source | dataset | .html | .RData |
---|---|---|---|
eurostat | nama_10_pe | 2024-11-05 | 2024-10-08 |
eurostat | namq_10_pe | 2024-11-01 | 2024-10-09 |
Population and employment
Data - Eurostat
Info
LAST_COMPILE
LAST_COMPILE |
---|
2024-11-05 |
Last
Code
%>%
namq_10_pe group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2024Q2 | 1221 |
na_item
Code
%>%
namq_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 |
---|---|---|
EMP_DC | Total employment domestic concept | 25809 |
SAL_DC | Employees domestic concept | 24690 |
SELF_DC | Self-employed domestic concept | 24690 |
POP_NC | Total population national concept | 23042 |
EMP_NC | Total employment national concept | 21644 |
SAL_NC | Employees national concept | 20874 |
SELF_NC | Self-employed national concept | 20874 |
s_adj
Code
%>%
namq_10_pe left_join(s_adj, by = "s_adj") %>%
group_by(s_adj, S_adj) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
s_adj | S_adj | Nobs |
---|---|---|
SCA | Seasonally and calendar adjusted data | 72232 |
NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 60292 |
SA | Seasonally adjusted data, not calendar adjusted data | 23203 |
CA | Calendar adjusted data, not seasonally adjusted data | 5896 |
freq
Code
%>%
namq_10_pe left_join(freq, by = "freq") %>%
group_by(freq, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
freq | Freq | Nobs |
---|---|---|
Q | Quarterly | 161623 |
unit
Code
%>%
namq_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 | 65911 |
PCH_SM_PER | Percentage change compared to same period in previous year (based on persons) | 63723 |
PCH_PRE_PER | Percentage change on previous period (based on persons) | 31989 |
geo
Code
%>%
namq_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
%>%
namq_10_pe group_by(time) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Population Table
Code
%>%
namq_10_pe filter(time %in% c("2019Q1", "2009Q1", "1999Q1", "1989Q1"),
== "POP_NC",
na_item %in% c("SCA", "SA"),
s_adj == "THS_PER") %>%
unit select(geo, s_adj, time, values) %>%
mutate(values = round(values/1000, 1)) %>%
left_join(geo, by = "geo") %>%
spread(time, values) %>%
arrange(- `2009Q1`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Employment Table
Code
%>%
namq_10_pe filter(time %in% c("2019Q1", "2009Q1", "1999Q1", "1989Q1"),
== "EMP_DC",
na_item %in% c("SCA", "SA"),
s_adj == "THS_PER") %>%
unit select(geo, s_adj, time, values) %>%
mutate(values = round(values/1000, 1)) %>%
left_join(geo, by = "geo") %>%
spread(time, values) %>%
arrange(- `2019Q1`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Eurozone
Last observation
Code
%>%
namq_10_pe filter(time == max(time),
== "POP_NC") %>%
na_item spread(unit, values) %>%
select_if(~ n_distinct(.) > 1) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, everything()) %>%
print_table_conditional
Previous observation
Code
%>%
namq_10_pe filter(time %in% c("2023Q4", "2023Q3"),
== "POP_NC",
na_item == "EA20") %>%
geo spread(time, values) %>%
select_if(~ n_distinct(.) > 1) %>%
print_table_conditional
unit | 2023Q3 | 2023Q4 |
---|---|---|
PCH_SM_PER | 0.5 | 0.5 |
THS_PER | 350371.4 | 350876.6 |
France Evolution
1999 -
NSA
Code
%>%
namq_10_pe filter(geo == "FR",
== "THS_PER",
unit == "NSA") %>%
s_adj left_join(na_item, by = "na_item") %>%
%>%
quarter_to_date filter(date >= as.Date("1999-01-01")) %>%
group_by(na_item) %>%
mutate(values = 100*values/values[1]) %>%
select_if(~ n_distinct(.) > 1) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Na_item)) +
theme_minimal() +
scale_x_date(breaks = seq(1999, 2024, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item))
SA
Code
%>%
namq_10_pe filter(geo == "FR",
== "THS_PER",
unit == "SA") %>%
s_adj left_join(na_item, by = "na_item") %>%
%>%
quarter_to_date filter(date >= as.Date("1999-01-01")) %>%
group_by(na_item) %>%
mutate(values = 100*values/values[1]) %>%
select_if(~ n_distinct(.) > 1) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Na_item)) +
theme_minimal() +
scale_x_date(breaks = seq(1999, 2024, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.7),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item))