HICP (2015 = 100) - monthly data (annual rate of change)

Data - Eurostat

Author

Info

source	dataset	.html	.RData
eurostat	prc_hicp_manr	2024-12-21	2024-12-22
eurostat	prc_hicp_midx	2024-11-01	2024-12-22

Data on inflation

source	dataset	.html	.RData
bis	CPI	2024-07-01	2022-01-20
ecb	CES	2024-12-22	2024-12-22
eurostat	nama_10_co3_p3	2024-12-14	2024-12-14
eurostat	prc_hicp_cow	2024-11-22	2024-10-08
eurostat	prc_hicp_ctrb	2024-11-22	2024-10-08
eurostat	prc_hicp_inw	2024-11-05	2024-11-23
eurostat	prc_hicp_manr	2024-12-21	2024-12-22
eurostat	prc_hicp_midx	2024-11-01	2024-12-22
eurostat	prc_hicp_mmor	2024-11-22	2024-11-23
eurostat	prc_ppp_ind	2024-11-22	2024-10-08
eurostat	sts_inpp_m	2024-06-24	2024-12-21
eurostat	sts_inppd_m	2024-12-21	2024-12-21
eurostat	sts_inppnd_m	2024-06-24	2024-12-16
fred	cpi	2024-12-22	2024-12-22
fred	inflation	2024-12-22	2024-12-22
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

Données sur l’inflation en France

source	dataset	.html	.RData
insee	bdf2017	2024-11-22	2023-11-21
insee	ILC-ILAT-ICC	2024-11-22	2024-12-22
insee	INDICES_LOYERS	2024-11-22	2024-12-22
insee	IPC-1970-1980	2024-11-22	2024-12-22
insee	IPC-1990	2024-11-22	2024-12-22
insee	IPC-2015	2024-12-16	2024-12-22
insee	IPC-PM-2015	2024-12-21	2024-12-22
insee	IPCH-2015	2024-12-21	2024-12-22
insee	IPGD-2015	2024-12-16	2024-12-16
insee	IPLA-IPLNA-2015	2024-11-22	2024-12-22
insee	IPPI-2015	2024-12-21	2024-12-16
insee	IRL	2024-11-22	2024-11-22
insee	SERIES_LOYERS	2024-11-22	2024-11-22
insee	T_CONSO_EFF_FONCTION	2024-11-22	2024-07-18

LAST_COMPILE

LAST_COMPILE
2024-12-22

Last

Code

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

time	Nobs
2024M11	16236

HICP Releases

Last Release. November 17, 2022. pdf
Release Schedule. pdf
Date of releases. html
Flash releases.

Code

tibble(release = "2021-11", key = "11563387", date = "30112021") %>%
  add_row(release = "2021-12", key = "14442438", date = "07012022") %>%
  add_row(release = "2022-02", key = "14358044", date = "02032022") %>%
  add_row(release = "2022-03", key = "14442438", date = "01042022") %>%
  add_row(release = "2022-09", key = "15131964", date = "30092022") %>%
  add_row(release = "2022-10", key = "15131964", date = "31102022") %>%
  mutate(en = paste0('<a  target=_blank href=https://ec.europa.eu/eurostat/documents/2995521/', key, '/2-', date, '-AP-EN.pdf > en </a>')) %>%
  mutate(fr = paste0('<a  target=_blank href=https://ec.europa.eu/eurostat/documents/2995521/', key, '/2-', date, '-AP-FR.pdf > fr </a>')) %>%
  mutate(de = paste0('<a  target=_blank href=https://ec.europa.eu/eurostat/documents/2995521/', key, '/2-', date, '-AP-DE.pdf > de </a>')) %>%
  select(-key, -date) %>%
  arrange(desc(release)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Full series of indices (approx. 2 weeks later).

Code

tibble(release = "2021-11", key = "2995521", date = "17122021") %>%
  add_row(release = "2022-08", key = "16092022", date = "30112021") %>%
  add_row(release = "2022-09", key = "15131946", date = "19102022") %>%
  # https://ec.europa.eu/eurostat/documents/2995521/15265521/2-17112022-AP-EN.pdf
  add_row(release = "2022-10", key = "15265521", date = "17112022") %>%
  mutate(en = paste0('<a  target=_blank href=https://ec.europa.eu/eurostat/documents/2995521/', key, '/2-', date, '-AP-EN.pdf > en </a>')) %>%
  mutate(fr = paste0('<a  target=_blank href=https://ec.europa.eu/eurostat/documents/2995521/', key, '/2-', date, '-AP-FR.pdf > fr </a>')) %>%
  mutate(de = paste0('<a  target=_blank href=https://ec.europa.eu/eurostat/documents/2995521/', key, '/2-', date, '-AP-DE.pdf > de </a>')) %>%
  select(-key, -date) %>%
  arrange(desc(release)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Code

i_g("bib/eurostat/2-30112021-AP-EN/euro-area-annual-inflation-components.png")

