HICP - country weights

Data - Eurostat

Author

Info

source	dataset	.html	.RData
eurostat	prc_hicp_cow	2024-11-05	2024-10-08

Données sur l’inflation en France

source	dataset	.html	.RData
insee	bdf2017	2024-11-09	2023-11-21
insee	ILC-ILAT-ICC	2024-11-09	2024-11-22
insee	INDICES_LOYERS	2024-11-09	2024-11-22
insee	IPC-1970-1980	2024-11-09	2024-11-22
insee	IPC-1990	2024-11-09	2024-11-22
insee	IPC-2015	2024-11-21	2024-11-22
insee	IPC-PM-2015	2024-11-21	2024-11-22
insee	IPCH-2015	2024-11-22	2024-11-22
insee	IPGD-2015	2024-11-21	2024-11-21
insee	IPLA-IPLNA-2015	2024-11-09	2024-11-22
insee	IPPI-2015	2024-11-21	2024-11-21
insee	IRL	2024-11-09	2024-11-09
insee	SERIES_LOYERS	2024-11-09	2024-11-09
insee	T_CONSO_EFF_FONCTION	2024-11-09	2024-07-18

Data on inflation

source	dataset	.html	.RData
bis	CPI	2024-07-01	2022-01-20
ecb	CES	2024-11-21	2024-11-21
eurostat	nama_10_co3_p3	2024-11-08	2024-10-09
eurostat	prc_hicp_cow	2024-11-05	2024-10-08
eurostat	prc_hicp_ctrb	2024-11-05	2024-10-08
eurostat	prc_hicp_inw	2024-11-05	2024-11-21
eurostat	prc_hicp_manr	2024-11-21	2024-11-21
eurostat	prc_hicp_midx	2024-11-01	2024-11-21
eurostat	prc_hicp_mmor	2024-11-05	2024-11-21
eurostat	prc_ppp_ind	2024-11-05	2024-10-08
eurostat	sts_inpp_m	2024-06-24	2024-11-21
eurostat	sts_inppd_m	2024-11-21	2024-11-21
eurostat	sts_inppnd_m	2024-06-24	2024-11-21
fred	cpi	2024-11-21	2024-11-21
fred	inflation	2024-11-21	2024-11-21
imf	CPI	2024-06-20	2020-03-13
oecd	MEI_PRICES_PPI	2024-09-15	2024-04-15
oecd	PPP2017	2024-04-16	2023-07-25
oecd	PRICES_CPI	2024-04-16	2024-04-15
wdi	FP.CPI.TOTL.ZG	2023-01-15	2024-09-18
wdi	NY.GDP.DEFL.KD.ZG	2024-09-18	2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code

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

time	Nobs
2024	91

statinfo

Code

prc_hicp_cow %>%
  left_join(statinfo, by = "statinfo") %>%
  group_by(statinfo, Statinfo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

statinfo	Statinfo	Nobs
COWEA19	Country weights for EA19 (euro area 2015-2022)	580
COWEA20	Country weights for EA20 (euro area from 2023)	525
COWEA	Country weights for the euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023)	477
COWEEA	Country weights for EEA (European Economic Area)	386
COWEU	Country weights for European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020)	328
COWEU27_2020	Country weights for EU27 (from 2020)	321
COWEU28	Country weights for EU28 (2013-2020)	304

geo

Code

prc_hicp_cow %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code

prc_hicp_cow %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

time	Nobs
2024	91
2023	91
2022	93
2021	93
2020	104
2019	106
2018	106
2017	106
2016	106
2015	106
2014	109
2013	112
2012	110
2011	110
2010	113
2009	113
2008	116
2007	122
2006	121
2005	121
2004	121
2003	101
2002	101
2001	101
2000	104
1999	46
1998	66
1997	66
1996	66

EA-19

time

Code

prc_hicp_cow %>%
  filter(statinfo == "COWEA19",
         geo != "EA19") %>%
  select(-statinfo) %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

time	Nobs
2024	19
2023	19
2022	19
2021	19
2020	19
2019	19
2018	19
2017	19
2016	19
2015	19
2014	19
2013	19
2012	19
2011	19
2010	19
2009	19
2008	19
2007	19
2006	19
2005	19
2004	19
2003	19
2002	19
2001	19
2000	19
1999	19
1998	19
1997	19
1996	19

2022 Table

Code

prc_hicp_cow %>%
  filter(time == "2022",
         statinfo == "COWEA19",
         geo != "EA19") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  arrange(-COWEA19) %>%
  mutate(cumsum = cumsum(COWEA19)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019 Table

Code

prc_hicp_cow %>%
  filter(time == "2019",
         statinfo == "COWEA19",
         geo != "EA19") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  arrange(-COWEA19) %>%
  mutate(cumsum = cumsum(COWEA19)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Germany, France, Italy, Spain

Code

prc_hicp_cow %>%
  filter(statinfo == "COWEA19",
         geo %in% c("DE", "FR", "IT", "ES")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Netherlands, Belgium, Austria, Portugal

Code

prc_hicp_cow %>%
  filter(statinfo == "COWEA19",
         geo %in% c("NL", "BE", "AT", "PT")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Greece, Finland, Ireland, Slovakia

Code

prc_hicp_cow %>%
  filter(statinfo == "COWEA19",
         geo %in% c("EL", "FI", "IE", "SK")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Lithuania, Slovenia, Luxembourg, Latvia

Code

prc_hicp_cow %>%
  filter(statinfo == "COWEA19",
         geo %in% c("LT", "SI", "LU", "LV")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .1),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Estonia, Cyprus, Malta

Code

prc_hicp_cow %>%
  filter(statinfo == "COWEA19",
         geo %in% c("EE", "CY", "MT")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .05),
                     labels = percent_format(a = .01)) + 
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

All

2022

Code

prc_hicp_cow %>%
  filter(time == "2022") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019

Code

prc_hicp_cow %>%
  filter(time == "2019") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

PECO

Bulgarie, Croatie, Estonie, Hongrie, Lettonie, Lituanie, Pologne, Roumanie, Slovénie, Slovaquie, République tchèque.

List

Code

Geo_PECO <- c("Bulgaria", "Croatia", "Estonia", "Hungary", "Lithuania", "Poland",
              "Romania", "Slovenia", "Slovakia", "Czechia")

EA19

2022 Table

Code

prc_hicp_cow %>%
  filter(time == "2022",
         statinfo == "COWEA19",
         geo != "EA19") %>%
  left_join(geo, by = "geo") %>%
  filter(Geo %in% Geo_PECO) %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  arrange(-COWEA19) %>%
  mutate(cumsum = cumsum(COWEA19)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}