Utertiguk

Anguniagaq 5: Suiaassutsit naligiissitaanerat

Agguaqatigiissillugu isertitat

GS Agguaqatigiissillugu isertitat suiaassuseq, najugaq ilinniagarlu malillugit
INXPI104_raw <- 
  "INXPI104" %>% 
  statgl_url(lang = language) %>% 
  statgl_fetch(
    "level of education" = px_all(),
    unit                 = 3,
    gender               = 1:2,
    age                  = c(0, 4),
    "type of income"     = 1,
    time                 = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()


INXPI104 <- 
  INXPI104_raw %>%
  filter(
    value != "NA",
    age   == unique(INXPI104_raw %>% pull(age))[1]
    ) %>% 
  rename(
    "edu"  = `level of education`,
    "type" = `type of income`
    ) %>% 
  mutate(
    edu  = edu %>% fct_inorder(),
    type = type %>%  str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws()
    )

INXPI104 %>% 
  ggplot(aes(
    x     = time %>% as.numeric(),
    y     = value,
    color = gender %>% fct_rev()
  )) +
  geom_line(size = 2) +
  facet_wrap(~ edu) +
  theme_statgl() +
  scale_color_statgl() +
  scale_y_continuous(labels = function(x) format(x, big.mark = ".", scientific = FALSE)) +
  scale_x_continuous(breaks = scales:: pretty_breaks()) + 
  labs(
    title    = INXPI104 %>% pull(type) %>% unique(),
    subtitle = INXPI104 %>% pull(unit) %>% unique(),
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg5$figs$fig1$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


tab <- 
  INXPI104 %>% 
  filter(time >= Sys.time() %>% year() - 7) %>% 
  mutate(time = time %>% fct_rev()) %>% 
  spread(time, value) %>% 
  select(-age)
  

tab %>% 
  select(-c(edu, unit, type)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["type"]] %>%  table()) %>% 
  pack_rows(index = tab[["edu"]] %>% table()) %>% 
  add_footnote(tab %>% pull(unit) %>% unique(), notation = "symbol")
2024 2023 2022 2021 2020 2019
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut
Meeqqat atuarfiat
Angutit 246.130 242.372 234.324 216.600 221.765 220.133
Arnat 186.549 178.302 172.812 165.146 162.849 159.516
Ilinniarnertuutut ilinniarneq
Angutit 349.248 338.409 327.912 321.539 306.222 307.544
Arnat 228.977 215.094 213.660 202.973 200.076 195.379
Inuussutissarsiutinik ilinniarneq
Angutit 432.780 419.922 409.975 391.283 399.353 385.654
Arnat 321.476 309.519 304.231 290.398 284.749 282.021
Kort videregående uddannelse
Angutit 353.615 327.698 310.500 306.983 275.872 268.076
Arnat 272.260 242.656 228.568 203.095 198.828 212.133
Mellemlang videregående uddannelse
Angutit 596.076 571.541 550.200 539.642 528.675 535.483
Arnat 462.415 444.865 439.964 427.006 416.231 410.856
Ingerlaqqilluni ilinniarnerit
Angutit 827.517 789.057 760.808 766.408 759.769 767.176
Arnat 671.863 614.281 609.123 601.243 589.539 575.814
* Inuit agguaqatigiissinneri tunngavigalugit isertitaat (kr.) 
INXPI104 <- 
  INXPI104_raw %>%
  filter(
    value != "NA",
    age   == unique(INXPI104_raw %>% pull(age))[2]
  ) %>% 
  rename(
    "edu"  = `level of education`,
    "type" = `type of income`
  ) %>% 
  mutate(
    edu  = edu %>% fct_inorder(),
    type = type %>%  str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws()
  )

