Tilbage

---

Delmål 3: Sundhed og trivsel

Aborter

---

GS Aldersstandardiseret rate hos kvinder i alderen 12-49 år

# Helper function:
age_adjust <- function(count, pop, rate = NULL, stdpop){
  if (missing(count) & !missing(pop) & is.null(rate)) {
    count <- rate * pop
  }
  
  if (missing(pop) & !missing(count) & is.null(rate)) {
    pop <- count/rate
    pop[!is.finite(pop)] <- 0
  }
  
  if (is.null(rate) & !missing(count) & !missing(pop)){
    rate <- count/pop
    rate[!is.finite(rate)] <- 0
  }

  cruderate <- sum(count)/sum(pop)
  stdwt <- stdpop/sum(stdpop)
  dsr <- sum(stdwt * rate)
  
  tibble(`crude_rate` = cruderate, `std_rate` = dsr)
}

# Import
SUDA2_raw <- 
  statgl_url("SUXA2", lang = "da") |>  
  statgl_fetch(.eliminate_rest = FALSE) |> 
  as_tibble() |> 
    rename(Aborter = value)

# Tidy
SUDA_2 <- 
  SUDA2_raw |> 
  as_tibble() |>  
  spread(enhed, Aborter) |> 
  mutate_at(c(1, 2), strtoi)

# Standardize
SUDA_2_2000 <- SUDA_2 |>  filter(tid == 2000) |>  pull(Middelfolketal)

SUDA2_std <- 
  SUDA_2 |> 
  group_by(tid) |>  
  summarise(age_adjust(Aborter, Middelfolketal, stdpop = SUDA_2_2000) * 1000)

# Plot
SUDA2_std |> 
  ggplot(aes(x = tid, y = std_rate)) +
  geom_line(size = 2, color = statgl:::statgl_cols("darkblue"))+
  theme_statgl() +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  labs(
    title    = sdg3$figs$fig1$title[language], 
    x        = " ", 
    y        = sdg3$figs$fig1$y_lab[language], 
    subtitle = sdg3$figs$fig1$sub[language],
    caption  = sdg3$figs$fig1$cap[language]
    )

Statistikbanken

Vis tabel

# Helper function:
age_adjust <- function(count, pop, rate = NULL, stdpop){
  if (missing(count) & !missing(pop) & is.null(rate)) {
    count <- rate * pop
  }
  
  if (missing(pop) & !missing(count) & is.null(rate)) {
    pop <- count/rate
    pop[!is.finite(pop)] <- 0
  }
  
  if (is.null(rate) & !missing(count) & !missing(pop)){
    rate <- count/pop
    rate[!is.finite(rate)] <- 0
  }

  
  cruderate <- sum(count)/sum(pop)
  stdwt <- stdpop/sum(stdpop)
  dsr <- sum(stdwt * rate)
  
  tibble(`crude_rate` = cruderate, `std_rate` = dsr)
}

# Import
SUDA2_raw <- 
  statgl_url("SUXA2", lang = "da") |>  
  statgl_fetch(.eliminate_rest = FALSE) |> 
  as_tibble() |> 
  rename(Aborter = value)

# Tidy
SUDA_2 <- 
  SUDA2_raw |> 
  as_tibble() |>  
  spread(enhed, Aborter) |>  
  mutate_at(c(1, 2), strtoi)

# Standardize
SUDA_2_2000 <- SUDA_2 |>  filter(tid == 2000) |>  pull(Middelfolketal)

SUDA2_std <- 
  SUDA_2 |> 
  group_by(tid) |>  
  summarise(age_adjust(Aborter, Middelfolketal, stdpop = SUDA_2_2000) * 1000,
            Aborter = sum(Aborter), Middelfolketal = sum(Middelfolketal))


vec        <- 2:5
names(vec) <- c(
    sdg3$figs$fig1$cols$col2[language],
    sdg3$figs$fig1$cols$col3[language],
    sdg3$figs$fig1$cols$col4[language],
    sdg3$figs$fig1$cols$col5[language]
    )

# Table
SUDA2_std |> 
  #arrange(desc(tid)) %>% 
  filter(tid > year(Sys.time()) - 7) |> 
  rename(vec) |> 
  gather(key, value, -tid) |>  
  mutate(
    key   = key |>  fct_inorder(),
    value = value |>  round(1),
    tid   = tid |>  factor(levels = unique(tid))
    ) |> 
  spread(1, 3) |>  
  rename(" " = 1) |>  
  statgl_table()

	2019	2020	2021	2022
Aborter pr. 1000	64,7	63,7	60,0	61,7
Aldersstandardiseret rate pr. 1000 kvinder, 12-49 år	56,2	55,8	52,8	54,5
Aborter	902,0	889,0	842,0	870,0
Middelfolketal	13.941,0	13.953,0	14.029,0	14.097,0

Senest opdateret: 20. juni 2025

Middellevetid

---

GS Middellevetid for 0 og 1 - årige personer født i Grønland

# Import
BEXBBDTB_raw <- 
  "BEXBBDTB" |>  
  statgl_url(lang = "da") |>  
  statgl_fetch(
    "place of birth" = "N",
    gender           = "t",
    age              = 0:1,
    calcbase         = "q5",
    measure          = "ex",
    time             = px_all(),
    .col_code        = TRUE
  ) |> 
  as_tibble()

# Plot
BEXBBDTB_raw |> 
  ggplot(aes(
    x     = as.numeric(time),
    y     = value,
    color = age
  )) +
  geom_line(size = 2) +
  theme_statgl() +
  scale_color_statgl() +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg3$figs$fig2$title[language],
    subtitle = sdg3$figs$fig2$sub[language],
    x        = " ",
    y        = BEXBBDTB_raw[["measure"]][[1]],
    color    = sdg3$figs$fig2$color[language],
    caption  = sdg3$figs$fig2$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
tab <- 
  BEXBBDTB_raw |> 
  select(age, time, measure, calcbase, value) |>  
  unite(combi, measure, calcbase, sep = ", ") |>  
  filter(time >= max(as.numeric(time)) - 9) |> 
  spread(time, value)

