Tilbage

Delmål 4: Kvalitetsuddannelse

Børn i dagtilbud, 3-5 år

FN 4.2.2 Antal børn i dagtilbud (3-5 år)
# 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]
  )

Statistikbanken 


# 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
Børn 3-5 år
Vuggestue 153 231 345 371
Børnehave 780 777 731 741
Intergrede institutioner 1.000 965 981 926
Dagplejer 109 106 120 115
Andre offentlige dagtilbud NA NA NA NA
* Antal børn i dagtilbud 
# 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]
  )

Statistikbanken

# 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
Børn 3-5 år
By
Andre offentlige dagtilbud NA NA NA NA
Børnehave 776 771 722 717
Dagplejer 3 6 4 11
Intergrede institutioner 981 939 954 901
Vuggestue 122 193 303 330
Bygd
Andre offentlige dagtilbud NA NA NA NA
Børnehave 4 6 9 24
Dagplejer 106 100 116 104
Intergrede institutioner 19 26 27 25
Vuggestue 31 38 42 41
* Antal børn i dagtilbud

Trintest-resultater

FN 4.1.1 Løsningssikkerhed for trintests i folkeskolens 3. og 7. klasse
# 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]
  )

Statistikbanken

Metode 


# 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. klasse
  1. klasse
Matematik,3. klasse Grønlandsk,3. klasse Engelsk,3. klasse Dansk,3. klasse Matematik,7. klasse Grønlandsk,7. klasse Engelsk,7. klasse Dansk,7. klasse
Løsningssikkerhed (pct. rigtige)
2023 52 48 NA 48 41 59 86 45
2022 48 41 NA 41 41 62 82 51
2021 51 48 NA 47 40 61 73 50
2020 51 41 NA 50 41 61 73 57
2019 52 45 NA 55 41 66 61 57 



# 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]
  )

Statistikbanken

Metode 


# 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. klasse
  1. klasse
Dansk
Engelsk
Grønlandsk
Matematik
Dansk
Engelsk
Grønlandsk
Matematik
By,Dansk,3. klasse Bygd,Dansk,3. klasse By,Engelsk,3. klasse Bygd,Engelsk,3. klasse By,Grønlandsk,3. klasse Bygd,Grønlandsk,3. klasse By,Matematik,3. klasse Bygd,Matematik,3. klasse By,Dansk,7. klasse Bygd,Dansk,7. klasse By,Engelsk,7. klasse Bygd,Engelsk,7. klasse By,Grønlandsk,7. klasse Bygd,Grønlandsk,7. klasse By,Matematik,7. klasse Bygd,Matematik,7. klasse
Løsningssikkerhed (pct. rigtige)
2023 50 36 NA NA 48 49 53 45 47 40 88 73 57 66 43 40
2022 41 43 NA NA 41 52 47 52 54 40 86 53 62 61 41 39
2021 48 39 NA NA 48 47 52 50 52 45 76 54 59 62 40 41
2020 50 52 NA NA 41 57 50 51 59 43 78 41 61 62 42 37
2019 55 55 NA NA 43 50 52 56 59 46 64 51 66 70 41 41



Folkeskolens afgangseksamen

GS Prøvekarakterer for folkeskolens afgangselever
# 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]
  )

Statistikbanken

Metode 


# 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
Karaktergennemsnit
Grønlandsk
Færdighedsprøve 4,73 3,56 3,72 3,99
Mundtlig 6,50 5,96 6,81 6,54
Skriftlig 4,81 5,35 5,48 4,75
Dansk
Færdighedsprøve 5,03 4,47 4,14 4,05
Mundtlig 4,54 5,36 4,85 6,15
Skriftlig 4,07 3,36 3,58 3,82
Matematik
Færdighedsprøve 5,19 4,94 4,89 4,82
Mundtlig 4,62 4,88 5,24 5,58
Skriftlig 2,44 2,17 2,52 2,98
Engelsk
Færdighedsprøve 5,05 4,90 5,20 5,56
Mundtlig 5,32 6,49 6,52 6,99
Skriftlig 3,99 4,11 4,51 4,56
* Folkeskolens afgangselever


På grund af Covid-19 har der ikke været afholdt afgangseksamen i 2020.