Code

i_g("bib/eurostat/2-30112021-AP-EN/HICP-202111.png")

coicop

All

Code

prc_hicp_manr %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            `2022M09, EA19` = values[geo == "EA19" & time == "2022M09"]) %>%
  print_table_conditional()

2-digit

Code

prc_hicp_manr %>%
  filter(nchar(coicop) == 4) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            last_inflation = values[geo == "EA19" & time == last_time_full_release]) %>%
  setNames(c("coicop", "Coicop", "Nobs", paste0(last_time_full_release, ", EA19"))) %>%
  print_table_conditional()

coicop	Coicop	Nobs	2024M11, EA19
CP00	All-items HICP	13684	2.2
CP01	Food and non-alcoholic beverages	13684	2.0
CP02	Alcoholic beverages, tobacco and narcotics	13684	5.6
CP03	Clothing and footwear	13684	1.4
CP04	Housing, water, electricity, gas and other fuels	13684	2.7
CP05	Furnishings, household equipment and routine household maintenance	13684	-0.1
CP06	Health	13684	3.3
CP07	Transport	13684	-0.1
CP08	Communications	13684	-3.0
CP09	Recreation and culture	13684	2.2
CP10	Education	13637	4.1
CP11	Restaurants and hotels	13684	4.4
CP12	Miscellaneous goods and services	13684	4.4
FOOD	Food including alcohol and tobacco	13137	2.7
FUEL	Liquid fuels and fuels and lubricants for personal transport equipment	13114	-7.5
SERV	Services (overall index excluding goods)	12231	3.9

3-digit

Code

prc_hicp_manr %>%
  filter(nchar(coicop) == 5) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            last_inflation = values[geo == "EA19" & time == last_time_full_release]) %>%
  setNames(c("coicop", "Coicop", "Nobs", paste0(last_time_full_release, ", EA19"))) %>%
  print_table_conditional()

4-digit

Code

prc_hicp_manr %>%
  filter(nchar(coicop) == 6) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            last_inflation = values[geo == "EA19" & time == last_time_full_release]) %>%
  setNames(c("coicop", "Coicop", "Nobs", paste0(last_time_full_release, ", EA19"))) %>%
  print_table_conditional()

5-digit

Code

prc_hicp_manr %>%
  filter(nchar(coicop) == 7) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            last_inflation = values[geo == "EA19" & time == last_time_full_release]) %>%
  setNames(c("coicop", "Coicop", "Nobs", paste0(last_time_full_release, ", EA19"))) %>%
  print_table_conditional()

Other

Code

prc_hicp_manr %>%
  filter(nchar(coicop) > 7) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            last_inflation = values[geo == "EA19" & time == last_time_full_release]) %>%
  setNames(c("coicop", "Coicop", "Nobs", paste0(last_time_full_release, ", EA19"))) %>%
  print_table_conditional()

geo

Code

prc_hicp_manr %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  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_manr %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

Last

Code

prc_hicp_manr %>%
  filter(time == last_time) %>%
  select_if(function(col) length(unique(col)) > 1) %>%
  left_join(geo, by = "geo") %>%
  left_join(coicop, by = "coicop") %>%
  select(geo, Geo, coicop, Coicop, values) %>%
  print_table_conditional()

Europe, United States (Proxy-HICP)

How much data ?

Code

prc_hicp_manr %>%
  filter(geo %in% c("US", "EA20"),
         time == "2023M09") %>%
  group_by(time, coicop) %>%
  filter(n() == 2) %>%
  ungroup %>%
  left_join(coicop, by = "coicop") %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  select(-unit, -geo, - time) %>%
  spread(Geo, values)

# # A tibble: 13 × 5
#    freq  coicop Coicop                          `Euro area HICP` `US Proxy-HICP`
#    <chr> <chr>  <chr>                                      <dbl>           <dbl>
#  1 M     CP00   All-items HICP                               4.3             2.6
#  2 M     CP01   Food and non-alcoholic beverag…              9.1             2.3
#  3 M     CP02   Alcoholic beverages, tobacco a…              7.4             4.2
#  4 M     CP03   Clothing and footwear                        3               2.6
#  5 M     CP04   Housing, water, electricity, g…             -2.3             3.9
#  6 M     CP05   Furnishings, household equipme…              4.8             1.3
#  7 M     CP06   Health                                       2.9             2.8
#  8 M     CP07   Transport                                    3.6            -0.1
#  9 M     CP08   Communications                              -0.3            -0.9
# 10 M     CP09   Recreation and culture                       5.6             4.8
# 11 M     CP10   Education                                    3.2             3.1
# 12 M     CP11   Restaurants and hotels                       6.7             5.2
# 13 M     CP12   Miscellaneous goods and servic…              5.2             3.8