# Table
tab |> 
  select(-combi) |> 
  rename(" " = 1) |>  
  statgl_table() |> 
  pack_rows(index = tab[["combi"]] |>  table())

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Middellevetid, 5 år
0	70,1	70,2	70,7	71,1	71,1	71,2	71,2	71,1	71,1	71,1
1	69,3	69,5	70,0	70,4	70,4	70,5	70,4	70,4	70,4	70,5

Dødelighed

---

GS Aldersstandardiseret dødelighedsrate

# Helper function:
age_adjust <- function(count, pop, rate = NULL, stdpop){
  if (missing(count) & !missing(pop) & is.null(rate)) {
    count <- rate * pop
  }
  
  if (missing(pop) & !missing(count) & is.null(rate)) {
    pop <- count/rate
    pop[!is.finite(pop)] <- 0
  }
  
  if (is.null(rate) & !missing(count) & !missing(pop)){
    rate <- count/pop
    rate[!is.finite(rate)] <- 0
  }

 
  cruderate <- sum(count)/sum(pop)
  stdwt <- stdpop/sum(stdpop)
  dsr <- sum(stdwt * rate)
  
  tibble(`crude_rate` = cruderate, `std_rate` = dsr)
}

# Import
BEDBBDM1_raw <-
  statgl_url("BEXBBDM1", lang = "da") |>  
  statgl_fetch(
    type      = px_all(),
    age       = px_all(),
    .col_code = TRUE) |> 
  as_tibble() |> 
    rename(c(
      "alder"    = 1,
      "art"      = 2,
      "tid"      = 3,
      "Dødsfald" = 4
      ))

BEDBBM1 <- 
  BEDBBDM1_raw |>  
  as_tibble() |>  
  spread(art, Dødsfald) |>  
  mutate_at(1:2, strtoi) 

BEDBBM1_2000 <- 
  BEDBBM1 |>  
  filter(tid == 2000) |>  
  pull(Middelfolketal)

BEDBBM1_std <- BEDBBM1 |> 
  group_by(tid) |> 
  summarise(age_adjust(Døde, Middelfolketal, stdpop = BEDBBM1_2000) * 1000) |> 
  ungroup()

BEDBBM1_std |> 
  ggplot(aes(
    x = tid, 
    y = std_rate
    )) +
  geom_line(size = 2, color = statgl:::statgl_cols("darkblue")) +
  theme_statgl() +
  labs(
    title    = sdg3$figs$fig4$title[language],
    subtitle = sdg3$figs$fig4$sub[language],
    y        = sdg3$figs$fig4$y_lab[language],
    x        = " ",
    caption  = sdg3$figs$fig4$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Helper function:
age_adjust <- function(count, pop, rate = NULL, stdpop){
  if (missing(count) & !missing(pop) & is.null(rate)) {
    count <- rate * pop
  }
  
  if (missing(pop) & !missing(count) & is.null(rate)) {
    pop <- count/rate
    pop[!is.finite(pop)] <- 0
  }
  
  if (is.null(rate) & !missing(count) & !missing(pop)){
    rate <- count/pop
    rate[!is.finite(rate)] <- 0
  }

  cruderate <- sum(count)/sum(pop)
  stdwt <- stdpop/sum(stdpop)
  dsr <- sum(stdwt * rate)
  
  tibble(`crude_rate` = cruderate, `std_rate` = dsr)
}

# Import
BEDBBDM1_raw <-
  statgl_url("BEXBBDM1", lang = "da") |> 
  statgl_fetch(
    type      = px_all(),
    age       = px_all(),
    .col_code = TRUE) |> 
  as_tibble() |> 
    rename(c(
      "alder"    = 1,
      "art"      = 2,
      "tid"      = 3,
      "Dødsfald" = 4
      ))

BEDBBM1 <- 
  BEDBBDM1_raw |>  
  as_tibble() |>  
  spread(art, Dødsfald) |>  
  mutate_at(1:2, strtoi) 

BEDBBM1_2000 <- 
  BEDBBM1 |>  
  filter(tid == 2000) |>  
  pull(Middelfolketal)

BEDBBM1_std <- BEDBBM1 |> 
  group_by(tid) |> 
  summarise(age_adjust(Døde, Middelfolketal, stdpop = BEDBBM1_2000) * 1000) |> 
  ungroup()

vec <- 2:3
names(vec) <- c(
  sdg3$figs$fig4$cols$col2[language], 
  sdg3$figs$fig4$cols$col3[language]
  )

BEDBBM1_std |> 
  arrange(desc(tid)) |>  
  filter(tid > year(Sys.time()) - 7) |>  
  rename(vec) |> 
  gather(key, value, -tid) |>  
  mutate(
    tid   = tid |>  factor(levels = unique(tid)), 
    value = value |>  round(3)
    ) |> 
  spread(1, 3) |>  
  rename(" " = 1) |>  
  statgl_table()

	2024	2023	2022	2021	2020	2019
Crude Rate	9,31	9,43	9,28	9,40	9,27	9,78
Std. Rate	5,50	5,38	5,60	5,79	5,70	6,07

# Helper function:
age_adjust <- function(count, pop, rate = NULL, stdpop){
  if (missing(count) & !missing(pop) & is.null(rate)) {
    count <- rate * pop
  }
  
  if (missing(pop) & !missing(count) & is.null(rate)) {
    pop <- count/rate
    pop[!is.finite(pop)] <- 0
  }
  
  if (is.null(rate) & !missing(count) & !missing(pop)){
    rate <- count/pop
    rate[!is.finite(rate)] <- 0
  }

  
  cruderate <- sum(count)/sum(pop)
  stdwt <- stdpop/sum(stdpop)
  dsr <- sum(stdwt * rate)
  
  tibble(`crude_rate` = cruderate, `std_rate` = dsr)
}

# Import
BEDBBDM1_raw <-
  statgl_url("BEXBBDM1", lang = language) |> 
  statgl_fetch(
    type      = px_all(),
    age       = px_all(),
    gender    = c("M", "K"),
    .col_code = TRUE) |> 
  as_tibble()

