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 = "da") %>% 
  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())

# 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 = TRUE)) +
  labs(
    title = "Børn i dagtilbud, 3-5 år",
    x = " ",
    y = "Antal børn",
    fill = " ",
    caption = "Kilde: https://bank.stat.gl/OFDUKN1"
  )

Statistikbanken


# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  spread(1, 4)

# Table
OFXUKN1 %>% 
  select(time, OFXUKN1_raw %>% pull(1) %>% unique()) %>% 
  statgl_table(col.names = c(" ", OFXUKN1_raw %>% pull(1) %>% unique())) %>% 
  pack_rows(index = OFXUKN1 %>% pull(1) %>% table())
Vuggestue Børnehave Intergrede institutioner Dagplejer Andre offentlige dagtilbud
Børn 3-5 år
2014 26 761 1.149 91 26
2015 11 771 1.137 104 24
2016 15 680 1.125 91 21
2017 10 694 1.036 104 0
2018 143 769 879 105 0
2019 153 780 1.000 109 0
# Import
OFXUKN1_raw <-
  statgl_url("OFXUKN1", lang = "da") %>%
  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(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 = TRUE)) +
  labs(
    title = "Børn i dagtilbud efter bosted, 3-5 år",
    x = " ",
    y = "Antal børn",
    fill = NULL,
    caption = "Kilde: https://bank.stat.gl/OFDUKN1"
  )

Statistikbanken
# Transform
OFXUKN1 <- 
  OFXUKN1_raw %>% 
  unite(combi, 1, 2, sep = ", ")

header_vector <- c(rep(length(unique(pull(OFXUKN1, 1)))/2, length(unique(pull(OFXUKN1_raw, 1)))))
names(header_vector) <- c(unique(pull(OFXUKN1_raw, 1))[1], unique(pull(OFXUKN1_raw, 1))[2])

# Table
OFXUKN1 %>% 
  spread(1, 4) %>% 
  select(time, OFXUKN1 %>% pull(1) %>% unique) %>% 
  statgl_table(replace_0s = TRUE,
               col.names = c(" ", rep(OFXUKN1_raw %>% pull(2) %>% unique(), length(unique(pull(OFXUKN1_raw, 1)))))) %>% 
  add_header_above(c(" ", header_vector)) %>% 
  pack_rows(index = OFXUKN1 %>% spread(1, 4) %>% pull(1) %>% table())
By
Bygd
Vuggestue Børnehave Intergrede institutioner Dagplejer Andre offentlige dagtilbud Vuggestue Børnehave Intergrede institutioner Dagplejer Andre offentlige dagtilbud
Børn 3-5 år
2014 26 761 1.051 4 - - - 98 87 26
2015 11 771 1.056 5 - - - 81 99 24
2016 15 680 1.058 4 - - - 67 87 21
2017 10 694 975 2 - - - 61 102 -
2018 116 765 861 2 - 27 4 18 103 -
2019 122 776 981 3 - 31 4 19 106 -

Trintest-resultater


FN 4.1.1 Løsningssikkerhed for trintests i folkeskolens 3. og 7. klasse
# Import
UDXTKB_raw <-
  statgl_url("UDXTKB", lang = "da") %>%
  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())

# 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 = "Trintest-resultater",
    subtitle = "Løsningssikkerhed (pct. rigtige)",
    x = " ",
    y = " ",
    color = "Fag",
    caption = "Kilde: https://bank.stat.gl/UDDTKB"
  )

Statistikbanken

Metode


# Transform
UDXTKB <-
  UDXTKB_raw %>% 
  unite(combi, 2, 1, sep = ", ")

header_vector <-
  rep(UDXTKB_raw %>% pull(1) %>% unique() %>% length(),
    UDXTKB_raw %>% pull(2) %>% unique() %>% length())

names(header_vector) <-
  UDXTKB_raw %>% pull(2) %>% unique()