2020-

All

Code

coicop_short <- tribble(
  ~ coicop, ~ Coicop,
  "CP01", "Food",
  "CP02", "Alcohol, Tobacco",
  "CP03", "Clothing",
  "CP04", "Housing, heating",
  "CP05", "Furnishings",
  "CP06", "Health",
  "CP07", "Transport",
  "CP08", "Communications",
  "CP09", "Recreation",
  "CP10", "Education",
  "CP11", "Restaurants, Hotels",
  "CP12", "Misc G&S")
prc_hicp_manr %>%
  filter(coicop != "CP00",
         geo %in% c("EA20", "US")) %>%
  group_by(time, coicop) %>%
  filter(n() == 2) %>%
  ungroup %>%
  month_to_date %>%
  filter(date >= as.Date("2021-03-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  left_join(coicop_short, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-09-01"), as.Date("2024-09-01"), "6 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_vline(xintercept = as.Date("2023-09-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-09-01"), linetype = "dotted") + 
  facet_wrap(~paste(coicop, Coicop, sep = " - ")) + 
  geom_text_repel(data = . %>% filter(date %in% c(as.Date("2023-09-01"),
                                                  as.Date("2022-09-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1), color = Geo), 
                  fontface ="plain", size = 3)

All

Code

coicop_short <- tribble(
  ~ coicop, ~ Coicop,
  "CP01", "Food",
  "CP02", "Alcohol, Tobacco",
  "CP03", "Clothing",
  "CP04", "Housing, heating",
  "CP05", "Furnishings",
  "CP06", "Health",
  "CP07", "Transport",
  "CP08", "Communications",
  "CP09", "Recreation",
  "CP10", "Education",
  "CP11", "Restaurants, Hotels",
  "CP12", "Misc G&S")
prc_hicp_manr %>%
  filter(coicop != "CP00",
         geo %in% c("EA20", "US")) %>%
  group_by(time, coicop) %>%
  filter(n() == 2) %>%
  ungroup %>%
  month_to_date %>%
  filter(date >= as.Date("2021-03-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  left_join(coicop_short, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-09-01"), as.Date("2024-09-01"), "6 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_vline(xintercept = as.Date("2023-10-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted") + 
  facet_wrap(~paste(coicop, Coicop, sep = " - ")) + 
  geom_text_repel(data = . %>% filter(date %in% c(as.Date("2023-10-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1), color = Geo), 
                  fontface ="plain", size = 3)

CP00 - All-items HICP

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("EA", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP01 - Food and non-alcoholic beverages

Code

prc_hicp_manr %>%
  filter(coicop == "CP01",
         geo %in% c("EA", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Food Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP02 - Alcoholic beverages, tobacco and narcotics

Code

prc_hicp_manr %>%
  filter(coicop == "CP02",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Food Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP03 - Clothing and footwear

Code

prc_hicp_manr %>%
  filter(coicop == "CP03",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Clothing and footwear Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP04 - Housing, water, electricity, gas and other fuels

Code

prc_hicp_manr %>%
  filter(coicop == "CP04",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Housing, water, electricity, gas and other fuels (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP05 - Furnishings, household equipment and routine household maintenance

Code

prc_hicp_manr %>%
  filter(coicop == "CP05",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Furnishings, household equipment Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP06 - Health

Code

prc_hicp_manr %>%
  filter(coicop == "CP06",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Health Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP07 - Transport

Code

prc_hicp_manr %>%
  filter(coicop == "CP07",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Transport Inflation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP08 - Communications

Code

prc_hicp_manr %>%
  filter(coicop == "CP08",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation, Communications (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

CP09 - Recreation and culture

Code

prc_hicp_manr %>%
  filter(coicop == "CP09",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation, Recreation and culture (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP10 - Education

Code

prc_hicp_manr %>%
  filter(coicop == "CP10",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation, Education (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP11 - Restaurants and hotels

Code

prc_hicp_manr %>%
  filter(coicop == "CP11",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation, Restaurants and hotels (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP12 - Miscellaneous goods and services

Code

prc_hicp_manr %>%
  filter(coicop == "CP12",
         geo %in% c("EA20", "US")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Euro area HICP", "US Proxy-HICP")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Inflation, Miscellaneous goods and services (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date %in% c(max(date), as.Date("2022-06-01"),
                                                  as.Date("2022-10-01"))),
                  aes(x = date, y = values, label = percent(values, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3) + 
  geom_vline(xintercept = as.Date("2022-06-01"), linetype = "dotted") + 
  geom_vline(xintercept = as.Date("2022-10-01"), linetype = "dotted")