BEDBBDM1 <- 
  BEDBBDM1_raw |>  
  as_tibble() |>  
  spread(type, value) |>  
  mutate_at(c(1, 3), strtoi) |> 
  rename(c("Death" = 4, "Meanpopulation" = 5))

BEDBBDM1_2000 <- 
  BEDBBDM1 |>  
  arrange(time, gender, age) |>  
  filter(time == 2000) |>  
  pull(Meanpopulation)

BEDBBDM1_std <- 
  BEDBBDM1 |>  
  group_by(time, gender) |>  
  arrange(age) |>  
  summarise(age_adjust(Death, Meanpopulation, stdpop = BEDBBDM1_2000) * 1000) |>  
  ungroup()

BEDBBDM1_std |> 
  ggplot(aes(
    x     = time, 
    y     = std_rate, 
    color = gender
  )) +
  geom_line(size = 2) + 
  theme_statgl() +
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = sdg3$figs$fig5$title[language], 
    subtitle = sdg3$figs$fig5$sub[language],
    color    = " ", 
    x        = " ",
    y        = sdg3$figs$fig5$y_lab[language],
    caption  = sdg3$figs$fig4$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Helper function:
age_adjust <- function(count, pop, rate = NULL, stdpop){
  if (missing(count) & !missing(pop) & is.null(rate)) {
    count <- rate * pop
  }
  
  if (missing(pop) & !missing(count) & is.null(rate)) {
    pop <- count/rate
    pop[!is.finite(pop)] <- 0
  }
  
  if (is.null(rate) & !missing(count) & !missing(pop)){
    rate <- count/pop
    rate[!is.finite(rate)] <- 0
  }

  cruderate <- sum(count)/sum(pop)
  stdwt <- stdpop/sum(stdpop)
  dsr <- sum(stdwt * rate)
  
  tibble(`crude_rate` = cruderate, `std_rate` = dsr)
}

# Import
BEDBBDM1_raw <-
  statgl_url("BEXBBDM1", lang = language) |> 
  statgl_fetch(
    type      = px_all(),
    age       = px_all(),
    gender    = c("M", "K"),
    .col_code = TRUE) |> 
  as_tibble()

BEDBBDM1 <- 
  BEDBBDM1_raw |>  
  spread(type, value) |>  
  mutate_at(c(1, 3), strtoi) |> 
  rename(c("Death" = 4, "Meanpopulation" = 5))

BEDBBDM1_2000 <- 
  BEDBBDM1 |>  
  arrange(time, gender, age) |>  
  filter(time == 2000) |>  
  pull(Meanpopulation)

BEDBBDM1_std <- 
  BEDBBDM1 |>  
  group_by(time, gender) |>  
  arrange(age) |> 
  summarise(age_adjust(Death, Meanpopulation, stdpop = BEDBBDM1_2000) * 1000) |> 
  ungroup()

vec        <- 2:3
names(vec) <- 
  c(
    sdg3$figs$fig4$cols$col2[language], 
    sdg3$figs$fig4$cols$col3[language]
    )

step <- 
  BEDBBDM1_std |> 
  filter(time > year(Sys.time()) - 5) |>  
  unite(combi, 1, 2, sep = ",") |> 
  rename(vec) |> 
  gather(key, value, -combi) |>  
  mutate(
    combi = combi |>  fct_inorder(),
    key   = key |>  fct_inorder(),
    value = value |>  round(3)
  ) |> 
  spread(1, 3)

vecvec   <- step[-1] |>  colnames() |>  str_split(",") |>  unlist()
head_vec <- vecvec[c(T, F)] |>  table()
col_vec  <- vecvec[c(F, T)]

step |> 
  rename(" " = 1) |>  
  statgl_table(
    col.names = c(" ", col_vec)
    ) |> 
  add_header_above(c(" ", head_vec))

	2021		2022		2023		2024
	2021,Kvinder	2021,Mænd	2022,Kvinder	2022,Mænd	2023,Kvinder	2023,Mænd	2024,Kvinder	2024,Mænd
Crude Rate	8,72	10,01	7,96	10,5	7,33	11,30	7,55	10,89
Std. Rate	5,05	6,42	4,55	6,5	3,89	6,74	4,18	6,71

Tuberkulose

---

FN 3.3.2 Nye tuberkulosetilfælde pr. 100.000 indbyggere

# Import
DISE01_raw <- 
  "https://pxweb.nhwstat.org:443/Prod/sq/31c3d851-c0ad-4728-8ee0-9b2b252cc48b.csv" |> 
  read_csv() |> 
  as_tibble()

# Transform
DISE01 <- 
  DISE01_raw |> 
  pivot_longer(cols = c(`Greenland Men`, `Greenland Women`), names_to = "sex", values_to = "Greenland") |> 
  mutate(
    sex  = sex |>  str_replace("Greenland Men",   sdg3$figs$fig6$groups$group1[language] |>  unlist()),
    sex  = sex |>  str_replace("Greenland Women", sdg3$figs$fig6$groups$group2[language] |>  unlist()),
    Greenland = as.numeric(Greenland)
  ) |> 
  filter(Year > 2002)

# Plot
DISE01 |> 
  ggplot(aes(
    x     = Year,
    y     = Greenland,
    color = sex
  )) +
  geom_line(size = 2) +
  theme_statgl() +
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = sdg3$figs$fig6$title[language],
    subtitle = sdg3$figs$fig6$sub[language],
    x        = " ",
    y        = sdg3$figs$fig6$y_lab[language],
    color    = " ",
    caption  = sdg3$figs$fig6$cap[language]
  )

