HICP (2015 = 100) - annual data (average index and rate of change)

Data - Eurostat

Author

François Geerolf

Info

Last observation: Annual: 2025 (N = 32,549)

First observation: Annual: 1996 (N = 6,256)

Last data update: 13 aoû 2026, 22:28. Last compile: 14 aoû 2026, 11:56

Structure

NULL

source dataset Title .html .rData
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2026-08-13 2026-08-13
eurostat prc_hicp_aind HICP (2015 = 100) - annual data (average index and rate of change) 2026-08-13 2026-08-12
eurostat prc_hicp_cow HICP - country weights 2026-08-13 2026-08-12
eurostat nama_10_co3_p3 Final consumption expenditure of households by consumption purpose (COICOP 3 digit) 2026-08-13 2026-08-13

Data on inflation

source dataset Title .html .rData
bis CPI Consumer Price Index 2026-08-12 2026-08-12
ecb CES Consumer Expectations Survey 2026-08-12 2026-08-12
eurostat nama_10_co3_p3 Final consumption expenditure of households by consumption purpose (COICOP 3 digit) 2026-08-13 2026-08-13
eurostat prc_hicp_cow HICP - country weights 2026-08-13 2026-08-12
eurostat prc_hicp_ctrb Contributions to euro area annual inflation (in percentage points) 2026-08-13 2026-08-12
eurostat prc_hicp_inw HICP - item weights 2026-08-13 2026-08-12
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2026-08-13 2026-08-13
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2026-08-13 2026-08-13
eurostat prc_hicp_mmor HICP (2015 = 100) - monthly data (monthly rate of change) 2026-08-13 2026-08-13
eurostat prc_ppp_ind Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates 2026-08-13 2026-08-12
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2026-08-13 2026-08-12
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2026-08-13 2026-08-12
eurostat sts_inppnd_m Producer prices in industry, non domestic market - monthly data 2026-08-13 2026-08-13
fred cpi Consumer Price Index 2026-08-12 2026-08-12
fred inflation Inflation 2026-08-12 2026-08-12
imf CPI Consumer Price Index (CPI) 2026 February - CPI_2026_FEB_VINTAGE 2026-08-12 2026-08-12
oecd MEI_PRICES_PPI Producer Prices - MEI_PRICES_PPI 2026-08-13 2026-08-02
oecd PPP2017 2017 PPP Benchmark results 2026-08-13 2026-08-02
oecd PRICES_CPI Consumer price indices (CPIs) 2026-08-13 2026-08-02
wdi FP.CPI.TOTL.ZG Inflation, consumer prices (annual %) 2026-08-12 2026-08-12
wdi NY.GDP.DEFL.KD.ZG Inflation, GDP deflator (annual %) 2026-08-12 2026-08-12

LAST_COMPILE

LAST_COMPILE
2026-08-14

Last

Code 
prc_hicp_aind |>
  group_by(time) |>
  summarise(Nobs = n()) |>
  arrange(desc(time)) |>
  head(2) |>
  print_table_conditional()
time Nobs
2025 32549
2024 32554

unit

Code 
prc_hicp_aind |>
  left_join(unit, by = "unit") |>
  group_by(unit, Unit) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
unit Unit Nobs
INX_A_AVG Annual average index 310631
RCH_A_AVG Annual average rate of change 294715
CID_EA Core inflation differential vis-à-vis the euro area 696

coicop

Code 
prc_hicp_aind |>
  left_join(coicop, by = "coicop") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

geo

Code 
prc_hicp_aind |>
  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

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

Années

2025

Tous

Code 
coicop <- read_parquet("coicop_fr.parquet")
prc_hicp_aind |>
  filter(time == "2025",
         geo %in% c("DE", "FR", "IT", "EA20"),
         unit == "RCH_A_AVG") %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(coicop, by = "coicop") |>
  spread(geo, values) |>
  select(coicop, Coicop, FR, EA20, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Sélection

Code 
coicop <- read_parquet("coicop_fr.parquet")
prc_hicp_aind |>
  filter(time == "2025",
         geo %in% c("DE", "FR", "IT", "EA20"),
         unit == "RCH_A_AVG",
         coicop %in% c(paste0("CP0", 0:9), paste0("CP1", 0:3), "CP041", "CP0830")) %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(coicop, by = "coicop") |>
  spread(geo, values) |>
  select(coicop, Coicop, FR, EA20, everything()) |>
  View()

