sdg2020

FN 4.2.2 Number of children in day care (3-5 years)

# Import
OFXUKN1_raw <-
  statgl_url("OFXUKN1", lang = language) %>% 
  statgl_fetch(
    born_var   = 3:5,
    inst_type2 = 1:5,
    .col_code  = TRUE
    ) %>% 
  as_tibble()

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  mutate(across(where(is.integer), ~ if_else(is.na(.x), 0, .x))) |> 
  summarise(value = sum(value), .by = c(inst_type2, aar)) |> 
  mutate(
    aar = aar %>% make_date(),
    inst_type2 = inst_type2 %>% fct_inorder(),
    alder = "Børn 3-5 år"
    )

# Plot
OFXUKN1 %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = inst_type2
  )) +
  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$title[language],
    subtitle = OFXUKN1[[4]][1],
    x        = " ",
    y        = sdg4$figs$fig1$y_lab[language],
    fill     = " ",
    caption  = sdg4$figs$fig1$cap[language]
  )

StatBank

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  #arrange(desc(time)) %>% 
  filter(aar >= year(Sys.time()) - 5) %>% 
  mutate(across(where(is.integer), ~ if_else(is.na(.x), 0, .x))) |> 
  summarise(value = sum(value), .by = c(inst_type2, aar)) |> 
  mutate(
    aar = aar %>% factor(levels = unique(aar)),
    inst_type2 = inst_type2 %>% fct_inorder(),
    alder = "Børn 3-5 år"
    ) %>% 
  spread(aar, value)

# 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	2023
Børn 3-5 år
Nursery	59	48	102	149	189
Childcare	1.870	1.917	1.943	1.876	1.869
Intergrated daycare	26	23	21	27	10
Childminding	107	104	120	114	115
Other Daycare	1	6	9	5	18
^* Number of children in day care

# Import
OFXUKN1_raw <-
  statgl_url("OFXUKN1", lang = language) %>% 
  statgl_fetch(
    born_var   = 3:5,
    inst_type2 = 1:5,
    bosted     = 1:2,
    .col_code  = TRUE
    ) %>% 
  as_tibble()

# Transform
OFXUKN1 <-
  OFXUKN1_raw %>% 
  mutate(
    inst_type2 = inst_type2 %>% fct_inorder(),
    bosted  = bosted %>% fct_inorder(),
    aar = aar %>% make_date(),
    born_var = "Børn 3-5 år",
    across(where(is.integer), ~ if_else(is.na(.x), 0, .x))
    ) |> 
  summarise(value = sum(value), .by = c(inst_type2, born_var, bosted, aar))

# Plot
OFXUKN1 %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = inst_type2
    )) +
  geom_col() +
  facet_wrap(~ bosted, scales = "free_y") +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = FALSE, nrow = 2)) +
  labs(
    title    = sdg4$figs$fig2$title[language],
    subtitle = OFXUKN1[[2]][1],
    x        = " ",
    y        = sdg4$figs$fig2$y_lab[language],
    fill     = NULL,
    caption  = sdg4$figs$fig2$cap[language]
  )

StatBank

# Transform
OFXUKN1 <- 
  OFXUKN1_raw %>% 
  mutate(born_var = "Børn 3-5 år",
    across(where(is.integer), ~ if_else(is.na(.x), 0, .x))) |> 
  #arrange(desc(time)) %>%
  summarise(value = sum(value), .by = c(inst_type2, born_var, bosted, aar)) |> 
  filter(aar >= year(Sys.time()) - 5) %>% 
  mutate(aar = aar %>% factor(levels = unique(aar))) %>% 
  spread(aar, value) |> 
  arrange(bosted)

# Table
OFXUKN1 %>% 
  select(-c(2, 3)) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = table(OFXUKN1[[2]])) %>% 
  pack_rows(index = table(OFXUKN1[[3]])) %>% 
  add_footnote(
    sdg4$figs$fig2$foot[language], 
    notation = "symbol"
    )

	2019	2020	2021	2022	2023
Børn 3-5 år
Settlement
Childcare	43	61	58	61	79
Childminding	104	94	109	97	106
Intergrated daycare	26	23	21	27	10
Nursery	7	1	8	16	11
Other Daycare	0	0	0	0	0
Town
Childcare	1.827	1.856	1.885	1.815	1.790
Childminding	3	10	11	17	9
Intergrated daycare	0	0	0	0	0
Nursery	52	47	94	133	178
Other Daycare	1	6	9	5	18
^* Number of children in day care