CP00

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("EA19", "US")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "US", color2, color)) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_2flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Mean of Inflation

All - Unweighted

Code

eurozone_countries <- c("Austria", "Belgium", "Cyprus", "Estonia", "Finland",
                        "France", "Germany", "Greece", "Ireland", "Italy",
                        "Latvia", "Lithuania", "Luxembourg", "Malta", "Netherlands",
                        "Portugal", "Slovakia", "Slovenia", "Spain", "Euro area - 19 countries  (from 2015)")

CEEC_countries <- c("Bulgaria", "Czech Republic", "Estonia", "Hungary", "Lithuania",
                    "Latvia", "Poland", "Romania", "Slovenia", "Slovakia")

prc_hicp_manr_2022M09 <- prc_hicp_manr %>%
  filter(time == "2022M09",
         coicop == "CP00") %>%
  left_join(geo, by = "geo") %>%
  select(Geo, inflation_2022M09 = values)

prc_hicp_manr_2022M10 <- prc_hicp_manr %>%
  filter(time == "2022M10",
         coicop == "CP00") %>%
  left_join(geo, by = "geo") %>%
  select(Geo, inflation_2022M10 = values)

prc_hicp_manr_2022M09_Energy <- prc_hicp_manr %>%
  filter(time == "2022M09",
         coicop == "NRG") %>%
  left_join(geo, by = "geo") %>%
  select(Geo, inflation_2022M09_NRG = values)

prc_hicp_manr_2022M10_Energy <- prc_hicp_manr %>%
  filter(time == "2022M10",
         coicop == "NRG") %>%
  left_join(geo, by = "geo") %>%
  select(Geo, inflation_2022M10_NRG = values)


coicop <- tibble(coicop = "CP00", Coicop = "Headline inflation") %>%
  add_row(coicop = "TOT_X_NRG_FOOD", Coicop = "Core inflation")
prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(Nobs = n(),
            values = mean(values)) %>%
  mutate(values = values/100) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation, Unweighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

All - Weighted

Core, Headline

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values/1000),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sum(values*country_weights/100)) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

CP01, CP02, CP03, CP04

Code

load_data("eurostat/coicop.RData")
prc_hicp_manr %>%
  filter(coicop %in% c("CP01", "CP02", "CP03", "CP04"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values/1000),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sum(values*country_weights/100)) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

All - Weighted, Unweighted

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(`Unweighted` = mean(values/100),
            `Weighted` = Hmisc::wtd.mean(values/100, country_weights)) %>%
  gather(weighted_yes_no, values, -date, -coicop) %>%
  arrange(desc(date)) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = weighted_yes_no)) + 
  theme_minimal() + xlab("") + ylab("Mean Inflation, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_linetype_manual(values = c("dashed", "solid")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.75, 0.80),
        legend.title = element_blank())

Compare: Standard Deviation, IQR max minus min

All - Unweighted

HICP Inflation

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  month_to_date %>%
  group_by(date) %>%
  filter(n() == 19) %>%
  summarise(`Standard Deviation` = sd(values/100),
            `Interquartile Range (IQR)` = quantile(values/100, 0.75)-quantile(values/100, 0.25)) %>%
  gather(Variable, values, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Variable, linetype = Variable)) + 
  theme_minimal() + xlab("") + ylab("HICP Inflation, Std, IQR, Unweighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630", "#003399")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.7, 0.90),
        legend.title = element_blank())

Core Inflation

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  month_to_date %>%
  group_by(date) %>%
  filter(n() == 19) %>%
  summarise(`Standard Deviation` = sd(values/100),
            `Interquartile Range (IQR)` = quantile(values/100, 0.75)-quantile(values/100, 0.25),
            `Maximum - Minimum` = max(values/100) - min(values/100)) %>%
  gather(Variable, values, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Variable)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation, Std, IQR, Max-Min, Unweighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630", "#003399")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.7, 0.90),
        legend.title = element_blank())

All - Weighted

HICP Inflation

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values),
            by = c("geo", "year")) %>%
  month_to_date %>%
  filter(!is.na(values), !is.na(country_weights)) %>%
  group_by(date) %>%
  arrange(date) %>%
  filter(n() == 19) %>%
  summarise(`Standard Deviation` = sqrt(Hmisc::wtd.var(values/100, country_weights)),
            `Interquartile Range (IQR)` = Hmisc::wtd.quantile(values/100, country_weights, probs = 0.75) -
              Hmisc::wtd.quantile(values/100, country_weights, probs = 0.25)) %>%
  gather(Variable, values, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Variable, linetype = Variable)) + 
  theme_minimal() + xlab("") + ylab("HICP Inflation, Std, IQR, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630", "#003399")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.7, 0.90),
        legend.title = element_blank())

