Utertiguk

Anguniagaq 4: Ilinniartitaaneq pitsaassusilik

Meeqqat atualinnginnermi ulluunerani neqeroorutiniittut, 3-niik 5-inut ukiullit

FN 4.2.2 Meeqqat atualinnginnermi ulluunerani neqeroorutiniittut (3-5-inik ukiullit)
# Import
OFXUKN1_raw <-
  statgl_url("OFXUKN1", lang = language) %>% 
  statgl_fetch(
    born_var   = 3:5,
    inst_type2 = 1:5,
    .col_code  = TRUE
    ) %>% 
  as_tibble()

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  mutate(across(where(is.integer), ~ if_else(is.na(.x), 0, .x))) |> 
  summarise(value = sum(value), .by = c(inst_type2, aar)) |> 
  mutate(
    aar = aar %>% make_date(),
    inst_type2 = inst_type2 %>% fct_inorder(),
    alder = "Børn 3-5 år"
    )

# Plot
OFXUKN1 %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = inst_type2
  )) +
  geom_col() +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = FALSE, nrow = 2)) +
  labs(
    title    = sdg4$figs$fig1$title[language],
    subtitle = OFXUKN1[[4]][1],
    x        = " ",
    y        = sdg4$figs$fig1$y_lab[language],
    fill     = " ",
    caption  = sdg4$figs$fig1$cap[language]
  )

Kisitsisaataasivik 


# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  #arrange(desc(time)) %>% 
  filter(aar >= year(Sys.time()) - 5) %>% 
  mutate(across(where(is.integer), ~ if_else(is.na(.x), 0, .x))) |> 
  summarise(value = sum(value), .by = c(inst_type2, aar)) |> 
  mutate(
    aar = aar %>% factor(levels = unique(aar)),
    inst_type2 = inst_type2 %>% fct_inorder(),
    alder = "Børn 3-5 år"
    ) %>% 
  spread(aar, value)

# Table
OFXUKN1 %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = table(OFXUKN1[[2]])) %>% 
  add_footnote(
    sdg4$figs$fig1$foot[language], 
    notation = "symbol"
    )
2021 2022 2023 2024
Børn 3-5 år
Meeraaqqeriviit 102 149 189 151
Meeqqeriviit 1.943 1.876 1.869 1.895
Meeqqeriviit meeraaqqeriviillu akuleriit 21 27 10 4
Angerlarsimafimmi paarsisartut 120 114 115 130
Pisortat neqeroorutaat allat 9 5 18 3
* Meeqqat amerlassusaat, 3-5-inik ukiullit. 
# Import
OFXUKN1_raw <-
  statgl_url("OFXUKN1", lang = language) %>% 
  statgl_fetch(
    born_var   = 3:5,
    inst_type2 = 1:5,
    bosted     = 1:2,
    .col_code  = TRUE
    ) %>% 
  as_tibble()

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  mutate(
    inst_type2 = inst_type2 %>% fct_inorder(),
    bosted  = bosted %>% fct_inorder(),
    aar = aar %>% make_date(),
    born_var = "Børn 3-5 år",
    across(where(is.integer), ~ if_else(is.na(.x), 0, .x))
    ) |> 
  summarise(value = sum(value), .by = c(inst_type2, born_var, bosted, aar))

# Plot
OFXUKN1 %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = inst_type2
    )) +
  geom_col() +
  facet_wrap(~ bosted, scales = "free_y") +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = FALSE, nrow = 2)) +
  labs(
    title    = sdg4$figs$fig2$title[language],
    subtitle = OFXUKN1[[2]][1],
    x        = " ",
    y        = sdg4$figs$fig2$y_lab[language],
    fill     = NULL,
    caption  = sdg4$figs$fig2$cap[language]
  )

Kisitsisaataasivik

# Transform
OFXUKN1 <- 
  OFXUKN1_raw %>% 
  mutate(born_var = "Børn 3-5 år",
    across(where(is.integer), ~ if_else(is.na(.x), 0, .x))) |> 
  #arrange(desc(time)) %>%
  summarise(value = sum(value), .by = c(inst_type2, born_var, bosted, aar)) |> 
  filter(aar >= year(Sys.time()) - 5) %>% 
  mutate(aar = aar %>% factor(levels = unique(aar))) %>% 
  spread(aar, value) |> 
  arrange(bosted)

# Table
OFXUKN1 %>% 
  select(-c(2, 3)) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = table(OFXUKN1[[2]])) %>% 
  pack_rows(index = table(OFXUKN1[[3]])) %>% 
  add_footnote(
    sdg4$figs$fig2$foot[language], 
    notation = "symbol"
    )
2021 2022 2023 2024
Børn 3-5 år
Illoqarfiit
Angerlarsimafimmi paarsisartut 11 17 9 7
Meeqqeriviit 1.885 1.815 1.790 1.833
Meeqqeriviit meeraaqqeriviillu akuleriit 0 0 0 0
Meeraaqqeriviit 94 133 178 140
Pisortat neqeroorutaat allat 9 5 18 3
Nunaqarfiit
Angerlarsimafimmi paarsisartut 109 97 106 123
Meeqqeriviit 58 61 79 62
Meeqqeriviit meeraaqqeriviillu akuleriit 21 27 10 4
Meeraaqqeriviit 8 16 11 11
Pisortat neqeroorutaat allat 0 0 0 0
* Meeqqat amerlassusaat, 3-5-inik ukiullit.

