README.md
In expersso/eurostatR: Programmatic Access to the Eurostat API
Introduction
The eurostatR package package provides an R interface to the Eurostat API.
To install the development version:
library(devtools)
install_github("expersso/eurostatR")
Example usage
Start by retrieving a dataframe with all available data sets:
library(eurostatR)
flows <- get_dataflows()
head(flows)
## id
## 1 DS-008573
## 2 DS-016890
## 3 DS-016893
## 4 DS-016894
## 5 DS-018995
## 6 DS-022469
## description
## 1 Sold production, exports and imports for steel by PRODCOM list (NACE Rev. 1.1) - monthly data
## 2 EU trade since 1988 by CN8
## 3 EU trade since 1995 by HS6
## 4 EU trade since 1988 by HS2-HS4
## 5 EU trade since 1988 by SITC
## 6 EXTRA EU trade since 1999 by mode of transport (NSTR)
Now, let's say we're interested in the share of employees with a doctorate, broken down by occupation:
flows[grep("doctorate", flows$description),]
## id
## 319 cdh_c_neu
## 322 cdh_e_diss
## 323 cdh_e_fos
## 324 cdh_e_fsp
## 325 cdh_e_lgt
## 326 cdh_e_mob
## 327 cdh_e_occ1
## 328 cdh_e_occ2
## 329 cdh_e_rlgt
## 330 cdh_e_une
## 332 cdh_m_mov
## 333 cdh_m_reg
## description
## 319 Non-EU doctorate holders in total doctorate holders (%)
## 322 Level of dissatisfaction of employed doctorate holders by reason and sex (%)
## 323 Employed doctorate holders in non managerial and non professional occupations by fields of science (%)
## 324 Employed doctorate holders working as researchers by sex, fields of science and sectors of performance (%)
## 325 Employed doctorate holders by length of stay with the same employer and sectors of performance (%)
## 326 Employed doctorate holders working as researchers by job mobility and sectors of performance over the last 10 years (%)
## 327 Employed doctorate holders by occupation (ISCO_88, %)
## 328 Employed doctorate holders by occupation (ISCO_08, %)
## 329 Employed doctorate holders working as researchers by length of stay with the same employer (%)
## 330 Unemployment rate of doctorate holders by fields of science
## 332 National doctorate holders having lived or stayed abroad in the past 10 years by reason for returning into the country (%)
## 333 National doctorate holders having lived or stayed abroad in the past 10 years by previous region of stay (%)
id <- "cdh_e_occ1"
Before getting the data, we need to know the available dimensions for this data set:
dims <- get_dimensions(id)
str(dims)
## List of 3
## $ dimensions:List of 5
## ..$ FREQ :'data.frame': 7 obs. of 2 variables:
## .. ..$ id : chr [1:7] "D" "W" "Q" "A" ...
## .. ..$ description: chr [1:7] "Daily" "Weekly" "Quarterly" "Annual" ...
## ..$ GEO :'data.frame': 18 obs. of 2 variables:
## .. ..$ id : chr [1:18] "BG" "DK" "DE" "EE" ...
## .. ..$ description: chr [1:18] "Bulgaria" "Denmark" "Germany (until 1990 former territory of the FRG)" "Estonia" ...
## ..$ ISCO88:'data.frame': 26 obs. of 2 variables:
## .. ..$ id : chr [1:26] "TOTAL" "ISCO1" "ISCO2" "ISCO21" ...
## .. ..$ description: chr [1:26] "Total" "Legislators, senior officials and managers" "Professionals" "Physical, mathematical and engineering science professionals" ...
## ..$ UNIT :'data.frame': 1 obs. of 2 variables:
## .. ..$ id : chr "PC"
## .. ..$ description: chr "Percentage"
## ..$ Y_GRAD:'data.frame': 2 obs. of 2 variables:
## .. ..$ id : chr [1:2] "TOTAL" "Y_GE1990"
## .. ..$ description: chr [1:2] "Total" "1990 and after"
## $ other :List of 2
## ..$ OBS_FLAG :'data.frame': 12 obs. of 2 variables:
## .. ..$ id : chr [1:12] "f" "u" "d" "e" ...
## .. ..$ description: chr [1:12] "forecast" "low reliability" "definition differs, see metadata" "estimated" ...
## ..$ OBS_STATUS:'data.frame': 3 obs. of 2 variables:
## .. ..$ id : chr [1:3] "na" "-" "0"
## .. ..$ description: chr [1:3] "not available" "not applicable or real zero or zero by default" "less than half of the unit used"
## $ key : chr "cdh_e_occ1.FREQ.Y_GRAD.UNIT.ISCO88.GEO"
The last value of the returned list (dims$key) gives the general form of the key that we need to request. By inspecting the dimensions in the list, we find that FREQ should be A (annual), Y_GRAD should be TOTAL, and UNIT should be PC (percent). The dimension ISCO88 gives the different occupations, and since we want all of these, we leave that dimension empty. Finally we want to look at date for Germany and the US (DE and US), which we specify with a +.