Core Inflation

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values),
            by = c("geo", "year")) %>%
  month_to_date %>%
  filter(!is.na(values), !is.na(country_weights)) %>%
  group_by(date) %>%
  filter(n() == 19) %>%
  summarise(`Standard Deviation` = sqrt(Hmisc::wtd.var(values/100, country_weights)),
            `Interquartile Range (IQR)` = Hmisc::wtd.quantile(values/100, country_weights, probs = 0.75) -
              Hmisc::wtd.quantile(values/100, country_weights, probs = 0.25)) %>%
  gather(Variable, values, -date) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Variable, linetype = Variable)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation, Std, IQR, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630", "#003399")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.7, 0.90),
        legend.title = element_blank())

Weighted, Unweighted

HICP Inflation

Core Inflation

Standard Deviation of Inflation

All - Unweighted

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sd(values/100)) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Unweighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

All - Weighted

Headline, Core

All

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK", "HR")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(geo, by = "geo") %>%
  #filter(!(Geo %in% intersect(eurozone_countries, CEEC_countries))) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-20 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

2017-

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK", "HR")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-20 (%)") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

Table

Code

instant_time <- "2022M10"
load_data("eurostat/coicop.RData")
prc_hicp_manr %>%
  filter(geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK"),
         time == instant_time) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  group_by(coicop) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  arrange(-values) %>%
  transmute(coicop, Coicop, `Weighted Std.` = paste0(round(values, 1), " %")) %>%
  head(9) %>%
  print_table_conditional()

coicop	Coicop	Weighted Std.
CP0451	Electricity	73.7 %
AP_NRG	Administered prices, energy	51.8 %
ELC_GAS	Electricity, gas, solid fuels and heat energy	51.7 %
CP045	Electricity, gas and other fuels	48.9 %
CP04521	Natural gas and town gas	46.3 %
CP0452	Gas	44 %
CP04549	Other solid fuels	38.3 %
CP0454	Solid fuels	35.3 %
CP07332	International flights	34.8 %

CP0451, CP07332, CP0452, CP0454

All

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP0451", "CP07332", "CP0452", "CP0454"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(geo, by = "geo") %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

2017-

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP0451", "CP07332", "CP0452", "CP0454"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(geo, by = "geo") %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

CP01, CP02, CP03, CP05

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP01", "CP02", "CP03", "CP05"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(geo, by = "geo") %>%
  filter(!(Geo %in% intersect(eurozone_countries, CEEC_countries))) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

CP06, CP07, CP08, CP09

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP06", "CP07", "CP08", "CP09"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(geo, by = "geo") %>%
  filter(!(Geo %in% intersect(eurozone_countries, CEEC_countries))) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

CP10, CP11, CP12

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP10", "CP11", "CP12"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(geo, by = "geo") %>%
  filter(!(Geo %in% intersect(eurozone_countries, CEEC_countries))) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(values = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

All - Weighted, Unweighted

Code

coicop <- tibble(coicop = "CP00", Coicop = "Headline inflation") %>%
  add_row(coicop = "TOT_X_NRG_FOOD", Coicop = "Core inflation")
prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  summarise(`Unweighted` = sqrt(Hmisc::wtd.var(values/100, uniform_weights)),
            `Weighted` = sqrt(Hmisc::wtd.var(values/100, country_weights))) %>%
  gather(weighted_yes_no, values, -date, -coicop) %>%
  left_join(coicop, by = "coicop") %>%
  arrange(desc(date)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = weighted_yes_no)) + 
  theme_minimal() + xlab("") + ylab("Standard Deviation of Inflation, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_linetype_manual(values = c("dashed", "solid")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.75, 0.80),
        legend.title = element_blank())

Interquartile range (IQR) of Inflation

All - Unweighted

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  filter(n() == 19) %>%
  summarise(values = quantile(values/100, probs = 0.75) - quantile(values/100, probs = 0.25)) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Interquartile range of Inflation, Unweighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

All - Weighted

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  filter(n() == 19) %>%
  summarise(values = Hmisc::wtd.quantile(values/100, country_weights, probs = 0.75) -
              Hmisc::wtd.quantile(values/100, country_weights, probs = 0.25)) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("Interquartile range of Inflation, Weighted, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())