FN 4.1.1 Problem-solving proficienty (pct. correct) in grade test results for children in 3rd and 7th grade (lower-secondary school)

# 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]
  )

StatBank

Method

# 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]]))

	3rd grade				7th grade
	Mathematics,3rd grade	Greenlandic,3rd grade	English,3rd grade	Danish,3rd grade	Mathematics,7th grade	Greenlandic,7th grade	English,7th grade	Danish,7th grade
Problem-solving proficiency (pct. correct)
2024	49	45	NA	41	40	56	84	42
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]
  )

StatBank

Method

# 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]]))

	3rd grade								7th grade
	Danish		English		Greenlandic		Mathematics		Danish		English		Greenlandic		Mathematics
	Town,Danish,3rd grade	Settlement,Danish,3rd grade	Town,English,3rd grade	Settlement,English,3rd grade	Town,Greenlandic,3rd grade	Settlement,Greenlandic,3rd grade	Town,Mathematics,3rd grade	Settlement,Mathematics,3rd grade	Town,Danish,7th grade	Settlement,Danish,7th grade	Town,English,7th grade	Settlement,English,7th grade	Town,Greenlandic,7th grade	Settlement,Greenlandic,7th grade	Town,Mathematics,7th grade	Settlement,Mathematics,7th grade
Problem-solving proficiency (pct. correct)
2024	42	38	NA	NA	43	48	48	56	45	34	86	55	55	57	39	40
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

GS Marks for pupils in lower secondary education

# Import
UDXFKK_raw <-
  statgl_url("UDXFKK", lang = language) %>% 
  statgl_fetch(
    unit             = "andel",
    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]
  )

StatBank

Method

# 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	2024
Grade Point Average
Greenlandic
Mark - Oral	6,50	5,94	6,81	6,48	6,80
Mark - Proficiency Test	4,74	3,52	3,67	3,97	4,86
Mark - Written	4,81	5,35	5,48	4,75	5,30
Danish
Mark - Oral	4,54	5,40	4,91	6,15	4,63
Mark - Proficiency Test	5,04	4,51	4,22	4,12	3,69
Mark - Written	4,11	3,39	3,64	3,90	3,18
Mathematics
Mark - Oral	4,62	4,89	5,25	5,58	5,60
Mark - Proficiency Test	5,21	4,95	4,91	4,84	4,81
Mark - Written	2,45	2,17	2,54	3,02	2,48
English
Mark - Oral	5,32	6,49	6,50	7,03	7,55
Mark - Proficiency Test	5,09	4,90	5,22	5,67	5,62
Mark - Written	4,01	4,12	4,57	4,63	5,34
^* School leavers - lower secondary education

Due to Covid-19 there has not been held final exams in 2020.

GS Transition from lower secondary education to upper secondary education

# Import
UDXTRFA1_raw <-
  statgl_url("UDXTRFA1", lang = language) %>% 
  statgl_fetch(
    aar       = 2,
    status    = px_all(),
    dim_aar   = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXTRFA1 <-
  UDXTRFA1_raw %>%
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  mutate(dim_aar = dim_aar %>% make_date())

  


# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x    = dim_aar,
    y    = value,
    fill = 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]
  )

StatBank

Method

# Transform
UDXTRFA1 <- 
  UDXTRFA1_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(dim_aar >= year(Sys.time()) - 8) %>% 
  mutate(dim_aar = dim_aar %>% factor(levels = unique(dim_aar))) %>% 
  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
Dropped out	109	117	108	82	95	92
Graduated	6	4	7	5	7	3
In education	220	264	243	250	270	257
Not enrolled in education	320	301	340	311	312	357
^* Number of people - transition from lower secondary education to upper secondary education (2 years after graduation)

# Import
UDXTRFA1_raw <-
  statgl_url("UDXTRFA1", lang = language) %>% 
  statgl_fetch(
    aar       = 2,
    status    = px_all(),
    dim_aar   = px_all(),
    sex       = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXTRFA1 <-
  UDXTRFA1_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  mutate(dim_aar = dim_aar %>% make_date())

# Plot
UDXTRFA1 %>% 
  ggplot(aes(
    x    = dim_aar,
    y    = value,
    fill = status
  )) +
  geom_col(position = "fill") +
  facet_wrap(~ sex) +
  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]
  )

