Contributions to euro area annual inflation (in percentage points)

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData

eurostat

prc_hicp_ctrb

 2024-06-20 2024-06-08

Data on inflation

source dataset .html .RData

bis

CPI

 2024-06-19 2022-01-20

ecb

CES

 2024-06-19 2024-01-12

eurostat

nama_10_co3_p3

 2024-06-20 2024-06-08

eurostat

prc_hicp_cow

 2024-06-24 2024-06-08

eurostat

prc_hicp_ctrb

 2024-06-20 2024-06-08

eurostat

prc_hicp_inw

 2024-06-20 2024-06-23

eurostat

prc_hicp_manr

 2024-06-20 2024-06-08

eurostat

prc_hicp_midx

 2024-06-20 2024-06-23

eurostat

prc_hicp_mmor

 2024-06-20 2024-06-18

eurostat

prc_ppp_ind

 2024-06-20 2024-06-08

eurostat

sts_inpp_m

 2024-06-20 2024-06-18

eurostat

sts_inppd_m

 2024-06-20 2024-06-08

eurostat

sts_inppnd_m

 2024-06-20 2024-06-08

fred

cpi

 2024-06-20 2024-06-07

fred

inflation

 2024-06-18 2024-06-07

imf

CPI

 2024-06-20 2020-03-13

oecd

MEI_PRICES_PPI

 2024-06-20 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 2024-04-14

wdi

NY.GDP.DEFL.KD.ZG

 2024-04-14 2024-04-14

LAST_COMPILE

LAST_COMPILE
2024-jui-24

Last

Code 
prc_hicp_ctrb %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2024M04 420
2024M03 420

coicop

All

Code 
prc_hicp_ctrb %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

2-digit

Code 
prc_hicp_ctrb %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  filter(nchar(coicop) == 4) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
coicop Coicop Nobs
CP01 Food and non-alcoholic beverages 268
CP02 Alcoholic beverages, tobacco and narcotics 268
CP03 Clothing and footwear 268
CP04 Housing, water, electricity, gas and other fuels 268
CP05 Furnishings, household equipment and routine household maintenance 268
CP06 Health 268
CP07 Transport 268
CP08 Communications 268
CP09 Recreation and culture 268
CP10 Education 268
CP11 Restaurants and hotels 268
CP12 Miscellaneous goods and services 268
FOOD Food including alcohol and tobacco 268
SERV Services (overall index excluding goods) 268

3-digit

Code 
prc_hicp_ctrb %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  filter(nchar(coicop) == 5) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

4-digit

Code 
prc_hicp_ctrb %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  filter(nchar(coicop) == 6) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

time

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

Main decomposition

2019-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("FOOD", "NRG", "IGD_NNRG", "SERV"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  mutate(Coicop_factor = factor(coicop, levels = c("SERV", "IGD_NNRG", "NRG", "FOOD"),
                         labels = c("Services", "Manufactured goods",
                                    "Energy", "Food"))) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop_factor), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_fill_manual(values = c("orange", "red", "blue", "darkgreen")) +
  scale_x_date(breaks ="3 months",
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.75),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("FOOD", "NRG", "IGD_NNRG", "SERV"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(Coicop_factor = factor(coicop, levels = c("SERV", "IGD_NNRG", "NRG", "FOOD"),
                         labels = c("Services", "Manufactured goods",
                                    "Energy", "Food"))) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop_factor), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_fill_manual(values = c("orange", "red", "blue", "darkgreen")) +
  scale_x_date(breaks ="3 months",
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.75),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2021-

Code 
line <- prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "TOT_X_NRG_FOOD"),
         geo == "EA") %>%
  month_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  mutate(line_en = factor(coicop, levels = c("CP00", "TOT_X_NRG_FOOD"),
                          labels = c("HICP inflation", "Core inflation (without energy, food)")))

prc_hicp_ctrb %>%
  filter(coicop %in% c("FOOD", "NRG", "IGD_NNRG", "SERV"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  mutate(Coicop_factor = factor(coicop, levels = c("SERV", "IGD_NNRG", "NRG", "FOOD"),
                         labels = c("Services", "Manufactured goods",
                                    "Energy", "Food"))) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop_factor), alpha = 1) +
  geom_line(data = line, aes(linetype = line_en), size = 1.2) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_fill_manual(values = c("orange", "red", "blue", "darkgreen")) +
  scale_x_date(breaks = "2 months",
               expand = c(.01, 0), date_labels = "%b %Y") +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2022-

