Utertiguk

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Anguniagaq 13: Silap pissusia suliniuteqarfigalugu

DCE-mit nukissiuutinik atuinermi silaannarmik mingutsitsisumik aniatitsinermik naatsorsuutit

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GS Nukissiuutinik atuinermi silaannarmik mingutsitsisumik aniatitsineq suliaqarfik malillugu

# Import
ENX1EM1_raw <-
  read_csv(
    paste0("https://bank.stat.gl:443/sq/53af71be-9a37-48a3-983c-e98720f29d40.csv", "?lang=", language),
    locale = locale(encoding = "latin1"))

# Transform
ENX1EM1 <- 
  ENX1EM1_raw %>% 
  rename(
    "gas"    = 1,
    "sector" = 2,
    "time"   = 3,
    "value"  = 4
  ) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    time   = time %>% make_date(),
    value  = value / 1000
  )

# Plot
ENX1EM1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gas
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg13$figs$fig1$title[language],
    subtitle =  ENX1EM1[[2]][1],
    x        = " ",
    y        = sdg13$figs$fig1$y_lab[language],
    fill     = sdg13$figs$fig1$fill[language],
    caption  = sdg13$figs$fig1$cap[language]
  )

Kisitsisaataasivik

Periaaseq

Rapport

Tabel nuisiguk

# Transform
ENX1EM1 <- 
  ENX1EM1_raw %>% 
  rename(
    "gas"    = 1,
    "sector" = 2,
    "time"   = 3,
    "value"  = 4
  ) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    value  = value / 1000,
    value  = round(value, 3)
  ) %>% 
  filter(time >= year(Sys.time()) - 8) %>% 
  spread(1, ncol(.)) %>% 
  arrange(desc(time))

# Table
ENX1EM1 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(year_col = " ") %>% 
  pack_rows(index = table(ENX1EM1[[1]])) %>% 
  add_footnote(sdg13$figs$fig1$foot[language], notation = "symbol")

	SF6	HFC	Lattergassi N2O	Metani CH4	Kuldioxidi CO2
Gassimik silaannarmik kissattoortitsisussamik aniatitsineq tamakkiisumik
2023	0,003	11,49	13,41	15,9	611
2022	0,003	12,20	12,30	16,1	589
2021	0,003	13,02	10,19	16,2	548
2020	0,003	12,94	10,02	16,1	552
2019	0,003	11,14	9,53	16,0	528
2018	0,003	9,76	8,71	16,1	526
2017	0,003	10,09	8,52	15,9	538
* CO2-mik aniatitsineq tonsinngorlugu tuusintikkaartunik.

# Import
ENX1EM1_raw <-
  read_csv(
    paste0("https://bank.stat.gl:443/sq/ca6d79eb-6230-4f07-ba49-c486f6e1edd4.csv", "?lang=", language),
    locale = locale(encoding = "latin1"))


# Transform
ENX1EM1 <-
  ENX1EM1_raw %>% 
  rename(
    "time"   = 1,
    "gas"    = 2,
    "sector" = 3,
    "value"  = 4
  ) %>% 
  filter(sector != unique(ENX1EM1_raw[[3]])[5]) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    sector = sector %>% str_remove_all("[1-5]|\\.") %>% trimws(),
    sector = sector %>% fct_inorder(),
    value  = value / 1000
  )


ENX1EM1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gas
  )) +
  geom_area() +
  facet_wrap(~ sector, scales = "free") +
   scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title   = sdg13$figs$fig2$title[language],
    x       = " ",
    y       = sdg13$figs$fig2$y_lab[language],
    fill    = str_to_title(colnames(ENX1EM1_raw)[2]),
    caption = sdg13$figs$fig2$cap[language]
  )