INXPI104 %>% 
  ggplot(aes(
    x     = time %>% as.numeric(),
    y     = value,
    color = gender %>% fct_rev()
  )) +
  geom_line(size = 2) +
  facet_wrap(~ edu) +
  theme_statgl() +
  scale_color_statgl() +
  scale_y_continuous(labels = function(x) format(x, big.mark = ".", scientific = FALSE)) +
  scale_x_continuous(breaks = scales:: pretty_breaks()) + 
  labs(
    title    = paste0(
      INXPI104 %>% pull(type) %>% unique(), ", ", 
      INXPI104 %>% pull(age) %>% unique()
    ),
    subtitle = INXPI104 %>% pull(unit) %>% unique(),
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg5$figs$fig1$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


tab <- 
  INXPI104 %>% 
  filter(time >= Sys.time() %>% year() - 7) %>% 
  mutate(time = time %>% fct_rev()) %>% 
  spread(time, value) %>% 
  unite(type, type, age, sep = ", ")


tab %>% 
  select(-c(edu, unit, type)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["type"]] %>%  table()) %>% 
  pack_rows(index = tab[["edu"]] %>% table()) %>% 
  add_footnote(tab %>% pull(unit) %>% unique(), notation = "symbol")
2024 2023 2022 2021 2020 2019
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut, 30-34 ukiullit
Meeqqat atuarfiat
Angutit 252.434 262.244 252.055 230.804 226.090 229.122
Arnat 191.654 186.574 176.554 171.386 165.637 161.100
Ilinniarnertuutut ilinniarneq
Angutit 320.323 314.355 293.683 296.680 289.586 313.604
Arnat 238.633 209.734 202.595 207.268 191.671 188.423
Inuussutissarsiutinik ilinniarneq
Angutit 432.038 417.811 406.392 380.457 357.371 347.546
Arnat 285.259 278.977 275.243 265.726 260.237 266.759
Kort videregående uddannelse
Angutit 388.152 359.467 350.995 349.079 306.309 294.879
Arnat 278.932 246.090 251.569 208.128 222.469 208.038
Mellemlang videregående uddannelse
Angutit 497.088 485.544 496.798 498.912 473.930 446.026
Arnat 403.970 386.579 383.420 377.972 359.267 359.695
Ingerlaqqilluni ilinniarnerit
Angutit 556.644 556.060 519.394 503.610 522.714 483.043
Arnat 537.490 496.914 482.401 471.482 472.983 460.440
* Inuit agguaqatigiissinneri tunngavigalugit isertitaat (kr.)

Qinikkat suiaassuseq malillugu agguataarnerat

FN 5.5.1 Arnat Inatsisartuni ilaasortat annertussusaat
# Import
ELEC03_raw <- 
  "https://pxweb.nordicstatistics.org:443/sq/e32a8e0a-b8fb-41ec-bbef-31c3bde52f42.csv" |> 
  read.csv() |> 
  as_tibble()

vec <- 1:24
names(vec) <- c("country", 2003:2025)

# Transform
ELEC03 <- 
  ELEC03_raw |> 
  rename(vec) |> 
  mutate(across(everything(), as.numeric),
         country = "greenland") |> 
  pivot_longer(cols = c("2003", "2004", "2005", "2006", "2007", "2008", "2009", 
                        "2010", "2011", "2012", "2013", "2014", "2015", "2016",
                        "2017", "2018", "2019", "2020", "2021", "2022", "2023",
                        "2024", "2025"),
               names_to = "time",
               values_to = "value") |> 
  drop_na(value)

# Plot
ELEC03 |> 
  ggplot(aes(
    x = time,
    y = value,
    fill = country
  )) +
  geom_col() +
  theme_statgl() +
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title   = sdg5$figs$fig2$title[language],
    x       = " ",
    y       = sdg5$figs$fig2$y_lab[language],
    fill    = " ",
    caption = sdg5$figs$fig2$cap[language]
  )

Nordic Statistics

Periaaseq 


col0 <- sdg5$figs$fig2$col0[language]

# Tabel
ELEC03 |> 
  spread(time, value) |> 
  mutate(country = col0) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg5$figs$fig2$foot[language], notation = "symbol")
2005 2009 2013 2014 2015 2016 2017 2018 2020 2021 2025
Arnat amerlassusaat 42 29 41 43 33 33 31 42 47 32 45
* Ukiup naanerani amerlassusaat

Aningaasaatikilliortut

GS Innuttaasut akornanni aningaasaatikilliortut annertussusaat suiaassuseq malillugu
# Import 
SOXOU01_raw <-
  statgl_url("SOXOU01", lang = language) %>%
  statgl_fetch(
    "inventory variable" = c("Andel50", "Andel60"),
    gender               = 1:2,
    year                 = px_all(),
    .col_code            = TRUE
    ) %>% 
    as_tibble()

# Transform
SOXOU01 <-
  SOXOU01_raw %>% 
  mutate(
    year   = year %>%  make_date(),
    gender = gender %>% fct_inorder()
    )

