Utertiguk

---

Anguniagaq 8: Suliffiit naapertuuttut aningaasarsiornikkullu siuariartorneq

BNP innuttaasumut ataatsimut

---

FN 8.1.1 Ukiumut innuttaasumut ataatsimut BNP-p ineriartornera

# Import
NRX10_raw <- 
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units     = "K",
    account   = "02",
    time       = px_all(),
    .col_code = TRUE
               ) %>% 
  as_tibble()

# transform 
NRX10 <- 
  NRX10_raw %>% 
  mutate(time = time %>% as.numeric()) %>% 
  mutate(value = (value - lag(value)) / lag(value))


NRX10 %>% 
  ggplot(aes(
    x = time,
    y = value,
    fill = account
  )) +
  geom_col() +
  scale_fill_statgl() + 
  theme_statgl() +
  theme(legend.position = "none") +
  scale_y_continuous(labels = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig1$title[language],
    subtitle = NRX10[[1]][1],
    x        = " ",
    y        = sdg8$fig$fig1$y_lab[language],
    fill     = " ",
    caption  = sdg8$fig$fig1$cap[language]
  )

Kisitsisaataasivik, BNP

Kisitsisaataasivik, suliffillit

Tabel nuisiguk

tab <- 
  NRX10 %>% 
  mutate(value = value*100) %>% 
  filter(time >= max(time) - 5) %>% 
  arrange(desc(time)) %>% 
  select(-account) %>% 
  mutate(time = time %>% as.character())


if(language != "en"){
  
  table <- 
    tab %>% 
    select(-units) %>% 
    mutate(value = value %>% round(1)) %>% 
    rename(" " = 1, "Realvækst" = value) %>% 
    statgl_table() %>% 
    pack_rows(index = tab[[1]] %>% table()) %>% 
    add_footnote(sdg8$fig$fig1$foot1[language], notation = "symbol")
  
} else {
  
   table <- 
    tab %>% 
    select(-units) %>% 
    mutate(value = value %>% round(1)) %>% 
    rename(" " = 1, "Real growth rate" = value) %>% 
    statgl_table() %>% 
    pack_rows(index = tab[[1]] %>% table()) %>% 
    add_footnote(sdg8$fig$fig1$foot1[language], notation = "symbol")

}


table

	Realvækst
Akiusimasunit kisitat (2010-imi akit)
2023	0,8
2022	1,7
2021	1,0
2020	0,1
2019	2,6
2018	0,6
* Ineriartorneq procentinngorlugu (2010-mi akit, akiusimasunit kisitat)

Suliffillit

---

GS Innuttaasunut tamanut naleqqiullugu suliffillit annertussusaat

ARXBFB05_raw <-
  statgl_url("ARXBFB05", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    komdist   = px_all(),
    opg_var   = "H",
    .col_code = T
  ) |> 
  as_tibble()


# Transform
ARXBFB05 <- 
  ARXBFB05_raw %>% 
  mutate(aar = aar %>% make_date(),
         komdist = komdist %>% factor(levels = unique(komdist)))

# Plot
ARXBFB05 %>% 
  filter(komdist == ARXBFB05[[1]][1]) %>% 
  ggplot(aes(
    x     = aar,
    y     = value,
    color = komdist
    )) +
  geom_line(linewidth = 2) +
  facet_wrap(~ komdist, scales = "free") +
    scale_y_continuous(labels = scales::percent_format(
      scale      = 1, 
      accuracy   = 0.1, 
      big.mark   = ".",
      decimal.mark = ","
      )) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg8$fig$fig2$title[language],
    subtitle = ARXBFB05[[1]][1],
    x        = " ",
    y        = " ",
    caption  = sdg8$fig$fig2$cap[language]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# Transform
ARXBFB05 <- 
  ARXBFB05_raw %>% 
  filter(
    komdist == ARXBFB05_raw[[1]][1],
    aar     >= year(Sys.time()) - 7
    ) %>% 
  arrange(desc(aar))

vec        <- 1:2
names(vec) <- c(" ", sdg8$fig$fig2$cols$col2[language])

# Table
ARXBFB05 %>% 
  select(2, ncol(.)) %>% 
  rename(vec) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXBFB05[[3]] %>% table()) %>% 
  add_footnote(ARXBFB05[[1]][1], notation = "symbol")

	Suliffillit annertussusaat
Agguaqatigiissillugu qaammammut suliffillit innuttaasut amerlassusaannut naleqqiullugit (pct.)
2023	67,6
2022	67,2
2021	66,6
2020	66,0
2019	66,6
2018	66,1
* Nuna tamakkerlugu (kommunit avataanni inissisimasut ilanngullugit)

