| source | dataset | .html | .RData |
|---|---|---|---|
| 2024-06-20 | 2024-06-18 |
HICP - item weights
Data - Eurostat
Info
Données sur l’inflation en France
| source | dataset | .html | .RData |
|---|---|---|---|
| 2024-06-19 | 2023-11-21 | ||
| 2024-06-19 | 2024-06-18 | ||
| 2024-06-19 | 2024-06-18 | ||
| 2024-06-19 | 2024-06-18 | ||
| 2024-06-19 | 2024-06-18 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-05-16 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-06-19 | ||
| 2024-06-19 | 2024-04-01 |
Data on inflation
| source | dataset | .html | .RData |
|---|---|---|---|
| 2024-06-19 | 2022-01-20 | ||
| 2024-06-19 | 2024-01-12 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-18 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-18 | ||
| 2024-06-20 | 2024-06-18 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-18 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-08 | ||
| 2024-06-20 | 2024-06-07 | ||
| 2024-06-18 | 2024-06-07 | ||
| 2024-06-18 | 2020-03-13 | ||
| 2024-06-19 | 2024-04-15 | ||
| 2024-04-16 | 2023-07-25 | ||
| 2024-04-16 | 2024-04-15 | ||
| 2023-01-15 | 2024-04-14 | ||
| 2024-04-14 | 2024-04-14 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-06-20 |
Last
Code
prc_hicp_inw %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(3) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024 | 18491 |
| 2023 | 18694 |
| 2022 | 18692 |
coicop
All
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n(),
`2022, France` = values[geo == "FR" & time == "2022"],
`2022, Germany` = values[geo == "DE" & time == "2022"]) %>%
print_table_conditional()2-digit
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(nchar(coicop) == 4) %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n(),
`2022, France` = values[geo == "FR" & time == "2022"],
`2022, Germany` = values[geo == "DE" & time == "2022"]) %>%
print_table_conditional()| coicop | Coicop | Nobs | 2022, France | 2022, Germany |
|---|---|---|---|---|
| CP00 | All-items HICP | 1190 | 1000.00 | 1000.00 |
| CP01 | Food and non-alcoholic beverages | 1190 | 165.55 | 126.57 |
| CP02 | Alcoholic beverages, tobacco and narcotics | 1190 | 44.94 | 44.96 |
| CP03 | Clothing and footwear | 1190 | 39.81 | 43.16 |
| CP04 | Housing, water, electricity, gas and other fuels | 1190 | 174.41 | 252.20 |
| CP05 | Furnishings, household equipment and routine household maintenance | 1190 | 59.28 | 60.90 |
| CP06 | Health | 1190 | 45.48 | 57.50 |
| CP07 | Transport | 1190 | 158.40 | 149.44 |
| CP08 | Communications | 1190 | 30.32 | 29.44 |
| CP09 | Recreation and culture | 1190 | 80.25 | 97.20 |
| CP10 | Education | 1190 | 3.76 | 9.72 |
| CP11 | Restaurants and hotels | 1190 | 73.73 | 39.42 |
| CP12 | Miscellaneous goods and services | 1190 | 124.05 | 89.49 |
| FOOD | Food including alcohol and tobacco | 1143 | 210.49 | 171.53 |
| FUEL | Liquid fuels and fuels and lubricants for personal transport equipment | 1141 | 47.34 | 53.47 |
| SERV | Services (overall index excluding goods) | 1068 | 435.93 | 440.98 |
3-digit
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(nchar(coicop) == 5) %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n(),
`2022, France` = values[geo == "FR" & time == "2022"],
`2022, Germany` = values[geo == "DE" & time == "2022"]) %>%
print_table_conditional()4-digit
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(nchar(coicop) == 6) %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n(),
`2022, France` = values[geo == "FR" & time == "2022"],
`2022, Germany` = values[geo == "DE" & time == "2022"]) %>%
print_table_conditional()5-digit
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(nchar(coicop) == 7) %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n(),
`2022, France` = values[geo == "FR" & time == "2022"],
`2022, Germany` = values[geo == "DE" & time == "2022"]) %>%
print_table_conditional()Other
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(nchar(coicop) > 7) %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n(),
`2022, France` = values[geo == "FR" & time == "2022"],
`2022, Germany` = values[geo == "DE" & time == "2022"]) %>%
print_table_conditional()geo
Code
prc_hicp_inw %>%
left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}time
Code
prc_hicp_inw %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}France
Table
Code
prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(geo == "FR",
substr(coicop, 1, 2) == "CP" ,
coicop != "CP00",
time %in% c("2017", "2023")) %>%
select(-geo) %>%
spread(time, values) %>%
print_table_conditional()2-digit
Code
`table1-2digit` <- prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(geo == "FR",