key <- "cdh_e_occ1.A.TOTAL.PC..DE+US"
doctorates <- get_data(key)
head(doctorates)
## isco88 unit y_grad geo freq obstime obsvalue obs_status
## 1 ISCO1 PC TOTAL DE A 2009 NA na
## 2 ISCO1 PC TOTAL DE A 2006 4.47 <NA>
## 3 ISCO1 PC TOTAL US A 2009 NA na
## 4 ISCO1 PC TOTAL US A 2006 10.49 <NA>
## 5 ISCO2 PC TOTAL DE A 2009 NA na
## 6 ISCO2 PC TOTAL DE A 2006 84.14 <NA>
We can now plot these data as a parallel coordinates plot:
library(dplyr)
library(ggplot2)
doctorates <- left_join(doctorates, dims$dimensions$ISCO88,
by = c("isco88" = "id"))
# Factor order for occupation variable, in descending order
lvls <- doctorates %>%
filter(geo == "US") %>%
arrange(obsvalue) %>%
.$description
doctorates %>%
filter(obstime == 2006) %>%
filter(!is.na(obsvalue)) %>%
filter(description != "Professionals") %>%
ggplot(aes(x = factor(description, lvls),
y = obsvalue, fill = geo, color = geo, group = geo)) +
geom_line() +
geom_point() +
coord_flip() +
theme_light(8) +
labs(y = "% share of employees", x = NULL,
title = "Employed doctorate holders by occupation")
The chart tells us e.g. that doctorates are much more prevalent among teaching professionals in the US than they are in Germany.
Disclaimer
This package is in no way officially related to, or endorsed by, the Eurostat.
expersso/eurostatR documentation built on May 16, 2019, 9:44 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Introduction
The eurostatR package package provides an R interface to the Eurostat API.
To install the development version:
library(devtools)
install_github("expersso/eurostatR")
Example usage
Start by retrieving a dataframe with all available data sets:
library(eurostatR)
flows <- get_dataflows()
head(flows)
## id
## 1 DS-008573
## 2 DS-016890
## 3 DS-016893
## 4 DS-016894
## 5 DS-018995
## 6 DS-022469
## description
## 1 Sold production, exports and imports for steel by PRODCOM list (NACE Rev. 1.1) - monthly data
## 2 EU trade since 1988 by CN8
## 3 EU trade since 1995 by HS6
## 4 EU trade since 1988 by HS2-HS4
## 5 EU trade since 1988 by SITC
## 6 EXTRA EU trade since 1999 by mode of transport (NSTR)
Now, let's say we're interested in the share of employees with a doctorate, broken down by occupation:
flows[grep("doctorate", flows$description),]
## id
## 319 cdh_c_neu
## 322 cdh_e_diss
## 323 cdh_e_fos
## 324 cdh_e_fsp
## 325 cdh_e_lgt
## 326 cdh_e_mob
## 327 cdh_e_occ1
## 328 cdh_e_occ2
## 329 cdh_e_rlgt
## 330 cdh_e_une
## 332 cdh_m_mov
## 333 cdh_m_reg
## description
## 319 Non-EU doctorate holders in total doctorate holders (%)
## 322 Level of dissatisfaction of employed doctorate holders by reason and sex (%)
## 323 Employed doctorate holders in non managerial and non professional occupations by fields of science (%)
## 324 Employed doctorate holders working as researchers by sex, fields of science and sectors of performance (%)
## 325 Employed doctorate holders by length of stay with the same employer and sectors of performance (%)
## 326 Employed doctorate holders working as researchers by job mobility and sectors of performance over the last 10 years (%)
## 327 Employed doctorate holders by occupation (ISCO_88, %)
## 328 Employed doctorate holders by occupation (ISCO_08, %)
## 329 Employed doctorate holders working as researchers by length of stay with the same employer (%)
## 330 Unemployment rate of doctorate holders by fields of science
## 332 National doctorate holders having lived or stayed abroad in the past 10 years by reason for returning into the country (%)
## 333 National doctorate holders having lived or stayed abroad in the past 10 years by previous region of stay (%)
id <- "cdh_e_occ1"
Before getting the data, we need to know the available dimensions for this data set:
dims <- get_dimensions(id)
str(dims)
