Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/sts_inlb_m.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-11-21 |
Data - Eurostat
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/sts_inlb_m.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-11-21 |
LAST_COMPILE |
---|
2024-11-22 |
%>%
sts_inlb_m group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2024M09 | 2645 |
%>%
sts_inlb_m left_join(nace_r2, by = "nace_r2") %>%
group_by(nace_r2, Nace_r2) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
sts_inlb_m 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 | 911401 |
NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 798948 |
CA | Calendar adjusted data, not seasonally adjusted data | 569235 |
%>%
sts_inlb_m left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
unit | Unit | Nobs |
---|---|---|
I15 | Index, 2015=100 | 671160 |
I21 | Index, 2021=100 | 634619 |
I10 | Index, 2010=100 | 398731 |
PCH_SM | Percentage change compared to same period in previous year | 318740 |
PCH_PRE | Percentage change on previous period | 256334 |
%>%
sts_inlb_m left_join(indic_bt, by = "indic_bt") %>%
group_by(indic_bt, Indic_bt) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
indic_bt | Indic_bt | Nobs |
---|---|---|
WAGE | Wages and salaries | 993082 |
HW | Hours worked by employees | 761070 |
EMP | Persons employed | 525432 |
%>%
sts_inlb_m left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
%>%
sts_inlb_m group_by(time) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
sts_inlb_m filter(nace_r2 == "C",
%in% c("FR", "DE", "IT"),
geo == "SCA",
s_adj == "I15") %>%
unit select(geo, indic_bt, time, values) %>%
group_by(indic_bt) %>%
mutate(values = 100*values/values[time == "2000M01"]) %>%
left_join(indic_bt, by = "indic_bt") %>%
%>%
month_to_date ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Indic_bt)) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
sts_inlb_m filter(nace_r2 == "C",
%in% c("PT"),
geo == "SCA",
s_adj == "I15") %>%
unit select(geo, indic_bt, time, values) %>%
group_by(indic_bt) %>%
mutate(values = 100*values/values[time == "2000M01"]) %>%
left_join(indic_bt, by = "indic_bt") %>%
%>%
month_to_date ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Indic_bt)) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
sts_inlb_m filter(nace_r2 == "C",
%in% c("AT"),
geo == "SCA",
s_adj == "I15") %>%
unit select(geo, indic_bt, time, values) %>%
group_by(indic_bt) %>%
mutate(values = 100*values/values[time == "2000M01"]) %>%
left_join(indic_bt, by = "indic_bt") %>%
%>%
month_to_date ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Indic_bt)) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
sts_inlb_m filter(nace_r2 == "C",
%in% c("SE"),
geo == "SCA",
s_adj == "I15") %>%
unit select(geo, indic_bt, time, values) %>%
group_by(indic_bt) %>%
mutate(values = 100*values/values[time == "2000M01"]) %>%
left_join(indic_bt, by = "indic_bt") %>%
%>%
month_to_date ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Indic_bt)) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(-60, 300, 10))