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 2016
Børn 3-5 år
Vuggestue 231 153 143 10 15
Børnehave 777 780 769 694 680
Intergrede institutioner 965 1.000 879 1.036 1.125
Dagplejer 106 109 105 104 91
Andre offentlige dagtilbud NA NA NA NA 21
* 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 2016
Børn 3-5 år
By
Andre offentlige dagtilbud NA NA NA NA -
Børnehave 771 776 765 694 680
Dagplejer 6 3 2 2 4
Intergrede institutioner 939 981 861 975 1.058
Vuggestue 193 122 116 10 15
Bygd
Andre offentlige dagtilbud NA NA NA NA 21
Børnehave 6 4 4 - -
Dagplejer 100 106 103 102 87
Intergrede institutioner 26 19 18 61 67
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
2016 65 50 NA 57 46 66 63 60 



# 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
2016 59 44 NA NA 49 54 66 61 62 48 63 53 64 69 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 2016
Karaktergennemsnit
Grønlandsk
Færdighedsprøve 3,56 4,73 5,71 5,07 4,88
Mundtlig 5,96 6,50 5,99 5,98 5,80
Skriftlig 5,34 4,81 5,34 5,52 5,34
Dansk
Færdighedsprøve 4,36 5,03 4,64 5,37 5,12
Mundtlig 5,36 4,54 4,32 5,07 5,23
Skriftlig 3,26 4,07 3,86 4,19 4,55
Matematik
Færdighedsprøve 4,94 5,19 5,19 5,17 5,30
Mundtlig 4,87 4,62 5,27 5,08 4,93
Skriftlig 2,13 2,44 2,12 2,74 2,98
Engelsk
Færdighedsprøve 4,78 5,05 5,01 5,65 4,97
Mundtlig 6,50 5,32 3,96 4,75 5,27
Skriftlig 3,92 3,99 3,54 3,59 3,53
* 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]
  )