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(
    keyfigures             = "_3sum_ald_3_5_x",
    "daycare institutions" = 1:5,
    .col_code              = TRUE
    ) %>% 
  as_tibble()

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  mutate(
    time = time %>% make_date(),
    `daycare institutions` = `daycare institutions` %>% fct_inorder()
    )

# Plot
OFXUKN1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `daycare institutions`
  )) +
  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$title[language],
    subtitle = OFXUKN1[[2]][1],
    x        = " ",
    y        = sdg4$figs$fig1$y_lab[language],
    fill     = " ",
    caption  = sdg4$figs$fig1$cap[language]
  )

Kisitsisaataasivik 


# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(
    time = time %>% factor(levels = unique(time)),
    `daycare institutions` = `daycare institutions` %>% fct_inorder()
    ) %>% 
  spread(3, 4)

# 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"
    )
2019 2020 2021 2022
Meeqqat 3-5-nik ukiullit
Meeraaqqeriviit 153 231 345 371
Meeqqeriviit 780 777 731 741
Meeqqeriviit akuleriit 1.000 965 981 926
Angerlarsimafimmi paarsisartut 109 106 120 115
Pisortat neqeroorutaat allat NA NA NA NA
* Meeqqat amerlassusaat, 3-5-inik ukiullit. 
# Import
OFXUKN1_raw <-
  statgl_url("OFXUKN1", lang = language) %>%
  statgl_fetch(
    keyfigures             = "_3sum_ald_3_5_x",
    "daycare institutions" = 1:5,
    residence              = c("By", "Bygd"),
    .col_code              = TRUE
    ) %>% 
  as_tibble()

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  mutate(
    `daycare institutions` = `daycare institutions` %>% fct_inorder(),
    residence  = residence %>% fct_inorder(),
    time = time %>% make_date(),
    )

# Plot
OFXUKN1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `daycare institutions`
    )) +
  geom_col() +
  facet_wrap(~ residence, scales = "free_y") +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = FALSE, nrow = 2)) +
  labs(
    title    = sdg4$figs$fig2$title[language],
    subtitle = OFXUKN1[[3]][1],
    x        = " ",
    y        = sdg4$figs$fig2$y_lab[language],
    fill     = NULL,
    caption  = sdg4$figs$fig2$cap[language]
  )

Kisitsisaataasivik

# Transform
OFXUKN1 <- 
  OFXUKN1_raw %>% 
  #arrange(desc(time)) %>%
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(4, 5)

# Table
OFXUKN1 %>% 
  select(-c(1, 3)) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = table(OFXUKN1[[3]])) %>% 
  pack_rows(index = table(OFXUKN1[[1]])) %>% 
  add_footnote(
    sdg4$figs$fig2$foot[language], 
    notation = "symbol"
    )
2019 2020 2021 2022
Meeqqat 3-5-nik ukiullit
Illoqarfiit
Angerlarsimafimmi paarsisartut 3 6 4 11
Meeqqeriviit 776 771 722 717
Meeqqeriviit akuleriit 981 939 954 901
Meeraaqqeriviit 122 193 303 330
Pisortat neqeroorutaat allat NA NA NA NA
Nunaqarfiit
Angerlarsimafimmi paarsisartut 106 100 116 104
Meeqqeriviit 4 6 9 24
Meeqqeriviit akuleriit 19 26 27 25
Meeraaqqeriviit 31 38 42 41
Pisortat neqeroorutaat allat NA NA NA NA
* 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)
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
2020 NA 50 51 41 73 57 41 61
2019 NA 55 52 45 61 57 41 66 



# 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)
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
2020 41 57 50 51 50 52 NA NA 61 62 42 37 59 43 78 41
2019 43 50 52 56 55 55 NA NA 66 70 41 41 59 46 64 51



