Info
LAST_DOWNLOAD
Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/nrg_cb_gasm.RData")$mtime)) %>%
print_table_conditional()
Last
Code
nrg_cb_gasm %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()
nrg_bal
Code
nrg_cb_gasm %>%
left_join(nrg_bal, by = "nrg_bal") %>%
group_by(nrg_bal, Nrg_bal) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
IC_CAL_MG |
Inland consumption - calculated as defined in MOS GAS |
11442 |
IMP |
Imports |
11379 |
STK_CHG_MG |
Stock changes - as defined in MOS GAS |
11137 |
EXP |
Exports |
10971 |
IPRD |
Indigenous production |
10913 |
DL |
Distribution losses |
10748 |
TI_EHG_MAP |
Transformation input - electricity and heat generation - main activity producers |
9871 |
IC_OBS |
Inland consumption - observed |
9520 |
STATDIFF |
Statistical differences |
4052 |
TOS |
Transfer from other sources |
3744 |
INTMARB |
International maritime bunkers |
3609 |
FLARE |
Flared |
3266 |
VENT |
Vented |
3266 |
STK_CHG_CG |
Change in stock - cushion gas |
1500 |
FC_OTH |
Final consumption - other sectors |
844 |
FC_IND |
Final consumption - industry sector |
778 |
unit
List
Code
nrg_cb_gasm %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
TJ_GCV |
Terajoule (gross calorific value - GCV) |
60848 |
MIO_M3 |
Million cubic metres |
46192 |
geo
Code
nrg_cb_gasm %>%
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_cb_gasm %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()
Examples
https://ec.europa.eu/eurostat/en/web/products-eurostat-news/w/DDN-20230419-1
Natural gas consumption reduction (August 2022-March 2023 vs. 2017-2022 average)
Code
include_graphics("https://ec.europa.eu/eurostat/documents/4187653/16179929/Natural_Gas_Consumption_Reduction.png")
Replicate
Average August 2022
Code
nrg_cb_gasm %>%
month_to_date %>%
filter(unit == "TJ_GCV",
nrg_bal == "IC_CAL_MG") %>%
mutate(period = case_when(date >= as.Date("2022-08-01") ~ "August 2022 - March 2023",
(date >= as.Date("2021-08-01") & date <= as.Date("2022-03-01")) |
(date >= as.Date("2020-08-01") & date <= as.Date("2021-03-01")) |
(date >= as.Date("2019-08-01") & date <= as.Date("2020-03-01")) |
(date >= as.Date("2018-08-01") & date <= as.Date("2019-03-01")) |
(date >= as.Date("2017-08-01") & date <= as.Date("2018-03-01")) ~ "2017-2022",
TRUE ~ NA)) %>%
filter(!is.na(period)) %>%
group_by(geo, period) %>%
summarise(mean = mean(values)) %>%
spread(period, mean) %>%
mutate(change = `August 2022 - March 2023`/`2017-2022`-1) %>%
arrange(change)
# # A tibble: 39 × 4
# # Groups: geo [39]
# geo `2017-2022` `August 2022 - March 2023` change
# <chr> <dbl> <dbl> <dbl>
# 1 MD 4304. 2099. -0.512
# 2 LV 4664. 2632. -0.436
# 3 FI 8532. 5004. -0.414
# 4 EE 1757. 1059. -0.397
# 5 SE 4212. 2735. -0.351
# 6 NL 132481. 86993. -0.343
# 7 LU 2913. 1942. -0.333
# 8 LT 7879. 5258. -0.333
# 9 FR 167551. 112466. -0.329
# 10 HU 40090. 27364. -0.317
# # ℹ 29 more rows
2017-2022
Code
nrg_cb_gasm %>%
month_to_date %>%
filter(date >= as.Date("2017-01-01"),
date <= as.Date("2022-12-31"),
unit == "TJ_GCV",
nrg_bal == "IC_CAL_MG") %>%
group_by(geo) %>%
summarise(energy = mean(values))
# # A tibble: 39 × 2
# geo energy
# <chr> <dbl>
# 1 AL 188.
# 2 AT 28594.
# 3 BE 56622.
# 4 BG 9823.
# 5 CY 0
# 6 CZ 27524.
# 7 DE 286278.
# 8 DK 9525.
# 9 EE 1445.
# 10 EL 17878.
# # ℹ 29 more rows
EU natural gas consumption (calculated), 2017-2023
Code
include_graphics("https://ec.europa.eu/eurostat/documents/4187653/16179929/Natural_Gas_Consumption_Calculated_2017-2023.png")