Tilbage

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Delmål 4: Kvalitetsuddannelse

Børn i dagtilbud, 3-5 år

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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

Vis tabel

# 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 Vis tabel

# 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

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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

Vis tabel

# 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))

	klasse				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

Vis tabel

# 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())

	klasse								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

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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

Vis tabel

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

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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