All - Weighted, Unweighted

Code

prc_hicp_manr %>%
  filter(coicop %in% c("TOT_X_NRG_FOOD", "CP00"),
         geo %in% c("AT", "BE", "CY", "DE", "EE", "EL", "ES", "FI", "FR", "IE",
                    "IT", "LT", "LU", "LV", "MT", "NL", "PT", "SI", "SK")) %>%
  mutate(year = substr(time, 1, 4)) %>%
  left_join(prc_hicp_cow %>%
              filter(statinfo == "COWEA19", geo != "EA19") %>%
              transmute(geo, year = time, country_weights = values, uniform_weights = 1),
            by = c("geo", "year")) %>%
  month_to_date %>%
  group_by(coicop, date) %>%
  filter(n() == 19) %>%
  summarise(`Unweighted` = quantile(values/100, probs = 0.75) -
              quantile(values/100, probs = 0.25),
            `Weighted` = Hmisc::wtd.quantile(values/100, country_weights, probs = 0.75) -
              Hmisc::wtd.quantile(values/100, country_weights, probs = 0.25)) %>%
  gather(weighted_yes_no, values, -date, -coicop) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop, linetype = weighted_yes_no)) + 
  theme_minimal() + xlab("") + ylab("Interquartile range (IQR) of Inflation, EA-19 (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#1E1C1C", "#A81630")) + 
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.45, 0.80),
        legend.title = element_blank())

Contributions to inflation

Eurozone

Code

prc_hicp_inw_2022 <- prc_hicp_inw %>%
  filter(time == "2022",
         geo == "EA") %>%
  mutate(values = values/1000) %>%
  select(-time) %>%
  rename(weights = values) %>%
  select_if(~ n_distinct(.) > 1)

prc_hicp_manr_last <- prc_hicp_manr %>%
  filter(time == "2022M03",
         geo == "EA") %>%
  select_if(~ n_distinct(.) > 1)

prc_hicp_inw_2022 %>%
  inner_join(prc_hicp_manr_last, by = c("coicop")) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(contributions = weights*values) %>%
  arrange(-contributions) %>%
  select(coicop, Coicop, everything()) %>%
  print_table_conditional()

France, Italy, Germany

Code

prc_hicp_inw_2022 <- prc_hicp_inw %>%
  filter(time == "2022",
         geo %in% c("DE", "FR", "IT")) %>%
  mutate(values = values/1000) %>%
  select(-time) %>%
  rename(weights = values) %>%
  select_if(~ n_distinct(.) > 1)

prc_hicp_manr_last <- prc_hicp_manr %>%
  filter(time == "2022M03",
         geo %in% c("DE", "FR", "IT")) %>%
  select_if(~ n_distinct(.) > 1)

prc_hicp_inw_2022 %>%
  inner_join(prc_hicp_manr_last, by = c("coicop", "geo")) %>%
  left_join(coicop, by = "coicop") %>%
  left_join(geo, by = "geo") %>%
  mutate(contributions = weights*values) %>%
  select(coicop, Coicop, Geo, contributions) %>%
  spread(Geo, contributions) %>%
  print_table_conditional()

CP0612

Table

Code

prc_hicp_manr %>%
  filter(coicop == "CP0612",
         time %in% c("2021M01", "2021M01", "2021M11")) %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  arrange(`2021M11`) %>%
  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 .}

France, Germany, Spain, Italy

Code

prc_hicp_manr %>%
  filter(coicop == "CP0612",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP0612 - Other medical products") +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-40, 20, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Share of Energy VS Rise in inflation

Share of Energy, Total Inflation

Code

nrg_weights <- prc_hicp_inw %>%
  filter(time %in% c("2022"),
         coicop %in% c("AP_NRG", "NRG")) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(values/10, 1)) %>%
  select(Geo, coicop, values) %>%
  spread(coicop, values) %>%
  transmute(Geo, `Energy, Non Administered Prices` = NRG - AP_NRG,
            `Energy, Administered Prices` = AP_NRG,
            Energy = NRG) %>%
  arrange(-`Energy, Non Administered Prices`)

prc_hicp_manr_2022M09 %>%
  inner_join(prc_hicp_manr_2022M09_Energy, by = "Geo") %>%
  inner_join(nrg_weights, by = "Geo") %>%
  filter(Geo %in% eurozone_countries) %>%
  mutate(contribution_Energy = inflation_2022M09_NRG*Energy/100) %>%
  ggplot(.) + theme_minimal() + xlab("Inflation, contribution of Energy (%)") + ylab("Total Inflation (%)") +
  geom_point(aes(x = `contribution_Energy`/100, y = `inflation_2022M09`/100)) +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = `contribution_Energy`/100, y = `inflation_2022M09`/100), 
              linetype = 2, method = "lm", color = "#F2A900") +
  geom_text_repel(aes(x = `contribution_Energy`/100, y = `inflation_2022M09`/100, label = Geo))