Quality

Code 
compare_coicop <- function(CPname, legend.position = c(0.2, 0.2), start = 1996, geos = c("DE", "FR", "IT", "ES", "NL"), ytick = 10){
  
  inflation <- prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
          coicop == CPname,
         geo %in% geos) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  filter(date >= as.Date(paste0(start, "-01-01"))) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Price index")
  
  cons <- nama_10_co3_p3 |>
  filter(unit == "PD15_EUR",
         geo %in% geos,
         coicop == CPname) |>
  left_join(geo, by = "geo") |>
  year_to_date() |>
  filter(date >= as.Date(paste0(start, "-01-01"))) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Consumption Deflator")
  
  cons |>
  bind_rows(inflation) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color, linetype = variable)) +
  theme_minimal()  + 
  scale_color_identity() + xlab("") + ylab(CPname) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = legend.position,
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 1000, ytick))
}


compare_coicop2 <- function(CPname, CPname2, legend.position = c(0.2, 0.2), start = 1996, geos = c("DE", "FR", "IT", "ES", "NL"), ytick = 10){
  
  inflation <- prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
          coicop == CPname,
         geo %in% geos) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  left_join(colors, by = c("Geo" = "country")) |>
  filter(date >= as.Date(paste0(start, "-01-01"))) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Price index")
  
  cons <- nama_10_co3_p3 |>
  filter(unit == "PD15_EUR",
         geo %in% geos,
         coicop == CPname2) |>
  left_join(geo, by = "geo") |>
  year_to_date() |>
  filter(date >= as.Date(paste0(start, "-01-01"))) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Consumption Deflator")
  
  cons |>
  bind_rows(inflation) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color, linetype = variable)) +
  theme_minimal()  + 
  scale_color_identity() + xlab("") + ylab(CPname) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = legend.position,
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 1000, ytick))
}

CP00

All

Code 
compare_coicop2("CP00", "TOTAL", legend.position = c(0.2, 0.8))

DE, FR

Code 
compare_coicop2("CP00", "TOTAL", geos = c("DE", "FR"), legend.position = c(0.2, 0.8))

CP09

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP09"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP09") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

CP08 - Communications

Annual Inflation

Code 
inflation_CP08 <- prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP08"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Price index")

inflation_CP08 |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP08") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

Annual Consumption Deflator Inflation

Code 
cons_CP08 <- nama_10_co3_p3 |>
  filter(unit == "PD15_EUR",
         geo %in% c("FR", "NL", "IT", "DE", "ES"),
         coicop == "CP08") |>
  left_join(geo, by = "geo") |>
  year_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Consumption Deflator")

cons_CP08 |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_flags +
  scale_color_identity() + xlab("") + ylab("Communications (08)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10))

Bind

Code 
compare_coicop("CP08")

CP081 - Postal services

Bind

Code 
compare_coicop("CP081", legend.position = c(0.2, 0.8))

CP082_083

CP0820

Bind

Code 
compare_coicop2("CP0820", "CP082", legend.position = c(0.2, 0.8))

CP0830

Bind

Code 
compare_coicop2("CP0830", "CP083", legend.position = c(0.2, 0.2))

Annual Consumption Deflator Inflation

Code 
cons_CP083 <- nama_10_co3_p3 |>
  filter(unit == "PD15_EUR",
         geo %in% c("FR", "NL", "IT", "DE", "ES"),
         coicop == "CP083") |>
  left_join(geo, by = "geo") |>
  year_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  ungroup() |>
  mutate(variable = "Consumption Deflator")

cons_CP083 |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_flags +
  scale_color_identity() + xlab("") + ylab("Communications (083)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10))

CP071

Bind

Code 
compare_coicop("CP071", legend.position = c(0.2, 0.8))

CP041

Bind

Code 
compare_coicop("CP041", legend.position = c(0.2, 0.8))

1999-

Code 
compare_coicop("CP041", legend.position = c(0.2, 0.8), start = 1999)

2000-

Code 
compare_coicop("CP041", legend.position = c(0.2, 0.8), start = 2000)

CP01

Bind

Code 
compare_coicop("CP01", legend.position = c(0.2, 0.8))

CP02

Bind

Code 
compare_coicop("CP02", legend.position = c(0.2, 0.8))