# Plot
SOXOU01 %>% 
  mutate(`inventory variable` = `inventory variable` %>% str_to_sentence()) %>% 
  ggplot(aes(
    x    = year,
    y    = value,
    fill = gender)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ",")
    ) +
  facet_wrap(~ `inventory variable`) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg5$figs$fig3$title[language],
    subtitle = sdg5$figs$fig3$sub[language],
    x        = " ", 
    y        = " ", 
    fill     = " ",
    caption  = sdg5$figs$fig3$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
SOXOU01 <-
  SOXOU01_raw %>% 
  arrange(desc(year)) %>% 
  filter(year >= year(Sys.time()) - 5) %>% 
  mutate(year = year %>% fct_inorder()) %>% 
  unite(combi, 1, 2, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(1, 3)

vec      <- SOXOU01[-1] %>% colnames() %>% str_split(",") %>% unlist() %>% str_to_sentence()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]

# Table
SOXOU01 %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  add_footnote(
    sdg5$figs$fig3$foot[language],
    notation = "symbol"
    )
50%-imik ataatsisut agguaqatigiissinnerini amerlassusaat
60%-imik ataatsisut agguaqatigiissinnerini amerlassusaat
50%-imik ataatsisut agguaqatigiissinnerini amerlassusaat,Arnat 50%-imik ataatsisut agguaqatigiissinnerini amerlassusaat,Angutit 60%-imik ataatsisut agguaqatigiissinnerini amerlassusaat,Arnat 60%-imik ataatsisut agguaqatigiissinnerini amerlassusaat,Angutit
2024 4,5 5,3 8,0 9,2
2023 3,9 5,0 7,7 8,7
2022 4,1 4,7 7,7 8,6
2021 3,8 4,3 7,4 7,9
* Isertitat agguaqatigiissinnerini 50 imaluunniit 60 %-it ataallugit isertitallit annertussusaat procentinngorlugit.

Meeqqat atuarfianni alloriarfinni misilitsinnernit angusat

GS Meeqqat atuarfianni alloriarfinni misilitsinnernit angusat suiaassuseq malillugu
# Import
UDXTKK_raw <-
  statgl_url("UDXTKK", lang = language) %>%
  statgl_fetch(subject   = px_all(),
               grade     = px_all(),
               sex       = 1:2,
               unit      = "B",
               time      = px_all(),
               .col_code = TRUE
               ) %>% 
    as_tibble()

# Transform
UDXTKK <- 
  UDXTKK_raw %>% 
  mutate(
    time     = time %>% make_date(),
     subject =  subject %>% fct_inorder()
    )

fig_legend   <- statgl_url("UDXTKK", lang = language) %>% statgl_fetch() %>% select(1) %>% colnames()
fig_title    <- (statgl_url("UDXTKK", lang = language) %>% statgl_meta())$title
fig_subtitle <- UDXTKK_raw[["unit"]] %>% unique()
  
# Plot
UDXTKK %>% 
  ggplot(aes(
    x = time,
    y = value,
    color = subject
  )) +
  geom_line(size = 2) +
  facet_grid(grade ~ sex) +
  theme_statgl() + 
  scale_color_statgl() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = fig_title,
    subtitle = fig_subtitle,
    x        = " ",
    y        = " ",
    color    = fig_legend,
    caption  = sdg5$figs$fig4$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


UDXTKK <- 
  UDXTKK_raw %>% 
  mutate(
    subject = subject %>% fct_inorder(),
    grade   = grade %>% fct_inorder(),
    sex     = sex %>% fct_inorder()
    ) %>% 
  arrange(subject, time) %>% 
  unite(combi, 2, 1, 3, sep = ",") %>% 
  spread(1, 4) %>% 
  arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5)

vec       <- UDXTKK %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec1 <- rep((vec[c(F, T, F)])[1:8] %>% table(), 2)
head_vec2 <- vec[c(T, F, F)] %>% table()
col_vec   <- vec[c(F, F, T)] 