Meeqqat atuarfianni soraarummeernerit

GS Meeqqat atuarfianni inaarutaasumik misilitsinnernit karakteerit
# 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,
    .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")
2019 2021 2022 2023
Agguaqatigiissillugu Karakteeri
Kalaallisut
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 4,81 5,35 5,48 4,75
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 6,50 5,96 6,81 6,54
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 4,73 3,56 3,72 3,99
Qallunaatut
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 4,07 3,36 3,58 3,82
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 4,54 5,36 4,85 6,15
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 5,03 4,47 4,14 4,05
Matematikki
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 2,44 2,17 2,52 2,98
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 4,62 4,88 5,24 5,58
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 5,19 4,94 4,89 4,82
Tuluttut
Inaarutaasumik Misilitsinnermi Karakteeri - Allattariarsorneq 3,99 4,11 4,51 4,56
Inaarutaasumik Misilitsinnermi Karakteeri - Oqaluttariarsorneq 5,32 6,49 6,52 6,99
Inaarutaasumik Misilitsinnermi Karakteeri - Piginnaasat 5,05 4,90 5,20 5,56
* 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(
    "number of years after lower secondary education" = 2,
    "educational status"                              = px_all(),
    "graduation year"                                 = px_all(),
    .col_code                                         = TRUE
    ) %>% 
  as_tibble()

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

  


# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x    = `graduation year`,
    y    = value,
    fill = `educational 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(`graduation year` <= year(Sys.time()) - 3) %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(`graduation year` >= year(Sys.time()) - 8) %>% 
  mutate(`graduation year` = `graduation year` %>% factor(levels = unique(`graduation year`))) %>% 
  spread(3, 4)

# Table
UDXTRFA1 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  add_footnote(
    sdg4$figs$fig6$foot[language],
    notation = "symbol"
  )
2016 2017 2018 2019 2020 2021
Naammassinnissimasut 6 4 7 5 7 0
Suli ingerlatsisut 220 264 241 251 272 0
Unitsitsisut 109 117 108 82 91 0
Aallartitsisimanngitsut 320 301 342 310 314 0
* Meeqqat atuarfianniit inuusuttut ilinniarfiinut ingerlaqqinnerit (meeqqat atuarfianni soraarummeereernermi ukiut marluk qaangiunnerini), ilinniartut amerlassusaat. 



# Import
UDXTRFA1_raw <-
  statgl_url("UDXTRFA1", lang = language) %>%
  statgl_fetch(
    "number of years after lower secondary education" = 2,
    "educational status"                              = px_all(),
    "graduation year"                                 = px_all(),
    gender                                            = px_all(),
    .col_code                                         = TRUE
    ) %>% 
  as_tibble()

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

# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x    = `graduation year`,
    y    = value,
    fill = `educational status`
  )) +
  geom_col(position = "fill") +
  facet_wrap(~ gender) +
  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(`graduation year` <= year(Sys.time()) - 3) %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(`graduation year` >= year(Sys.time()) - 8) %>% 
  mutate(`graduation year` = `graduation year` %>% factor(levels = unique(`graduation year`))) %>% 
  spread(4, 5) %>% 
  arrange(`educational 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"
  )
2016 2017 2018 2019 2020 2021
Naammassinnissimasut
Angutit 152 153 173 173 172 0
Arnat 168 148 169 137 142 0
Suli ingerlatsisut
Angutit 5 3 7 3 7 0
Arnat 1 1 0 2 0 0
Unitsitsisut
Angutit 107 111 99 103 121 0
Arnat 113 153 142 148 151 0
Aallartitsisimanngitsut
Angutit 43 53 45 45 41 0
Arnat 66 64 63 37 50 0
* Karaktergennemsnit, Folkeskolens Afgangselever



Ilinniarnertuunngorniarnermiit ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinneq

GS Ilinniarnertuunngorniarnermiit ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinnerit
# Import
UDXTRGU2_raw <-
  statgl_url("UDXTRGU2", lang = language) %>%
  statgl_fetch(
    "number of years after graduation" = 2,
    "educational status"               = px_all(),
    "graduation year"                  = px_all(),
    .col_code = TRUE) %>% 
  as_tibble()

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

# Plot
UDXTRGU2 %>% 
  ggplot(aes(
    x    = `graduation year`,
    y    = value,
    fill = `educational 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(`graduation year` >= year(Sys.time()) - 8 & `graduation year` < year(Sys.time()) - 2) %>% 
  mutate(`graduation year` = `graduation year` %>% factor(levels = unique(`graduation year`))) %>% 
  spread(3, 4)

# Table
UDXTRGU2 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  add_footnote(
    sdg4$figs$fig8$foot[language],
    notation = "symbol"
    )
2016 2017 2018 2019 2020 2021
Naammassinnissimasut 28 29 32 37 34 0
Suli ingerlatsisut 157 134 137 129 114 0
Unitsitsisut 69 61 46 49 54 0
Aallartitsisimanngitsut 91 104 89 88 93 0
* Ilinniarnertuunngorniarnermiik ingerlariaqqiffiusumik ilinniarnermut ingerlaqqinneq (ilinniarnertuunngoreernermik ukiut marluk qaangiunnerini), ilinniartut amerlassusaat. 



