (brexit); European Labour Force"
## [7] "Survey; Unemployment rate; Total; Age 15 to 74; Total; Seasonally adjusted, not"
## [8] "working day adjusted"
We now join together the two data sets:
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
unemp <- unemp %>% mutate(obstime = convert_dates(obstime)) %>% group_by(ref_area, obstime = year(obstime)) %>% summarise(obsvalue = mean(obsvalue)) %>% ungroup() %>% select(ref_area, obstime, "unemp" = obsvalue)
## `summarise()` has grouped output by 'ref_area'. You can override using the
## `.groups` argument.
wages <- wages %>% mutate(obstime = as.numeric(obstime)) %>% select(ref_area, obstime, "wage" = obsvalue) df <- left_join(unemp, wages)
## Joining with `by = join_by(ref_area, obstime)`
## # A tibble: 6 × 4
## ref_area obstime unemp wage
## <chr> <dbl> <dbl> <dbl>
## 1 AT 2000 3.89 55.9
## 2 AT 2001 4.01 57.0
## 3 AT 2002 4.39 58.4
## 4 AT 2003 4.78 59.6
## 5 AT 2004 5.88 60.6
## 6 AT 2005 6.03 62.3
Finally, we plot the annual change in wages against the annual change in unemployment for all countries:
library(ggplot2)
df %>%
filter(complete.cases(.)) %>%
group_by(ref_area) %>%
mutate(d_wage = c(NA, diff(wage)) / lag(wage),
d_unemp = c(NA, diff(unemp))) %>%
ggplot(aes(x = d_unemp, y = d_wage)) +
geom_point() +
facet_wrap(~ref_area, scales = "free") +
theme_bw(8) +
theme(strip.background = element_blank()) +
geom_smooth(method = "lm") +
labs(x = "\nAnnual change in unemployment", y = "Annual change in wages\n",
title = "Relationship between wages and unemployment\n")
## `geom_smooth()` using formula = 'y ~ x'
This package is in no way officially related to, or endorsed by, the ECB.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4