# Table
UDXTKK %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  mutate_all(~replace(., is.na(.), 0)) %>% 
  statgl_table(col.names = c(" ", col_vec),
               replace_0s = TRUE) %>% 
  add_header_above(c(" ", head_vec1)) %>% 
  add_header_above(c(" ", head_vec2)) %>% 
  pack_rows(index = UDXTKK[["unit"]] %>% table())
  1. klassi
  1. klassi
Kalaallisut
Matematikki
Qallunaatut
Tuluttut
Kalaallisut
Matematikki
Qallunaatut
Tuluttut
  1. klassi,Kalaallisut,Niviarsiaqqat
  1. klassi,Kalaallisut,Nukappiaqqat
  1. klassi,Matematikki,Niviarsiaqqat
  1. klassi,Matematikki,Nukappiaqqat
  1. klassi,Qallunaatut,Niviarsiaqqat
  1. klassi,Qallunaatut,Nukappiaqqat
  1. klassi,Tuluttut,Niviarsiaqqat
  1. klassi,Tuluttut,Nukappiaqqat
  1. klassi,Kalaallisut,Niviarsiaqqat
  1. klassi,Kalaallisut,Nukappiaqqat
  1. klassi,Matematikki,Niviarsiaqqat
  1. klassi,Matematikki,Nukappiaqqat
  1. klassi,Qallunaatut,Niviarsiaqqat
  1. klassi,Qallunaatut,Nukappiaqqat
  1. klassi,Tuluttut,Niviarsiaqqat
  1. klassi,Tuluttut,Nukappiaqqat
Inerniliillaqqissuseq (eqqortut pct.-inngorlugit)
2024 48 43 46 51 43 39 0 0 59 50 38 41 45 41 88 80
2023 48 48 48 56 48 45 0 0 64 54 42 41 50 42 90 82
2022 43 40 48 48 44 39 0 0 66 57 41 41 59 44 86 75
2021 50 45 49 53 48 46 0 0 66 54 38 41 59 47 76 71

Karakteerit agguaqatigiissinnerat

GS Misilitsinnermi karakteerit agguaqatigiissinnerat suiaassuseq malillugu
# Import
UDXFKK_raw <-
  statgl_url("UDXFKK", lang = language) %>% 
  statgl_fetch(unit             = "andel",
               grade            = "FO",
               subject          = c("01", "02", "03", "04"),
               "type of grades" = 56:58,
               sex              = 1:2,
               time             = px_all(),
               .col_code = TRUE) %>% 
    as_tibble()

# Transform
UDXFKK <-
  UDXFKK_raw %>% 
  separate(`type of grades`, c("split1", "split2"),  " - ") %>% 
  mutate(split2 = split2 %>% str_to_title(),
         split1 = split1 %>% str_to_lower(),
         time = time %>% make_date()) %>% 
  unite(combi, 2, 4, sep = ", ")

fig_title    <- (statgl_url("UDXFKK", lang = language) %>% statgl_meta())$title
fig_y        <- UDXFKK[["unit"]] %>% unique() %>% str_to_title()
fig_subtitle <- UDXFKK[["combi"]] %>% unique()

# Plot
UDXFKK %>% 
  ggplot(aes(
    x     = time,
    y     = value, 
    color = sex
    )) +
  geom_line(size = 1.5) +
  facet_grid(split2 ~ subject) +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ",", 
    accuracy     = 1
    )) +
  theme_statgl() + 
  scale_color_statgl(guide = guide_legend(reverse = TRUE)) +
  labs(
    title    = fig_title,
    subtitle = fig_subtitle,
    x        = " ",
    y        = fig_y,
    color    = " ",
    caption  = sdg5$figs$fig5$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXFKK <-
  UDXFKK_raw %>% 
  filter(time >= year(Sys.Date()) - 6,
         value != "NA") %>% 
  separate(`type of grades`, c("split1", "split2"),  " - ") %>% 
  mutate(split2 = split2 %>% str_to_title(),
         split1 = split1 %>% str_to_lower()) %>% 
  unite(combi1, 2, 4, sep = ", ") %>% 
  unite(combi2, 3, 4, sep = ",") %>% 
  spread(3, ncol(.)) %>% 
  arrange(desc(time))

vec      <- UDXFKK %>% select(-(1:4)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]