nchar(coicop) == 4,
substr(coicop, 1, 2) == "CP" ,
coicop != "CP00",
time %in% c("2017", "2023")) %>%
select(-geo) %>%
spread(time, values)
`table1-2digit` %>%
print_table_conditional()| freq | coicop | Coicop | 2017 | 2023 |
|---|---|---|---|---|
| A | CP01 | Food and non-alcoholic beverages | 160.04 | 161.90 |
| A | CP02 | Alcoholic beverages, tobacco and narcotics | 42.41 | 40.95 |
| A | CP03 | Clothing and footwear | 49.60 | 40.13 |
| A | CP04 | Housing, water, electricity, gas and other fuels | 157.96 | 164.05 |
| A | CP05 | Furnishings, household equipment and routine household maintenance | 58.53 | 55.61 |
| A | CP06 | Health | 44.59 | 42.25 |
| A | CP07 | Transport | 159.15 | 164.75 |
| A | CP08 | Communications | 31.85 | 27.58 |
| A | CP09 | Recreation and culture | 89.23 | 80.92 |
| A | CP10 | Education | 3.81 | 4.80 |
| A | CP11 | Restaurants and hotels | 83.10 | 99.18 |
| A | CP12 | Miscellaneous goods and services | 119.73 | 117.87 |
Code
`table1-2digit` %>%
gt::gt() %>%
gt::gtsave(filename = "prc_hicp_inw_files/figure-html/table1-2digit-1.png")3-digit: CP082_083
Code
`table1-3digit` <- prc_hicp_inw %>%
left_join(coicop, by = "coicop") %>%
filter(geo == "FR",
nchar(coicop) == 5 | coicop == "CP082_083",
substr(coicop, 1, 2) == "CP" ,
coicop != "CP00",
time %in% c("2017", "2023")) %>%
select(-geo) %>%
spread(time, values)
`table1-3digit` %>%
print_table_conditional()Code
`table1-3digit` %>%
gt::gt() %>%
gt::gtsave(filename = "prc_hicp_inw_files/figure-html/table1-3digit-1.png")CP082, CP083, CP08, CP091, CP053
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP0820", "CP0830", "CP08", "CP091", "CP053"),
geo %in% c("FR")) %>%
year_to_date %>%
left_join(coicop, by = "coicop") %>%
ggplot() + theme_minimal() + ylab("") + xlab("") +
geom_line(aes(x = date, y = values/1000, color = paste(coicop, Coicop))) +
#scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.005),
labels = percent_format(a = .1))
CP01, CP04, CP07
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP01", "CP04", "CP07"),
geo %in% c("FR")) %>%
year_to_date %>%
left_join(coicop, by = "coicop") %>%
ggplot() + theme_minimal() + ylab("") + xlab("") +
geom_line(aes(x = date, y = values/1000, color = Coicop)) +
#scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP05, CP09, CP11, CP12
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP05", "CP09", "CP11", "CP12"),
geo %in% c("FR")) %>%
year_to_date %>%
left_join(coicop, by = "coicop") %>%
ggplot() + theme_minimal() + ylab("") + xlab("") +
geom_line(aes(x = date, y = values/1000, color = Coicop)) +
#scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP02, CP03, CP06, CP08
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP02", "CP03", "CP06", "CP08"),
geo %in% c("FR")) %>%
year_to_date %>%
left_join(coicop, by = "coicop") %>%
ggplot() + theme_minimal() + ylab("") + xlab("") +
geom_line(aes(x = date, y = values/1000, color = Coicop)) +
#scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.73, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
Part Energie
2022
Code
prc_hicp_inw %>%
filter(time %in% c("2022"),
coicop %in% c("AP_NRG", "NRG", "CP045", "CP0722")) %>%
left_join(geo, by = "geo") %>%
mutate(values = round(values/10, 1)) %>%
select(Geo, coicop, values) %>%
spread(coicop, values) %>%
transmute(Geo,
`Energy, Non Adm` = NRG - AP_NRG,
`Electricity, gas and other fuels` = CP045,
`Fuels and lubricants` = CP0722,
`Energy, Adm` = AP_NRG,
Energy = NRG) %>%
arrange(-`Energy, Non Adm`) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}France, Germany, Italy
2020
Code
prc_hicp_inw %>%
filter(time == "2020",
geo %in% c("FR", "DE", "IT")) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, Geo, values) %>%
spread(Geo, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}2010
Code
prc_hicp_inw %>%
filter(time == "2010",
geo %in% c("FR", "DE", "IT")) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, Geo, values) %>%
spread(Geo, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}2000
Code
prc_hicp_inw %>%
filter(time == "2000",
geo %in% c("FR", "DE", "IT")) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, Geo, values) %>%
spread(Geo, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}France - 2020, 2010, 2000
2-digit
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("FR"),
nchar(coicop) == 4) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}3-digit
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("FR"),
nchar(coicop) %in% c(4, 5)) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}All
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("FR")) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Germany - 2020, 2010, 2000