Meeqqat atuarfianni alloriarfinni misilitsinnernit angusat

FN 4.1.1 Meeqqat atuarfianni 3.klassini aamma 7.klassini alloriarfinni misilitsinnerni inerniliillaqqissuseq
# Import
UDXTKB_raw <-
  statgl_url("UDXTKB", lang = language) %>%
  statgl_fetch(
    subject   = px_all(),
    grade     = c(3, 7),
    unit      = "B",
    .col_code = TRUE) %>% 
  as_tibble()

# Transform
UDXTKB <-
  UDXTKB_raw %>% 
  mutate(
    time     = time %>% make_date(),
     subject =  subject %>% fct_inorder()
    )

# Plot
UDXTKB %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = subject
    )) +
  geom_line(size = 2) +
  facet_wrap(~ grade) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title    = sdg4$figs$fig3$title[language],
    subtitle = UDXTKB[[3]][1],
    x        = " ",
    y        = " ",
    color    = sdg4$figs$fig3$color[language],
    caption  = sdg4$figs$fig3$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXTKB <- 
  UDXTKB_raw %>% 
  arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  arrange(grade, desc(subject)) %>% 
  unite(combi, 1, 2, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(1, ncol(.))

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

# Table
UDXTKB %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = table(UDXTKB[[1]]))
  1. klassi
  1. klassi
Tuluttut,3. klassi Qallunaatut,3. klassi Matematikki,3. klassi Kalaallisut,3. klassi Tuluttut,7. klassi Qallunaatut,7. klassi Matematikki,7. klassi Kalaallisut,7. klassi
Inerniliillaqqissuseq (eqqortut pct.-inngorlugit)
2024 NA 41 49 45 84 42 40 56
2023 NA 48 52 48 86 45 41 59
2022 NA 41 48 41 82 51 41 62
2021 NA 47 51 48 73 50 40 61 



# Import
UDXTKB_raw <-
  statgl_url("UDXTKB", lang = language) %>%
  statgl_fetch(
    subject              = px_all(),
    grade                = c(3, 7),
    unit                 = "B",
    "place of residence" = 1:2,
    .col_code            = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXTKB <-
  UDXTKB_raw %>% 
  mutate(
    time = time %>% make_date(),
    `place of residence` = `place of residence` %>% fct_inorder(),
    subject = subject %>% fct_inorder()
    )

# Plot
UDXTKB %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = subject
  )) +
  geom_line(size = 2) +
  facet_grid(grade ~ `place of residence`) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title    = sdg4$figs$figX$title_fig4,
    subtitle = UDXTKB[[4]][1],
    x        = " ",
    y        = " ",
    color    = sdg4$figs$fig4$color[language],
    caption  = sdg4$figs$fig4$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXTKB <- 
  UDXTKB_raw %>% 
  arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(time = time %>% fct_inorder()) %>% 
  arrange(grade, subject) %>% 
  unite(combi, 1, 2, 3, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(1, 4) 

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

UDXTKB %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec1)) %>% 
  add_header_above(c(" ", head_vec2)) %>% 
  pack_rows(index = table(UDXTKB[[1]]))
  1. klassi
  1. klassi
Kalaallisut
Matematikki
Qallunaatut
Tuluttut
Kalaallisut
Matematikki
Qallunaatut
Tuluttut
Illoqarfiit,Kalaallisut,3. klassi Nunaqarfiit,Kalaallisut,3. klassi Illoqarfiit,Matematikki,3. klassi Nunaqarfiit,Matematikki,3. klassi Illoqarfiit,Qallunaatut,3. klassi Nunaqarfiit,Qallunaatut,3. klassi Illoqarfiit,Tuluttut,3. klassi Nunaqarfiit,Tuluttut,3. klassi Illoqarfiit,Kalaallisut,7. klassi Nunaqarfiit,Kalaallisut,7. klassi Illoqarfiit,Matematikki,7. klassi Nunaqarfiit,Matematikki,7. klassi Illoqarfiit,Qallunaatut,7. klassi Nunaqarfiit,Qallunaatut,7. klassi Illoqarfiit,Tuluttut,7. klassi Nunaqarfiit,Tuluttut,7. klassi
Inerniliillaqqissuseq (eqqortut pct.-inngorlugit)
2024 43 48 48 56 42 38 NA NA 55 57 39 40 45 34 86 55
2023 48 49 53 45 50 36 NA NA 57 66 43 40 47 40 88 73
2022 41 52 47 52 41 43 NA NA 62 61 41 39 54 40 86 53
2021 48 47 52 50 48 39 NA NA 59 62 40 41 52 45 76 54