# Table
UDXFKK %>% 
  select(-(1:2), -4) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = UDXFKK[[1]] %>% str_to_title() %>% table()) %>% 
  pack_rows(index = UDXFKK[["time"]] %>% table() %>% rev()) %>% 
  add_footnote(UDXFKK[[2]] %>% unique(),
               notation = "symbol")
Kalaallisut
Matematikki
Qallunaatut
Tuluttut
Kalaallisut,Allattariarsorneq Kalaallisut,Oqaluttariarsorneq Kalaallisut,Piginnaasat Matematikki,Allattariarsorneq Matematikki,Oqaluttariarsorneq Matematikki,Piginnaasat Qallunaatut,Allattariarsorneq Qallunaatut,Oqaluttariarsorneq Qallunaatut,Piginnaasat Tuluttut,Allattariarsorneq Tuluttut,Oqaluttariarsorneq Tuluttut,Piginnaasat
Agguaqatigiissillugu Karakteeri
2024
Niviarsiaqqat 6,14 7,47 5,57 2,41 5,74 4,51 3,58 5,00 4,05 5,58 7,16 5,73
Nukappiaqqat 4,45 6,11 4,15 2,55 5,47 5,11 2,79 4,30 3,36 5,10 7,90 5,54
2023
Niviarsiaqqat 5,65 6,74 4,49 3,07 5,37 4,49 4,28 6,13 4,18 4,73 7,34 5,52
Nukappiaqqat 3,84 6,30 3,50 2,89 5,81 5,14 3,39 6,16 3,92 4,39 6,60 5,60
2022
Niviarsiaqqat 6,18 7,49 3,98 2,61 5,22 4,84 4,22 5,55 4,71 5,07 6,74 5,55
Nukappiaqqat 4,60 5,87 3,37 2,41 5,26 4,95 2,78 3,86 3,43 3,82 6,27 4,76
2021
Niviarsiaqqat 6,31 6,21 3,94 2,17 4,94 4,84 4,00 5,74 4,93 4,40 6,36 5,03
Nukappiaqqat 4,18 5,67 3,11 2,16 4,79 5,06 2,59 4,89 3,93 3,75 6,66 4,73
* Meeqqat atuarfianni naggataarlutik atuartut, inaarutaasumik misilitsinnermi karakteeri


Covid-19 peqqutaalluni 2020-mi naggataarutaasumik soraarummeertoqanngilaq.

Qaffasinnerpaatut ilinniagaq naammassisimasaq

GS 35-t 39-llu akornanni ukiullit qaffasinnerpaatut ilinniakkat naammassisimasaat suiaassuseq malillugu
# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch(
    ISCED11_level = px_all(),
    Sex           = px_all(),
    Aar           = px_all(),
    alder_grp     = "35-39",
    .col_code     = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISCPROD <-
  UDXISCPROD_raw %>% 
  filter(ISCED11_level != UDXISCPROD_raw[[2]][1]) %>% 
  mutate(
    ISCED11_level = ISCED11_level %>% factor(level = unique(ISCED11_level) %>% rev()),
    Aar           = Aar %>% make_date()
    )

# Plot
UDXISCPROD %>% 
  arrange(ISCED11_level) %>% 
  ggplot(aes(
    x    = Aar,
    y    = value,
    fill = ISCED11_level
  )) +
  geom_area(position = "fill") +
  facet_wrap(~ Sex) +
  scale_y_continuous(labels = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl(base_size = 11) +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE, nrow = 3)) +
  labs(
    title    = sdg5$figs$fig6$title[language],
    subtitle = unique(UDXISCPROD[["age"]]),
    x        = " ",
    y        = " ",
    fill     = NULL,
    caption  = sdg5$figs$fig6$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


UDXISCPROD <- 
  UDXISCPROD_raw %>% 
  filter(
    ISCED11_level != UDXISCPROD_raw[[2]][1],
    Aar > year(Sys.Date()) - 7
    ) %>% 
  mutate(
    ISCED11_level = ISCED11_level %>% factor(levels = unique(ISCED11_level))
    ) %>% 
  arrange(ISCED11_level, desc(Aar)) %>% 
  unite(combi, 3, 4, sep = "-") %>% 
  mutate(combi = combi %>% factor(level = unique(combi))) %>% 
  spread(3, 4, sep = "-")

vec      <- (UDXISCPROD %>% select(-(1:2)) %>% colnames() %>% str_split("-") %>% unlist())[c(F, T, T)]
head_vec <- vec[c(F, T)] %>% table() %>% rev()
col_vec  <- vec[c(T, F)]

