Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/ei_bsin_m_r2.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-11-21 |
Data - Eurostat
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/ei_bsin_m_r2.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2024-11-21 |
LAST_COMPILE |
---|
2024-11-22 |
%>%
ei_bsin_m_r2 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2024M10 | 528 |
%>%
ei_bsin_m_r2 left_join(indic, by = "indic") %>%
group_by(indic, Indic) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
indic | Indic | Nobs |
---|---|---|
BS-IPE | Production expectations over the next 3 months | 26584 |
BS-IPT | Production development observed over the past 3 months | 26416 |
BS-IOB | Assessment of order-book levels | 26404 |
BS-ISFP | Assessment of the current level of stocks of finished products | 26248 |
BS-ICI | Industrial confidence indicator | 26068 |
BS-IEOB | Assessment of export order-book levels | 25878 |
BS-ISPE | Selling price expectations over the next 3 months | 25695 |
BS-IEME-BAL | Employment expectations over the next 3 months - industry | 25530 |
BS-IEME | Employment expectations over the next 3 months | 25358 |
%>%
ei_bsin_m_r2 left_join(s_adj, by = "s_adj") %>%
group_by(s_adj, S_adj) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
s_adj | S_adj | Nobs |
---|---|---|
SA | Seasonally adjusted data, not calendar adjusted data | 117126 |
NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 117055 |
%>%
ei_bsin_m_r2 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_bsin_m_r2 group_by(time) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
%>%
ei_bsin_m_r2 filter(indic == "BS-IPE",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-IPE",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-IPE",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("2000-01-01")) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-IPE",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("2015-01-01")) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-ICI",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-ICI",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-ICI",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
group_by(geo) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date filter(date >= as.Date("2000-01-01")) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Industrial confidence indicator") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 5))
%>%
ei_bsin_m_r2 filter(indic == "BS-IOB",
%in% c("FR", "DE", "IT"),
geo == "NSA") %>%
s_adj select(geo, time, values) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
%>%
month_to_date left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + ylab("Assessment of order books levels") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + add_3flags + theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-2000, 2000, 10))