# Import
UDXTRGU2_raw <-
  statgl_url("UDXTRGU2", lang = language) %>%
  statgl_fetch(
    "number of years after graduation" = 2,
    "educational status"               = px_all(),
    "graduation year"                  = px_all(),
    gender                             = px_all(),
    .col_code                          = TRUE
    ) %>% 
  as_tibble()

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

# Plot
UDXTRGU2 %>% 
  ggplot(aes(
    x    = `graduation year`,
    y    = value,
    fill = `educational status`
  )) +
  geom_col(position = "fill") +
  facet_wrap( ~ gender) +
  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(`graduation year` >= year(Sys.time()) - 7 & `graduation year` < year(Sys.time()) - 2) %>% 
  mutate(`graduation year` = `graduation year` %>% factor(levels = unique(`graduation year`))) %>% 
  spread(4, 5) %>% 
  arrange(`educational 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"
  )
2017 2018 2019 2020 2021
Naammassinnissimasut
Angutit 51 38 50 44 0
Arnat 53 51 38 49 0
Suli ingerlatsisut
Angutit 10 13 10 12 0
Arnat 19 19 27 22 0
Unitsitsisut
Angutit 61 52 47 32 0
Arnat 73 85 82 82 0
Aallartitsisimanngitsut
Angutit 22 13 16 23 0
Arnat 39 33 33 31 0
* 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(
    "level of education" = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(time                = time %>% make_date(),
        `level of education` = `level of education` %>%  fct_inorder() %>% fct_rev(),
        value                = value * 10^-3)

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `level of education`
  )) +
  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(time >= year(Sys.time()) - 6) %>% 
  mutate(
         `level of education` = `level of education` %>% factor(levels = unique(`level of education`)),
         time                 = time %>% factor(levels = unique(time)),
         ) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig10$foot[language],
    notation = "symbol"
    )
2018 2019 2020 2021 2022
Ilinniarnertuunngorniarneq 1.156 1.121 1.171 1.164 1.147
Inuussutissarsiornermik ilinniarneq 1.133 1.081 1.131 1.029 1.027
Angusanik qaffassaanerit 60 29 29 14 18
Ingerlariaqqiffiusumik ilinniarneq naatsoq 199 195 172 157 184
Bachelorinngorniarneq 315 324 374 358 346
Professionsbachelorinngorniarneq 586 560 550 528 524
Kandidatinngorniarneq 170 183 171 167 160
* Ilinniakkamik ingerlatsisut amerlassusaat. 



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

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

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = country
  )) +
  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(time >= year(Sys.time()) - 6) %>% 
  mutate(
    time    = time %>% fct_inorder(),
    country = country %>% fct_inorder
    ) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig11$foot[language],
    notation = "symbol"
    )
2018 2019 2020 2021 2022
Kalaallit Nunaanni Atuarfiit 3.068 2.952 3.073 2.952 2.903
Danmarkimi atuarfiit 520 516 501 445 481
Nunani allani atuarfiit 31 25 24 20 22
* Ilinniakkamik ingerlatsisut amerlassussaat.



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

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

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gender
  )) +
  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(time >= year(Sys.time()) - 6) %>% 
  mutate(time = time %>% fct_inorder()) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig12$foot[language],
    notation = "symbol"
    )
2018 2019 2020 2021 2022
Angutit 1.424 1.373 1.364 1.292 1.262
Arnat 2.195 2.120 2.234 2.125 2.144
* Ilinniakkamik ingerlatsisut amerlassusaat.



