source | dataset | .html | .RData |
---|---|---|---|
eurostat | nama_10_pe | 2024-11-01 | 2024-10-08 |
eurostat | namq_10_pe | 2024-11-01 | 2024-10-09 |
Population and employment - nama_10_pe
Data - Eurostat
Info
LAST_COMPILE
LAST_COMPILE |
---|
2024-11-05 |
Last
Code
%>%
nama_10_pe group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2023 | 478 |
na_item
Code
%>%
nama_10_pe 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 |
---|---|---|
POP_NC | Total population national concept | 2567 |
EMP_DC | Total employment domestic concept | 2454 |
SAL_DC | Employees domestic concept | 2391 |
SELF_DC | Self-employed domestic concept | 2391 |
EMP_NC | Total employment national concept | 2049 |
SAL_NC | Employees national concept | 2002 |
SELF_NC | Self-employed national concept | 2002 |
unit
Code
%>%
nama_10_pe left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
unit | Unit | Nobs |
---|---|---|
THS_PER | Thousand persons | 8056 |
PCH_PRE_PER | Percentage change on previous period (based on persons) | 7800 |
geo
Code
%>%
nama_10_pe left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
time
Code
%>%
nama_10_pe group_by(time) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Population Table
English
Code
%>%
nama_10_pe filter(time %in% c("2019", "2009", "1999", "1989"),
== "POP_NC",
na_item == "THS_PER") %>%
unit select(geo, time, values) %>%
mutate(values = round(values/1000, 1)) %>%
left_join(geo, by = "geo") %>%
spread(time, values) %>%
arrange(- `2019`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
French
Code
load_data("eurostat/geo_fr.RData")
%>%
nama_10_pe filter(time %in% c("2019", "2009", "1999", "1989"),
== "POP_NC",
na_item == "THS_PER") %>%
unit select(geo, time, values) %>%
mutate(values = round(values/1000, 1)) %>%
left_join(geo, by = "geo") %>%
spread(time, values) %>%
arrange(- `2019`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
png table
Code
include_graphics3("bib/eurostat/nama_10_pe_ex1.png")
Employment Table
Code
load_data("eurostat/geo.RData")
%>%
nama_10_pe filter(time %in% c("2019", "2009", "1999", "1989"),
== "EMP_DC",
na_item == "THS_PER") %>%
unit select(geo, time, values) %>%
mutate(values = round(values/1000, 1)) %>%
left_join(geo, by = "geo") %>%
spread(time, values) %>%
arrange(- `2019`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Employment / Population Table
Code
load_data("eurostat/geo_fr.RData")
%>%
nama_10_pe filter(time %in% c("2019", "2009", "1999", "1989"),
%in% c("EMP_DC", "POP_NC"),
na_item == "THS_PER") %>%
unit select(geo, na_item, time, values) %>%
spread(na_item, values) %>%
transmute(geo, time, values = 100*EMP_DC/POP_NC) %>%
mutate(values = round(values, 1)) %>%
left_join(geo, by = "geo") %>%
spread(time, values) %>%
arrange(- `2019`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Poland, Germany, Hungary
Code
%>%
nama_10_pe filter(geo %in% c("PL", "DE", "HU"),
%in% c("EMP_DC", "POP_NC"),
na_item == "THS_PER") %>%
unit select(geo, na_item, time, values) %>%
spread(na_item, values) %>%
transmute(geo, time, values = EMP_DC/POP_NC) %>%
%>%
year_to_date left_join(geo, by = "geo") %>%
+ geom_line(aes(x = date, y = values, color = Geo, linetype = Geo)) +
ggplot scale_color_manual(values = viridis(4)[1:3]) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
labels = date_format("%y")) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("") +
scale_y_continuous(breaks = 0.01*seq(-30, 70, 1),
labels = percent_format(a = 1))
France, Italy, Spain
Code
%>%
nama_10_pe filter(geo %in% c("FR", "IT", "ES"),
%in% c("EMP_DC", "POP_NC"),
na_item == "THS_PER") %>%
unit select(geo, na_item, time, values) %>%
spread(na_item, values) %>%
transmute(geo, time, values = EMP_DC/POP_NC) %>%
%>%
year_to_date left_join(geo, by = "geo") %>%
+ geom_line(aes(x = date, y = values, color = Geo, linetype = Geo)) +
ggplot scale_color_manual(values = viridis(4)[1:3]) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
labels = date_format("%y")) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("") +
scale_y_continuous(breaks = 0.01*seq(-30, 70, 1),
labels = percent_format(a = 1))