Meeqqat atuarfianni soraarummeernerit

GS Meeqqat atuarfianni inaarutaasumik misilitsinnernit karakteerit
# 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,
    .col_code        = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXFKK <-
  UDXFKK_raw %>% 
  mutate(
    `type of grades` = `type of grades` %>% str_remove_all("Prøvekarakter -") %>% trimws() %>% str_to_title(),
    subject          = subject %>% fct_inorder(),
    time             = time %>% make_date()
    )

# Plot
UDXFKK %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = `type of grades`
    )) +
  geom_line(size = 2) +
  facet_wrap( ~ subject, ncol = 2) +
  theme_statgl() + 
  scale_color_statgl(guide = guide_legend(nrow = 3)) +
  labs(
    title   = sdg4$figs$fig5$title[language],
    color   = sdg4$figs$fig5$color[language],
    x       = " ",
    y       = sdg4$figs$fig5$y_lab[language],
    caption = sdg4$figs$fig5$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXFKK <-
  UDXFKK_raw %>% 
  mutate(
    `type of grades` = `type of grades` %>% 
      str_remove_all("Prøvekarakter -") %>%
      trimws() %>%
      str_to_title()
    ) %>% 
  #arrange(desc(time)) %>% 
  filter(
    value != "NA",
    time >= year(Sys.time()) - 5
    ) %>% 
  mutate(
    subject = subject %>% fct_inorder(),
    time = time %>% factor(levels = unique(time)),
    ) %>% 
  spread(5, 6) %>% 
  arrange(subject)

# Table
UDXFKK %>% 
  select(-(1:3)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(UDXFKK[[1]] %>% str_to_title())) %>% 
  pack_rows(index = table(UDXFKK[[3]])) %>% 
  add_footnote(UDXFKK[[2]][1], notation = "symbol")
2021 2022 2023 2024
Agguaqatigiissillugu Karakteeri
Kalaallisut
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 5,35 5,48 4,75 5,30
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 5,96 6,81 6,54 6,80
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 3,56 3,72 3,99 4,86
Qallunaatut
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 3,36 3,58 3,82 3,18
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 5,36 4,85 6,15 4,63
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 4,47 4,14 4,05 3,70
Matematikki
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 2,17 2,52 2,98 2,48
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 4,88 5,24 5,58 5,60
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 4,94 4,89 4,82 4,81
Tuluttut
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 4,11 4,51 4,56 5,34
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 6,49 6,52 6,99 7,55
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 4,90 5,20 5,56 5,63
* Meeqqat atuarfianni naggataarlutik atuartut


Covid-19 peqqutaalluni 2020-mi naggataarutaasumik soraarummeertoqanngilaq.



Meeqqat atuarfianniit ilinniakkanut ingerlaqqinneq

GS Meeqqat atuarfianniit inuusuttut ilinniarfiinut ingerlaqqinnerit
# Import
UDXTRFA1_raw <-
  statgl_url("UDXTRFA1", lang = language) %>% 
  statgl_fetch(
    aar       = 2,
    status    = px_all(),
    dim_aar   = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXTRFA1 <-
  UDXTRFA1_raw %>%
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  mutate(dim_aar = dim_aar %>% make_date())

  


# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x    = dim_aar,
    y    = value,
    fill = status
  )) +
  geom_col(position = "fill") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  scale_fill_statgl(reverse = TRUE) +
  theme_statgl() +
  labs(
    title    = sdg4$figs$fig6$title[language],
    subtitle = sdg4$figs$fig6$sub[language],
    x        = sdg4$figs$fig6$x_lab[language],
    y        = " ",
    fill     = sdg4$figs$fig6$fill[language],
    caption  = sdg4$figs$fig6$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXTRFA1 <- 
  UDXTRFA1_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(dim_aar >= year(Sys.time()) - 8) %>% 
  mutate(dim_aar = dim_aar %>% factor(levels = unique(dim_aar))) %>% 
  spread(3, 4)

# Table
UDXTRFA1 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  add_footnote(
    sdg4$figs$fig6$foot[language],
    notation = "symbol"
  )
2018 2019 2020 2021 2022 2023
Naammassisimasut 7 5 7 3 6 0
Suli ingerlatsisut 243 250 269 252 226 0
Unitsitsisut 108 82 96 97 116 0
Aallartitsisimanngitsut 340 311 312 357 330 0
* Meeqqat atuarfianniit inuusuttut ilinniarfiinut ingerlaqqinnerit (meeqqat atuarfianni soraarummeereernermi ukiut marluk qaangiunnerini), ilinniartut amerlassusaat. 