Tabel, NOMESCO

Vis tabel

# Transform
DISE01 <- 
  DISE01_raw |> 
  pivot_longer(cols = c(`Greenland Men`, `Greenland Women`), names_to = "sex", values_to = "Greenland") |> 
  mutate(
    sex  = sex |>  str_replace("Greenland Men",   sdg3$figs$fig6$groups$group1[language] |>  unlist()),
    sex  = sex |>  str_replace("Greenland Women", sdg3$figs$fig6$groups$group2[language] |>  unlist())
  ) |> 
  filter(Year >= year(Sys.time()) - 7) |> 
  select(-`Greenland Men and Women`) |> 
  spread(1,3)

# Table
DISE01 |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  pack_rows(index = rep(sdg3$figs$fig6$index[language] |>  unlist(), length(DISE01[[1]])) |>  table()) |>  
  add_footnote(
    sdg3$figs$fig6$foot[language], 
    notation = "symbol"
  )

	2018	2019	2020	2021	2022	2023
Diagnosticerede tilfælde af tuberkulose
Kvinder	..	..	..	..	..	..
Mænd	..	..	..	..	..	..
* Per hundrede tusinde personer

Selvmord

---

FN 3.4.3 Selvmordsrate

# Import
SUDLDM2_raw <- 
  read_csv(paste0("https://bank.stat.gl:443/sq/3efbaaab-3db0-4b90-8f7b-18c556afe4e4", "?lang=", language),
    locale = locale(encoding = "latin1"))

BEDSTM1_raw <- 
  read_csv(paste0("https://bank.stat.gl:443/sq/e8c2ed7c-ed03-471b-87e1-40d658b78bd4", "?lang=", language))

# Transform
Selvmord <-
  SUDLDM2_raw |> 
  left_join(BEDSTM1_raw) |>  
  rename(
    "cause"      = 1,
    "time"       = 2,
    "suicide"    = 3,
    "population" = 4
    ) |> 
  mutate(rate = suicide / population * 10^5,
         time = time |>  make_date()) |> 
  filter(rate > 0)

# Plot
Selvmord |> 
  ggplot(aes(
    x = time,
    y = rate,
    color = statgl:::statgl_cols("darkblue")
  )) +
  geom_line(size = 2) +
  theme_statgl() + scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title   = Selvmord[[1]][1],
    x       = " ",
    y       = sdg3$figs$fig7$y_lab[language],
    caption = sdg3$figs$fig7$cap[language]
  )

Statistikbanken

Vis tabel

# Transform
Selvmord <-
  SUDLDM2_raw |> 
  left_join(BEDSTM1_raw) |>  
  rename(
    "cause"      = 1,
    "time"       = 2,
    "suicide"    = 3,
    "population" = 4
    ) |> 
  mutate(rate = (suicide / population * 10^5) |>  round(2)) |>  
  #arrange(desc(time)) %>% 
  filter(rate > 0, 
         time >= year(Sys.time()) - 7) |> 
  select(-(3:4)) |> 
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(2, 3)

# Table
Selvmord |> 
  rename(" " = 1) |>  
  statgl_table() |> 
  add_footnote(
    sdg3$figs$fig7$foot[language], 
    notation = "symbol"
    )

	2018	2019	2020	2021	2022	2023
Selvmord og selvmordsforsøg	80,5	82,2	73,1	83,3	70,7	70,7
* per hundrede tusinde personer

Børnedødelighed

---

FN 3.2.1 Dødelighed hos børn under 5 år

# Import, dødelighed
BEXBBDM1_raw <-
  statgl_url("BEXBBDM1", lang = language) |> 
  statgl_fetch(
    age       = 0:4,
    type      = "D",
    .col_code = TRUE
    ) |> 
  as_tibble()

# Import, levendefødte
BEXBBLK1_raw <-
  statgl_url("BEXBBLK1", lang = language) |> 
  statgl_fetch(
    type      = "L",
    .col_code = TRUE
    ) |> 
  as_tibble()

child_mortality <-
  BEXBBDM1_raw |>  
  spread(2, 4) |>  
  spread(1, 3) |>  
  mutate(sum = `0` + `1` + `2` + `3` + `4`) |> 
  select(-(2:6)) |> 
  left_join(BEXBBLK1_raw |>  spread(1, 3)) |>  
  rename(
    "mortality"  = 2,
    "population" = 3
  ) |> 
  mutate(rate = mortality / population * 1000,
         time = time |>  make_date())

# Plot
child_mortality |> 
  ggplot(aes(
    x = time,
    y = rate,
    color = statgl:::statgl_cols("darkblue")
    )) +
  geom_line(size = 2, color = statgl:::statgl_cols("darkblue")) +
  expand_limits(y = 0) +
  theme_statgl() +
  labs(
    title    = sdg3$figs$fig8$title[language],
    subtitle = sdg3$figs$fig8$sub[language],
    x        = " ",
    y        = sdg3$figs$fig8$y_lab[language],
    caption  = sdg3$figs$fig8$cap[language]
  )

Statistikbanken

Vis tabel

# Transform
child_mortality <-
  BEXBBDM1_raw |>  
  spread(2, 4) |>  
  spread(1, 3) |>  
  mutate(sum = `0` + `1` + `2` + `3` + `4`) |> 
  select(-(2:6)) |> 
  left_join(BEXBBLK1_raw |>  spread(1, 3)) |>  
  rename(
    "mortality"  = 2,
    "population" = 3
    ) |> 
  mutate(rate = mortality / population * 1000,
         rate = rate %>% round(3)) |>  
  select(-(2:3)) |> 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) |>  
  mutate(time = time |>  factor(levels = unique(time)),
         var = sdg3$figs$fig8$cols$col3[language]) |>  
  spread(1, 2)