Overgang fra folkeskole til videre uddannelse

GS Overgang fra folkeskolen til ungdomsuddannelse
# 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]
  )

Statistikbanken

Metode 


# 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
Afbrudt 109 117 108 82 91 0
Aktiv 220 264 241 251 272 0
Ej påbegyndt 320 301 342 310 314 0
Gennemført 6 4 7 5 7 0
* Antal personer, overgang fra folkeskolen til ungdomsuddannelse (2 år efter folkeskolens afgangsprøve). 



# 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]
  )

Statistikbanken

Metode 


# 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
Afbrudt
Kvinder 66 64 63 37 50 0
Mænd 43 53 45 45 41 0
Aktiv
Kvinder 113 153 142 148 151 0
Mænd 107 111 99 103 121 0
Ej påbegyndt
Kvinder 168 148 169 137 142 0
Mænd 152 153 173 173 172 0
Gennemført
Kvinder 1 1 0 2 0 0
Mænd 5 3 7 3 7 0
* Antal personer, overgang fra folkeskolen til ungdomsuddannelse (2 år efter folkeskolens afgangsprøve).



Overgang fra gymnasium til videre uddannelse

GS Overgang fra gymnasial uddannelse til videre uddannelse
# 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]
  )

Statistikbanken

Metode 


# 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
Afbrudt 69 61 46 49 54 0
Aktiv 157 134 137 129 114 0
Ej påbegyndt 91 104 89 88 93 0
Gennemført 28 29 32 37 34 0
* Antal personer, overgang fra folkeskolen til ungdomsuddannelse (2 år efter folkeskolens afgangsprøve). 



# 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]
  )

Statistikbanken

Metode 


# 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
Afbrudt
Kvinder 39 33 33 31 0
Mænd 22 13 16 23 0
Aktiv
Kvinder 73 85 82 82 0
Mænd 61 52 47 32 0
Ej påbegyndt
Kvinder 53 51 38 49 0
Mænd 51 38 50 44 0
Gennemført
Kvinder 19 19 27 22 0
Mænd 10 13 10 12 0
* Antal personer, overgang fra gymnasium til ungdomsuddannelse (2 år efter studentereksamen).

Aktive studerende i uddannelse

GS Antal aktive studerende i uddannelse efter uddannelsesniveau og land
# 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]
  )

Statistikbanken 


# 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
Gymnasial uddannelse 1.156 1.121 1.171 1.164 1.147
Erhvervsuddannelse 1.133 1.081 1.131 1.029 1.027
Suppleringskurser 60 29 29 14 18
Kort videregående uddannelse 199 195 172 157 184
Bacheloruddannelse 315 324 374 358 346
Professionsbacheloruddannelse 586 560 550 528 524
Kandidatuddannelse 170 183 171 167 160
* Antal personer, aktive studerende i uddannelse. 



# 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] 
  )

Statistikbanken 


# 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
Skoler i Grønland 3.068 2.952 3.073 2.952 2.903
Skoler i Danmark 520 516 501 445 481
Skoler i udlandet 31 25 24 20 22
* Antal personer, aktive studerende i uddannelse.



FN 4.3.1 Antal aktive studerende i uddannelse efter køn
# 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]
  )

Statistikbanken 


# 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
Kvinder 2.195 2.120 2.234 2.125 2.144
Mænd 1.424 1.373 1.364 1.292 1.262
* Antal personer, aktive studerende i uddannelse.