# Table
UDXTKB %>% 
  spread(1, 4) %>% 
  select(-1) %>% 
  statgl_table(col.names = c(" ", 
                             rep(c(unique(pull(UDXTKB_raw, 1))[2],
                                   unique(pull(UDXTKB_raw, 1))[4], 
                                   unique(pull(UDXTKB_raw, 1))[1],
                                   unique(pull(UDXTKB_raw, 1))[3]),
                                 UDXTKB_raw %>% pull(2) %>% unique() %>% length())
                             )) %>% 
  pack_rows(index = UDXTKB %>% spread(1, 4) %>% pull(1) %>% table()) %>% 
  add_header_above(c(" ", header_vector))
  1. klasse
  1. klasse
Dansk Engelsk Grønlandsk Matematik Dansk Engelsk Grønlandsk Matematik
Løsningssikkerhed (pct. rigtige)
2009 48 NA 44 61 60 49 61 51
2010 45 NA 45 60 54 49 59 46
2011 49 NA 49 65 59 54 62 47
2012 48 NA 47 63 58 52 62 47
2013 52 NA 51 61 64 53 62 45
2014 48 NA 47 63 59 53 62 47
2015 50 NA 51 64 57 53 64 48
2016 57 NA 50 65 60 63 66 46
2017 55 NA 49 63 62 63 66 46
2018 54 NA 50 52 57 57 63 41
2019 55 NA 45 52 57 61 66 41
2020 50 NA 42 51 57 73 61 41



# Import
UDXTKB_raw <-
  statgl_url("UDXTKB", lang = "da") %>%
  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())

# 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 = "Trintest-resultater efter bosted",
    subtitle = "Løsningssikkerhed (pct. rigtige)",
    x = " ",
    y = " ",
    color = "Fag",
    caption = "Kilde: https://bank.stat.gl/UDDTKB"
  )

Statistikbanken

Metode


# Transform
UDXTKB <-
  UDXTKB_raw %>% 
  unite(combi, 3, 2, 1, sep = ", ") %>% 
  spread(1, 4)

residence_col <- UDXTKB_raw %>% pull(1) %>% unique()
subject_col   <- UDXTKB_raw %>% pull(2) %>% unique()
grade_col     <- UDXTKB_raw %>% pull(3) %>% unique()

col_names <- rep(residence_col, length(subject_col) * length(grade_col))

header_vector1 <- rep(length(residence_col), length(subject_col) * length(residence_col))
names(header_vector1) <- rep(c(subject_col[2], subject_col[4], subject_col[1], subject_col[3]), length(residence_col))

header_vector2 <- rep(length(subject_col) * length(residence_col), length(grade_col))
names(header_vector2) <- grade_col

# Table
UDXTKB %>% 
  select(-1) %>% 
  statgl_table(col.names = c(" ", col_names)) %>% 
  add_header_above(c(" ", header_vector1)) %>% 
  add_header_above(c(" ", header_vector2)) %>% 
  pack_rows(index = UDXTKB %>% pull(1) %>% table())
  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)
2009 50 38 NA NA 44 48 61 61 62 48 49 43 60 63 51 49
2010 46 43 NA NA 45 48 60 59 58 47 50 46 59 60 46 51
2011 50 41 NA NA 48 51 65 64 61 53 54 51 62 63 47 52
2012 48 43 NA NA 44 50 63 63 60 53 54 49 62 65 46 49
2013 53 47 NA NA 49 63 59 70 67 51 56 51 62 65 45 46
2014 50 46 NA NA 47 53 63 65 62 47 56 47 61 67 47 47
2015 51 47 NA NA 50 55 64 64 59 50 56 47 62 70 48 51
2016 59 44 NA NA 49 54 66 61 62 48 63 53 64 69 46 44
2017 57 48 NA NA 48 52 63 64 66 41 65 42 66 64 46 44
2018 56 45 NA NA 50 51 54 46 59 47 59 49 62 66 40 43
2019 55 55 NA NA 43 50 52 56 59 46 64 51 66 70 41 41
2020 50 53 NA NA 41 59 50 53 59 43 78 43 61 62 42 37