## List of 3
## $ dimensions:List of 5
## ..$ FREQ :'data.frame': 7 obs. of 2 variables:
## .. ..$ id : chr [1:7] "D" "W" "Q" "A" ...
## .. ..$ description: chr [1:7] "Daily" "Weekly" "Quarterly" "Annual" ...
## ..$ GEO :'data.frame': 18 obs. of 2 variables:
## .. ..$ id : chr [1:18] "BG" "DK" "DE" "EE" ...
## .. ..$ description: chr [1:18] "Bulgaria" "Denmark" "Germany (until 1990 former territory of the FRG)" "Estonia" ...
## ..$ ISCO88:'data.frame': 26 obs. of 2 variables:
## .. ..$ id : chr [1:26] "TOTAL" "ISCO1" "ISCO2" "ISCO21" ...
## .. ..$ description: chr [1:26] "Total" "Legislators, senior officials and managers" "Professionals" "Physical, mathematical and engineering science professionals" ...
## ..$ UNIT :'data.frame': 1 obs. of 2 variables:
## .. ..$ id : chr "PC"
## .. ..$ description: chr "Percentage"
## ..$ Y_GRAD:'data.frame': 2 obs. of 2 variables:
## .. ..$ id : chr [1:2] "TOTAL" "Y_GE1990"
## .. ..$ description: chr [1:2] "Total" "1990 and after"
## $ other :List of 2
## ..$ OBS_FLAG :'data.frame': 12 obs. of 2 variables:
## .. ..$ id : chr [1:12] "f" "u" "d" "e" ...
## .. ..$ description: chr [1:12] "forecast" "low reliability" "definition differs, see metadata" "estimated" ...
## ..$ OBS_STATUS:'data.frame': 3 obs. of 2 variables:
## .. ..$ id : chr [1:3] "na" "-" "0"
## .. ..$ description: chr [1:3] "not available" "not applicable or real zero or zero by default" "less than half of the unit used"
## $ key : chr "cdh_e_occ1.FREQ.Y_GRAD.UNIT.ISCO88.GEO"
The last value of the returned list (dims$key) gives the general form of the key that we need to request. By inspecting the dimensions in the list, we find that FREQ should be A (annual), Y_GRAD should be TOTAL, and UNIT should be PC (percent). The dimension ISCO88 gives the different occupations, and since we want all of these, we leave that dimension empty. Finally we want to look at date for Germany and the US (DE and US), which we specify with a +.
key <- "cdh_e_occ1.A.TOTAL.PC..DE+US"
doctorates <- get_data(key)
head(doctorates)
## isco88 unit y_grad geo freq obstime obsvalue obs_status
## 1 ISCO1 PC TOTAL DE A 2009 NA na
## 2 ISCO1 PC TOTAL DE A 2006 4.47 <NA>
## 3 ISCO1 PC TOTAL US A 2009 NA na
## 4 ISCO1 PC TOTAL US A 2006 10.49 <NA>
## 5 ISCO2 PC TOTAL DE A 2009 NA na
## 6 ISCO2 PC TOTAL DE A 2006 84.14 <NA>
We can now plot these data as a parallel coordinates plot:
library(dplyr)
library(ggplot2)
doctorates <- left_join(doctorates, dims$dimensions$ISCO88,
by = c("isco88" = "id"))
# Factor order for occupation variable, in descending order
lvls <- doctorates %>%
filter(geo == "US") %>%
arrange(obsvalue) %>%
.$description
doctorates %>%
filter(obstime == 2006) %>%
filter(!is.na(obsvalue)) %>%
filter(description != "Professionals") %>%
ggplot(aes(x = factor(description, lvls),
y = obsvalue, fill = geo, color = geo, group = geo)) +
geom_line() +
geom_point() +
coord_flip() +
theme_light(8) +
labs(y = "% share of employees", x = NULL,
title = "Employed doctorate holders by occupation")
The chart tells us e.g. that doctorates are much more prevalent among teaching professionals in the US than they are in Germany.
Disclaimer
This package is in no way officially related to, or endorsed by, the Eurostat.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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