# Import
UDXTRFA1_raw <-
  statgl_url("UDXTRFA1", lang = language) %>% 
  statgl_fetch(
    aar       = 2,
    status    = px_all(),
    dim_aar   = px_all(),
    sex       = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXTRFA1 <-
  UDXTRFA1_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  mutate(dim_aar = dim_aar %>% make_date())

# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x    = dim_aar,
    y    = value,
    fill = status
  )) +
  geom_col(position = "fill") +
  facet_wrap(~ sex) +
  scale_y_continuous(labels  = scales::percent_format()) +
  scale_fill_statgl(reverse = TRUE) +
  theme_statgl() +
  labs(
    title    = sdg4$figs$fig7$title[language],
    subtitle = sdg4$figs$fig7$sub[language],
    x        = sdg4$figs$fig7$x_lab[language],
    y        = " ",
    fill     = sdg4$figs$fig7$fill[language],
    caption  = sdg4$figs$fig7$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXTRFA1 <- 
  UDXTRFA1_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(dim_aar >= year(Sys.time()) - 8) %>% 
  mutate(dim_aar = dim_aar %>% factor(levels = unique(dim_aar))) %>% 
  spread(4, 5) %>% 
  arrange(status)
  
# Table
UDXTRFA1 %>% 
  select(-1, -3) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = table(UDXTRFA1[[3]])) %>% 
  add_footnote(
    sdg4$figs$fig7$foot[language],
    notation = "symbol"
  )
2018 2019 2020 2021 2022 2023
Naammassisimasut
Angutit 171 174 170 181 169 0
Arnat 169 137 142 176 161 0
Suli ingerlatsisut
Angutit 7 3 7 2 6 0
Arnat 0 2 0 1 0 0
Unitsitsisut
Angutit 101 102 119 111 94 0
Arnat 142 148 150 141 132 0
Aallartitsisimanngitsut
Angutit 45 45 45 44 45 0
Arnat 63 37 51 53 71 0
* Karaktergennemsnit, Folkeskolens Afgangselever



Ilinniarnertuunngorniarnermiit ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinneq

GS Ilinniarnertuunngorniarnermiit ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinnerit
# Import
UDXTRGU2_raw <-
  statgl_url("UDXTRGU2", lang = language) %>% 
  statgl_fetch(
    aar     = 2,
    status  = px_all(),
    dim_aar = px_all(),
    .col_code = TRUE) %>% 
  as_tibble()

# Transform
UDXTRGU2 <-
  UDXTRGU2_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 2) |> 
  mutate(dim_aar = dim_aar %>% make_date())

# Plot
UDXTRGU2 %>% 
  ggplot(aes(
    x    = dim_aar,
    y    = value,
    fill = status
    )) +
  geom_col(position = "fill") +
  scale_y_continuous(labels  = scales::percent_format(
    scale = 100, 
    accuracy = 1, 
    big.mark = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg4$figs$fig8$title[language],
    subtitle = sdg4$figs$fig8$sub[language],
    x        = sdg4$figs$fig8$x_lab[language],
    y        = " ",
    fill     = sdg4$figs$fig8$fill[language],
    caption  = sdg4$figs$fig8$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXTRGU2 <-
  UDXTRGU2_raw %>% 
  filter(dim_aar >= year(Sys.time()) - 9 & dim_aar < year(Sys.time()) - 3) %>% 
  mutate(dim_aar = dim_aar %>% factor(levels = unique(dim_aar))) %>% 
  spread(3, 4)

# Table
UDXTRGU2 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  add_footnote(
    sdg4$figs$fig8$foot[language],
    notation = "symbol"
    )
2017 2018 2019 2020 2021 2022
Naammassisimasut 29 32 35 36 26 31
Suli ingerlatsisut 134 137 131 112 124 125
Unitsitsisut 61 46 50 54 67 45
Aallartitsisimanngitsut 104 89 87 93 95 68
* Ilinniarnertuunngorniarnermiik ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinneq (ilinniarnertuunngoreernermik ukiut marluk qaangiunnerini), ilinniartut amerlassusaat. 



# Import
UDXTRGU2_raw <-
  statgl_url("UDXTRGU2", lang = language) %>% 
  statgl_fetch(
    aar       = 2,
    status    = px_all(),
    dim_aar   = px_all(),
    sex       = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXTRGU2 <- 
  UDXTRGU2_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) |> 
  mutate(dim_aar = dim_aar %>% make_date())

# Plot
UDXTRGU2 %>% 
  ggplot(aes(
    x    = dim_aar,
    y    = value,
    fill = status
  )) +
  geom_col(position = "fill") +
  facet_wrap( ~ sex) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1,
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg4$figs$fig9$title[language],
    subtitle = sdg4$figs$fig9$sub[language],
    x        = sdg4$figs$fig9$x_lab[language],
    y        = " ",
    fill     = sdg4$figs$fig9$fill[language],
    caption  = sdg4$figs$fig9$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXTRGU2 <-
  UDXTRGU2_raw %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(dim_aar >= year(Sys.time()) - 8 & dim_aar < year(Sys.time()) - 3) %>% 
  mutate(dim_aar = dim_aar %>% factor(levels = unique(dim_aar))) %>% 
  spread(4, 5) %>% 
  arrange(status)