# Table
child_mortality |> 
  rename(" " = 1) |>  
  statgl_table() |> 
  add_footnote(
    sdg3$figs$fig8$foot[language], 
    notation = "symbol"
    )

	2020	2021	2022	2023	2024
Børnedødelighed	12	15,8	13,4	12,6	14,6
* Dødelighedsrate per tusinde levendefødte

Alkohol og tobak

---

FN 3.5.2 Tilførsel af alkohol per person over 14 år.

url <- paste0("https://bank.stat.gl:443/api/v1/", language, "/Greenland/AL/AL10/ALXALK1.px")

# Import
ALXALK1_raw <-
  url |> 
  statgl_fetch(
    unit      = 1,
    type      = 0:2,
    category  = 1,
    .col_code = TRUE
    ) |> 
  as_tibble()

# Transform
ALXALK1 <-
  ALXALK1_raw |>  
  mutate(time = time |>  make_date())
  
# Plot
ALXALK1 |> 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = type
    )) +
  geom_area() +
  theme_statgl() +
  scale_fill_statgl(palette = "autumn") +
  labs(
    title    = sdg3$figs$fig9$title[language],
    subtitle = sdg3$figs$fig9$sub[language],
    x        = " ",
    y        = sdg3$figs$fig9$y_lab[language],
    fill     = sdg3$figs$fig9$fill[language],
    caption  = sdg3$figs$fig9$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
ALXALK1 <-
  ALXALK1_raw |> 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) |>  
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(4, 5)

# Table
ALXALK1 |> 
  select(-c(1, 3)) |>  
  rename(" " = 1) |> 
  statgl_table() |> 
  pack_rows(index = table(ALXALK1[[3]]))

	2020	2021	2022	2023	2024
Liter pr. person over 14 år
Spiritus	0,77	0,86	0,87	0,81	0,66
Vin	1,68	1,65	1,52	1,43	1,39
Øl	6,31	6,28	5,86	6,26	6,02

FN 3.a.1 Indførsel af cigaretter pr. dag pr. person over 14 år

url <- paste0("https://bank.stat.gl:443/api/v1/", language, "/Greenland/AL/AL40/ALXTOB2.px")

# Import
ALXTOB2_raw <-
  url |> 
  statgl_fetch(
    unit      = 3,
    type      = 0:1,
    .col_code = TRUE
    ) |> 
  as_tibble()

  
# Transform
ALXTOB2 <-
  ALXTOB2_raw |> 
  mutate(time = time |>  make_date())

# Plot
ALXTOB2 |> 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = type
    )) +
  geom_area() +
  theme_statgl() +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  scale_fill_statgl(palette = "autumn") +
  scale_y_continuous(labels = scales::comma_format(
    decimal.mark = ",", 
    big.mark     = "."
    )) +
  labs(
    title    = sdg3$figs$fig10$title[language],
    subtitle = sdg3$figs$fig10$sub[language],
    x        = " ",
    y        = sdg3$figs$fig10$y_lab[language],
    fill     = sdg3$figs$fig10$fill[language],
    caption  = sdg3$figs$fig10$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
ALXTOB2 <-
  ALXTOB2_raw |> 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) |>  
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(2, 4)

# Table
ALXTOB2 |> 
  select(-1) |>  
  rename(" " = 1) |>  
  statgl_table() |> 
  pack_rows(index = table(ALXTOB2[[1]]))

	2020	2021	2022	2023	2024
Antal stk. pr. dag pr. person over 14 år
Almindelige cigaretter	3,28	2,82	3,02	3,20	2,45
Rulletobak	2,46	2,40	2,13	1,78	2,78

Offentligt ansatte i sundhedsvæsenet

---

FN 3.c.1 Offentligt ansatte i sundhedsvæsenet

# Import
OFXOA1_raw <-
  statgl_url("OFXOA1", lang = language) |> 
  statgl_fetch(
    opg_var = px_all(),
    cofog_hoved = "07",
    .col_code            = TRUE
    ) |> 
  as_tibble()

# Transform
OFXOA1 <-
  OFXOA1_raw |>  
  mutate(
    taar  = taar |>  make_date(),
    value = value * 10^-3
    )

# Plot
OFXOA1 |> 
  ggplot(aes(
    x    = taar,
    y    = value,
    fill = opg_var
  )) +
  geom_col(position = "dodge") +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title   = sdg3$figs$fig11$title[language],
    x       = " ",
    y       = sdg3$figs$fig11$y_lab[language],
    fill    = " ",
    caption = sdg3$figs$fig11$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
OFXOA1 <- 
  OFXOA1_raw |> 
  #arrange(desc(time)) %>% 
  filter(taar >= year(Sys.time()) - 5) |>  
  mutate(taar = taar |>  factor(levels = unique(taar))) |>  
  spread(1, 3)

# Table
OFXOA1 |> 
  rename(" " = 1) |>  
  statgl_table() |> 
  add_footnote(
    sdg3$figs$fig11$foot[language], 
    notation = "symbol"
    )

	value	Sundhedsvæsen
2020	1.607	Fuldtidsbeskæftigede
2020	3.350	Ansatte
2021	1.654	Fuldtidsbeskæftigede
2021	3.561	Ansatte
2022	1.650	Fuldtidsbeskæftigede
2022	3.574	Ansatte
2023	1.668	Fuldtidsbeskæftigede
2023	3.787	Ansatte
* Antal personer i sundhedsvæsnet

:::

Fødselsvægt

---

GS Fødselsvægt

# Import
BEDLL1_raw <- 
  statgl_url("BEXLL1", lang = language) |>  
  statgl_fetch(
    time      = px_all(),
    weight    = 0:9,
    .col_code = TRUE
  ) |> 
  as_tibble()

