Supply, transformation and consumption of gas - monthly data

Data - Eurostat

Author

Info

LAST_DOWNLOAD

Code

tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/nrg_cb_gasm.RData")$mtime)) %>%
  print_table_conditional()

LAST_DOWNLOAD
2024-11-21

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code

nrg_cb_gasm %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2024M10	371

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()

nrg_bal	Nrg_bal	Nobs
IC_CAL_MG	Inland consumption - calculated as defined in MOS GAS	11261
IMP	Imports	11198
STK_CHG_MG	Stock changes - as defined in MOS GAS	10956
EXP	Exports	10790
IPRD	Indigenous production	10736
DL	Distribution losses	10580
TI_EHG_MAP	Transformation input - electricity and heat generation - main activity producers	9691
IC_OBS	Inland consumption - observed	9340
STATDIFF	Statistical differences	3872
TOS	Transfer from other sources	3567
INTMARB	International maritime bunkers	3442
FLARE	Flared	3128
VENT	Vented	3128
STK_CHG_CG	Change in stock - cushion gas	1336
FC_OTH	Final consumption - other sectors	752
FC_IND	Final consumption - industry sector	696

unit

List

Code

nrg_cb_gasm %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

unit	Unit	Nobs
TJ_GCV	Terajoule (gross calorific value - GCV)	59561
MIO_M3	Million cubic metres	44912

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.                      2052. -0.523
#  2 LV          4664.                      2563. -0.450
#  3 FI          8532.                      4992. -0.415
#  4 EE          1757.                      1029. -0.414
#  5 NL        132481.                     84806. -0.360
#  6 LU          2913.                      1873. -0.357
#  7 LT          7879.                      5080. -0.355
#  8 SE          4212.                      2730. -0.352
#  9 HU         40090.                     26021. -0.351
# 10 AT         33722.                     22012. -0.347
# # 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")