2021M11

Code

prc_hicp_manr_2021M11 <- prc_hicp_manr %>%
  filter(time == "2021M11",
         coicop == "CP00") %>%
  left_join(geo, by = "geo") %>%
  select(Geo, inflation_2021M11 = values)
prc_hicp_manr_2021M11 %>%
  inner_join(nrg_weights, by = "Geo") %>%
  arrange(-inflation_2021M11) %>%
  ggplot(.) + theme_minimal() + xlab("Share of energy (%)") + ylab("Inflation (%)") +
  geom_point(aes(x = `Energy, Non Administered Prices`/100, y = `inflation_2021M11`/100)) +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = `Energy, Non Administered Prices`/100, y = `inflation_2021M11`/100), 
              linetype = 2, method = "lm", color = "#F2A900") +
  geom_text_repel(aes(x = `Energy, Non Administered Prices`/100, y = `inflation_2021M11`/100, label = Geo))

2022M10 - Inflation

Code

prc_hicp_manr_2022M10 %>%
  inner_join(nrg_weights, by = "Geo") %>%
  arrange(-inflation_2022M10) %>%
  ggplot(.) + theme_minimal() + xlab("Share of energy (%)") + ylab("Inflation (%)") +
  geom_point(aes(x = `Energy, Non Administered Prices`/100, y = `inflation_2022M10`/100)) +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = `Energy, Non Administered Prices`/100, y = `inflation_2022M10`/100), 
              linetype = 2, method = "lm", color = "#F2A900") +
  geom_text_repel(aes(x = `Energy, Non Administered Prices`/100, y = `inflation_2022M10`/100, label = Geo))

Energy

List - Online

List. html

Code

i_g("bib/eurostat/NRG_aggregate.png")

List - COICOP

Code

coicop_NRG <- c("CP0451", "CP04521", "CP04522", "CP0453", "CP04541", "CP04549", "CP0455",
                "CP07221", "CP07222", "CP07223")

prc_hicp_manr %>%
  filter(coicop %in% coicop_NRG) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n(),
            `2022M03, France` = values[geo == "FR" & time == "2022M03"]) %>%
  print_table_conditional()

coicop	Coicop	Nobs	2022M03, France
CP0451	NA	13137	6.0
CP04521	NA	4548	41.3
CP04522	NA	4116	8.3
CP0453	NA	10055	84.0
CP04549	NA	4360	5.8
CP0455	NA	9548	52.9
CP07221	NA	5101	43.5
CP07222	NA	5040	30.5
CP07223	NA	3194	12.0

All

Code

load_data("eurostat/coicop_fr.RData")
prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "TOT_X_NRG", "TOT_X_NRG_FOOD_NP"),
         geo %in% c("EA")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.4, 0.90),
        legend.title = element_blank())

Code

load_data("eurostat/coicop.RData")

2010-

Code

load_data("eurostat/coicop_fr.RData")
prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "TOT_X_NRG", "TOT_X_NRG_FOOD_NP"),
         geo %in% c("EA")) %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.4, 0.90),
        legend.title = element_blank())

Code

load_data("eurostat/coicop.RData")

2016-

Code

load_data("eurostat/coicop_fr.RData")
prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "TOT_X_NRG", "TOT_X_NRG_FOOD_NP"),
         geo %in% c("EA")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  #scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.4, 0.90),
        legend.title = element_blank())

Code

load_data("eurostat/coicop.RData")

France, Germany, Italy, Netherlands, Europe

CP04521 - Town gas

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP04521",
         geo %in% c("FR", "BE", "IT", "EA19", "NL")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation, CP04521 - Natural gas and town gas (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 400, 20),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  geom_hline(yintercept = 0, linetype = "dashed")

CP0451 - Electricity

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0451",
         geo %in% c("FR", "DE", "IT", "EA19", "NL", "BE")) %>%
  month_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation, CP0451 - Electricité (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "6 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 400, 20),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_6flags +
  geom_hline(yintercept = 0, linetype = "dashed")

France, Germany, Italy, Spain, Europe

CP00

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT", "EA19", "US")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT", "EA19", "US")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_6flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

France, Germany, Belgium, Netherlands

Total

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "BE", "NL")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Core

Code

prc_hicp_manr %>%
  filter(coicop == "TOT_X_NRG_FOOD",
         geo %in% c("FR", "DE", "BE", "NL")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP04521 - Natural gas and town gas

Code

prc_hicp_manr %>%
  filter(coicop == "CP04521",
         geo %in% c("FR", "DE", "BE", "NL")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Natural gas and twon gas") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-300, 300, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

France, Germany, Italy, Spain, Europe

CP00

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT", "NL", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_6flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP041

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP041",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP041",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP041",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP041",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "1 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

1 year

Code

prc_hicp_manr %>%
  filter(coicop == "CP041",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= max(date) - years(1)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "1 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .2),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

AP_NRG

All

Code

prc_hicp_manr %>%
  filter(coicop == "AP_NRG",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "AP_NRG",
         geo %in% c("FR", "ES", "IT", "EA19")) %>%
  # group_by(geo) %>%
  # summarise(Nobs = n()) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "AP_NRG",
         geo %in% c("FR", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP04521 - Natural gas and town gas