StatBank

Method

# Transform
UDXTRFA1 <- 
  UDXTRFA1_raw %>% 
  filter(dim_aar <= year(Sys.time()) - 3) %>% 
  #arrange(desc(`graduation year`)) %>% 
  filter(dim_aar >= year(Sys.time()) - 8) %>% 
  mutate(dim_aar = dim_aar %>% factor(levels = unique(dim_aar))) %>% 
  spread(4, 5) %>% 
  arrange(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
Dropped out
Men	43	53	45	45	44	42
Women	66	64	63	37	51	50
Graduated
Men	5	3	7	3	7	2
Women	1	1	0	2	0	1
In education
Men	107	111	101	102	120	113
Women	113	153	142	148	150	144
Not enrolled in education
Men	152	153	171	174	170	181
Women	168	148	169	137	142	176
^* Number of people - transition from lower secondary education to upper secondary education (2 years after graduation)

GS Transition from upper secondary education to further education

# 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]
  )

StatBank

Method

# 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
Dropped out	69	61	46	49	54	0
Graduated	28	29	32	37	34	0
In education	157	134	137	129	114	0
not enrolled in education	91	104	89	88	93	0
^* Number of people - transition from lower secondary education to further education (2 years after graduation)

# 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]
  )

StatBank

Method

# 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
Dropped out
Men	22	13	16	23	0
Women	39	33	33	31	0
Graduated
Men	10	13	10	12	0
Women	19	19	27	22	0
In education
Men	61	52	47	32	0
Women	73	85	82	82	0
not enrolled in education
Men	51	38	50	44	0
Women	53	51	38	49	0
^* Number of people - transition from upper secondary education to further education (2 years after graduation)

GS Number of students by level of education and country

# Import
UDXISC11B_raw <-
  statgl_url("UDXISC11B", lang = language) %>% 
  statgl_fetch(
    isced = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(taar = taar %>% make_date(),
        isced = isced %>%  fct_inorder() %>% fct_rev(),
        value = value * 10^-3)

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = isced
  )) +
  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]
  )

StatBank

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 6) %>% 
  mutate(
         isced = isced %>% factor(levels = unique(isced)),
         taar  = taar %>% factor(levels = unique(taar)),
         ) %>% 
  spread(2, 3)

# Table
UDXISC11B %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(
    sdg4$figs$fig10$foot[language],
    notation = "symbol"
    )

	2018	2019	2020	2021	2022	2023
Upper secondary education	1.156	1.120	1.170	1.162	1.134	1.083
Vocational education and training	1.143	1.088	1.137	1.027	1.008	940
Supplementary examination courses	60	29	29	14	18	22
Short-cycle higher education	189	186	167	155	163	159
Bachelors programme	313	323	373	359	347	343
Professional bachelors programme	587	561	550	527	535	527
Masters programme	169	181	169	164	153	153
^* Number of active students

# Import
UDXISC11B_raw <-
  statgl_url("UDXISC11B", lang = language) %>% 
  statgl_fetch(
    skoleomr   = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Translate
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(
    taar     = taar %>% make_date(),
    skoleomr = skoleomr %>% fct_reorder(value),
    value    = value * 10^-3
    )

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = skoleomr
  )) +
  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] 
  )

StatBank

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 6) %>% 
  mutate(
    taar    = taar %>% fct_inorder(),
    skoleomr = skoleomr %>% 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	2023
Schools in Greenland	3.066	2.948	3.070	2.943	2.855	2.753
Schools in Denmark	520	515	501	445	481	458
Schools abroad	31	25	24	20	22	16
^* Number of students

FN 4.3.1 Number of students by gender

# Import
UDXISC11B_raw <-
  statgl_url("UDXISC11B", lang = language) %>% 
  statgl_fetch(
    sex       = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  mutate(
    taar  = taar %>% make_date(),
    sex   = sex %>% reorder(value),
    value = value * 10^-3
    )

# Plot
UDXISC11B %>% 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = sex
  )) +
  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]
  )

StatBank

# Transform
UDXISC11B <-
  UDXISC11B_raw %>% 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 6) %>% 
  mutate(taar = taar %>% 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	2023
Men	1.424	1.370	1.363	1.291	1.240	1.178
Women	2.193	2.118	2.232	2.117	2.118	2.049
^* Number of students

GS Number of graduates

# Import
UDXISC11D_raw <-
  statgl_url("UDXISC11D", lang = language) %>% 
  statgl_fetch(
    Isced     = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11D <-
  UDXISC11D_raw %>%
  mutate(
    slutaar              = slutaar %>% make_date(),
    id                   = row_number(),
    Isced = Isced %>% str_remove("uddannelse"),
    Isced = Isced %>% fct_reorder(id, .fun = min, na.rm = TRUE) %>% fct_rev()
  )