Suliffissaaleqineq

---

FN 8.5.2 Ukiut malillugit suliffissaaleqinerup annertussusaa

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    alder_grp = "1",
    opg_var   = "P",
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
ARXLED3 <-
  ARXLED3_raw %>% 
  mutate(aar = aar %>% make_date()) %>% 
  unite(combi, 1, 2, 4, sep = ", ")

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar,
    y     = value,
    color = combi
    )) +
  geom_line(size = 2) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 0.1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig3$title[language],
    subtitle = ARXLED3[[1]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg8$fig$fig3$cap[language]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# Transform

ARXLED3 %>% 
  mutate(aar = aar %>% year()) %>% 
  filter(aar > year(Sys.time()) - 8) %>% 
  spread(aar, value) %>% 
  mutate(combi = sdg8$fig$fig3$cols$ncol[language][[1]]) %>% 
  rename(" " = 1) %>% 
  statgl_table()

	2018	2019	2020	2021	2022	2023
Suliffissaaleqineq procentinngorlugu	5	4,3	4,5	3,7	3,2	2,9

ARXLED4_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    opg_var   = "P",
    alder_grp = px_all(),
    .col_code = T
    ) %>% 
  as_tibble()

# Transform
ARXLED4 <- 
  ARXLED4_raw %>% 
  filter(alder_grp != ARXLED4_raw[[2]][1]) %>% 
  arrange(desc(aar)) %>% 
  mutate(alder_grp = alder_grp %>% factor(levels = unique(alder_grp)),
         aar = aar %>% make_date()) %>% 
  unite(combi, 1, 4, sep = ", ")

# Plot
ARXLED4 %>% 
  ggplot(aes(
    x     = aar, 
    y     = value,
    color = alder_grp
    )) +
  geom_line(linewidth = 1.5) +
  theme_statgl() + 
  scale_color_statgl() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig4$title[language],
    subtitle = ARXLED4[[1]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg8$fig$fig4$cap[language]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# Transform
ARXLED4 <- 
  ARXLED4_raw %>% 
  filter(
    alder_grp != ARXLED4_raw[[2]][1],
    aar >= year(Sys.time()) - 6
    ) %>% 
  arrange(desc(aar)) %>% 
  mutate(
    alder_grp = alder_grp %>% factor(levels = unique(alder_grp)),
    aar = aar %>% factor(levels = unique(aar))
    ) %>% 
  unite(combi, 1, 2, sep = ", ") %>% 
  spread(3, ncol(.))

vec        <- ARXLED4[[2]] %>% length()
names(vec) <- sdg8$fig$fig4$index[language]


# Table
ARXLED4 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = vec) %>% 
  pack_rows(index = table(ARXLED4$combi)) |> 
  add_footnote(ARXLED4_raw[[1]][1], notation = "symbol")

	Agguaqatigiissillugu qaammammut suliffissaaleqineq pct.-inngorlugu
Suliffissaaleqineq procentinngorlugu
Nuna tamakkerlugu, 18-19-inik ukiullit
2023	5,3
2022	5,0
2021	6,8
2020	8,5
2019	7,6
Nuna tamakkerlugu, 20-24-nik ukiullit
2023	3,7
2022	4,2
2021	4,8
2020	6,1
2019	6,2
Nuna tamakkerlugu, 25-29-nik ukiullit
2023	2,6
2022	3,1
2021	3,2
2020	4,5
2019	4,7
Nuna tamakkerlugu, 30-34-nik ukiullit
2023	2,8
2022	3,2
2021	3,3
2020	4,4
2019	4,0
Nuna tamakkerlugu, 35-39-nik ukiullit
2023	2,4
2022	2,8
2021	3,2
2020	4,2
2019	4,0
Nuna tamakkerlugu, 40-44-nik ukiullit
2023	2,8
2022	2,6
2021	3,4
2020	3,9
2019	3,7
Nuna tamakkerlugu, 45-49-nik ukiullit
2023	2,2
2022	2,4
2021	2,9
2020	3,9
2019	4,0
Nuna tamakkerlugu, 50-54-inik ukiullit
2023	2,7
2022	3,4
2021	4,0
2020	4,4
2019	3,9
Nuna tamakkerlugu, 55-59-inik ukiullit
2023	3,4
2022	3,2
2021	3,9
2020	4,3
2019	3,7
Nuna tamakkerlugu, 60-inik ukiullit-soraarneruss. ukiussarititat
2023	3,4
2022	3,5
2021	3,6
2020	4,2
2019	4,2
* Nuna tamakkerlugu

GS Najugaq malillugu suliffissaaleqineq procentinngorlugu

# Import

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR40/ARXLED3.px")