# Table
UDXTRGU2 %>% 
  select(-c(1, 3)) %>% 
  rename("  " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = UDXTRGU2[[3]] %>% table()) %>% 
  add_footnote(
    sdg4$figs$fig9$foot[language],
    notation = "symbol"
  )
2018 2019 2020 2021 2022
Naammassisimasut
Angutit 38 49 44 37 32
Arnat 51 38 49 58 36
Suli ingerlatsisut
Angutit 13 9 12 12 9
Arnat 19 26 24 14 22
Unitsitsisut
Angutit 52 49 32 33 46
Arnat 85 82 80 91 79
Aallartitsisimanngitsut
Angutit 13 16 23 21 13
Arnat 33 34 31 46 32
* Ilinniarnertuunngorniarnermiik ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinneq (ilinniarnertuunngoreernermik ukiut marluk qaangiunnerini), ilinniartut amerlassusaat.

Ilinniakkamik ingerlatsitsut

GS Ilinniakkamik ingerlatsisut amerlassusaat ilinniakkap qaffasissusaa aamma nuna malillugit
# Import
UDXISC11B_raw <-
  statgl_url("UDXISC11B", lang = language) %>% 
  statgl_fetch(
    isced = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(taar = taar %>% make_date(),
        isced = isced %>%  fct_inorder() %>% fct_rev(),
        value = value * 10^-3)

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = isced
  )) +
  geom_col() +
   guides(fill = guide_legend(nrow = 4, byrow = TRUE)) +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = FALSE)) +
  labs(
    title   = sdg4$figs$fig10$title[language],
    x       = " ",
    y       = sdg4$figs$fig10$y_lab[language],
    fill    = NULL,
    caption = sdg4$figs$fig10$cap[language]
  )

Kisitsisaataasivik 


# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 6) %>% 
  mutate(
         isced = isced %>% factor(levels = unique(isced)),
         taar  = taar %>% factor(levels = unique(taar)),
         ) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig10$foot[language],
    notation = "symbol"
    )
2020 2021 2022 2023 2024
Ilinniarnertuunngorniarneq 1.170 1.161 1.129 1.064 1.071
Inuussutissarsiornermik ilinniarneq 1.136 1.025 1.001 921 876
Angusanik qaffassaanerit 29 14 19 22 26
Ingerlariaqqiffiusumik ilinniarneq naatsoq 167 155 162 155 162
Bachelorinngorniarneq 373 359 346 333 353
Professionsbachelorinngorniarneq 550 527 528 511 528
Kandidatinngorniarneq 170 165 155 154 150
* Ilinniakkamik ingerlatsisut amerlassusaat. 



# Import
UDXISC11B_raw <-
  statgl_url("UDXISC11B", lang = language) %>% 
  statgl_fetch(
    skoleomr   = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Translate
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(
    taar     = taar %>% make_date(),
    skoleomr = skoleomr %>% fct_reorder(value),
    value    = value * 10^-3
    )

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = skoleomr
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE)) +
  labs(
    title   = sdg4$figs$fig11$title[language],
    x       = " ",
    y       = sdg4$figs$fig11$y_lab[language],
    fill    = " ",
    caption = sdg4$figs$fig11$cap[language] 
  )

Kisitsisaataasivik 


# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 6) %>% 
  mutate(
    taar    = taar %>% fct_inorder(),
    skoleomr = skoleomr %>% fct_inorder
    ) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig11$foot[language],
    notation = "symbol"
    )
2020 2021 2022 2023 2024
Kalaallit Nunaanni Atuarfiit 3.061 2.922 2.806 2.638 2.580
Danmarkimi atuarfiit 510 464 512 506 569
Nunani allani atuarfiit 24 20 22 16 17
* Ilinniakkamik ingerlatsisut amerlassussaat.



FN 4.3.1 Ilinniakkamik ingerlatsisut amerlassusaat suiaassuseq malillugu
# Import
UDXISC11B_raw <-
  statgl_url("UDXISC11B", lang = language) %>% 
  statgl_fetch(
    sex       = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(
    taar  = taar %>% make_date(),
    sex   = sex %>% reorder(value),
    value = value * 10^-3
    )

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = sex
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl() +
  labs(
    title   = sdg4$figs$fig12$title[language],
    x       = " ",
    y       = sdg4$figs$fig12$y_lab[language],
    fill    = " ",
    caption = sdg4$figs$fig12$cap[language]
  )

Kisitsisaataasivik 


# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 6) %>% 
  mutate(taar = taar %>% fct_inorder()) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig12$foot[language],
    notation = "symbol"
    )
2020 2021 2022 2023 2024
Angutit 1.363 1.289 1.231 1.150 1.160
Arnat 2.232 2.117 2.109 2.010 2.006
* Ilinniakkamik ingerlatsisut amerlassusaat.