UDXISCPROD %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE, col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = UDXISCPROD[["alder_grp"]] %>% table())
2024
2023
2022
2021
2020
combi-Arnat-2024 combi-Angutit-2024 combi-Arnat-2023 combi-Angutit-2023 combi-Arnat-2022 combi-Angutit-2022 combi-Arnat-2021 combi-Angutit-2021 combi-Arnat-2020 combi-Angutit-2020
Ukiut 35-39
Atuarfik tunngaviliivik, 10.klasse tikillugu 783 1.228 705 1.199 655 1.125 627 1.068 631 1.037
Ilinniarnertuunngorniarneq 117 99 112 84 104 79 105 70 91 63
Inuussutissarsiornermik ilinniarneq 619 686 611 678 583 658 584 697 601 688
Angusanik qaffassaaneq 41 48 37 43 37 32 32 26 30 23
Ingerlariaqqiffiusumik ilinniarneq naatsoq 106 98 96 81 84 75 88 70 87 76
Bachelorinngorniarneq 43 17 41 15 41 17 35 13 35 13
Professionsbachelorinngorniarneq 347 109 332 111 320 114 320 98 301 94
Kandidatinngorniarneq 119 69 109 70 111 68 105 68 86 70
Ilisimatuunngorniarneq 8 3 8 4 6 3 6 3 6 1

Suliffeqarneq

GS Najugaqavissut akornanni pingaarnertut suliffillit, inuussutissarsiorfiit aamma suiaassuseq malillugit
ARXBFB01_raw <-
  statgl_url("ARXBFB01", lang = language) |> 
  statgl_fetch(
    beskbrch  = c("01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16"),
    sex       = c("M","K"),
    opg_var   = "G",
    aar       = px_all(),
    .col_code = T
  ) |> 
  as_tibble()

# Transform
ARXBFB01 <-
  ARXBFB01_raw %>% 
  mutate(
    aar     = aar %>% make_date(),
    beskbrch = beskbrch %>% fct_reorder(value) %>% fct_rev()
    ) %>% 
  arrange(beskbrch)

# Plot
ARXBFB01 %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = sex
    )) +
  geom_area() +
  facet_wrap(~ beskbrch, scales = "free", labeller = label_wrap_gen()) +
  theme_statgl(base_size = 8) + 
  scale_fill_statgl(reverse = TRUE) +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
    )) +
    labs(
      title = unique(ARXBFB01[[4]]),
      subtitle = sdg5$figs$fig7$title[language],
      x        = " ",
      y        = sdg5$figs$fig7$y_lab[language],
      fill     = " ",
      caption  = sdg5$figs$fig7$cap[language]
      )

Kisitsisaataasivik

Periaaseq 


ARXBFB01 <- 
  ARXBFB01_raw %>% 
  filter(aar >= year(Sys.time()) - 6) %>% 
  mutate(beskbrch = beskbrch %>% fct_reorder(value) %>% fct_rev()) %>% 
  arrange(beskbrch, aar) %>% 
  unite(combi, 2, 3, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(2, ncol(.))

vec      <- ARXBFB01 %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(F, T)] %>% table()
col_vec  <- vec[c(T, F)]

ARXBFB01 %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = ARXBFB01[[2]] %>% table())
2020
2021
2022
2023
2024
Angutit,2020 Arnat,2020 Angutit,2021 Arnat,2021 Angutit,2022 Arnat,2022 Angutit,2023 Arnat,2023 Angutit,2024 Arnat,2024
Agguaqatigiissillugu qaammammut saniatigooralugu suliffillit
Pisortat allaffissornerat kiffartuussinerallu 3.883 8.858 3.966 8.931 3.929 8.929 3.817 8.919 3.863 8.973
Aalisarneq aalisakkanillu tunisassiornermi niuerneq 3.862 708 3.662 669 3.655 658 3.799 683 3.677 649
Niuertunik pilersuineq atungassanillu nioqquteqarneq 1.494 1.425 1.533 1.496 1.552 1.530 1.575 1.508 1.608 1.506
Sanaartorneq sanaartortitsinerlu 1.865 178 2.103 206 2.129 204 2.080 195 1.998 187
Assartuineq assartugassalerinerlu 1.546 460 1.514 448 1.563 484 1.596 495 1.568 500
Akunnittarfiit neriniartarfiillu 317 356 360 436 376 480 423 510 436 486
Paasissutissalerineq attaveqaatilerinerlu 422 197 415 197 382 186 375 184 359 174
Allaffissornikkut kiffartuussinerit 272 160 246 149 234 154 241 175 267 201
Nukissiuutinik imermillu pilersuineq 361 77 361 77 348 69 353 71 359 72
Kiffartuussilluni inuussutissarsiorfiit allat 154 151 158 149 156 155 155 165 143 172
Namminersortunit, ilisimatusarnikkut teknikkikkullu suliaqartunit kiffartuussinerit 158 109 173 116 177 123 179 120 166 115
Inissiaateqarneq 152 104 143 98 139 112 155 121 136 120
Aningaaseriviit aningaasaliisarfiillu 75 129 76 131 78 140 84 145 109 196
Nioqqutissiorneq 185 50 189 49 195 52 193 59 176 52
Aatsitassarsiorneq 65 25 85 34 73 34 56 29 59 34
Nunalerineq, orpippassualerineq nunalerinermilu tunisassiorneq niuernerlu 85 19 92 18 80 18 68 15 70 15