Kisitsisaataasivik

Periaaseq

Rapport

Tabel nuisiguk

# Transform
ENX1EM1 <-
  ENX1EM1_raw %>% 
  rename(
    "time"   = 1,
    "gas"    = 2,
    "sector" = 3,
    "value"  = 4
  ) %>% 
  filter(
    sector != unique(ENX1EM1_raw[[3]])[5],
    time >= year(Sys.time())- 8,
    value != 0) %>% 
  mutate(
    gas    = gas %>% fct_inorder() %>% fct_rev(),
    sector = sector %>% str_remove_all("[1-5]|\\.") %>% trimws(),
    sector = sector %>% fct_inorder(),
    value  = value / 1000
  ) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(1, ncol(.)) %>% 
  arrange(sector)

# Table
ENX1EM1 %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(ENX1EM1[[2]])) %>% 
  add_footnote(sdg13$figs$fig2$foot[language],
               notation = "symbol")

	2017	2018	2019	2020	2021	2022	2023
Nukimmik atuineq
Lattergassi N2O	2,096	2,039	2,045	2,092	2,093	2,291	2,331
Metani CH4	1,369	1,387	1,378	1,442	1,430	1,501	1,515
Kuldioxidi CO2	532,476	520,309	522,563	545,965	542,840	583,722	605,406
Suliffissuaqarnermi atortussiassat kemiimi akulerunnerat
SF6	0,003	0,003	0,003	0,003	0,003	0,003	0,003
HFC	10,094	9,761	11,144	12,944	13,023	12,204	11,487
Lattergassi N2O	0,001	0,001	0,001	0,001	0,001	0,001	0,001
Metani CH4	0,001	0,001	0,001	0,001	0,001	0,001	0,001
Kuldioxidi CO2	0,721	0,949	1,014	0,854	0,715	0,718	0,745
Nunalerineq
Lattergassi N2O	1,628	1,433	2,093	2,207	2,375	4,250	5,035
Metani CH4	7,086	7,234	7,086	7,197	7,225	7,052	6,907
Kuldioxidi CO2	0,004	0,004	0,004	0,004	0,004	0,004	0,004
Eqqakkanik aqutsineq
Lattergassi N2O	4,750	5,185	5,337	5,672	5,667	5,712	5,999
Metani CH4	7,477	7,478	7,483	7,488	7,498	7,506	7,509
Kuldioxidi CO2	3,385	3,409	3,427	3,450	3,474	3,493	3,511
* CO2-mik aniatitsineq 1000 tonsinngorlugu

Gassimik silaannarmik kissattoortitsisussamik nukissiuutinik atuinernit anitsinermit atuinermi kisitsisit pingaarnerit

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GS Gassimik silaannarmik kissattoortitsisussamik nukissiuutinik atuinernit anitsinermit atuinermi kisitsisit pingaarnerit

ENX6KEY_raw <- 
  statgl_url("ENX6KEY", lang = "da") |> 
  statgl_fetch(
    "key figure" = 16:18,
    time = px_all(),
    .col_code = T
  ) |> 
  as_tibble()

# Transform
ENX6KEY <- 
  ENX6KEY_raw |> 
  rename(key = `key figure`) |> 
  mutate(time = as.numeric(time))



# Plot
ENX6KEY |> 
  ggplot(aes(
    x = time,
    y = value,
    color = key
  )) +
  geom_line(size = 2) +
  facet_wrap(~ key, nrow = 3, scales = "free_y") +
  theme_statgl() +
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg13$figs$fig5$title[language],
    subtitle = sdg13$figs$fig5$sub[language],
    y        = " ",
    x        = " ",
    caption  = sdg13$figs$fig5$cap[language]
  )

Kisitsisaataasivik

Tabel nuisiguk

#Table
ENX6KEY %>% 
  filter(time >= year(Sys.time()) - 8) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(time, value) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE)

	2017	2018	2019	2020	2021	2022	2023
Faktisk emission i alt [1.000 ton CO2e]	535,7	523,4	525,7	549,4	546,2	587,4	609,1
Faktisk emission i alt pr. indbygger [ton CO2e]	9,5	9,3	9,4	9,7	9,6	10,4	10,7
Faktisk emission pr. BNP-enhed [ton CO2e pr. mio. BNP]	36,3	35,1	34,4	35,6	35,2	NA	NA