Ilinniarnerit naammassineqarsimasut

GS Ilinniarnerit naammassineqarsimasut amerlassusaat
# Import
UDXISC11D_raw <-
  statgl_url("UDXISC11D", lang = language) %>% 
  statgl_fetch(
    Isced     = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11D <-
  UDXISC11D_raw %>%
  mutate(
    slutaar              = slutaar %>% make_date(),
    id                   = row_number(),
    Isced = Isced %>% str_remove("uddannelse"),
    Isced = Isced %>% fct_reorder(id, .fun = min, na.rm = TRUE) %>% fct_rev()
  )

# Plot
UDXISC11D %>% 
  ggplot(aes(
    x    = slutaar,
    y    = value,
    fill = Isced
    )) +
  geom_col() +
  scale_y_continuous(labels = scales::number_format(
    accuracy     = 1,
    big.mark     = ".",
    decimal.mark = ",")) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE, nrow = 4)) +
  labs(
    title    = sdg4$figs$fig13$title[language],
    subtitle = sdg4$figs$fig13$sub[language],
    x        = " ",
    y        = sdg4$figs$fig13$y_lab[language],
    fill     = sdg4$figs$fig13$fill[language],
    caption  = sdg4$figs$fig13$cap[language] 
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  #arrange(desc(time)) %>% 
  filter(slutaar >= year(Sys.time()) - 6) %>% 
  mutate(
    Isced    = Isced %>% fct_inorder(),
    slutaar  = slutaar %>% fct_inorder()
    ) %>% 
  spread(2, 3)

# Table
UDXISC11D %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig13$foot[language],
    notation = "symbol"
    )
2020 2021 2022 2023 2024
Ilinniarnertuunngorniarneq 310 318 282 333 293
Inuussutissarsiornermik ilinniarneq 411 448 376 403 362
Angusanik qaffassaanerit 125 141 119 99 89
Ingerlariaqqiffiusumik ilinniarneq naatsoq 68 66 62 62 62
Bachelorinngorniarneq 47 54 55 56 53
Professionsbachelorinngorniarneq 107 119 103 88 95
Kandidatinngorniarneq 40 35 32 35 28
* Qaffasinnerpaatut ilinniakat naammassineqarsimasut, naammassinnissimasut amerlassusaat. 



# Import
UDXISC11D_raw <-
  statgl_url("UDXISC11D", lang = language) %>% 
  statgl_fetch(
    Isced     = px_all(),
    sex       = px_all(),
    skoleomr  = c("A_SG", "B_SD"),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  mutate(
    Isced    = Isced %>% str_remove("uddannelse") %>% trimws(),
    Isced    = Isced %>% fct_inorder() %>% fct_rev(),
    sex      = sex  %>% fct_inorder(),
    skoleomr = skoleomr %>% fct_inorder,
    slutaar  = slutaar    %>% make_date()
  )

# Plot
UDXISC11D %>% 
  ggplot(aes(
    x = slutaar,
    y = value, 
    fill = Isced
  )) +
  geom_col() +
  facet_grid(skoleomr ~ sex, 
             scales = "free_y") +
  scale_y_continuous(labels = scales::number_format(
    accuracy = 1, 
    big.mark = ".",
    decimal.mark = ","
    )) +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, 
                    guide = guide_legend(reverse = TRUE, nrow = 4)) +
  labs(
    title    = sdg4$figs$fig14$title[language],
    subtitle = sdg4$figs$fig14$sub[language],
    x        = " ",
    y        = sdg4$figs$fig14$y_lab[language],
    fill     = sdg4$figs$fig14$fill[language],
    caption  = sdg4$figs$fig14$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  #arrange(desc(time)) %>% 
  filter(slutaar >= year(Sys.time()) - 4) %>% 
  mutate(
    slutaar  = slutaar %>% fct_inorder(),
    Isced    = Isced %>% fct_inorder(),
    skoleomr = skoleomr %>% fct_inorder()
    ) %>% 
  unite(combi, 2, 4, sep = ",") %>%  
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(2, 4)

vec      <- UDXISC11D[-(1:2)] %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- table(vec[c(F, T)]) %>% rev()
col_vec  <- vec[c(T, F)]

