Code
tibble(DOWNLOAD_TIME = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/ei_isin_m.RData")$mtime)) %>%
print_table_conditional()
DOWNLOAD_TIME |
---|
2024-10-09 |
Data - Eurostat
tibble(DOWNLOAD_TIME = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/ei_isin_m.RData")$mtime)) %>%
print_table_conditional()
DOWNLOAD_TIME |
---|
2024-10-09 |
%>%
ei_isin_m group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2024M09 | 18 |
load_data("eurostat/indic_fr.RData")
%>%
ei_isin_m left_join(indic, by = "indic") %>%
group_by(indic, Indic) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
indic | Indic | Nobs |
---|---|---|
IS-IP | Indice de production | 586374 |
IS-ITT | Indice du chiffre d'affaires - total | 560397 |
IS-ITD | Indice du chiffre d'affaires - marché intérieur | 549651 |
IS-ITND | Indice du chiffre d'affaires - marché extérieur | 355918 |
IS-PPI | Prix à la production de l'indice du marché intérieur (Indice des prix à la production) (NSA) | 255531 |
IS-WSI | Indice des salaires et traitements bruts | 133598 |
IS-EPI | Indice du nombre de personnes occupées | 109322 |
IS-HWI | Indice des heures travaillées | 107274 |
IS-IMPR | Indices des prix à l'importation (NSA) | 105615 |
IS-IMPX | Indices des prix à l'importation - hors zone euro (NSA) | 66043 |
IS-IMPZ | Indices des prix à l'importation - zone euro (NSA) | 60262 |
%>%
ei_isin_m left_join(indic, by = "indic") %>%
group_by(indic, Indic) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
indic | Indic | Nobs |
---|---|---|
IS-IP | Indice de production | 586374 |
IS-ITT | Indice du chiffre d'affaires - total | 560397 |
IS-ITD | Indice du chiffre d'affaires - marché intérieur | 549651 |
IS-ITND | Indice du chiffre d'affaires - marché extérieur | 355918 |
IS-PPI | Prix à la production de l'indice du marché intérieur (Indice des prix à la production) (NSA) | 255531 |
IS-WSI | Indice des salaires et traitements bruts | 133598 |
IS-EPI | Indice du nombre de personnes occupées | 109322 |
IS-HWI | Indice des heures travaillées | 107274 |
IS-IMPR | Indices des prix à l'importation (NSA) | 105615 |
IS-IMPX | Indices des prix à l'importation - hors zone euro (NSA) | 66043 |
IS-IMPZ | Indices des prix à l'importation - zone euro (NSA) | 60262 |
load_data("eurostat/indic.RData")
%>%
ei_isin_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 .} {
%>%
ei_isin_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 |
---|---|---|
NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 1177857 |
SCA | Seasonally and calendar adjusted data | 1035979 |
CA | Calendar adjusted data, not seasonally adjusted data | 676149 |
%>%
ei_isin_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 |
---|---|---|
I2015 | Index, 2015=100 | 1465020 |
I2021 | NA | 1424965 |
%>%
ei_isin_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 .} {
%>%
ei_isin_m group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-IP",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "1997M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_log10(breaks = seq(-60, 300, 10))
Turnover indices serve to provide a monthly measurement of trends in the activity of companies in the industrial, construction, retail, personal services, wholesale and miscellaneous services to enterprises sectors. They are elaborated each month from the monthly declarations (CA3) made by enterprises falling within the normal tax arrangements for the payment of value added tax (VAT). These indices are determined at the finest level of the French classification of activities (that is, the sub-classes of NAF rev.2) then aggregated to provide indices for the different levels of the composite nomenclatures (NA, NACE). The resultant indices are in volume in the retail and personal services sectors, and in value in the other sectors. The series are disseminated raw or seasonally and calendar effect adjusted.
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITT",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj filter(!is.na(values)) %>%
select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2005M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date ggplot() + ylab("Turnover index - total") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITD",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2005M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date ggplot() + ylab("Turnover index - domestic market") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITND",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2005M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date ggplot() + ylab("Turnover index - non-domestic market") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "MIG_DCOG",
== "IS-IP",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "1997M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Durable Goods - Industrial Production") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "MIG_COG",
== "IS-IP",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "1997M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date ggplot() + ylab("Consumer Goods - Industrial Production") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "MIG_CAG",
== "IS-IP",
indic %in% c("FR", "DE", "IT"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "1997M01"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date ggplot() + ylab("Capital Goods - Industrial Production") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITT",
indic %in% c("FR", "DE", "IT", "ES"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2020M02"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("2020-02-01")) %>%
ggplot() + ylab("Turnover index - all") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_4flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
scale_x_date(breaks = "2 months",
labels = date_format("%b %y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITT",
indic %in% c("FR", "DE", "IT", "ES"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2020M02"]) %>%
left_join(geo, by = "geo") %>%
%>%
month_to_date filter(date >= as.Date("2020-02-01")) %>%
group_by(Geo) %>%
arrange(date) %>%
mutate(values = values-100,
values = cumsum(values)) %>%
ggplot() + ylab("Turnover index - all (Cum. Sum)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_4flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
scale_x_date(breaks = "2 months",
labels = date_format("%b %y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = seq(-300, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITND",
indic %in% c("FR", "DE", "IT", "ES"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2020M02"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("2020-02-01")) %>%
ggplot() + ylab("Turnover index - external market") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_3flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITND",
indic %in% c("FR", "DE", "IT", "ES"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2020M02"]) %>%
left_join(geo, by = "geo") %>%
%>%
month_to_date filter(date >= as.Date("2020-02-01")) %>%
group_by(Geo) %>%
arrange(date) %>%
mutate(values = values-100,
values = cumsum(values)) %>%
ggplot() + ylab("Turnover index - external market (Cum. Sum)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_4flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = seq(-300, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITD",
indic %in% c("FR", "DE", "IT", "ES"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2020M02"]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("2020-02-01")) %>%
ggplot() + ylab("Turnover index - internal market") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) + add_4flags +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %y")) +
theme(legend.position = "none") +
scale_y_log10(breaks = seq(-60, 300, 10))
%>%
ei_isin_m filter(nace_r2 == "C",
== "IS-ITD",
indic %in% c("FR", "DE", "IT", "ES"),
geo == "SCA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
mutate(values = 100*values/values[time == "2020M02"]) %>%
left_join(geo, by = "geo") %>%
%>%
month_to_date filter(date >= as.Date("2020-02-01")) %>%
group_by(Geo) %>%
arrange(date) %>%
mutate(values = values-100,
values = cumsum(values)) %>%
ggplot() + ylab("Turnover index - internal market (Cum. Sum)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = Geo)) +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %y")) + add_4flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = seq(-300, 300, 10))