Production in industry - quarterly data - sts_inpr_q

Data - Eurostat

~/data/eurostat/

nace_r2

sts_inpr_q %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

s_adj

sts_inpr_q %>%
  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
CA	Calendar adjusted data, not seasonally adjusted data	1386841
SCA	Seasonally and calendar adjusted data	1342796
NSA	Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)	659945

unit

sts_inpr_q %>%
  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	1391116
I10	Index, 2010=100	973090
PCH_SM	Percentage change compared to same period in previous year	515106
PCH_PRE	Percentage change on previous period	510270

indic_bt

sts_inpr_q %>%
  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
PROD	Volume index of production	3389582

geo

sts_inpr_q %>%
  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 .}

time

sts_inpr_q %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Industrial Production

France, Germany, Italy

sts_inpr_q %>%
  filter(nace_r2 == "C",
         unit == "I15",
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "1997Q1"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  quarter_to_date %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  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")) +
  geom_image(data = tibble(date = rep(as.Date("2022-01-01"), 3),
                           value = c(110, 140, 85),
                           image = c("~/Dropbox/bib/flags/france.png",
                                     "~/Dropbox/bib/flags/germany.png",
                                     "~/Dropbox/bib/flags/italy.png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))