2-digit
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("DE"),
nchar(coicop) == 4) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}3-digit
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("DE"),
nchar(coicop) %in% c(4,5)) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}All
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("DE")) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Euro Area - 2020, 2010, 2000
2-digit
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("EA"),
nchar(coicop) == 4) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}3-digit
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("EA"),
nchar(coicop) %in% c(4,5)) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}All
Code
prc_hicp_inw %>%
filter(time %in% c("2000", "2010", "2020"),
geo %in% c("EA")) %>%
left_join(coicop, by = "coicop") %>%
select(coicop, Coicop, time, values) %>%
spread(time, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}2020 - Housing
Javascript
Code
prc_hicp_inw %>%
filter(time %in% c("2020"),
coicop %in% c("CP04", "CP041", "CP043", "CP044", "CP045")) %>%
left_join(coicop, by = "coicop") %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, Coicop, values) %>%
spread(Coicop, values) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}2020 - Total / Rents Housing
Javascript
Code
prc_hicp_inw %>%
filter(time %in% c("2020"),
coicop %in% c("CP04", "CP041")) %>%
left_join(geo, by = "geo") %>%
mutate(values = round(values/10, 1)) %>%
select(Geo, coicop, values) %>%
spread(coicop, values) %>%
arrange(-CP041) %>%
rename(Housing = CP04, `Actual rentals` = CP041) %>%
mutate_at(vars(-Geo), funs(paste0(., "%"))) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}png
Code
i_g("bib/eurostat/prc_hicp_inw_ex2.png")
Rent Share in HICP VS Share of renters
Code
i_g("data/eurostat/ilc_lvho02_files/figure-html/rent-share-CP041-correlation-1.png")
France, Germany, Italy, Europe, Spain
CP04 - Housing, water, electricity, gas and other fuels
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP04"),
geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("Housing, water, electricity, gas and other fuels ") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_5flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP041 - Actual rentals for housing
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP041"),
geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("Actual rentals for housing") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_5flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP10 - Education
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP10"),
geo %in% c("FR", "DE", "ES", "IT", "EA19")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("CP10 - Education") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_5flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(a = .1))
France, Germany, Italy
CP0820
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP0820"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("CP0820") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(a = .1))
CP091
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP091"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("CP091") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(a = .1))
CP10 - Education
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP10"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("CP10 - Education") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(a = .1))
CP06 - Health
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP06"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("Health ") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP061 - Medical products, appliances and equipment
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP061"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("Health ") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP062 - Out-patient services
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP062"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("Health ") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.01),
labels = percent_format(a = 1))
CP063 - Hospital Services
Code
prc_hicp_inw %>%
filter(coicop %in% c("CP063"),
geo %in% c("FR", "DE", "IT")) %>%
year_to_date %>%
left_join(geo, by = "geo") %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot() + theme_minimal() + ylab("Health ") + xlab("") +
geom_line(aes(x = date, y = values, color = color)) + add_3flags +
scale_color_identity() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 1, 0.001),
labels = percent_format(a = .1))