# Table
UDXISC11D %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  pack_rows(index = table(UDXISC11D[[1]])) %>% 
  add_header_above(c(" ", head_vec))
2024
2023
2022
Angutit,2022 Angutit,2023 Angutit,2024 Arnat,2022 Arnat,2023 Arnat,2024
Ilinniarnertuunngorniarneq
Kalaallit Nunaanni Atuarfiit 86 84 107 163 214 166
Danmarkimi atuarfiit 17 6 7 13 26 11
Inuussutissarsiornermik ilinniarneq
Kalaallit Nunaanni Atuarfiit 164 166 147 198 222 197
Danmarkimi atuarfiit 8 6 7 5 8 10
Angusanik qaffassaanerit
Kalaallit Nunaanni Atuarfiit 41 27 30 75 67 59
Danmarkimi atuarfiit 1 2 0 2 3 0
Ingerlariaqqiffiusumik ilinniarneq naatsoq
Kalaallit Nunaanni Atuarfiit 17 11 18 27 35 29
Danmarkimi atuarfiit 8 5 7 10 10 7
Bachelorinngorniarneq
Kalaallit Nunaanni Atuarfiit 8 15 16 24 29 22
Danmarkimi atuarfiit 7 6 3 14 5 11
Professionsbachelorinngorniarneq
Kalaallit Nunaanni Atuarfiit 17 15 10 75 52 65
Danmarkimi atuarfiit 4 9 6 7 12 14
Kandidatinngorniarneq
Kalaallit Nunaanni Atuarfiit 2 2 2 10 10 9
Danmarkimi atuarfiit 6 8 7 13 15 8



35-t 39-llu akornanni ukiullit ilinniarsimassusaat

GS 35-t 39-llu akornanni ukiullit ilinniagaasa qaffasissusaat
# Import
UDXISCPROF_raw <-
  statgl_url("UDXISCPROF", lang = language) %>% 
  statgl_fetch(
    alder_grp     = "35-39",
    ISCED11_level = c(20, 34, 35, 40, 50, 64, 65, 70, 80),
    .col_code     = TRUE
    ) %>% 
  as_tibble()
  
# Transform
UDXISCPROF <-
  UDXISCPROF_raw %>% 
  mutate(
    id = row_number(),
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse") %>% 
    fct_reorder(id, .fun = min, na.rm = T) %>% fct_rev()
    )

# Plot
UDXISCPROF %>% 
  mutate(Aar = Aar %>% make_date()) %>% 
  ggplot(aes(
    x    = Aar, 
    y    = value,
    fill = ISCED11_level
    )) +
  geom_area(position = "fill") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl(base_size = 11) +
  guides(fill = guide_legend(nrow = 3, byrow = TRUE)) +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE)) +
  labs(
    title    = sdg4$figs$fig15$title[language],
    subtitle = UDXISCPROF[[2]][1],
    x        = " ",
    y        = " ",
    fill     = NULL,
    caption  = sdg4$figs$fig15$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


# Transform
UDXISCPROF <- 
  UDXISCPROF_raw %>% 
  #arrange(desc(time)) %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  mutate(
    Aar           = Aar %>% fct_inorder(),
    ISCED11_level = ISCED11_level %>% fct_inorder()
    ) %>% 
  spread(3, 4)

# Table
UDXISCPROF %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(UDXISCPROF[[1]])) %>% 
  add_footnote(
    sdg4$figs$fig15$foot[language], 
    notation = "symbol"
    )
2021 2022 2023 2024
Ukiut 35-39
Atuarfik tunngaviliivik, 10.klasse tikillugu 1.695 1.780 1.904 2.011
Ilinniarnertuunngorniarneq 175 183 196 216
Inuussutissarsiornermik ilinniarneq 1.281 1.241 1.289 1.305
Angusanik qaffassaaneq 58 69 80 89
Ingerlariaqqiffiusumik ilinniarneq naatsoq 158 159 177 204
Bachelorinngorniarneq 48 58 56 60
Professionsbachelorinngorniarneq 418 434 443 456
Kandidatinngorniarneq 173 179 179 188
Ilisimatuunngorniarneq 9 9 12 11
* Qaffasinnerpaatut ilinniagaq naammassisimasaq, naammassinnissimasut amerlassusaat. 



# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch(
    alder_grp     = "35-39",
    ISCED11_level = c(20, 34, 35, 40, 50, 64, 65, 70, 80),
    Bsted         = px_all(),
    .col_code     = TRUE
    ) %>% 
  as_tibble()
  
# Transform
UDXISCPROD <-
  UDXISCPROD_raw %>% 
  mutate(
    id                   = row_number(),
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse") %>% 
           fct_reorder(id, .fun = min, na.rm = TRUE) %>% fct_rev(),
    Aar                 = Aar %>% make_date()
    )

# Plot
UDXISCPROD %>% 
  ggplot(aes(
    x    = Aar,
    y    = value,
    fill = ISCED11_level
    )) +
  geom_area(position = "fill") +
  facet_wrap(~ Bsted) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl(base_size = 11) +
  guides(fill = guide_legend(nrow = 3, byrow = TRUE)) +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE)) +
  labs(
    title    = sdg4$figs$fig16$title[language],
    subtitle = UDXISCPROD[[3]][1],
    x        = " ",
    y        = " ",
    fill     = NULL,
    caption  = sdg4$figs$fig16$cap[language]
  )

Kisitsisaataasivik

Periaaseq 


UDXISCPROD <-
  UDXISCPROD_raw %>% 
  #arrange(desc(time)) %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  mutate(
    Aar = Aar %>% fct_inorder(),
    ISCED11_level = ISCED11_level %>% fct_inorder()
  ) %>% 
  arrange(ISCED11_level) %>% 
  unite(combi, 2, 4, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(2, ncol(.))

