| source | dataset | .html | .RData |
|---|---|---|---|
| eurostat | prc_hicp_ctrb | 2024-11-01 | 2024-10-08 |
Contributions to euro area annual inflation (in percentage points)
Data - Eurostat
Info
Data on inflation
| source | dataset | .html | .RData |
|---|---|---|---|
| bis | CPI | 2024-07-01 | 2022-01-20 |
| ecb | CES | 2024-10-08 | 2024-01-12 |
| eurostat | nama_10_co3_p3 | 2024-11-05 | 2024-10-09 |
| eurostat | prc_hicp_cow | 2024-11-05 | 2024-10-08 |
| eurostat | prc_hicp_ctrb | 2024-11-01 | 2024-10-08 |
| eurostat | prc_hicp_inw | 2024-11-01 | 2024-11-05 |
| eurostat | prc_hicp_manr | 2024-11-01 | 2024-10-08 |
| eurostat | prc_hicp_midx | 2024-11-01 | 2024-11-05 |
| eurostat | prc_hicp_mmor | 2024-11-01 | 2024-10-08 |
| eurostat | prc_ppp_ind | 2024-11-01 | 2024-10-08 |
| eurostat | sts_inpp_m | 2024-06-24 | 2024-10-08 |
| eurostat | sts_inppd_m | 2024-10-09 | 2024-10-08 |
| eurostat | sts_inppnd_m | 2024-06-24 | 2024-10-08 |
| fred | cpi | 2024-11-01 | 2024-11-01 |
| fred | inflation | 2024-11-01 | 2024-11-01 |
| imf | CPI | 2024-06-20 | 2020-03-13 |
| oecd | MEI_PRICES_PPI | 2024-09-15 | 2024-04-15 |
| oecd | PPP2017 | 2024-04-16 | 2023-07-25 |
| oecd | PRICES_CPI | 2024-04-16 | 2024-04-15 |
| wdi | FP.CPI.TOTL.ZG | 2023-01-15 | 2024-09-18 |
| wdi | NY.GDP.DEFL.KD.ZG | 2024-09-18 | 2024-09-18 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-nov-05 |
Last
Code
prc_hicp_ctrb %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(2) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024M08 | 420 |
| 2024M07 | 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 | 272 |
| CP02 | Alcoholic beverages, tobacco and narcotics | 272 |
| CP03 | Clothing and footwear | 272 |
| CP04 | Housing, water, electricity, gas and other fuels | 272 |
| CP05 | Furnishings, household equipment and routine household maintenance | 272 |
| CP06 | Health | 272 |
| CP07 | Transport | 272 |
| CP08 | Communications | 272 |
| CP09 | Recreation and culture | 272 |
| CP10 | Education | 272 |
| CP11 | Restaurants and hotels | 272 |
| CP12 | Miscellaneous goods and services | 272 |
| FOOD | Food including alcohol and tobacco | 272 |
| SERV | Services (overall index excluding goods) | 272 |
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())