Code
%>%
tec00010 left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Data - Eurostat
%>%
tec00010 left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
tec00010 left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
unit | Unit | Nobs |
---|---|---|
CP_MEUR | Current prices, million euro | 488 |
PC_GDP | Percentage of gross domestic product (GDP) | 488 |
%>%
tec00010 left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
geo | Geo | Nobs |
---|---|---|
AL | Albania | 24 |
AT | Austria | 24 |
BA | Bosnia and Herzegovina | 24 |
BE | Belgium | 24 |
BG | Bulgaria | 24 |
CH | Switzerland | 24 |
CY | Cyprus | 24 |
CZ | Czechia | 24 |
DE | Germany | 24 |
DK | Denmark | 24 |
EA19 | Euro area - 19 countries (2015-2022) | 24 |
EA20 | Euro area – 20 countries (from 2023) | 24 |
EE | Estonia | 24 |
EL | Greece | 24 |
ES | Spain | 24 |
EU27_2020 | European Union - 27 countries (from 2020) | 24 |
FI | Finland | 24 |
FR | France | 24 |
HR | Croatia | 24 |
HU | Hungary | 24 |
IE | Ireland | 24 |
IS | Iceland | 24 |
IT | Italy | 24 |
LT | Lithuania | 24 |
LU | Luxembourg | 24 |
LV | Latvia | 24 |
ME | Montenegro | 24 |
MK | North Macedonia | 24 |
MT | Malta | 24 |
NL | Netherlands | 24 |
NO | Norway | 24 |
PL | Poland | 24 |
PT | Portugal | 24 |
RO | Romania | 24 |
RS | Serbia | 24 |
SE | Sweden | 24 |
SI | Slovenia | 24 |
SK | Slovakia | 24 |
TR | Türkiye | 24 |
XK | Kosovo* | 24 |
UK | United Kingdom | 16 |
%>%
tec00010 left_join(na_item, by = "na_item") %>%
group_by(na_item, Na_item) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
na_item | Na_item | Nobs |
---|---|---|
P3_S13 | Final consumption expenditure of general government | 976 |
%>%
tec00010 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
time | Nobs |
---|---|
2012 | 82 |
2013 | 82 |
2014 | 82 |
2015 | 82 |
2016 | 82 |
2017 | 82 |
2018 | 82 |
2019 | 82 |
2020 | 80 |
2021 | 80 |
2022 | 80 |
2023 | 80 |
%>%
tec00010 filter(geo %in% c("FR", "DE", "PT"),
== "PC_GDP") %>%
unit %>%
year_to_enddate left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Final consumption expenditure of general gvnt") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
labels = scales::percent_format(accuracy = 1))
%>%
tec00010 filter(geo %in% c("IT", "ES", "GR"),
== "PC_GDP") %>%
unit %>%
year_to_enddate left_join(geo, by = "geo") %>%
+ geom_line() + theme_minimal() +
ggplot aes(x = date, y = values/100, color = Geo, linetype = Geo) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
labels = date_format("%y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
xlab("") + ylab("Final consumption expenditure of general gvnt") +
scale_y_continuous(breaks = 0.01*seq(0, 200, 0.2),
labels = scales::percent_format(accuracy = 0.1))