Suliffissaaleqineq

GS Suiaassuseq 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(
    sex       = c("M", "K"),
    alder_grp = px_all(),
    aar       = px_all(),
    opg_var   = "P",
    .col_code = T
  ) |> 
  as_tibble()

# Transform
ARXLED3 <-
  ARXLED3_raw %>% 
  mutate(
    aar = aar %>% make_date(),
    alder_grp  = alder_grp %>% factor(levels = unique(alder_grp))
    )

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar, 
    y     = value,
    color = sex
    )) +
  geom_line(size = 1.5) +
  facet_wrap(~ alder_grp, scales = "free") +
  theme_statgl() + scale_color_statgl(reverse = TRUE) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    accuracy     = 1,
    big.mark     = ".",
    decimal.mark = ",")) +
  labs(
    title    = sdg5$figs$fig8$title[language],
    subtitle = sdg5$figs$fig8$sub[language],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg5$figs$fig8$cap[language]
    )

Kisitsisaataasivik

Periaaseq 


ARXLED3 <- 
  ARXLED3_raw %>% 
  select(-opg_var) |> 
  mutate(
    alder_grp = alder_grp %>% fct_inorder(),
    aar = aar %>% as.numeric()
    ) %>% 
  filter(aar > max(aar) - 5) %>% 
  arrange(alder_grp, aar) %>% 
  unite(combi, aar, sex, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(combi, value)
  
vec      <- ARXLED3 %>% select(-1) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]


ARXLED3 %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  row_spec(1, bold = TRUE) %>% 
  pack_rows(index = c("Ledighedsprocent" = ARXLED3[[1]] %>% length())) %>% 
  add_footnote(
    sdg5$figs$fig8$foot[language],
    notation = "symbol")
2020
2021
2022
2023
2024
2020,Angutit 2020,Arnat 2021,Angutit 2021,Arnat 2022,Angutit 2022,Arnat 2023,Angutit 2023,Arnat 2024,Angutit 2024,Arnat
Ledighedsprocent
Katillugit 4,9 4,2 3,9 3,4 3,4 3,0 3,2 2,7 3,7 3,0
18-19-inik ukiullit 8,4 8,6 7,1 6,4 4,8 5,3 5,1 5,5 8,3 5,7
20-24-nik ukiullit 6,3 6,0 4,8 4,9 4,1 4,4 3,8 3,5 5,2 4,7
25-29-nik ukiullit 4,7 4,2 3,4 3,0 3,4 2,8 2,9 2,4 3,9 3,2
30-34-nik ukiullit 4,2 4,5 3,2 3,4 3,4 3,1 2,9 2,8 3,4 3,0
35-39-nik ukiullit 4,4 4,1 3,2 3,2 2,7 2,8 2,3 2,5 2,7 2,5
40-44-nik ukiullit 4,4 3,4 3,5 3,2 2,8 2,4 3,0 2,5 3,2 2,5
45-49-nik ukiullit 4,0 3,8 3,1 2,7 2,6 2,1 2,3 2,1 3,0 2,5
50-54-inik ukiullit 5,1 3,7 4,3 3,7 3,8 3,0 2,9 2,5 3,0 2,8
55-59-inik ukiullit 4,9 3,6 4,4 3,2 3,6 2,8 3,9 2,8 3,9 2,9
60-inik ukiullit-soraarneruss. ukiussarititat 5,3 2,7 4,1 2,9 3,9 3,0 3,8 2,9 3,8 2,3
* Najugaqavissut 18-it 65-illu akornanni ukiullit agguaqatigiissillugu qaammammut suliffissaaleqinerat, procentinngorlugu.