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    alder_grp = "1",
    opg_var   = "P",
    bybygd    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
ARXLED3 <- 
  ARXLED3_raw %>% 
  mutate(aar = aar %>% make_date())

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar,
    y     = value, 
    color = bybygd
    )) +
  geom_line(linewidth = 2) +
  theme_statgl() + 
  scale_color_statgl() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig5$title[language],
    subtitle = ARXLED4[[2]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg8$fig$fig5$cap[language]
    )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# Transform
ARXLED3 <- 
  ARXLED3_raw %>% 
  filter(aar >= year(Sys.time()) - 6) %>% 
  arrange(aar) %>% 
  mutate(aar = aar %>% factor(levels = unique(aar))) %>% 
  unite(combi, distrikt, alder_grp, sep = ", ") %>% 
  spread(1, ncol(.))

vec        <- ARXLED3[[1]] %>% length()
names(vec) <- sdg8$fig$fig5$index[language]

# Table
ARXLED3 %>% 
  select(-c(1, 3)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(ARXLED3[[1]][1], notation = "symbol") %>% 
  row_spec(1, bold = TRUE) %>% 
  pack_rows(index = vec) |> 
  pack_rows(index = table(ARXLED3$bybygd))

	Nuna tamakkerlugu, Katillugit
Suliffissaaleqineq procentinngorlugu
Illoqarfiit
2019	4,1
2020	4,3
2021	3,4
2022	3,0
2023	2,7
Katillugit
2019	4,3
2020	4,5
2021	3,7
2022	3,2
2023	2,9
Nunaqarfiit il.il.
2019	6,1
2020	6,5
2021	5,4
2022	4,8
2023	4,7
* Illoqarfiit

Ilisimatusarneq ineriartortitsinerlu

---

GS Ilisimatusarnermut ineriartortitsinermullu aningaasartuutit BNP-mi annertussusaat

# Import
NRX09_raw <-
  statgl_url("NRX09", lang = language) %>% 
  statgl_fetch(
    units   = "L",
    account = px_all(),
    time    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

NRX09_raw <- NRX09_raw %>% filter(account %in% unique(NRX09_raw %>% pull(account))[7])

NRX10_raw <-
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units    = "L",
    account  = "01",
    time      = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
RD_GDP <- 
  NRX10_raw %>% 
  select(3:4) %>% 
  rename(
    "time" = 1,
    "GDP"  = 2
    ) %>% 
  left_join(
    NRX09_raw %>% 
      mutate(account = account %>% str_remove_all("[:digit:]") %>% trimws()) %>% 
      select(3:4) %>% 
      rename("RD" = 2)
    ) %>% 
  mutate(pct = RD / GDP) %>% 
  filter(pct != "NA")

# Plot
RD_GDP %>% 
  ggplot(aes(
    x    = time,
    y    = pct,
    fill = time
    )) +
  geom_col() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg8$fig$fig6$title[language],
    subtitle = NRX09_raw[[1]][1],
    x        = " ",
    y        = sdg8$fig$fig6$y_lab[language],
    caption  = sdg8$fig$fig6$cap[language]
  )

Kisitsisaataasivik, aningaasaliinerit (ilisimatusarneq ineriartortitsinerlu)

Kisitsisaataasivik, BNP

Tabel nuisiguk

# Transform
tab <- 
  RD_GDP %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  mutate(
    time = time %>% fct_inorder(),
    pct  = pct * 100,
    pct  = pct %>% round(1),
    var  = sdg8$fig$fig6$cols$col1[language]
    ) %>% 
  select(-(2:3)) %>% 
  spread(1, 2)

# Table
tab %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(NRX09_raw[[1]][1])) %>% 
  add_footnote(sdg8$fig$fig6$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023
Akit ingerlaavartut, mio. kr.
Ilisimatusarnermut ineriartortitsinermullu aningaasartuutit	1,8	1,8	1,6	1,5	1,9	2,1
* BNP-mi procentinngorlugu

Nunanut allanut angalasut

---

GS Timmisartumik ilaasartaammik nunanut allanut angalasut amerlassusaat

# Import
TUXUPAX_raw <-
  statgl_url("TUXUPAX", lang = language) %>%
  statgl_fetch(airport   = 0,
               month     = 0,
               time      = px_all(),
               .col_code = TRUE) %>% 
  as_tibble()