Code 
line <- prc_hicp_manr %>%
  filter(coicop %in% c("CP00", "TOT_X_NRG_FOOD"),
         geo == "EA") %>%
  month_to_date %>%
  filter(date >= as.Date("2022-01-01")) %>%
  mutate(line_en = factor(coicop, levels = c("CP00", "TOT_X_NRG_FOOD"),
                          labels = c("HICP inflation", "Core inflation (without energy, food)")))

prc_hicp_ctrb %>%
  filter(coicop %in% c("FOOD", "NRG", "IGD_NNRG", "SERV"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2022-01-01")) %>%
  mutate(Coicop_factor = factor(coicop, levels = c("SERV", "IGD_NNRG", "NRG", "FOOD"),
                         labels = c("Services", "Manufactured goods",
                                    "Energy", "Food"))) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop_factor), alpha = 1) +
  geom_line(data = line, aes(linetype = line_en), size = 1.2) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_fill_manual(values = c("orange", "red", "blue", "darkgreen")) +
  scale_x_date(breaks = "1 month",
               expand = c(.01, 0), date_labels = "%b %Y") +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Last: Biggest contributions to inflation

Only Last

Code 
last_time <- prc_hicp_ctrb %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  pull(time)

prc_hicp_ctrb %>%
  filter(time == last_time) %>%
  select_if(function(col) length(unique(col)) > 1) %>%
  left_join(coicop, by = "coicop") %>%
  arrange(-values) %>%
  select(coicop, Coicop, everything()) %>%
  print_table_conditional()

Average December 2021 - March 2022

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M12", "2021M01", "2021M02", "2022M03")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(values = mean(values)) %>%
  arrange(-values) %>%
  print_table_conditional()

Inflation since October 2021

Energy Aggregate

Code 
i_g("https://fgeerolf.com/bib/eurostat/NRG_aggregate.png")

Stack up

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452", "CP0453", "CP0454", "CP0455", "CP0722"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation, Energy Aggregate") +
  scale_x_date(breaks ="3 months",
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.75),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

All

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP045", "CP072", "CP01", "CP111"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  spread(coicop, values) %>%
  transmute(time,
            `Energy, Transport` = CP045 + CP072,
            `Energy, Transport, Food` = CP045 + CP072 + CP01,
            `Energy, Transport, Food, Catering` = CP045 + CP072 + CP01 + CP111) %>%
  gather(Coicop, values, -time) %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP045", "CP072", "CP01", "CP111"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  spread(coicop, values) %>%
  transmute(time,
            `Energy, Transport` = CP045 + CP072,
            `Energy, Transport, Food` = CP045 + CP072 + CP01,
            `Energy, Transport, Food, Catering` = CP045 + CP072 + CP01 + CP111) %>%
  gather(Coicop, values, -time) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP045", "CP072", "CP01", "CP111"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  spread(coicop, values) %>%
  transmute(time,
            `Energy, Transport` = CP045 + CP072,
            `Energy, Transport, Food` = CP045 + CP072 + CP01,
            `Energy, Transport, Food, Catering` = CP045 + CP072 + CP01 + CP111) %>%
  gather(Coicop, values, -time) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "3 months"),
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

New Aggregate

Composition

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP045", "CP072", "CP01", "CP11"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP045", "CP072", "CP01", "CP11"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2019-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP045", "CP072", "CP01", "CP11"),
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="3 months",
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

All

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP111) %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

Bars

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP111) %>%
  select(-coicop, -freq) %>%
  spread(Coicop, values) %>%
  month_to_date %>%
  mutate(`Super Core inflation` = `All-items HICP`-`Energy, Transport, Food, Catering`) %>%
  gather(Coicop, values, -date) %>%
  filter(Coicop != "All-items HICP") %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2017-

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP111) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

Bars

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP111) %>%
  select(-coicop, -freq) %>%
  spread(Coicop, values) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  mutate(`Super Core inflation` = `All-items HICP`-`Energy, Transport, Food, Catering`) %>%
  gather(Coicop, values, -date) %>%
  filter(Coicop != "All-items HICP") %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  geom_line(aes(x = as.Date("2022-02-24")), linetype = "dashed") +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2020-

Lines

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP111) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "3 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Bars

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP11) %>%
  select(-coicop, -freq) %>%
  spread(Coicop, values) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(`Super Core inflation` = `All-items HICP`-`Energy, Transport, Food, Restaurants&Hotels`) %>%
  gather(Coicop, values, -date) %>%
  filter(Coicop != "All-items HICP") %>%
  ggplot(., aes(x = date, y = values/100)) +
  geom_col(aes(fill = Coicop), alpha = 1) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "3 months"),
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2022-

Lines

Code 
prc_hicp_manr %>%
  filter(coicop == "CP00",
         geo == "EA") %>%
  select(-geo, -unit) %>%
  left_join(coicop, by = "coicop") %>%
  bind_rows(prc_hicp_ctrb_CP045_CP072_CP01_CP11) %>%
  month_to_date %>%
  filter(date >= as.Date("2022-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = Sys.Date(), by = "1 month"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