# Plot
UDXISC11D %>% 
  ggplot(aes(
    x    = slutaar,
    y    = value,
    fill = Isced
    )) +
  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] 
  )

StatBank

Method

# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  #arrange(desc(time)) %>% 
  filter(slutaar >= year(Sys.time()) - 6) %>% 
  mutate(
    Isced    = Isced %>% fct_inorder(),
    slutaar  = slutaar %>% 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	2023
Upper secondary education	304	314	310	318	282	333
Vocational education and training	429	399	411	448	376	404
Supplementary examination courses	75	111	125	141	119	98
Short-cycle higher education	67	55	68	66	62	62
Bachelors programme	43	53	47	54	55	56
Professional bachelors programme	134	129	107	119	102	88
Masters programme	34	35	40	35	32	35
^* Number of people - highest educational attainment

# Import
UDXISC11D_raw <-
  statgl_url("UDXISC11D", lang = language) %>% 
  statgl_fetch(
    Isced     = px_all(),
    sex       = px_all(),
    skoleomr  = c("A_SG", "B_SD"),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  mutate(
    Isced    = Isced %>% str_remove("uddannelse") %>% trimws(),
    Isced    = Isced %>% fct_inorder() %>% fct_rev(),
    sex      = sex  %>% fct_inorder(),
    skoleomr = skoleomr %>% fct_inorder,
    slutaar  = slutaar    %>% make_date()
  )

# Plot
UDXISC11D %>% 
  ggplot(aes(
    x = slutaar,
    y = value, 
    fill = Isced
  )) +
  geom_col() +
  facet_grid(skoleomr ~ sex, 
             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]
  )

StatBank

Method

# Transform
UDXISC11D <- 
  UDXISC11D_raw %>% 
  #arrange(desc(time)) %>% 
  filter(slutaar >= year(Sys.time()) - 4) %>% 
  mutate(
    slutaar  = slutaar %>% fct_inorder(),
    Isced    = Isced %>% fct_inorder(),
    skoleomr = skoleomr %>% 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))

	2023		2022		2021		2020
	Men,2020	Men,2021	Men,2022	Men,2023	Women,2020	Women,2021	Women,2022	Women,2023
Upper secondary education
Schools in Greenland	103	96	86	84	178	198	163	214
Schools in Denmark	9	7	17	6	13	14	13	26
Vocational education and training
Schools in Greenland	189	180	164	166	191	249	198	223
Schools in Denmark	14	11	8	6	17	8	5	8
Supplementary examination courses
Schools in Greenland	28	37	41	26	86	95	75	67
Schools in Denmark	1	2	1	2	10	7	2	3
Short-cycle higher education
Schools in Greenland	17	14	17	11	26	34	27	35
Schools in Denmark	12	5	8	5	13	13	10	10
Bachelors programme
Schools in Greenland	6	10	8	15	24	22	24	29
Schools in Denmark	5	5	7	6	12	14	14	5
Professional bachelors programme
Schools in Greenland	15	12	17	15	65	86	74	52
Schools in Denmark	11	6	4	9	16	13	7	12
Masters programme
Schools in Greenland	1	3	2	2	18	10	10	10
Schools in Denmark	3	10	6	8	13	11	13	15

GS Level of education among 35-39 year olds

# Import
UDXISCPROF_raw <-
  statgl_url("UDXISCPROF", lang = language) %>% 
  statgl_fetch(
    alder_grp     = "35-39",
    ISCED11_level = c(20, 34, 35, 40, 50, 64, 65, 70, 80),
    .col_code     = TRUE
    ) %>% 
  as_tibble()
  
# Transform
UDXISCPROF <-
  UDXISCPROF_raw %>% 
  mutate(
    id = row_number(),
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse") %>% 
    fct_reorder(id, .fun = min, na.rm = T) %>% fct_rev()
    )

# Plot
UDXISCPROF %>% 
  mutate(Aar = Aar %>% make_date()) %>% 
  ggplot(aes(
    x    = Aar, 
    y    = value,
    fill = ISCED11_level
    )) +
  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]
  )

StatBank

Method

# Transform
UDXISCPROF <- 
  UDXISCPROF_raw %>% 
  #arrange(desc(time)) %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  mutate(
    Aar           = Aar %>% fct_inorder(),
    ISCED11_level = ISCED11_level %>% 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	2023
35-39 years
Lower secondary education	1.600	1.669	1.696	1.781	1.901
Upper secondary education	155	154	175	183	197
Vocational education and training	1.173	1.223	1.247	1.229	1.292
Supplementary examination courses	145	118	91	80	80
Short-cycle higher education	144	163	158	159	176
Bachelors programme	50	48	48	58	56
Professional bachelors programme	388	394	417	433	442
Masters programme	157	156	173	179	180
Phd. Programmes	2	7	9	9	11
^* Number of people - highest educational attainment

# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch(
    alder_grp     = "35-39",
    ISCED11_level = c(20, 34, 35, 40, 50, 64, 65, 70, 80),
    Bsted         = px_all(),
    .col_code     = TRUE
    ) %>% 
  as_tibble()
  
# Transform
UDXISCPROD <-
  UDXISCPROD_raw %>% 
  mutate(
    id                   = row_number(),
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse") %>% 
           fct_reorder(id, .fun = min, na.rm = TRUE) %>% fct_rev(),
    Aar                 = Aar %>% make_date()
    )

# Plot
UDXISCPROD %>% 
  ggplot(aes(
    x    = Aar,
    y    = value,
    fill = ISCED11_level
    )) +
  geom_area(position = "fill") +
  facet_wrap(~ Bsted) +
  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]
  )

StatBank

Method

UDXISCPROD <-
  UDXISCPROD_raw %>% 
  #arrange(desc(time)) %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  mutate(
    Aar = Aar %>% fct_inorder(),
    ISCED11_level = ISCED11_level %>% fct_inorder()
  ) %>% 
  arrange(ISCED11_level) %>% 
  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"
    )

	2023		2022		2021		2020		2019
	Town,2019	Town,2020	Town,2021	Town,2022	Town,2023	Settlements,2019	Settlements,2020	Settlements,2021	Settlements,2022	Settlements,2023
Lower secondary education	1.319	1.360	1.389	1.506	1.618	281	309	307	275	283
Upper secondary education	147	148	162	171	183	8	6	13	12	14
Vocational education and training	1.065	1.120	1.140	1.125	1.180	108	103	107	104	112
Supplementary examination courses	135	112	86	76	77	10	6	5	4	3
Short-cycle higher education	144	161	157	155	171	0	2	1	4	5
Bachelors programme	49	48	48	58	55	1	0	0	0	1
Professional bachelors programme	376	383	403	411	427	12	11	14	22	15
Masters programme	156	155	173	179	180	1	1	0	0	0
Phd. Programmes	2	7	9	9	11	0	0	0	0	0
^* Number of people - highest educational attainment

FN 4.4.1 Proportions of 16-74 year olds with education in information and communication technology

# Import
UDXISCPROE_raw1 <-
  statgl_url("UDXISCPROE", lang = language) %>% 
  statgl_fetch(
    ISCED11_level  = c(35, 50, 64, 65, 70),
    ISCED11_sektor = c("06"),
    .col_code      = TRUE
    ) %>% 
  as_tibble()

UDXISCPROE_raw2 <-
  statgl_url("UDXISCPROE", lang = language) %>% 
  statgl_fetch(
    ISCED11_level = "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,
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse"),
    Aar           = Aar %>% make_date()
    )

# Plot
UDXISCPROE %>% 
  ggplot(aes(
    x    = Aar,
    y    = procent,
    fill = ISCED11_level
  )) +
  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]
  )

StatBank

# 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),
    ISCED11_level = ISCED11_level %>% str_remove("uddannelse")
    ) %>% 
  #arrange(desc(time)) %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  mutate(
    ISCED11_level = ISCED11_level %>% fct_inorder(),
    Aar           = Aar %>% 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	2023
Vocational education and training	0,2	0,2	0,2	0,2	0,2
Short-cycle higher education	0,2	0,2	0,2	0,2	0,2
Bachelors programme	0,0	0,0	0,0	0,0	0,0
Professional bachelors programme	0,0	0,0	0,0	0,0	0,0
Masters programme	0,0	0,0	0,0	0,0	0,0
^* Percentage of young people and adults with professional qualifications in information and communication technology

Goal 4: Quality Education

FN 4.2.2 Number of children in day care (3-5 years)

FN 4.1.1 Problem-solving proficienty (pct. correct) in grade test results for children in 3rd and 7th grade (lower-secondary school)

GS Marks for pupils in lower secondary education

GS Transition from lower secondary education to upper secondary education

GS Transition from upper secondary education to further education

GS Number of students by level of education and country

FN 4.3.1 Number of students by gender

GS Number of graduates

GS Level of education among 35-39 year olds

FN 4.4.1 Proportions of 16-74 year olds with education in information and communication technology