Folkeskolens afgangseksamen


GS Prøvekarakterer for folkeskolens afgangselever
# Import
UDXFKK_raw <-
  statgl_url("UDXFKK", lang = "da") %>%
  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())

# Plot
UDXFKK %>% 
  mutate(time = time %>% make_date()) %>% 
  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 = "Prøvekarakterer for folkeskolens afgangselever",
    color = "Karaktertype",
    x = "",
    y = "Karaktergennemsnit",
    caption = "Kilde: https://bank.stat.gl/UDDFKK"
  )

Statistikbanken

Metode


subject_col <- UDXFKK %>% pull(3) %>% unique()
grades_col  <- UDXFKK %>% pull(4) %>% unique()

col_names   <- rep(grades_col, length(subject_col))

header_vector <- rep(length(grades_col), length(subject_col))
names(header_vector) <- subject_col

foot <-
  UDXFKK %>% 
  unite(combi, 1, 2, sep = ", ") %>% 
  mutate(combi = combi %>% str_to_title()) %>% 
  pull(1) %>% 
  unique()

UDXFKK %>% 
  select(-(1:2)) %>% 
  unite(combi, 1, 2, sep = ", ") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(1, 3) %>% 
  statgl_table(col.names = c(" ", col_names)) %>% 
  add_header_above(c(" ",header_vector)) %>% 
  add_footnote(foot, notation = "symbol")
Grønlandsk
Dansk
Matematik
Engelsk
Skriftlig Mundtlig Færdighedsprøve Skriftlig Mundtlig Færdighedsprøve Skriftlig Mundtlig Færdighedsprøve Skriftlig Mundtlig Færdighedsprøve
2008 4,95 6,88 5,45 2,97 4,90 4,70 2,81 4,33 3,52 2,65 4,75 4,91
2009 5,18 6,19 6,18 3,78 4,65 4,49 3,02 4,74 4,12 3,39 4,47 5,72
2010 5,22 5,80 5,15 3,90 4,63 4,86 2,24 3,85 3,92 3,03 3,87 6,11
2011 5,55 6,44 5,95 4,16 5,21 5,20 3,80 4,59 3,92 2,86 3,27 5,89
2012 4,78 6,66 5,26 3,93 4,14 5,68 2,04 4,01 3,53 3,19 4,36 5,95
2013 5,10 6,41 5,12 4,14 4,88 6,46 4,10 4,51 5,20 3,19 5,07 5,26
2014 5,18 6,01 5,73 3,88 4,43 5,92 3,17 4,45 4,68 3,18 3,64 4,46
2015 5,42 5,91 5,74 4,03 4,11 5,61 2,71 4,07 4,63 3,09 3,68 4,44
2016 5,34 5,80 4,88 4,55 5,23 5,12 2,98 4,93 5,30 3,53 5,27 4,97
2017 5,52 5,98 5,07 4,19 5,07 5,37 2,74 5,08 5,17 3,59 4,75 5,65
2018 5,34 5,99 5,71 3,86 4,32 4,64 2,12 5,27 5,19 3,54 3,96 5,01
2019 4,81 6,50 4,73 4,07 4,54 5,03 2,44 4,62 5,19 3,99 5,32 5,05
2020 NA NA NA NA NA NA NA NA NA NA NA NA
* Karaktergennemsnit, Folkeskolens Afgangselever



Overgang fra folkeskole til videre uddannelse


GS Overgang fra folkeskolen til ungdomsuddannelse
# Import
UDXTRFA1_raw <-
  statgl_url("UDXTRFA1", lang = "da") %>% 
  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 %>%
  mutate(`graduation year` = `graduation year` %>% make_date())

# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x = `graduation year`,
    y = value,
    fill = `educational status`
  )) +
  geom_col(positi