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")
2022 2021 2020 2019 2018 2017
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut
Meeqqat atuarfiat
Angutit 234.135 221.379 221.751 220.133 219.867 212.780
Arnat 172.329 168.006 162.850 159.522 155.738 152.098
Ilinniarnertuutut ilinniarneq
Angutit 348.357 322.442 306.486 307.544 306.674 310.075
Arnat 233.118 206.330 200.436 195.745 188.368 186.094
Inuussutissarsiutinik ilinniarneq
Angutit 417.861 398.507 402.334 386.489 381.560 370.171
Arnat 295.870 281.768 274.182 272.668 265.089 257.890
Kort videregående uddannelse
Angutit 349.507 338.797 317.874 331.182 307.996 296.458
Arnat 320.252 306.207 302.860 302.482 294.940 291.796
Mellemlang videregående uddannelse
Angutit 554.668 548.204 528.469 535.014 530.823 555.195
Arnat 446.885 436.757 415.822 410.472 405.119 416.966
Ingerlaqqilluni ilinniarnerit
Angutit 768.439 767.868 757.895 765.664 713.391 780.811
Arnat 618.724 606.830 590.392 578.418 561.255 613.203
* 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")
2022 2021 2020 2019 2018 2017
Isertitat akileraarutinik allanilluunniit ilanngarneqanngitsut, 30-34 ukiullit
Meeqqat atuarfiat
Angutit 250.832 235.607 226.090 229.122 220.892 208.084
Arnat 176.076 171.912 165.499 161.197 158.658 155.093
Ilinniarnertuutut ilinniarneq
Angutit 299.808 294.967 289.586 313.604 273.332 301.519
Arnat 208.197 206.114 192.447 188.873 187.006 176.472
Inuussutissarsiutinik ilinniarneq
Angutit 408.290 381.070 357.371 347.546 331.054 325.784
Arnat 280.832 268.671 260.461 266.763 256.601 249.524
Kort videregående uddannelse
Angutit 348.301 342.909 306.309 294.879 347.271 336.414
Arnat 262.814 220.418 222.469 208.038 226.267 233.077
Mellemlang videregående uddannelse
Angutit 505.500 505.478 473.930 446.026 436.353 432.105
Arnat 387.396 378.566 359.046 359.464 360.720 376.074
Ingerlaqqilluni ilinniarnerit
Angutit 525.041 504.234 522.714 483.043 472.560 501.613
Arnat 485.566 473.687 472.983 460.440 441.388 492.136
* 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 2005 2009 2013 2014 2015 2016 2017 2018 2020 2021
Arnat amerlassusaat 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
2022 4,3 5,0 8,4 9,2
2021 4,0 4,5 7,9 8,3
2020 3,7 4,5 7,3 8,1
2019 3,5 4,1 7,0 7,8
* 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)
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
2019 50 39 52 51 59 50 0 0 70 61 43 40 60 48 67 53

Karakteerit agguaqatigiissinnerat

GS Misilitsinnermi karakteerit agguaqatigiissinnerat suiaassuseq malillugu
# Import
UDXFKK_raw <-
  statgl_url("UDXFKK", lang = language) %>% 
  statgl_fetch(unit             = "Avg",
               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
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
2018
Niviarsiaqqat 6,41 6,69 6,29 2,18 5,20 5,01 4,44 4,47 4,92 3,98 4,46 5,26
Nukappiaqqat 4,15 5,33 5,07 2,05 5,37 5,39 3,20 4,13 4,32 3,05 3,32 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("level of education" = px_all(),
               gender               = px_all(),
               time                 = px_all(),
               age                  = "35-39",
               .col_code = TRUE) %>% 
  as_tibble()

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

# Plot
UDXISCPROD %>% 
  arrange(`level of education`) %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `level of education`
  )) +
  geom_area(position = "fill") +
  facet_wrap(~ gender) +
  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(
    `level of education` != UDXISCPROD_raw[[2]][1],
    time > year(Sys.Date()) - 7
    ) %>% 
  mutate(
    `level of education` = `level of education` %>% factor(levels = unique(`level of education`))
    ) %>% 
  arrange(`level of education`, desc(time)) %>% 
  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[["age"]] %>% table())
2022
2021
2020
2019
2018
combi-Arnat-2022 combi-Angutit-2022 combi-Arnat-2021 combi-Angutit-2021 combi-Arnat-2020 combi-Angutit-2020 combi-Arnat-2019 combi-Angutit-2019 combi-Arnat-2018 combi-Angutit-2018
Ukiut 35-39
Atuarfik tunngaviliivik, 10.klasse tikillugu 681 1.153 627 1.068 632 1.037 614 986 626 936
Ilinniarnertuunngorniarneq 112 83 106 71 91 63 82 73 78 76
Inuussutissarsiornermik ilinniarneq 577 648 561 685 556 667 516 657 470 633
Angusanik qaffassaaneq 45 31 54 37 75 43 97 48 113 65
Ingerlariaqqiffiusumik ilinniarneq naatsoq 82 74 89 69 87 76 77 67 76 80
Bachelorinngorniarneq 37 15 35 13 35 13 32 18 34 17
Professionsbachelorinngorniarneq 299 102 319 98 300 94 295 93 286 82
Kandidatinngorniarneq 103 63 105 68 86 70 81 76 96 72
Ilisimatuunngorniarneq 5 1 6 3 6 1 2 0 4 3

Suliffeqarneq

GS Najugaqavissut akornanni pingaarnertut suliffillit, inuussutissarsiorfiit aamma suiaassuseq malillugit
# Import
url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR30/ARXBFB01.px")