# Transform
TUXUPAX <-
  TUXUPAX_raw %>% 
  mutate(value = value / 1000,
         time = time %>% make_date())

# Plot
TUXUPAX %>% 
  ggplot(aes(
    x    = time,
    y    = value, 
    fill = airport
    )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(plot.margin = margin(10, 10, 10, 10),
        legend.position = "none") +
  labs(
    title = sdg8$fig$fig7$title[language],
    x = " ",
    y = sdg8$fig$fig7$y_lab[language],
    caption = sdg8$fig$fig7$cap[language]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# Transform
TUXUPAX <-
  TUXUPAX_raw %>% 
  mutate(value = value) %>% 
  filter(time >= year(Sys.time()) - 7,
         value != "NA") %>% 
  arrange(desc(time))

vec <- 1:2
names(vec) <- c(" ",  sdg8$fig$fig7$cols$col2[language])


# Table
TUXUPAX %>% 
  select(-(1:2)) %>% 
  rename(vec) %>% 
  statgl_table() %>% 
  add_footnote(sdg8$fig$fig7$foot[language],
               notation = "symbol")

	Timmisartumik ilaasartaammik nunanut allanut angalasut
2024	96.817
2023	96.362
2022	85.484
2021	39.293
2020	30.785
2019	86.989
2018	85.306
* Ilaasut tuusintilikkuutaarlugit.

Inuusuttut sullinniakkat

---

GS Inuusuttut sullinniakkat

UDXUMG3_raw <- 
  statgl_url("UDXUMG3", lang = language) %>%
  statgl_fetch(
    alder        = px_all(),
    registrering = 5:7,
    aar          = px_all(),
    .col_code    = TRUE
  ) %>% 
  as_tibble()

#sdg8 <- read_yaml("S:/STATGS/VM/SDG_dokument/input/text/txt_08.yml")

lab_vec        <- 1:5
names(lab_vec) <- 
  c(
    "age",
    "time",
    sdg8$fig$fig8$tags$tag1[language] %>% unlist(),
    sdg8$fig$fig8$tags$tag2[language] %>% unlist(),
    sdg8$fig$fig8$tags$tag3[language] %>% unlist()
)


UDXUMG3 <-
  UDXUMG3_raw %>% 
  rename("status" = registrering, "age" = alder, "time" = aar) |> 
  mutate(status = status %>% fct_inorder()) %>% 
  spread(status, value) %>% 
  rename(
    "age"   = 1,
    "time"  = 2,
    "work"  = 3,
    "none"  = 4,
    "total" = 5
  ) %>% 
  mutate(edu = total - work - none) %>% 
  select(-total) %>% 
  rename(lab_vec) %>% 
  gather(status, value, -c(age, time)) %>% 
  mutate(time = time %>% as.numeric()) %>% 
  filter(time %in% c(min(time), mean(time), max(time))) %>%
  mutate(time = time %>% as.character() %>% fct_rev())
  

UDXUMG3 %>%
  ggplot(aes(
    x    = parse_number(age),
    y    = value,
    fill = status
  )) +
  geom_col(position = "fill") +
  facet_wrap(~ time) + 
  scale_x_continuous(labels = function(x) round(x)) +
  scale_y_continuous(labels = scales:: percent) + 
  scale_fill_statgl(reverse = TRUE) +
  theme_statgl() + 
  labs(
    title    = sdg8$fig$fig8$title[language],
    subtitle = sdg8$fig$fig8$sub[language],
    x        = sdg8$fig$fig8$x_lab[language],
    y        = " ",
    color    = colnames(UDXUMG3_raw)[2] %>% str_to_title(),
    caption  = sdg8$fig$fig8$cap[language]
)

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

tab <- 
  UDXUMG3 %>% 
  mutate(age = age %>% fct_inorder()) %>% 
  arrange(age, time, status) %>% 
  unite(combi, time, status, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(combi, value)

vec      <- tab %>% select(-1) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(TRUE, FALSE)] %>% table() %>% rev()
col_vec  <- vec[c(FALSE, TRUE)]

tab %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  add_footnote(sdg8$fig$fig8$foot[language], notation = "symbol")

	2023			2016
age	2023,Ilinniakkamik ingerlatsisut	2023,Suliffeqanngitsut ilinnianngitsullu	2023,Suliffillit	2016,Ilinniakkamik ingerlatsisut	2016,Suliffeqanngitsut ilinnianngitsullu	2016,Suliffillit
16-inik ukiullit	310	310	86	390	295	70
17-inik ukiullit	304	293	114	275	362	127
18-inik ukiullit	272	265	212	298	325	176
19-inik ukiullit	229	206	296	274	318	300
20-nik ukiullit	142	221	338	171	264	374
21-nik ukiullit	138	216	403	205	242	389
22-nik ukiullit	158	206	376	219	233	411
23-nik ukiullit	150	174	362	223	221	472
24-nik ukiullit	149	181	424	227	197	476
25-nik ukiullit	188	163	437	230	186	487
* Inuit amerlassusaat