%>%
tgs00026 left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
unit | Unit | Nobs |
---|---|---|
MIO_PPS_EU27_2020 | Million purchasing power standards (PPS, EU27 from 2020) | 2792 |
%>%
tgs00026 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()
time | Nobs |
---|---|
2021 | 50 |
2020 | 243 |
2019 | 250 |
2018 | 250 |
2017 | 250 |
2016 | 250 |
2015 | 250 |
2014 | 251 |
2013 | 251 |
2012 | 251 |
2011 | 251 |
2010 | 245 |
%>%
tgs00026 left_join(direct, by = "direct") %>%
group_by(direct, Direct) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
direct | Direct | Nobs |
---|---|---|
BAL | Balance | 2792 |
%>%
tgs00026 left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
tgs00026 filter(time == 2015,
nchar(geo) == 4) %>%
right_join(europe_NUTS2, by = "geo") %>%
filter(long >= -15, lat >= 33) %>%
ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
breaks = c(seq(0, 300, 50)),
values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "Disposable Income")
%>%
tgs00026 filter(time == 2019,
nchar(geo) == 4) %>%
right_join(europe_NUTS2, by = "geo") %>%
filter(long >= -15, lat >= 33) %>%
ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
geom_polygon() + coord_map() +
scale_fill_viridis_c(na.value = "white",
labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
breaks = c(seq(0, 300, 50)),
values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
theme_void() + theme(legend.position = c(0.25, 0.85)) +
labs(fill = "Disposable Income")