Ilinniarnerit naammassineqarsimasut

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

# Transform
UDXISC11D <-
  UDXISC11D_raw %>%
  mutate(
    time                 = time %>% make_date(),
    id                   = row_number(),
    `level of education` = `level of education` %>% str_remove("uddannelse"),
    `level of education` = `level of education` %>% fct_reorder(id, .fun = min, na.rm = TRUE) %>% fct_rev()
  )

# Plot
UDXISC11D %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `level of education`
    )) +
  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(time >= year(Sys.time()) - 6) %>% 
  mutate(
    `level of education` = `level of education` %>% fct_inorder(),
    time                 = time %>% fct_inorder()
    ) %>% 
  spread(2, 3)

# Table
UDXISC11D %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig13$foot[language],
    notation = "symbol"
    )
2018 2019 2020 2021 2022
Ilinniarnertuunngorniarneq 304 314 310 318 281
Inuussutissarsiornermik ilinniarneq 427 399 409 446 374
Angusanik qaffassaanerit 75 111 126 141 117
Ingerlariaqqiffiusumik ilinniarneq naatsoq 69 55 70 67 61
Bachelorinngorniarneq 43 54 47 55 56
Professionsbachelorinngorniarneq 134 129 107 119 102
Kandidatinngorniarneq 34 34 40 35 32
* Qaffasinnerpaatut ilinniakat naammassineqarsimasut, naammassinnissimasut amerlassusaat. 



# Import
UDXISC11D_raw <-
  statgl_url("UDXISC11D", lang = language) %>%
  statgl_fetch(
    "level of education" = px_all(),
    gender               = px_all(),
    country              = c("A_SG", "B_SD"),
    .col_code            = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  mutate(
    `level of education` = `level of education` %>% str_remove("uddannelse") %>% trimws(),
    `level of education` = `level of education` %>% fct_inorder() %>% fct_rev(),
    gender               = gender  %>% fct_inorder(),
    country              = country %>% fct_inorder,
    time                 = time    %>% make_date()
  )

# Plot
UDXISC11D %>% 
  ggplot(aes(
    x = time,
    y = value, 
    fill = `level of education`
  )) +
  geom_col() +
  facet_grid(country ~ gender, 
             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(time >= year(Sys.time()) - 4) %>% 
  mutate(
    time                 = time %>% fct_inorder(),
    `level of education` = `level of education` %>% fct_inorder(),
    country              = country %>% 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))
2022
2021
2020
Angutit,2020 Angutit,2021 Angutit,2022 Arnat,2020 Arnat,2021 Arnat,2022
Ilinniarnertuunngorniarneq
Kalaallit Nunaanni Atuarfiit 103 96 86 178 198 162
Danmarkimi atuarfiit 9 7 17 13 14 13
Inuussutissarsiornermik ilinniarneq
Kalaallit Nunaanni Atuarfiit 189 180 164 191 249 197
Danmarkimi atuarfiit 12 9 7 17 8 5
Angusanik qaffassaanerit
Kalaallit Nunaanni Atuarfiit 28 37 41 87 95 73
Danmarkimi atuarfiit 1 2 1 10 7 2
Ingerlariaqqiffiusumik ilinniarneq naatsoq
Kalaallit Nunaanni Atuarfiit 17 14 17 26 34 26
Danmarkimi atuarfiit 14 7 8 13 12 10
Bachelorinngorniarneq
Kalaallit Nunaanni Atuarfiit 6 10 8 24 22 23
Danmarkimi atuarfiit 5 5 8 12 15 15
Professionsbachelorinngorniarneq
Kalaallit Nunaanni Atuarfiit 15 12 17 65 86 74
Danmarkimi atuarfiit 11 6 4 16 13 7
Kandidatinngorniarneq
Kalaallit Nunaanni Atuarfiit 1 3 2 18 10 10
Danmarkimi atuarfiit 3 10 6 13 11 13



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(
    age                  = "35-39",
    "level of education" = c(20, 34, 35, 40, 50, 64, 65, 70, 80),
    .col_code = TRUE
    ) %>% 
  as_tibble()
  
# Transform
UDXISCPROF <-
  UDXISCPROF_raw %>% 
  mutate(
    id = row_number(),
    `level of education` = `level of education` %>% str_remove("uddannelse") %>% 
    fct_reorder(id, .fun = min, na.rm = T) %>% fct_rev()
    )

