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)) +
  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"
    )
2020 2019 2018 2017
Børn 3-5 år
Vuggestue 231 153 143 10
Børnehave 777 780 769 694
Intergrede institutioner 965 1.000 879 1.036
Dagplejer 106 109 105 104
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)) +
  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"
    )
2020 2019 2018 2017
Børn 3-5 år
By
Andre offentlige dagtilbud NA NA NA NA
Børnehave 771 776 765 694
Dagplejer 6 3 2 2
Intergrede institutioner 939 981 861 975
Vuggestue 193 122 116 10
Bygd
Andre offentlige dagtilbud NA NA NA NA
Børnehave 6 4 4 -
Dagplejer 100 106 103 102
Intergrede institutioner 26 19 18 61
Vuggestue 38 31 27 -
* 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 Grønlandsk Engelsk Dansk Matematik Grønlandsk Engelsk Dansk
Løsningssikkerhed (pct. rigtige)
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
2018 52 50 NA 54 41 63 57 57
2017 63 49 NA 55 46 66 63 62 



# 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 Bygd By Bygd By Bygd By Bygd By Bygd By Bygd By Bygd By Bygd
Løsningssikkerhed (pct. rigtige)
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
2018 56 45 NA NA 50 51 54 46 59 47 59 49 62 66 40 43
2017 57 48 NA NA 48 52 63 64 66 41 65 42 66 64 46 44



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() +
  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")
2021 2019 2018 2017
Karaktergennemsnit
Grønlandsk
Færdighedsprøve 3,56 4,73 5,71 5,07
Mundtlig 5,96 6,50 5,99 5,98
Skriftlig 5,35 4,81 5,34 5,52
Dansk
Færdighedsprøve 4,47 5,03 4,64 5,37
Mundtlig 5,36 4,54 4,32 5,07
Skriftlig 3,36 4,07 3,86 4,19
Matematik
Færdighedsprøve 4,94 5,19 5,19 5,17
Mundtlig 4,88 4,62 5,27 5,08
Skriftlig 2,17 2,44 2,12 2,74
Engelsk
Færdighedsprøve 4,90 5,05 5,01 5,65
Mundtlig 6,49 5,32 3,96 4,75
Skriftlig 4,11 3,99 3,54 3,59
* Folkeskolens afgangselever



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()) - 7) %>% 
  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"
  )
2019 2018 2017 2016 2015
Afbrudt - 103 117 108 107
Aktiv - 246 264 221 221
Ej påbegyndt - 342 301 320 330
Gennemført - 7 4 6 7
* 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()) - 7) %>% 
  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"
  )
2019 2018 2017 2016 2015
Afbrudt
Kvinder - 59 64 65 64
Mænd - 44 53 43 43
Aktiv
Kvinder - 146 153 114 126
Mænd - 100 111 107 95
Ej påbegyndt
Kvinder - 169 148 168 174
Mænd - 173 153 152 156
Gennemført
Kvinder - - 1 1 -
Mænd - 7 3 5 7
* 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 %>% 
  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

Vis tabel 


# Transform
UDXTRGU2 <-
  UDXTRGU2_raw %>% 
  arrange(desc(`graduation year`)) %>% 
  filter(`graduation year` >= year(Sys.time()) - 7) %>% 
  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"
    )
2019 2018 2017 2016 2015
Afbrudt - - 58 70 60
Aktiv - - 138 159 137
Ej påbegyndt - - 103 89 95
Gennemført - - 29 27 32
* 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 %>% 
  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()) - 6) %>% 
  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"
  )
2019 2018 2017 2016
Afbrudt
Kvinder - - 37 48
Mænd - - 21