# Transform
BEDLL1 <- 
  BEDLL1_raw |>  
  mutate(
    time   = time |>  as.numeric(),
    weight = weight |>  str_remove("gram") |>  trimws(),
    weight = weight |>  factor(levels = unique(weight))
  ) |> 
  filter(time %in% quantile(time)[-1])

# Plot
BEDLL1 |> 
  ggplot(aes(
    x    = weight,
    y    = value,
    fill = time |>  as.factor()
  )) +
  geom_col(position = "dodge2") +
  theme_statgl() + 
  theme(text = element_text(size = 20)) +
  scale_fill_statgl() +
  labs(
    title   = sdg3$figs$fig12$title[language],
    x       = sdg3$figs$fig12$x_lab[language],
    y       = sdg3$figs$fig12$y_lab[language],
    fill    = " ",
    caption = sdg3$figs$fig12$cap[language]
  )

Statistikbanken

Vis tabel

# Table
BEDLL1_raw |> 
  mutate(
    time   = time   |>  as.numeric(),
    weight = weight |>  factor(levels = unique(weight))
  ) |> 
  filter(time %in% quantile(time)[-1]) |>  
  arrange(desc(time)) |> 
  mutate(time = time |> factor(levels = unique(time))) |>  
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig12$foot[language], notation = "symbol")

	2010	2005	2000	1995
Under 1000 gram	3	1	3	2
1000-1499 gram	5	1	4	15
1500-1999 gram	15	8	5	7
2000-2499 gram	32	22	15	34
2500-2999 gram	83	119	91	129
3000-3499 gram	293	275	289	340
3500-3999 gram	273	303	294	361
4000-4499 gram	124	118	138	169
4500-4999 gram	31	33	34	36
Over 5000 gram	5	5	2	6
* Antal personer

Rusmidler

---

GS Rusmidler

# Import 
a <- c(3, 6, 16, 37, 49, 56, 3, 8, 24, 37, 52, 55, 2, 2, 6, 25, 43, 49, 1.5, 2, 6, 15, 26, 52)
b <- rep(c(11, 12, 13, 14, 15, 16), 4)
c <- c(rep(2006, 6), rep(2010, 6), rep(2014, 6), rep(2018, 6))

# Transform
hbsc <-
  data.frame(a, b, c) |> 
  as_tibble() |> 
  rename(
    "value" = 1,
    "age"   = 2,
    "time"  = 3
    ) |> 
  mutate(age = age |>  factor(levels = unique(age)))

# Plot  
hbsc |> 
  ggplot(aes(
    x = age,
    y = value,
    fill = age
  )) +
  geom_col() +
  facet_wrap(~ time) +
  xlab("age") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title   = sdg3$figs$fig13$title[language],
    x       = sdg3$figs$fig13$x_lab[language],
    y       = " ",
    caption = sdg3$figs$fig13$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

vec        <- 1
names(vec) <-  sdg3$figs$fig13$x_lab[language]
  
# Table
hbsc |> 
  arrange(desc(time)) |>  
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(time, value) |>  
  rename(vec) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig13$foot[language], notation = "symbol")

Alder	2018	2014	2010	2006
11	1,5	2	3	3
12	2,0	2	8	6
13	6,0	6	24	16
14	15,0	25	37	37
15	26,0	43	52	49
16	52,0	49	55	56
* Daglig rygning i % blandt 11-16-årige fra 2006-2018 (N=2.085 i 2018)

key1 <- sdg3$figs$fig14$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig14$keys$key2[language] |>  unlist()
key3 <- sdg3$figs$fig14$keys$key3[language] |>  unlist()


  
# Import
a <- c(6, 3, 4, 2, 3, 2)
b <- rep(c(2014, 2018), 3)
c <- c(rep(key1, 2), rep(key2, 2), rep(key3, 2))

hbsc <- 
  data.frame(b, a, c) |> 
  as_tibble() |> 
  rename(
    "time" = 1,
    "value" = 2,
    "key" = 3
  ) |> 
  mutate(
    key  = key  |>  factor(levels = unique(key)),
    time = time |>  factor(levels = unique(time))
    )

# Plot
hbsc |> 
  ggplot(aes(
    x = time,
    y = value,
    fill = time
  )) +
  geom_col() +
  facet_wrap(~ key) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  xlab("time") +
  labs(
    title   = sdg3$figs$fig14$title[language],
    y       = " ",
    x       = " ",
    caption = sdg3$figs$fig14$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

# Table
hbsc |> 
  #arrange(desc(time)) %>% 
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig14$foot[language], notation = "symbol")

	2014	2018
Sniffet mindst en gang	6	3
Sniffet i de sidste 12 måneder	4	2
Sniffet i de sidste 30 dage	3	2
* Andelen af 11-17-årige, der har sniffet i hele deres liv, i de sidste 12 måneder og i de sidste 30 dage (N=1.931-2.013).

key1 <- sdg3$figs$fig15$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig15$keys$key2[language] |>  unlist()
key3 <- sdg3$figs$fig15$keys$key3[language] |>  unlist()

  
  
# Import 
a <- c(27, 15, 17, 12, 9, 8)
b <- rep(c(2014, 2018), 3)
c <- c(rep(key1, 2), rep(key2, 2), rep(key3, 2))

# Transform
hbsc <-
  data.frame(b, c, a) |>  
  as_tibble() |> 
  rename(
    "time"  = 1,
    "key"   = 2,
    "value" = 3
  ) |> 
  mutate(
    key  = key  |>  factor(levels = unique(key)),
    time = time |>  factor(levels = unique(time))
    )

# Plot
hbsc |> 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = key
  )) +
  geom_col() +
  facet_wrap(~ key) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title   = sdg3$figs$fig15$title[language],
    y       = " ",
    x       = " ",
    caption = sdg3$figs$fig15$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

# Table
hbsc |> 
  #arrange(desc(time)) %>% 
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig15$foot[language], notation = "symbol")

	2014	2018
Hash mindst en gang	27	15
Hash de sidste 12 måneder	17	12
Hash de sidste 30 dage	9	8
* Andel af 15-17 årige, der angav at have prøvet hash, havde røget hash de sidste 12 måneder og i de sidste 30 dage (N=329 i 2018).