CP022

Bind

Code 
compare_coicop("CP022", legend.position = c(0.2, 0.8), ytick = 50)

CP03

Bind

Code 
compare_coicop("CP03", legend.position = c(0.2, 0.8))

CP04

Bind

Code 
compare_coicop("CP04", legend.position = c(0.2, 0.8))

CP05

Bind

Code 
compare_coicop("CP05", legend.position = c(0.2, 0.8))

CP06

Bind

Code 
compare_coicop("CP06", legend.position = c(0.2, 0.8))

CP07

Bind

Code 
compare_coicop("CP07", legend.position = c(0.2, 0.8))

CP09

Bind

Code 
compare_coicop("CP09")

CP091

Bind

Code 
compare_coicop("CP091")

CP10

Bind

Code 
compare_coicop("CP10", legend.position = c(0.2, 0.8))

CP11

Bind

Code 
compare_coicop("CP11", legend.position = c(0.2, 0.8))

CP12

Bind

Code 
compare_coicop("CP12", legend.position = c(0.2, 0.8))

CP082_083 - Telephone and telefax equipment

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP082_083"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP082_083") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

CP0820 - Telephone and telefax equipment

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP0820"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP0820 - Telephone and telefax equipment") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP082_083"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP082_083") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

CP091

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP091"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP091") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

CP071 - Cars

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP071"),
         geo %in% c("DE", "FR", "IT", "ES", "NL")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP071 - Cars") +
  scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

CP03 - Clothing

2017-

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP03"),
         geo %in% c("DE", "FR", "IT", "ES", "EA20")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  filter(date >= as.Date("2017-01-01")) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP03 - Clothing and footwear") +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 1), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

CP08202 - Mobile Telephone equipment

2017-

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP08202"),
         geo %in% c("DE", "FR", "IT", "ES", "EA20")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  arrange(time) |>
  year_to_date() |>
  group_by(Geo) |>
  arrange(date) |>
  filter(date >= as.Date("2017-01-01")) |>
  mutate(values = 100*values/values[1]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP08202 - Mobile Telephone equipment") +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 200, 10), 2, 3, 5, 15, 8, 4)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none")

Greece, Europe, France, Spain, Italy, Germany

2011-2013 -

HICP

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop == "CP00",
         geo %in% c("EL", "FR", "ES", "IT", "DE")) |>
  year_to_date() |>
  group_by(geo) |>
  mutate(values = 100*values/values[date == as.Date("2011-01-01")]) |>
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2016-01-01")) |>
  left_join(geo, by = "geo") |>
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("100 = Janv. 2011") +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_identity() + add_flags +
  scale_y_log10(breaks = seq(0, 200, 2)) +
  theme(legend.position = "none",
        legend.title = element_blank())

Rents

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop == "CP041",
         geo %in% c("EL", "FR", "ES", "IT", "DE")) |>
  year_to_date() |>
  group_by(geo) |>
  mutate(values = 100*values/values[date == as.Date("2011-01-01")]) |>
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2016-01-01")) |>
  left_join(geo, by = "geo") |>
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("100 = Janv. 2011") +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_identity() + add_flags +
  scale_y_log10(breaks = seq(0, 200, 2)) +
  theme(legend.position = "none",
        legend.title = element_blank())

Real Rents

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP041", "CP00"),
         geo %in% c("EL", "FR", "ES", "IT", "DE")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  spread(coicop, values) |>
  mutate(values = 100*CP041/CP00) |>
  year_to_date() |>
  group_by(geo) |>
  mutate(values = 100*values/values[date == as.Date("2011-01-01")]) |>
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2016-01-01")) |>
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Real Rents (100 = Janv. 2011)") +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_identity() + add_flags +
  scale_y_log10(breaks = seq(0, 200, 2)) +
  theme(legend.position = "none",
        legend.title = element_blank())

Europe - Real Rents

Value

Code 
prc_hicp_aind |>
  filter(unit == "INX_A_AVG",
         coicop %in% c("CP041", "CP00"),
         geo %in% c("EA")) |>
  left_join(tibble(geo = c("EA", "EA18", "EA19"),
                   Geo = c("Euro Area (time-dep geography)")), 
            by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  spread(coicop, values) |>
  mutate(values = 100*CP041/CP00) |>
  year_to_date() |>
  ggplot() + geom_line(aes(x = date, y = values)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.5, 0.85),
        legend.title = element_blank())