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP04521",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP04521",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP04521",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation, CP04521 - Natural gas and town gas (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 200, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

CP011 - Food

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP011",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP011",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP011",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation, CP011 - Alimentation (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

CP0111 - Bread and cereals

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0111",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0111",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0111",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0112 - Meat

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0112",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0112",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0112",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0114 - Milk, cheese and eggs

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0114",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0114",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0114",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0117 - Vegetables

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0117",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0117",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0117",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP07332 - International flights

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP07332",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP07332",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP07332",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0722 - Fuels and lubricants for personal transport equipment

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0722",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0722",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0722",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP072 - Operation of personal transport equipment

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP072",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP072",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP072",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0451 - Electricity

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0451",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0451",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0451",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0452 - Gas

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0452",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0452",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0452",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0453 - Liquid fuels

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0453",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0453",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0453",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0454 - Solid fuels

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0454",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0454",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0454",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP045 - Electricity, gas and other fuels

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP045",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP045",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP045",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP0455 - Heat energy

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP0455",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0455",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0455",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

NRG

All

Code

prc_hicp_manr %>%
  filter(coicop == "NRG",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "NRG",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "NRG",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

SERV_TRA

All

Code

prc_hicp_manr %>%
  filter(coicop == "SERV_TRA",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "SERV_TRA",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "SERV_TRA",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

FUEL

All

Code

prc_hicp_manr %>%
  filter(coicop == "FUEL",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 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_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "FUEL",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "FUEL",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 10),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

France, Germany, Italy, United Kingdom

CP00

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "UK", "IT")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags(4) +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "UK", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "UK", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

France, Germany, Italy, Spain

TOT_X_NRG_FOOD - Core Inflation

All

Code

prc_hicp_manr %>%
  filter(coicop == "TOT_X_NRG_FOOD",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation (%)") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags(5) +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "TOT_X_NRG_FOOD",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation (%)") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "TOT_X_NRG_FOOD",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation (%)") +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "TOT_X_NRG_FOOD",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation (%)") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

1 year

Code

prc_hicp_manr %>%
  filter(coicop == "TOT_X_NRG_FOOD",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(1)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Core Inflation (%)") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .2),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_5flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP00

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags(4) +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP01 - Food and non-alcoholic beverages

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP01",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP01",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 40, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP02 - Alcoholic beverages, tobacco and narcotics

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP02",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2010-

Code

prc_hicp_manr %>%
  filter(coicop == "CP02",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP02",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP02",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 40, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP03 - Clothing and footwear

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP03",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP03",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 40, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP04 - Housing, water, electricity, gas and other fuels

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP04",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP04",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP05 - Furnishings

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP05",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

CP06 - Health

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP06",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 0.5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP06",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 0.5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2021-

Code

prc_hicp_manr %>%
  filter(coicop == "CP06",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 0.5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP06",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 40, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP063

2020-

Code

prc_hicp_manr %>%
  filter(coicop == "CP0612",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("CP0612 - Other medical products") +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-40, 20, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP063",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 40, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP07 - Transport

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP07",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2021-

Code

prc_hicp_manr %>%
  filter(coicop == "CP07",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP07",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP08 - Communications

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP08",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP08",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP09 - Recreation and culture

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP09",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP09",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP10 - Education

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP10",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP10",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP11 - Restaurants and hotels

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP11",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP11",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP12 - Miscellenaous

2016-

Code

prc_hicp_manr %>%
  filter(coicop == "CP12",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

2 years

Code

prc_hicp_manr %>%
  filter(coicop == "CP12",
         geo %in% c("FR", "DE", "ES", "IT")) %>%
  month_to_date %>%
  filter(date >= Sys.Date() - years(2)) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Countries

France

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04", "CP11", "CP01"),
         geo %in% c("FR")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

Slovakia

All

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04", "CP11", "CP01"),
         geo %in% c("SK")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

2018-

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04", "CP11", "CP01"),
         geo %in% c("SK")) %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 2),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

Germany

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04", "CP11", "CP01"),
         geo %in% c("DE")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04"),
         geo %in% c("DE")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

Spain

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04", "CP11", "CP01"),
         geo %in% c("ES")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.25, 0.8),
        legend.title = element_blank())

Greece

Code

prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "CP04", "CP11", "CP01"),
         geo %in% c("EL")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot(.) + geom_line(aes(x = date, y = values/100, color = Coicop)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2050, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  
  theme(legend.position = c(0.25, 0.2),
        legend.title = element_blank())

Euro area VS European Union

All

Code

prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo %in% c("EA", "EU")) %>%
  month_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) +
  theme(legend.position = c(0.55, 0.90),
        legend.title = element_blank())