source | dataset | Title | Download | Compile |
---|---|---|---|---|
eurostat | nrg_pc_203_h | Gas prices for industrial consumers - bi-annual data (until 2007) | 2024-11-05 | [2024-11-01] |
ec | WOB | Weekly Oil Bulletin | 2024-08-25 | [2024-09-15] |
eurostat | ei_isen_m | Energy - monthly data | 2024-10-09 | [2024-11-05] |
eurostat | nrg_bal_c | Complete energy balances | 2024-10-08 | [2023-12-31] |
eurostat | nrg_pc_202 | Gas prices for household consumers - bi-annual data (from 2007 onwards) | 2024-10-08 | [2024-11-05] |
eurostat | nrg_pc_203 | Gas prices for non-household consumers - bi-annual data (from 2007 onwards) | 2024-10-08 | [2023-06-11] |
eurostat | nrg_pc_203_c | Gas prices components for non-household consumers - annual data | 2024-10-08 | [2024-11-05] |
eurostat | nrg_pc_203_v | Non-household consumption volumes of gas by consumption bands | 2024-10-08 | [2024-11-01] |
eurostat | nrg_pc_204 | Electricity prices for household consumers - bi-annual data (from 2007 onwards) | 2024-11-05 | [2024-11-01] |
eurostat | nrg_pc_205 | Electricity prices for non-household consumers - bi-annual data (from 2007 onwards) | 2024-10-08 | [2023-06-11] |
fred | energy | Energy | 2024-11-01 | [2024-11-01] |
iea | world_energy_balances_highlights_2022 | World Energy Balances Highlights (2022 edition) | 2023-04-24 | [2024-06-20] |
wb | CMO | World Bank Commodity Price Data (The Pink Sheet) | 2024-05-23 | [2024-06-20] |
wdi | EG.GDP.PUSE.KO.PP.KD | GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent) | 2024-09-18 | [2024-09-18] |
wdi | EG.USE.PCAP.KG.OE | Energy use (kg of oil equivalent per capita) | 2024-09-18 | [2024-09-18] |
yahoo | energy | Energy | 2024-11-05 | [2024-11-05] |
Gas prices for industrial consumers - bi-annual data (until 2007)
Data - Eurostat
Info
LAST_COMPILE
LAST_COMPILE |
---|
2024-11-05 |
Last
Code
%>%
nrg_pc_203_h group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(2) %>%
print_table_conditional()
time | Nobs |
---|---|
2007S2 | 1215 |
2007S1 | 1476 |
Info
consom
Code
%>%
nrg_pc_203_h left_join(consom, by = "consom") %>%
group_by(consom, Consom) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
consom | Consom | Nobs |
---|---|---|
4142050 | Industry - I1 (Annual consumption: 418.6 GJ; no load factor) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 3213 |
4142051 | Industry - I1 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 252 |
4142052 | Industry - I1 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 252 |
4142054 | Industry - I1 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 252 |
4142100 | Industry - I2 (Annual consumption: 4 186 GJ; load factor: 200 days) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 3600 |
4142101 | Industry - I2 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 252 |
4142102 | Industry - I2 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 252 |
4142104 | Industry - I2 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 252 |
4142150 | Industry - I3-1 (Annual consumption: 41 860 GJ; load factor: 200 days, 1 600 hours) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 5598 |
4142151 | Industry - I3-1 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 315 |
4142152 | Industry - I3-1 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 315 |
4142154 | Industry - I3-1 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 315 |
4142200 | Industry - I3-2 (Annual consumption: 41 860 GJ; load factor: 250 days, 4 000 hours) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 3393 |
4142201 | Industry - I3-2 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 315 |
4142202 | Industry - I3-2 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 315 |
4142204 | Industry - I3-2 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 315 |
4142250 | Industry - I4-1(Annual consumption: 418 600 GJ; load factor: 250 days, 4 000 hours) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 3429 |
4142251 | Industry - I4-1 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 315 |
4142252 | Industry - I4-1 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 315 |
4142254 | Industry - I4-1 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 315 |
4142300 | Industry - I4-2 (Annual consumption: 418 600 GJ; load factor: 330 days, 8 000 hours) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 2934 |
4142301 | Industry - I4-2 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 315 |
4142302 | Industry - I4-2 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 315 |
4142304 | Industry - I4-2 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 315 |
4142350 | Industry - I5 (Annual consumption: 4 186 000 GJ; load factor: 330 days, 8 000 hours) (for Belgium: fixed supply (non-erasable) for non-specific applications that can easily be substituted by residual fuel oils (CNE 1 P 1)) | 2070 |
4142351 | Industry - I5 (Belgium only: completely erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0 P 0.9)) | 63 |
4142352 | Industry - I5 (Belgium only: 50% erasable supply for non-specific applications that can easily be substituted by residual fuel oils (CNE 0.5 P 1)) | 63 |
4142354 | Industry - I5 (Belgium only: fixed supply (non-erasable) for specific applications that can not easily be substituted by residual fuel oils (CNE 1 P1.1)) | 63 |
currency
Code
%>%
nrg_pc_203_h left_join(currency, by = "currency") %>%
group_by(currency, Currency) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
currency | Currency | Nobs |
---|---|---|
EUR | Euro | 9906 |
NAC | National currency | 9906 |
NAT | National currency (former currencies of the euro area countries) | 9906 |
tax
Code
%>%
nrg_pc_203_h left_join(tax, by = "tax") %>%
group_by(tax, Tax) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
tax | Tax | Nobs |
---|---|---|
I_TAX | All taxes and levies included | 9906 |
X_TAX | Excluding taxes and levies | 9906 |
X_VAT | Excluding VAT and other recoverable taxes and levies | 9906 |
geo
Code
%>%
nrg_pc_203_h left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
time
Code
%>%
nrg_pc_203_h group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()
Band I1 : Consumption < 1 000 GJ
France, Germany, Italy, Spain
All
Code
%>%
nrg_pc_203_h filter(geo %in% c("FR", "DE", "IT", "ES"),
%in% c("I_TAX", "X_TAX"),
tax == "4142050",
consom == "EUR") %>%
currency left_join(geo, by = "geo") %>%
left_join(tax, by = "tax") %>%
%>%
semester_to_date left_join(colors, by = c("Geo" = "country")) %>%
+ geom_line(aes(x = date, y = values, color = color, linetype = Tax)) +
ggplot scale_color_identity() + theme_minimal() + add_8flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank()) +
xlab("") + ylab("Consumption < 1 000 GJ") +
scale_y_log10(breaks = seq(0.02, 0.2, 0.01),
labels = dollar_format(a = .01, pre = "", su = "€"))
Example
France
Log
Code
%>%
nrg_pc_203_h filter(geo == "FR",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Linear
Code
%>%
nrg_pc_203_h filter(geo == "FR",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Germany
Log
Code
%>%
nrg_pc_203_h filter(geo == "DE",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Linear
Code
%>%
nrg_pc_203_h filter(geo == "DE",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Italy
Log
Code
%>%
nrg_pc_203_h filter(geo == "IT",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Linear
Code
%>%
nrg_pc_203_h filter(geo == "IT",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Spain
Code
%>%
nrg_pc_203_h filter(geo == "ES",
== "EUR") %>%
currency left_join(tax, by = "tax") %>%
left_join(consom, by = "consom") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
semester_to_date + geom_line(aes(x = date, y = values, color = Consom, linetype = Tax)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 1, 0.01)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())