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

UDXISCPROD %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec), replace_0s = TRUE) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  add_footnote(
    sdg4$figs$fig16$foot[language], 
    notation = "symbol"
    )
2024
2023
2022
2021
Illoqarfik,2021 Illoqarfik,2022 Illoqarfik,2023 Illoqarfik,2024 Nunaqarfik,2021 Nunaqarfik,2022 Nunaqarfik,2023 Nunaqarfik,2024
Atuarfik tunngaviliivik, 10.klasse tikillugu 1.388 1.505 1.620 1.748 307 275 284 263
Ilinniarnertuunngorniarneq 162 171 182 204 13 12 14 12
Inuussutissarsiornermik ilinniarneq 1.170 1.135 1.178 1.198 111 106 111 107
Angusanik qaffassaaneq 57 67 77 88 1 2 3 1
Ingerlariaqqiffiusumik ilinniarneq naatsoq 157 155 172 199 1 4 5 5
Bachelorinngorniarneq 48 58 55 60 0 0 1 0
Professionsbachelorinngorniarneq 404 412 428 438 14 22 15 18
Kandidatinngorniarneq 173 179 179 188 0 0 0 0
Ilisimatuunngorniarneq 9 9 12 11 0 0 0 0
* Qaffasinnerpaatut ilinniagaq naammassisimaseq, naammassinnissimasut amerlassusaat.



Paasissutissalerinermi attaveqatigiinnermilu ingerlaatsit

FN 4.4.1 16-iniit 74-inut ukiullit akornanni paasissutissalerinermi attaveqatigiinnermilu ingerlaatsinik ilinniarsimasut annertussusaat
# Import
UDXISCPROE_raw1 <-
  statgl_url("UDXISCPROE", lang = language) %>% 
  statgl_fetch(
    ISCED11_level  = c(35, 50, 64, 65, 70),
    ISCED11_sektor = c("06"),
    .col_code      = TRUE
    ) %>% 
  as_tibble()

UDXISCPROE_raw2 <-
  statgl_url("UDXISCPROE", lang = language) %>% 
  statgl_fetch(
    ISCED11_level = "00",
    .col_code     = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISCPROE <-
  UDXISCPROE_raw1 %>% 
  rename(tæller = value) %>% 
  left_join(UDXISCPROE_raw2 %>% rename(nævner = value) %>% select(-1)) %>% 
  mutate(
    procent       = tæller / nævner * 100,
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse"),
    Aar           = Aar %>% make_date()
    )

# Plot
UDXISCPROE %>% 
  ggplot(aes(
    x    = Aar,
    y    = procent,
    fill = ISCED11_level
  )) +
  geom_col() +
  scale_y_continuous(labels = scales::percent_format(
    scale        = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE, palette  = "spring") +
  guides(fill = guide_legend(nrow = 2, byrow = TRUE)) +
  labs(
    title    = sdg4$figs$fig17$title[language],
    subtitle = sdg4$figs$fig17$sub[language],
    x        = " ",
    y        = " ",
    fill     = NULL,
    caption  = sdg4$figs$fig17$cap[language]
  )

Kisitsisaataasivik

# Transform
UDXISCPROE <-
  UDXISCPROE_raw1 %>% 
  rename(tæller = value) %>% 
  left_join(UDXISCPROE_raw2 %>% rename(nævner = value) %>% select(-1)) %>% 
  mutate(
    procent              = tæller / nævner * 100,
    procent              = procent %>% round(1),
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse")
    ) %>% 
  #arrange(desc(time)) %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  mutate(
    ISCED11_level = ISCED11_level %>% fct_inorder(),
    Aar           = Aar %>% fct_inorder()
  ) %>% 
  select(-c(2, 4:5)) %>% 
  spread(2, 3)
  
# Table
UDXISCPROE %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig17$foot[language],
    notation = "symbol")
2021 2022 2023 2024
Inuussutissarsiornermik ilinniarneq 0,3 0,3 0,3 0,3
Ingerlariaqqiffiusumik ilinniarneq naatsoq 0,2 0,2 0,2 0,2
Bachelorinngorniarneq 0,0 0,0 0,0 0,0
Professionsbachelorinngorniarneq 0,0 0,0 0,0 0,0
Kandidatinngorniarneq 0,0 0,0 0,0 0,1
* Inuusuttut inersimasullu 16-it 74-illu akornanni ukiullit paasissutissalerinermi attaveqatigiinnermilu ingerlaatsinik suliaqarnermi piginnaasallit annertussusaat procentinngorlugu.