# Import 
a <- c(92,90,82,56,35,24,89,84,70,52,29,21,80,83,82,82,80,74,98,98,96,84,68,51)
b <- c(rep(2006, 6), rep(2010, 6), rep(2014, 6), rep(2018, 6))
c <- rep(11:16, 4)

# Transform
hbsc <- 
data.frame(b, c, a) |> 
  as_tibble() |> 
  rename(
    time  = 1,
    age   = 2,
    value = 3
  ) |> 
  mutate(
    time = time |> factor(levels = unique(time)),
  )

# Plot
hbsc |> 
  ggplot(aes(
    x = age,
    y = value,
    color = time
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title   = sdg3$figs$fig16$title[language],
    y       = sdg3$figs$fig16$y_lab[language],
    x       = sdg3$figs$fig16$x_lab[language],
    color   = " ",
    caption = sdg3$figs$fig16$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

  hbsc |> 
  arrange(desc(time)) |>  
  mutate(time = time |> factor(levels = unique(time))) |> 
  spread(time, value) |> 
  rename("Alder" = 1) |> 
  statgl_table() |>  
  add_footnote(sdg3$figs$fig16$foot[language], notation = "symbol")

Alder	2018	2014	2010	2006
11	98	80	89	92
12	98	83	84	90
13	96	82	70	82
14	84	82	52	56
15	68	80	29	35
16	51	74	21	24
* Udvikling fra 2006 til 2018 i andelen, der aldrig har været fuld

Fysisk aktivitet

---

GS Fysisk aktivitet

# Import
key1 <- sdg3$figs$fig17$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig17$keys$key2[language] |>  unlist()

a <- c(33, 30, 27, 24, 24, 24, 15, 18)
b <- rep(c(2006, 2010, 2014, 2018), 2)
c <- c(rep(key1, 4), rep(key2, 4))

# Transform
hbsc <-
  data.frame(b, c, a) |> 
  as_tibble() |> 
  rename(
    "time"   = 1,
    "gender" = 2,
    "value"  = 3
  ) |> 
  mutate(gender = gender |> factor(levels = unique(gender)))

# Plot
hbsc |> 
  ggplot(aes(
    x     = time,
    y     = value,
    color = gender
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title   = sdg3$figs$fig17$title[language],
    y       = sdg3$figs$fig17$y_lab[language],
    x       = " ",
    color   = " ",
    caption = sdg3$figs$fig17$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

# Table
hbsc |> 
  #arrange(desc(time)) %>% 
  mutate(time = time |>  factor(levels = unique(time))) |> 
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig17$foot[language], notation = "symbol")

	2006	2010	2014	2018
Drenge	33	30	27	24
Piger	24	24	15	18
* Procent, Moderat fysisk aktivitet 60 minutter dagligt i 2006 til 2018 på køn (N=2.075 i 2018)

Kropsopfattelse

---

GS Kropsopfattelse

# Import
key1 <- sdg3$figs$fig18$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig18$keys$key2[language] |>  unlist()
key3 <- sdg3$figs$fig18$keys$key3[language] |>  unlist()
key4 <- sdg3$figs$fig18$keys$key4[language] |>  unlist()
key5 <- sdg3$figs$fig18$keys$key5[language] |>  unlist()

a <- c(3,3, 11,9,57,58,26,26,3,4)
b <- c(rep(key1, 2), rep(key2, 2), rep(key3, 2), rep(key4, 2), rep(key5, 2))
c <- rep(c(2014, 2018), 5)

# Transform
hbsc <- 
  data.frame(c, b, a) |> 
  as_tibble() |> 
  rename(
    "time"  = 1,
    "key"   = 2,
    "value" = 3
  ) |> 
  mutate(
    time = time |> factor(levels = unique(time)),
    key  = key  |> factor(levels = unique(key))
    )

# Plot
hbsc |> 
  ggplot(aes(
    x = key,
    y = value,
    fill = time
  )) +
  geom_col(position = "dodge2") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl() +
  labs(
    title   = sdg3$figs$fig18$title[language],
    y       = " ",
    x       = " ",
    fill    = " ",
    caption = sdg3$figs$fig18$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

# Table
hbsc |> 
  arrange(desc(time)) |> 
  mutate(time = time |> factor(levels = unique(time))) |> 
  spread(time, value) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig18$foot[language], notation = "symbol")

key	2018	2014
Alt for tynd	3	3
Lidt for tynd	9	11
Passende	58	57
Lidt for tyk	26	26
Alt for tyk	4	3
* Procent, Tilfredshed med egen krop (N=1.871 i 2018).

Sodavand

---

GS Sodavand

# Import
key1 <- sdg3$figs$fig19$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig19$keys$key2[language] |>  unlist()
key3 <- sdg3$figs$fig19$keys$key3[language] |>  unlist()

a <- c(32,22,20,23,14,35,38,34,9,31,44,41,42,13,35)
b <- rep(c(2002,2006,2010,2014,2018), 3)
c <- c(rep(key1, 5), rep(key2, 5), rep(key3, 5))

# Transform
hbsc <- 
data.frame(b, c, a) |> 
  as_tibble() |> 
  rename(
    "time"  = 1,
    "key"   = 2,
    "value" = 3
  ) |> 
  mutate(key = key |> factor(levels = unique(key)))

# Plot
hbsc |> 
  ggplot(aes(
    x     = time,
    y     = value,
    color = key
  )) +
  geom_line(size  = 2) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title   = sdg3$figs$fig19$title[language],
    x       = " ",
    y       = " ",
    color   = " ",
    caption = sdg3$figs$fig19$cap[language] 
  )

HBSC-undersøgelsen

Vis tabel

# Table
hbsc |> 
  #arrange(desc(time)) %>% 
  mutate(time = time |> factor(levels = unique(time))) |> 
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig19$foot[language], notation = "symbol")

	2002	2006	2010	2014	2018
Nuuk	32	22	20	23	14
Andre byer	35	38	34	9	31
Bygder	44	41	42	13	35
* Udviklingen fra 2002 til 2018 af andelen af elever, der drak sodavand med sukker dagligt.

