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 2018
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut
Meeqqat atuarfiat
Angutit 246.130 242.372 234.324 216.600 221.765 220.133 219.879
Arnat 186.549 178.302 172.812 165.146 162.849 159.516 155.746
Ilinniarnertuutut ilinniarneq
Angutit 349.248 338.409 327.912 321.539 306.222 307.544 306.674
Arnat 228.977 215.094 213.660 202.973 200.076 195.379 188.188
Inuussutissarsiutinik ilinniarneq
Angutit 432.780 419.922 409.975 391.283 399.353 385.654 378.860
Arnat 321.476 309.519 304.231 290.398 284.749 282.021 275.123
Kort videregående uddannelse
Angutit 353.615 327.698 310.500 306.983 275.872 268.076 263.876
Arnat 272.260 242.656 228.568 203.095 198.828 212.133 186.694
Mellemlang videregående uddannelse
Angutit 596.076 571.541 550.200 539.642 528.675 535.483 532.088
Arnat 462.415 444.865 439.964 427.006 416.231 410.856 405.089
Ingerlaqqilluni ilinniarnerit
Angutit 827.517 789.057 760.808 766.408 759.769 767.176 713.205
Arnat 671.863 614.281 609.123 601.243 589.539 575.814 560.462
* 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 2018
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut, 30-34 ukiullit
Meeqqat atuarfiat
Angutit 252.434 262.244 252.055 230.804 226.090 229.122 220.892
Arnat 191.654 186.574 176.554 171.386 165.637 161.100 158.492
Ilinniarnertuutut ilinniarneq
Angutit 320.323 314.355 293.683 296.680 289.586 313.604 273.332
Arnat 238.633 209.734 202.595 207.268 191.671 188.423 186.541
Inuussutissarsiutinik ilinniarneq
Angutit 432.038 417.811 406.392 380.457 357.371 347.546 331.144
Arnat 285.259 278.977 275.243 265.726 260.237 266.759 256.674
Kort videregående uddannelse
Angutit 388.152 359.467 350.995 349.079 306.309 294.879 347.271
Arnat 278.932 246.090 251.569 208.128 222.469 208.038 226.267
Mellemlang videregående uddannelse
Angutit 497.088 485.544 496.798 498.912 473.930 446.026 436.701
Arnat 403.970 386.579 383.420 377.972 359.267 359.695 360.720
Ingerlaqqilluni ilinniarnerit
Angutit 556.644 556.060 519.394 503.610 522.714 483.043 472.560
Arnat 537.490 496.914 482.401 471.482 472.983 460.440 441.388
* 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/6c4d7add-c65a-43ab-a60a-0119c13f9bd6.csv" |> 
  read.csv() |> 
  as_tibble()

vec <- 1:21
names(vec) <- c("country", 2003:2022)

# 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"),
               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")
X2023 X2024 2005 2009 2013 2014 2015 2016 2017 2018 2020 2021
Arnat amerlassusaat NA NA 42 29 41 43 33 33 31 42 47 32
* 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
2023 4,1 5,0 7,8 8,7
2022 4,1 4,8 7,7 8,6
2021 3,8 4,3 7,4 7,9
2020 3,7 4,5 7,3 8,1
* 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
2020 48 40 48 53 50 49 0 0 65 57 42 41 60 54 73 73

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
2019
Niviarsiaqqat 5,90 7,65 5,24 2,69 4,60 5,06 4,83 5,75 5,74 4,69 5,81 5,58
Nukappiaqqat 3,72 5,32 4,21 2,18 4,64 5,33 3,30 3,63 4,31 3,28 4,72 4,52
* 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
2019
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 combi-Arnat-2019 combi-Angutit-2019
Ukiut 35-39
Atuarfik tunngaviliivik, 10.klasse tikillugu 783 1.228 705 1.199 655 1.125 627 1.068 631 1.037 613 986
Ilinniarnertuunngorniarneq 117 99 112 84 104 79 105 70 91 63 82 73
Inuussutissarsiornermik ilinniarneq 619 686 611 678 583 658 584 697 601 688 589 686
Angusanik qaffassaaneq 41 48 37 43 37 32 32 26 30 23 24 20
Ingerlariaqqiffiusumik ilinniarneq naatsoq 106 98 96 81 84 75 88 70 87 76 77 67
Bachelorinngorniarneq 43 17 41 15 41 17 35 13 35 13 32 18
Professionsbachelorinngorniarneq 347 109 332 111 320 114 320 98 301 94 296 93
Kandidatinngorniarneq 119 69 109 70 111 68 105 68 86 70 81 76
Ilisimatuunngorniarneq 8 3 8 4 6 3 6 3 6 1 2 0

