HICP at constant taxes - monthly data (monthly rate of change) - prc_hicp_cmon

Data - Eurostat

Author

François Geerolf

Info

Last observation: Monthly: 2025M12 (N = 27,755)

First observation: Monthly: 2002M02 (N = 156)

Last data update: 23 jul 2026, 22:43. Last compile: 24 jul 2026, 03:16

Structure

France, Germany, Italy, Spain

All-items HICP, Monthly Rate of Change, 2019-

Code

prc_hicp_cmon %>%
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         unit == "RCH_M",
         coicop == "CP00") %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(2019, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("HICP, monthly rate of change") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 0.1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

France: All-Items, Energy, Food

Code

prc_hicp_cmon %>%
  filter(geo == "FR",
         unit == "RCH_M",
         coicop %in% c("CP00", "NRG", "FOOD")) %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Coicop)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(2019, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("HICP, monthly rate of change") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Latest Month by Country

Code

latest_m <- prc_hicp_cmon %>%
  filter(unit == "RCH_M",
         coicop == "CP00",
         !is.na(values)) %>%
  summarise(m = max(time)) %>%
  pull(m)

prc_hicp_cmon %>%
  filter(unit == "RCH_M",
         time == latest_m,
         coicop %in% c("CP00", "NRG", "FOOD", "SERV", "IGD", "TOT_X_NRG"),
         geo %in% c("FR", "DE", "IT", "ES", "EA20")) %>%
  select(coicop, Coicop, Geo, values) %>%
  spread(Geo, values) %>%
  print_table_conditional()

coicop	Coicop	Euro area – 20 countries (2023-2025)	France	Germany	Italy	Spain
CP00	All-items HICP	0.2	0.1	0.1	0.2	0.3
FOOD	Food including alcohol and tobacco	0.0	0.2	-0.1	-0.1	0.3
IGD	Industrial goods	-0.4	-0.6	-0.6	0.2	-0.3
NRG	Energy	-0.9	-1.6	-1.1	0.1	-0.3
SERV	Services (overall index excluding goods)	0.7	0.5	0.8	0.4	0.7
TOT_X_NRG	Overall index excluding energy	0.3	0.2	0.2	0.2	0.4