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")
2023 2022 2021 2020 2019 2018
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut
Meeqqat atuarfiat
Angutit 246.610 235.495 216.588 221.765 220.133 219.879
Arnat 178.784 172.638 165.161 162.866 159.533 155.737
Ilinniarnertuutut ilinniarneq
Angutit 343.299 330.860 321.803 306.222 307.544 306.674
Arnat 216.107 219.648 202.971 200.285 195.457 188.218
Inuussutissarsiutinik ilinniarneq
Angutit 426.171 412.919 392.943 402.291 386.489 381.488
Arnat 301.973 294.195 279.924 274.136 272.691 265.099
Kort videregående uddannelse
Angutit 355.763 339.158 337.080 317.874 331.182 307.996
Arnat 326.231 320.197 303.812 302.860 302.482 294.940
Mellemlang videregående uddannelse
Angutit 581.646 547.975 539.926 528.469 535.014 530.823
Arnat 447.214 438.987 426.767 415.880 410.435 405.057
Ingerlaqqilluni ilinniarnerit
Angutit 791.874 760.137 763.632 757.895 765.664 713.391
Arnat 615.166 610.568 602.025 590.392 577.631 560.352
* 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")
2023 2022 2021 2020 2019 2018
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut, 30-34 ukiullit
Meeqqat atuarfiat
Angutit 268.042 253.788 230.804 226.090 229.122 220.892
Arnat 187.337 176.863 171.427 165.678 161.194 158.488
Ilinniarnertuutut ilinniarneq
Angutit 316.108 297.391 296.680 289.586 313.604 273.332
Arnat 210.529 203.560 207.277 191.614 188.123 186.754
Inuussutissarsiutinik ilinniarneq
Angutit 426.184 408.503 380.457 357.371 347.546 331.054
Arnat 283.068 275.319 265.726 260.237 266.759 256.674
Kort videregående uddannelse
Angutit 359.357 352.560 349.079 306.309 294.879 347.271
Arnat 248.426 251.874 208.128 222.469 208.038 226.267
Mellemlang videregående uddannelse
Angutit 486.531 497.058 498.912 473.930 446.026 436.353
Arnat 387.513 382.637 377.972 359.267 359.695 360.720
Ingerlaqqilluni ilinniarnerit
Angutit 560.435 519.394 503.610 522.714 483.043 472.560
Arnat 498.549 482.339 471.482 472.983 460.440 441.388
* 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())
2023
2022
2021
2020
2019
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 703 1.198 656 1.125 628 1.068 632 1.037 614 986
Ilinniarnertuunngorniarneq 113 84 104 79 105 70 91 63 82 73
Inuussutissarsiornermik ilinniarneq 613 679 575 654 562 685 556 667 516 657
Angusanik qaffassaaneq 37 43 45 35 54 37 75 43 97 48
Ingerlariaqqiffiusumik ilinniarneq naatsoq 96 80 84 75 88 70 87 76 77 67
Bachelorinngorniarneq 41 15 41 17 35 13 35 13 32 18
Professionsbachelorinngorniarneq 331 111 319 114 319 98 300 94 295 93
Kandidatinngorniarneq 110 70 111 68 105 68 86 70 81 76
Ilisimatuunngorniarneq 7 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 = "da") |> 
  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
Mænd,2019 Kvinder,2019 Mænd,2020 Kvinder,2020 Mænd,2021 Kvinder,2021 Mænd,2022 Kvinder,2022 Mænd,2023 Kvinder,2023
Hovedbeskæftigelse i gennemsnit pr. måned
Offentlig forvaltning og service 3.810 8.721 3.889 8.859 3.971 8.931 3.933 8.927 3.819 8.911
Fiskeri og fiskerirelateret industri og handel 4.009 716 3.883 719 3.682 680 3.673 670 3.820 698
Engroshandel og detailhandel 1.488 1.424 1.495 1.417 1.534 1.486 1.553 1.519 1.564 1.490
Transport og godshåndtering 1.525 488 1.521 457 1.508 447 1.558 483 1.594 493
Bygge- og anlægsvirksomhed 1.773 175 1.850 179 2.090 207 2.101 204 2.044 195
Overnatningsfaciliteter og restaurationsvirksomhed 336 382 310 351 351 427 364 469 408 495
Information og kommunikation 431 197 419 196 413 195 379 184 373 182
Administrative tjenesteydelser og hjælpetjenester 297 187 297 165 250 152 244 159 261 188
Energi- og vandforsyning 362 75 360 77 360 77 348 69 353 71
Øvrige serviceerhverv 171 165 153 152 155 151 156 160 159 171
Fast ejendom 159 103 170 106 166 103 179 118 199 127
Liberale, videnskabelige og tekniske tjenesteydelser 151 111 158 109 174 116 176 123 179 119
Fremstillingsvirksomhed 159 49 166 47 173 50 177 52 172 58
Pengeinstitut og finansvirksomhed 63 122 75 127 72 128 64 137 64 143
Råstofindvinding 66 25 65 25 85 34 73 34 56 29
Landbrug, skovbrug og landbrugsrelateret industri og handel 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