ARXBFB01_raw <-
  url |> 
  statgl_fetch(
    industry             = c("01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16"),
    gender               = c("M","K"),
    "inventory variable" = "G",
    time                 = px_all(),
    .col_code            = T
  ) |> 
  as_tibble()

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

# Plot
ARXBFB01 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gender
    )) +
  geom_area() +
  facet_wrap(~ industry, 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(time >= year(Sys.time()) - 6) %>% 
  mutate(industry = industry %>% fct_reorder(value) %>% fct_rev()) %>% 
  arrange(industry, time) %>% 
  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())
2018
2019
2020
2021
2022
Angutit,2018 Arnat,2018 Angutit,2019 Arnat,2019 Angutit,2020 Arnat,2020 Angutit,2021 Arnat,2021 Angutit,2022 Arnat,2022
Agguaqatigiissillugu qaammammut saniatigooralugu suliffillit
Pisortat allaffissornerat kiffartuussinerallu 3.782 8.540 3.810 8.721 3.889 8.859 3.968 8.928 3.941 8.932
Aalisarneq aalisakkanillu tunisassiornermi niuerneq 3.979 708 4.009 716 3.880 719 3.680 683 3.672 671
Niuertunik pilersuineq atungassanillu nioqquteqarneq 1.482 1.387 1.488 1.424 1.498 1.417 1.540 1.485 1.556 1.519
Sanaartorneq sanaartortitsinerlu 1.781 168 1.773 175 1.850 179 2.089 206 2.102 205
Assartuineq assartugassalerinerlu 1.477 464 1.525 488 1.521 457 1.505 446 1.560 483
Akunnittarfiit neriniartarfiillu 341 406 336 382 310 351 351 425 363 466
Paasissutissalerineq attaveqaatilerinerlu 451 205 431 197 419 196 413 195 379 184
Allaffissornikkut kiffartuussinerit 313 212 296 187 297 165 248 152 243 159
Nukissiuutinik imermillu pilersuineq 355 68 362 75 360 77 358 77 348 69
Kiffartuussilluni inuussutissarsiorfiit allat 169 151 171 165 153 152 155 151 158 161
Namminersortunit, ilisimatusarnikkut teknikkikkullu suliaqartunit kiffartuussinerit 144 117 151 111 158 109 174 116 176 123
Inissiaateqarneq 136 98 159 103 170 106 166 103 180 118
Nioqqutissiorneq 159 42 159 49 166 47 173 50 176 52
Aningaaseriviit aningaasaliisarfiillu 58 110 63 122 75 127 72 128 64 137
Aatsitassarsiorneq 67 29 66 25 65 25 85 34 73 34
Nunalerineq, orpippassualerineq nunalerinermilu tunisassiorneq niuernerlu 82 17 81 19 85 19 92 18 80 18

Suliffissaaleqineq

GS Suiaassuseq malillugu suliffissaaleqineq procentinngorlugu
# Import

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

ARXLED3_raw <-
  url |> 
  statgl_fetch(
    gender               = c("M", "K"),
    age                  = px_all(),
    time                 = px_all(),
    "inventory variable" = "P",
    .col_code            = T
  ) |> 
  as_tibble()

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

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = time, 
    y     = value,
    color = gender
    )) +
  geom_line(size = 1.5) +
  facet_wrap(~ age, 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(-`inventory variable`) |> 
  mutate(
    age = age %>% fct_inorder(),
    time = time %>% as.numeric()
    ) %>% 
  filter(time > max(time) - 5) %>% 
  arrange(age, time) %>% 
  unite(combi, time, gender, 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")
2018
2019
2020
2021
2022
2018,Angutit 2018,Arnat 2019,Angutit 2019,Arnat 2020,Angutit 2020,Arnat 2021,Angutit 2021,Arnat 2022,Angutit 2022,Arnat
Ledighedsprocent
Katillugit 5,2 4,8 4,6 4,0 4,9 4,2 3,9 3,4 3,4 3,0
18-19-inik ukiullit 11,9 12,5 7,1 8,2 8,4 8,6 7,1 6,4 4,8 5,3
20-24-nik ukiullit 7,1 8,6 6,4 6,1 6,3 6,0 4,8 4,9 4,1 4,4
25-29-nik ukiullit 5,3 5,2 4,8 4,6 4,7 4,2 3,4 3,0 3,4 2,8
30-34-nik ukiullit 4,7 4,5 3,9 4,1 4,2 4,5 3,2 3,4 3,4 3,1
35-39-nik ukiullit 4,9 4,3 4,4 3,6 4,4 4,1 3,1 3,2 2,8 2,8
40-44-nik ukiullit 4,2 3,7 4,1 3,2 4,4 3,4 3,5 3,2 2,9 2,4
45-49-nik ukiullit 4,7 3,8 4,3 3,7 4,0 3,8 3,1 2,7 2,6 2,1
50-54-inik ukiullit 4,4 4,2 4,5 3,3 5,1 3,7 4,3 3,7 3,8 3,0
55-59-inik ukiullit 4,9 3,5 4,1 3,3 4,9 3,6 4,4 3,2 3,6 2,8
60-inik ukiullit-soraarneruss. ukiussarititat 5,7 3,0 5,0 3,0 5,3 2,7 4,1 2,9 3,9 3,0
* 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
2014 - 2018 68,8 73 68,6 72,3
* 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")
2018 2019 2020 2021 2022
Angut 185 190 165 148 128
Arnaq 838 897 867 834 718
* Inuit amerlassusaat