CP01, CP045, CP072, CP111

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP01", "CP045", "CP072", "CP111")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.2),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP01", "CP045", "CP072", "CP111")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.2),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP01", "CP045", "CP072", "CP111")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.2),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank())

Table

All

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M10", "2022M09")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(difference = `2022M09` - `2021M10`) %>%
  select(coicop, Coicop, everything()) %>%
  arrange(-difference) %>%
  print_table_conditional()

Only CP - 20 first

All

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M10", "2022M09"),
         substr(coicop, 1, 2) == "CP") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(difference = `2022M09` - `2021M10`) %>%
  select(coicop, Coicop, everything()) %>%
  arrange(-difference) %>%
  filter(difference > 0.1) %>%
  print_table_conditional()

2-digit

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M10", "2022M09"),
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 4) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(difference = `2022M09` - `2021M10`) %>%
  select(coicop, Coicop, everything()) %>%
  arrange(-`2022M09`) %>%
  filter(`2022M09` > 0.1) %>%
  print_table_conditional()
coicop Coicop 2021M10 2022M09 difference
CP04 Housing, water, electricity, gas and other fuels 1.40 3.71 2.31
CP01 Food and non-alcoholic beverages 0.32 2.30 1.98
CP07 Transport 1.45 1.58 0.13
CP11 Restaurants and hotels 0.20 0.70 0.50
CP05 Furnishings, household equipment and routine household maintenance 0.15 0.52 0.37
CP09 Recreation and culture 0.12 0.37 0.25
CP12 Miscellaneous goods and services 0.20 0.33 0.13
CP02 Alcoholic beverages, tobacco and narcotics 0.11 0.18 0.07
CP03 Clothing and footwear 0.04 0.17 0.13

3-digit

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M10", "2022M09"),
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 5) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(difference = `2022M09` - `2021M10`) %>%
  select(coicop, Coicop, everything()) %>%
  arrange(-`2022M09`) %>%
  filter(`2022M09` > 0.1) %>%
  print_table_conditional()
coicop Coicop 2021M10 2022M09 difference
CP045 Electricity, gas and other fuels 1.19 3.37 2.18
CP011 Food 0.29 2.15 1.86
CP072 Operation of personal transport equipment 1.16 1.11 -0.05
CP111 Catering services 0.16 0.44 0.28
CP071 Purchase of vehicles 0.17 0.33 0.16
CP112 Accommodation services 0.04 0.25 0.21
CP051 Furniture and furnishings, carpets and other floor coverings 0.08 0.20 0.12
CP056 Goods and services for routine household maintenance 0.02 0.16 0.14
CP121 Personal care 0.04 0.16 0.12
CP012 Non-alcoholic beverages 0.03 0.15 0.12
CP031 Clothing 0.03 0.14 0.11
CP041 Actual rentals for housing 0.09 0.14 0.05
CP093 Other recreational items and equipment, gardens and pets 0.06 0.14 0.08
CP043 Maintenance and repair of the dwelling 0.06 0.13 0.07
CP073 Transport services 0.12 0.13 0.01
CP021 Alcoholic beverages 0.03 0.11 0.08

4-digit

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M10", "2022M09"),
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 6) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(difference = `2022M09` - `2021M10`) %>%
  select(coicop, Coicop, everything()) %>%
  arrange(-`2022M09`) %>%
  filter(`2022M09` > 0.1) %>%
  print_table_conditional()
coicop Coicop 2021M10 2022M09 difference
CP0452 Gas 0.44 1.45 1.01
CP0451 Electricity 0.43 1.15 0.72
CP0722 Fuels and lubricants for personal transport equipment 1.02 0.82 -0.20
CP0453 Liquid fuels 0.29 0.54 0.25
CP0112 Meat 0.07 0.50 0.43
CP0114 Milk, cheese and eggs 0.05 0.44 0.39
CP0111 Bread and cereals 0.06 0.42 0.36
CP1111 Restaurants, cafés and the like 0.14 0.42 0.28
CP0711 Motor cars 0.15 0.31 0.16
CP0117 Vegetables 0.02 0.25 0.23
CP0511 Furniture and furnishings 0.08 0.18 0.10
CP0723 Maintenance and repair of personal transport equipment 0.08 0.16 0.08
CP0115 Oils and fats 0.04 0.14 0.10
CP0455 Heat energy 0.03 0.14 0.11
CP0113 Fish and seafood 0.03 0.13 0.10
CP0411 Actual rentals paid by tenants 0.08 0.13 0.05
CP0312 Garments 0.03 0.12 0.09
CP0561 Non-durable household goods 0.01 0.12 0.11
CP0733 Passenger transport by air 0.08 0.12 0.04
CP1213 Other appliances, articles and products for personal care 0.02 0.11 0.09

