Import prices in industry - monthly data - sts_inpi_m

Data - Eurostat

~/data/eurostat/

Info

LAST_DOWNLOAD

tibble(LAST_DOWNLOAD = as.Date(file.info("~/Dropbox/website/data/eurostat/sts_inpi_m.RData")$mtime)) %>%
  print_table_conditional()

LAST_DOWNLOAD
2022-11-17

LAST_COMPILE

LAST_COMPILE
2022-11-17

Last

sts_inpi_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2022M10	666

nace_r2

sts_inpi_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 .}

s_adj

sts_inpi_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)	3193698

unit

sts_inpi_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
PCH_PRE	Percentage change on previous period	895831
I15	Index, 2015=100	888862
PCH_SM	Percentage change compared to same period in previous year	851208
I10	Index, 2010=100	557797

indic_bt

sts_inpi_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
IMPR	Import price index	1643632
IMPX	Import price index - non euro area	851962
IMPZ	Import price index - euro area	698104

geo

sts_inpi_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 .}

time

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

Manufacturing

France, Germany, Italy

All

sts_inpi_m %>%
  filter(nace_r2 == "C",
         unit == "I15",
         geo %in% c("FR", "DE", "IT"),
         indic_bt == "IMPR") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M03"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab(" Import price index") + 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 = . %>%
               filter(date == as.Date("2013-01-01")) %>%
               mutate(date = as.Date("2013-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 2))

2005-

sts_inpi_m %>%
  filter(nace_r2 == "C",
         unit == "I15",
         geo %in% c("FR", "DE", "IT"),
         indic_bt == "IMPR") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M03"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2005-01-01")) %>%
  ggplot() + ylab(" Import price index") + 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 = . %>%
               filter(date == as.Date("2013-01-01")) %>%
               mutate(date = as.Date("2013-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 2))

B062

sts_inpi_m %>%
  filter(nace_r2 == "B062",
         unit == "I15",
         geo %in% c("FR", "DE", "IT"),
         indic_bt == "IMPR") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M03"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  ggplot() + ylab(" Import price index") + 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 = . %>%
               filter(date == as.Date("2013-01-01")) %>%
               mutate(date = as.Date("2013-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 2))