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())
2019
2020
2021
2022
2023
Angutit,2019 Arnat,2019 Angutit,2020 Arnat,2020 Angutit,2021 Arnat,2021 Angutit,2022 Arnat,2022 Angutit,2023 Arnat,2023
Agguaqatigiissillugu qaammammut saniatigooralugu suliffillit
Pisortat allaffissornerat kiffartuussinerallu 3.810 8.721 3.889 8.859 3.971 8.931 3.933 8.927 3.819 8.911
Aalisarneq aalisakkanillu tunisassiornermi niuerneq 4.009 716 3.883 719 3.682 680 3.673 670 3.820 698
Niuertunik pilersuineq atungassanillu nioqquteqarneq 1.488 1.424 1.495 1.417 1.534 1.486 1.553 1.519 1.564 1.490
Assartuineq assartugassalerinerlu 1.525 488 1.521 457 1.508 447 1.558 483 1.594 493
Sanaartorneq sanaartortitsinerlu 1.773 175 1.850 179 2.090 207 2.101 204 2.044 195
Akunnittarfiit neriniartarfiillu 336 382 310 351 351 427 364 469 408 495
Paasissutissalerineq attaveqaatilerinerlu 431 197 419 196 413 195 379 184 373 182
Allaffissornikkut kiffartuussinerit 297 187 297 165 250 152 244 159 261 188
Nukissiuutinik imermillu pilersuineq 362 75 360 77 360 77 348 69 353 71
Kiffartuussilluni inuussutissarsiorfiit allat 171 165 153 152 155 151 156 160 159 171
Inissiaateqarneq 159 103 170 106 166 103 179 118 199 127
Namminersortunit, ilisimatusarnikkut teknikkikkullu suliaqartunit kiffartuussinerit 151 111 158 109 174 116 176 123 179 119
Nioqqutissiorneq 159 49 166 47 173 50 177 52 172 58
Aningaaseriviit aningaasaliisarfiillu 63 122 75 127 72 128 64 137 64 143
Aatsitassarsiorneq 66 25 65 25 85 34 73 34 56 29
Nunalerineq, orpippassualerineq nunalerinermilu tunisassiorneq niuernerlu 81 19 85 19 92 18 80 18 68 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")
2019
2020
2021
2022
2023
2019,Angutit 2019,Arnat 2020,Angutit 2020,Arnat 2021,Angutit 2021,Arnat 2022,Angutit 2022,Arnat 2023,Angutit 2023,Arnat
Ledighedsprocent
Katillugit 4,6 4,0 4,9 4,2 3,9 3,4 3,4 3,0 3,1 2,7
18-19-inik ukiullit 7,1 8,2 8,4 8,6 7,1 6,4 4,8 5,3 5,1 5,5
20-24-nik ukiullit 6,4 6,1 6,3 6,0 4,8 4,9 4,1 4,4 3,8 3,5
25-29-nik ukiullit 4,8 4,6 4,7 4,2 3,4 3,0 3,4 2,8 2,9 2,3
30-34-nik ukiullit 3,9 4,1 4,2 4,5 3,2 3,4 3,4 3,1 2,9 2,8
35-39-nik ukiullit 4,4 3,6 4,4 4,1 3,2 3,2 2,7 2,8 2,3 2,5
40-44-nik ukiullit 4,1 3,2 4,4 3,4 3,5 3,2 2,8 2,4 3,0 2,5
45-49-nik ukiullit 4,3 3,7 4,0 3,8 3,1 2,7 2,6 2,1 2,3 2,1
50-54-inik ukiullit 4,5 3,3 5,1 3,7 4,3 3,7 3,8 3,0 2,9 2,5
55-59-inik ukiullit 4,1 3,3 4,9 3,6 4,4 3,2 3,6 2,8 3,9 2,8
60-inik ukiullit-soraarneruss. ukiussarititat 5,0 3,0 5,3 2,7 4,1 2,9 3,9 3,0 3,8 2,9
* 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()) - 10) %>% 
  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 68,1 72,5
* 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")
2019 2020 2021 2022 2023
Angut 190 165 148 128 94
Arnaq 897 867 834 718 625
* Inuit amerlassusaat