Fuldførte uddannelser

GS Antal fuldførte uddannelsesforløb
# 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] 
  )

Statistikbanken

Metode 


# 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
Gymnasial uddannelse 304 314 310 318 281
Erhvervsuddannelse 427 399 409 446 374
Suppleringskurser 75 111 126 141 117
Kort videregående uddannelse 69 55 70 67 61
Bacheloruddannelse 43 54 47 55 56
Professionsbacheloruddannelse 134 129 107 119 102
Kandidatuddannelse 34 34 40 35 32
* Antal personer, højest fuldførte uddannelsesforløb. 



# 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]
  )

Statistikbanken

Metode 


# 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
Mænd,2020 Mænd,2021 Mænd,2022 Kvinder,2020 Kvinder,2021 Kvinder,2022
Gymnasial uddannelse
Skoler i Grønland 103 96 86 178 198 162
Skoler i Danmark 9 7 17 13 14 13
Erhvervsuddannelse
Skoler i Grønland 189 180 164 191 249 197
Skoler i Danmark 12 9 7 17 8 5
Suppleringskurser
Skoler i Grønland 28 37 41 87 95 73
Skoler i Danmark 1 2 1 10 7 2
Kort videregående uddannelse
Skoler i Grønland 17 14 17 26 34 26
Skoler i Danmark 14 7 8 13 12 10
Bacheloruddannelse
Skoler i Grønland 6 10 8 24 22 23
Skoler i Danmark 5 5 8 12 15 15
Professionsbacheloruddannelse
Skoler i Grønland 15 12 17 65 86 74
Skoler i Danmark 11 6 4 16 13 7
Kandidatuddannelse
Skoler i Grønland 1 3 2 18 10 10
Skoler i Danmark 3 10 6 13 11 13



Uddannelsesniveau blandt 35-39-årige

GS Uddannelsesniveau blandt 35-39-årige
# 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]
  )

Statistikbanken

Metode 


# 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
35-39 år
Grundskole 10. klasses niveau 1.600 1.669 1.695 1.834
Gymnasial uddannelse 155 154 177 195
Erhvervsuddannelse 1.173 1.223 1.246 1.225
Suppleringskurser 145 118 91 76
Kort videregående uddannelse 144 163 158 156
Bacheloruddannelse 50 48 48 52
Professionsbacheloruddannelse 388 394 417 401
Kandidatuddannelse 157 156 173 166
Phd. og forskeruddannelse 2 7 9 6
* Antal personer, højest fuldførte uddannelse. 



# 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]
  )

Statistikbanken

Metode 


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
By,2019 By,2020 By,2021 By,2022 Bygd,2019 Bygd,2020 Bygd,2021 Bygd,2022
Grundskole 10. klasses niveau 1.319 1.360 1.388 1.555 281 309 307 279
Gymnasial uddannelse 147 148 164 183 8 6 13 12
Erhvervsuddannelse 1.065 1.120 1.139 1.122 108 103 107 103
Suppleringskurser 135 112 86 72 10 6 5 4
Kort videregående uddannelse 144 161 157 153 0 2 1 3
Bacheloruddannelse 49 48 48 52 1 0 0 0
Professionsbacheloruddannelse 376 383 403 381 12 11 14 20
Kandidatuddannelse 156 155 173 166 1 1 0 0
Phd. og forskeruddannelse 2 7 9 6 0 0 0 0
* Antal personer, højest fuldførte uddannelse.



Informations- og kommunikationsteknologi

FN 4.4.1 Andel af 16-74 årige med uddannelse indenfor informations- og kommunikationsteknologi
# 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]
  )

Statistikbanken

# 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
Erhvervs 0,2 0,2 0,2 0,2
Kort videregående 0,2 0,2 0,2 0,2
Bachelor 0,0 0,0 0,0 0,0
Professionsbachelor 0,0 0,0 0,0 0,0
Kandidat 0,0 0,0 0,0 0,0
* Procentvis andel af unge og voksne med faglige kvalifikationer inden for informations- og kommunikationsteknologi blandt 16-74 årige.