# Plot
UDXISCPROF %>% 
  mutate(time = time %>% make_date()) %>% 
  ggplot(aes(
    x    = time, 
    y    = value,
    fill = `level of education`
    )) +
  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(time >= year(Sys.time()) - 5) %>% 
  mutate(
    time                 = time %>% fct_inorder(),
    `level of education` = `level of education` %>% 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"
    )
2019 2020 2021 2022
Ukiut 35-39
Atuarfik tunngaviliivik, 10.klasse tikillugu 1.600 1.669 1.695 1.834
Ilinniarnertuunngorniarneq 155 154 177 195
Inuussutissarsiornermik ilinniarneq 1.173 1.223 1.246 1.225
Angusanik qaffassaaneq 145 118 91 76
Ingerlariaqqiffiusumik ilinniarneq naatsoq 144 163 158 156
Bachelorinngorniarneq 50 48 48 52
Professionsbachelorinngorniarneq 388 394 417 401
Kandidatinngorniarneq 157 156 173 166
Ilisimatuunngorniarneq 2 7 9 6
* Qaffasinnerpaatut ilinniagaq naammassisimasaq, naammassinnissimasut amerlassusaat. 



# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch(
    age                  = "35-39",
    "level of education" = c(20, 34, 35, 40, 50, 64, 65, 70, 80),
    "place of residence" = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()
  
# Transform
UDXISCPROD <-
  UDXISCPROD_raw %>% 
  mutate(
    id                   = row_number(),
    `level of education` = `level of education` %>% str_remove("uddannelse") %>% 
           fct_reorder(id, .fun = min, na.rm = TRUE) %>% fct_rev(),
    time                 = time %>% make_date()
    )

# Plot
UDXISCPROD %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `level of education`
    )) +
  geom_area(position = "fill") +
  facet_wrap(~ `place of residence`) +
  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(time >= year(Sys.time()) - 5) %>% 
  mutate(
    time = time %>% fct_inorder(),
    `level of education` = `level of education` %>% fct_inorder()
  ) %>% 
  arrange(`level of education`) %>% 
  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"
    )
2022
2021
2020
2019
Illoqarfik,2019 Illoqarfik,2020 Illoqarfik,2021 Illoqarfik,2022 Nunaqarfik,2019 Nunaqarfik,2020 Nunaqarfik,2021 Nunaqarfik,2022
Atuarfik tunngaviliivik, 10.klasse tikillugu 1.319 1.360 1.388 1.555 281 309 307 279
Ilinniarnertuunngorniarneq 147 148 164 183 8 6 13 12
Inuussutissarsiornermik ilinniarneq 1.065 1.120 1.139 1.122 108 103 107 103
Angusanik qaffassaaneq 135 112 86 72 10 6 5 4
Ingerlariaqqiffiusumik ilinniarneq naatsoq 144 161 157 153 0 2 1 3
Bachelorinngorniarneq 49 48 48 52 1 0 0 0
Professionsbachelorinngorniarneq 376 383 403 381 12 11 14 20
Kandidatinngorniarneq 156 155 173 166 1 1 0 0
Ilisimatuunngorniarneq 2 7 9 6 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(
    "level of education"  = c(35, 50, 64, 65, 70),
    "fields of education" = c("06"),
    .col_code             = TRUE
    ) %>% 
  as_tibble()

UDXISCPROE_raw2 <-
  statgl_url("UDXISCPROE", lang = language) %>% 
  statgl_fetch(
    "level of education" = "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,
    `level of education` = `level of education` %>% str_remove("uddannelse"),
    time                 = time %>% make_date()
    )

# Plot
UDXISCPROE %>% 
  ggplot(aes(
    x    = time,
    y    = procent,
    fill = `level of education`
  )) +
  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),
    `level of education` = `level of education` %>% str_remove("uddannelse")
    ) %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(
    `level of education` = `level of education` %>% fct_inorder(),
    time                 = time %>% 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")
2019 2020 2021 2022
Inuussutissarsiornermik ilinniarneq 0,2 0,2 0,2 0,2
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 0,0
* Inuusuttut inersimasullu 16-it 74-illu akornanni ukiullit paasissutissalerinermi attaveqatigiinnermilu ingerlaatsinik suliaqarnermi piginnaasallit annertussusaat procentinngorlugu.