source | dataset | Title | Download | Compile |
---|---|---|---|---|
eurostat | nrg_pc_203_v | Non-household consumption volumes of gas by consumption bands | 2024-10-08 | [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_h | Gas prices for industrial consumers - bi-annual data (until 2007) | 2024-11-05 | [2024-11-05] |
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] |
Non-household consumption volumes of gas by consumption bands
Data - Eurostat
Info
LAST_COMPILE
LAST_COMPILE |
---|
2024-11-05 |
Last
Code
%>%
nrg_pc_203_v group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
time | Nobs |
---|---|
2023 | 180 |
Info
Code
include_graphics("https://ec.europa.eu/eurostat/documents/4187653/14185664/Gas+and+Electricity+Prices+S1_2022.png")
nrg_cons
Code
%>%
nrg_pc_203_v left_join(nrg_cons, by = "nrg_cons") %>%
group_by(nrg_cons, Nrg_cons) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
nrg_cons | Nrg_cons | Nobs |
---|---|---|
GJ100000-999999 | Consumption from 100 000 GJ to 999 999 GJ - band I4 | 227 |
GJ_LT1000 | Consumption less than 1 000 GJ - band I1 | 227 |
GJ1000-9999 | Consumption from 1 000 GJ to 9 999 GJ -band I2 | 221 |
GJ10000-99999 | Consumption from 10 000 GJ to 99 999 GJ - band I3 | 221 |
GJ1000000-3999999 | Consumption from 1 000 000 GJ to 3 999 999 GJ - band I5 | 204 |
GJ_GE4000000 | Consumption 4 000 000 GJ or over - band I6 | 155 |
geo
Code
%>%
nrg_pc_203_v 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_v group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()
time | Nobs |
---|---|
2023 | 180 |
2022 | 188 |
2021 | 182 |
2020 | 187 |
2019 | 187 |
2018 | 175 |
2017 | 156 |
Example
France
Code
%>%
nrg_pc_203_v filter(geo == "FR") %>%
left_join(nrg_cons, by = "nrg_cons") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
year_to_date + geom_line(aes(x = date, y = values, color = Nrg_cons)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(1, 100, 1)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Germany
Code
%>%
nrg_pc_203_v filter(geo == "DE") %>%
left_join(nrg_cons, by = "nrg_cons") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
year_to_date + geom_line(aes(x = date, y = values, color = Nrg_cons)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(1, 100, 1)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Italy
Code
%>%
nrg_pc_203_v filter(geo == "IT") %>%
left_join(nrg_cons, by = "nrg_cons") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
year_to_date + geom_line(aes(x = date, y = values, color = Nrg_cons)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(1, 100, 1)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())
Spain
Code
%>%
nrg_pc_203_v filter(geo == "ES") %>%
left_join(nrg_cons, by = "nrg_cons") %>%
select_if(~ n_distinct(.) > 1) %>%
%>%
year_to_date + geom_line(aes(x = date, y = values, color = Nrg_cons)) +
ggplot theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(1, 100, 1)) +
theme(legend.position = c(0.45, 0.7),
legend.title = element_blank())