5-digit

Code 
prc_hicp_ctrb %>%
  filter(time %in% c("2021M10", "2022M09"),
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 7) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(coicop, by = "coicop") %>%
  mutate(difference = `2022M09` - `2021M10`) %>%
  select(coicop, Coicop, everything()) %>%
  arrange(-`2022M09`) %>%
  filter(`2022M09` > 0.1) %>%
  print_table_conditional()
coicop Coicop 2021M10 2022M09 difference
CP04521 Natural gas and town gas 0.41 1.42 1.01
CP07221 Diesel 0.46 0.50 0.04
CP07222 Petrol 0.54 0.29 -0.25
CP11111 Restaurants, cafés and dancing establishments 0.09 0.28 0.19
CP11201 Hotels, motels, inns and similar accommodation services 0.04 0.24 0.20
CP01145 Cheese and curd 0.02 0.19 0.17
CP07111 New motor cars 0.11 0.17 0.06
CP01113 Bread 0.03 0.16 0.13
CP05111 Household furniture 0.07 0.16 0.09
CP01171 Fresh or chilled vegetables other than potatoes and other tubers -0.01 0.15 0.16
CP01127 Dried, salted or smoked meat 0.02 0.14 0.12
CP07112 Second-hand motor cars 0.04 0.14 0.10
CP11112 Fast food and take away food services 0.05 0.14 0.09
CP01114 Other bakery products 0.01 0.12 0.11
CP01124 Poultry 0.02 0.12 0.10
CP07332 International flights 0.08 0.11 0.03
CP12132 Articles for personal hygiene and wellness, esoteric products and beauty products 0.02 0.11 0.09

Main components since

Energy Bills - “AP_NRG”, “NRG”, “CP045”, “CP0722”

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("NRG", "CP045", "CP0722")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("AP_NRG", "NRG", "CP045", "CP0722")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("AP_NRG", "NRG", "CP045", "CP0722")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Main

CP01, CP02, CP03

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP01", "CP02", "CP03")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP01", "CP02", "CP03")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP01", "CP02", "CP03")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

CP04, CP05, CP06

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP05", "CP06")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP05", "CP06")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP05", "CP06")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-10-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.2),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

CP07, CP08, CP09

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP07", "CP08", "CP09")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP07", "CP08", "CP09")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

CP10, CP11, CP12

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP10", "CP11", "CP12")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = paste(coicop, Coicop))) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP10", "CP11", "CP12")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

CP121, CP122, CP123, CP124, CP125, CP126, CP127

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP121", "CP122", "CP123", "CP124", "CP125", "CP126", "CP127")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = paste(coicop, Coicop))) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="5 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.75),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP121", "CP122", "CP123", "CP124", "CP125", "CP126")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.05),
                     labels = percent_format(accuracy = .01)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

Fuels

CP01, CP04, CP045

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP045", "CP01")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP045", "CP01")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP045", "CP01")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

CP01, CP04, CP041

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP041", "CP01")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP041", "CP01")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

CP04, CP045

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP045")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP04", "CP045")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

CP0451, CP0452, CP0453

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452", "CP0453")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452", "CP0453")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452", "CP0453")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

CP0451, CP0452

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0451", "CP0452")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

CP0454, CP0455

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0454", "CP0455")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.01),
                     labels = percent_format(accuracy = .01)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0454", "CP0455")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.01),
                     labels = percent_format(accuracy = .01)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Food

CP011, CP012, CP01

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP011", "CP012", "CP01")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP011", "CP012", "CP01")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP011", "CP012", "CP01")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank())

CP0111, CP0112, CP0113, CP0114, CP0115

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0111", "CP0112", "CP0113", "CP0114", "CP0115")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0111", "CP0112", "CP0113", "CP0114", "CP0115")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0111", "CP0112", "CP0113", "CP0114", "CP0115")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank())

CP0116, CP0117, CP0118, CP0119, CP012

All

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0116", "CP0117", "CP0118", "CP0119", "CP012")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

2017-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0116", "CP0117", "CP0118", "CP0119", "CP012")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(coicop, by = "coicop") %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks ="1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

2020-

Code 
prc_hicp_ctrb %>%
  filter(coicop %in% c("CP0116", "CP0117", "CP0118", "CP0119", "CP012")) %>%
  month_to_date %>%
  left_join(coicop, by = "coicop") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values/100, color = Coicop)) +
  theme_minimal() + xlab("") + ylab("Contributions to inflation") +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-10-01"), to = as.Date("2100-10-01"), by = "6 months"),
               labels = date_format("%Y %b")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 0.05),
                     labels = percent_format(accuracy = .01)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())