Inuuneqqortussuseq

GS 0-iniit 1-inut ukiullit inuuneqqortussusaat suiaassuseq malillugu
# Import
BEXDT5A_raw <-
  statgl_url("BEXDT5A", lang = language) %>% 
  statgl_fetch(type   = "E",
               gender = c("M", "K"),
               time   = px_all(),
               age    = 0:1,
               .col_code = TRUE) %>% 
  as_tibble()

# Transform
BEXDT5A <- 
  BEXDT5A_raw %>% 
    separate(time, c("startar", "slutar"),  " - ") %>% 
  mutate(slutar = slutar %>% make_date())

# Plot
BEXDT5A %>% 
  ggplot(aes(
    x     = slutar,
    y     = value,
    color = gender
    )) +
  geom_line(size = 2) +
  facet_wrap(~ age) +
    theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  labs(
    title    = sdg5$figs$fig9$title[language],
    subtitle = sdg5$figs$fig9$sub[language],
    x        = sdg5$figs$fig9$x_lab[language],
    y        = sdg5$figs$fig9$y_lab[language],
    color    = " ",
    caption  = sdg5$figs$fig9$cap[language]
    )

Kisitsisaataasivik 


# Transform
BEXDT5A <-
  BEXDT5A_raw %>% 
  arrange(desc(time), age) %>% 
  unite(combi, 2, 3, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(2, 4) %>% 
  arrange(desc(time)) %>% 
  mutate(timetime = time) %>% 
  separate(timetime, c("time1", "time2"), " - ") %>% 
  filter(time >= year(Sys.time()) - 20) %>% 
  select(-c("time1", "time2"))

vec      <- BEXDT5A %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(F, T)] %>% table()
col_vec  <- vec[c(T, F)]

# Table
BEXDT5A %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = BEXDT5A[[1]] %>% table()) %>% 
  add_footnote(
    sdg5$figs$fig9$foot[language], 
    notation = "symbol"
    )
0
1
Angutit,0 Arnat,0 Angutit,1 Arnat,1
Middellevetid
2015 - 2019 68,3 73,0 68,1 72,5
2014 - 2018 68,8 73,0 68,6 72,3
2013 - 2017 68,6 73,1 68,4 72,4
2012 - 2016 68,4 73,4 68,2 72,8
2011 - 2015 68,5 72,6 68,3 72,1
2010 - 2014 68,0 72,1 67,8 71,5
2009 - 2013 67,4 72,2 67,1 71,6
2008 - 2012 67,3 71,9 67,0 71,3
2007 - 2011 67,0 71,5 66,7 71,0
2006 - 2010 66,6 71,5 66,4 71,1
* 0-iniit 1-inut ukiullit inuuneqqortussusaat, Kalaallit Nunaanni inunngortut.


Erninermi suliffeqanngikkallarnermi ikiorsiissutit

FN 5.4.1
# Import
SOX007_raw <- 
  statgl_url("SOX007", lang = language) |> 
  statgl_fetch(
    gender    = 1:2,
    type      = 30,
    time      = px_all(),
    .col_code = T
  ) |> 
  as_tibble()

# Transform
SOX007 <- 
  SOX007_raw |> 
  mutate(value = as.numeric(value)) |> 
  select(-2)


# Plot
SOX007 |> 
  ggplot(aes(
    x     = as.integer(time),
    y     = value,
    color = gender
  )) +
  geom_line(size = 2) +
  theme_statgl() +
  scale_color_statgl() +
  labs(
    title   = sdg5$figs$fig10$title[language],
    x       = " ",
    y       = " ",
    color   = " ",
    caption = sdg5$figs$fig10$cap[language]
  )

Kisitsisaataasivik 


SOX007 |> 
  filter(time >= year(Sys.time()) - 6) |> 
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg5$figs$fig10$foot[language], notation = "symbol")
2020 2021 2022 2023
Angut 165 148 128 94
Arnaq 867 834 718 625
* Inuit amerlassusaat