Tandbørstning

---

GS Tandbørstning

# Import

key1 <- sdg3$figs$fig20$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig20$keys$key2[language] |>  unlist()
key3 <- sdg3$figs$fig20$keys$key3[language] |>  unlist()

a <- c(50,31,19,61,24,16,63,27,10,64,28,8)
b <- rep(c(2006,2010,2014,2018), 3)
c <- rep(c(key1, key2, key3), 4)

# Transform
hbsc <- 
  data.frame(b, c, a) |> 
  as_tibble() |> 
  rename(
    "time"  = 1,
    "key"   = 2,
    "value" = 3
  ) |> 
  mutate(
    key  = key |>  factor(levels = unique(key)),
    time = time |>  factor(levels = unique(time))
  )

# Plot
hbsc |> 
  ggplot(aes(
    x = key,
    y = value,
    fill = key
  )) +
  geom_col() +
  facet_wrap(~ time, nrow = 1) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_fill_statgl(guide = guide_legend(nrow = 3)) +
  theme(axis.text.x  = element_blank()) +
  labs(
    title   = sdg3$figs$fig20$title[language],
    y       = " ",
    x       = " ",
    fill    = " ",
    caption = sdg3$figs$fig20$cap[language]
  )

HBSC-undersøgelsen

Vis tabel

# Table
hbsc |> 
  #arrange(desc(time)) %>% 
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote()

	2006	2010	2014	2018
Mere end 1 gang om ugen	50	64	63	61
1 gang om dagen	24	31	28	27
Sjældere end dagligt	10	16	19	8

Telefonrådgivning

---

GS Telefonrådgivning

# Import
key1 <- sdg3$figs$fig21$keys$key1[language] |>  unlist()
key2 <- sdg3$figs$fig21$keys$key2[language] |>  unlist()
key3 <- sdg3$figs$fig21$keys$key3[language] |>  unlist()
key4 <- sdg3$figs$fig21$keys$key4[language] |>  unlist()

a <- c(1732, 272, 547,  1485, 1887, 382, 668, 1609, 636, 106, 182, 560)
b <- c(rep(2019, 4), rep(2020, 4), rep(2021, 4))
c <- rep(c(key1, key2, key3, key4), 3)

# Transform
tusaannga <- 
  data.frame(b, c, a) |> 
  as_tibble() |> 
  rename(
    "time"  = 1,
    "key"   = 2,
    "value" = 3
  ) |> 
  mutate(key = key |>  factor(levels = unique(key)))

tusaannga |> 
  filter(!key %in% c(key1, key2)) |>  
  ggplot(aes(
    x = time,
    y = value,
    fill = key
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg3$figs$fig21$title[language],
    subtitle = sdg3$figs$fig21$sub[language],
    x        = " ",
    y        = sdg3$figs$fig21$y_lab[language],
    fill     = " ",
    caption  = sdg3$figs$fig21$cap[language]
  )

Vis tabel

# Table
tusaannga |> 
  #arrange(desc(time)) %>% 
  mutate(time = time |>  factor(levels = unique(time))) |>  
  spread(1, 3) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig21$foot[language], notation = "symbol")

	2019	2020	2021
Voksen	1.732	1.887	636
Børn	272	382	106
SMS	547	668	182
Opkald	1.485	1.609	560
* Tal for 2021 gælder 1. jannuar til 23. april

Familiepleje

---

GS Familiepleje

# Import
SOXPV003_raw <- 
  statgl_url("SOXPV003", lang = language) |> 
  statgl_fetch(
    "first adress in greenland" = px_all(),
    year                        = px_top(3),
    .col_code                    = TRUE
  ) |> 
  as_tibble()

# Transform
SOXPV003 <- 
  SOXPV003_raw |> 
  filter(`first adress in greenland` != SOXPV003_raw[[1]][1]) |> 
  mutate(
    year                        = year |>  as.numeric(),
    `first adress in greenland` = `first adress in greenland` |>  fct_reorder(value, sum)
    )

# Plot
SOXPV003 |> 
  ggplot(aes(
    x = `first adress in greenland`,
    y = value,
    fill = year
  )) +
  geom_col() +
  coord_flip() +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  facet_wrap(~ year) +
  labs(
    title   = statgl_meta(statgl_url("SOXPV003", lang = language))[1],
    x       = statgl_meta(statgl_url("SOXPV003", lang = language))[2]$variables[[1]]$text |>  str_to_title(),
    y       = sdg3$figs$fig22$y_lab[language],
    caption = sdg3$figs$fig22$cap[language]
  )

Statistikbanken

Vis tabel

# Table
x_lab        <- 1
names(x_lab) <- statgl_meta(statgl_url("SOXPV003", lang = language))[2]$variables[[1]]$text |>  str_to_title()

tab <- 
  SOXPV003 |> 
  mutate(
    `first adress in greenland` = `first adress in greenland` |>  fct_rev(),
    year = year |>  factor(levels = unique(year)) |>  fct_rev()
    ) |> 
  spread(2, 3)

tab |> 
  rename(x_lab) |> 
  statgl_table() |> 
  add_footnote(sdg3$figs$fig22$foot[language], notation = "symbol")

Første Adresse I Grønland	2024	2023	2022
Tasiilaq	96	81	73
Nuuk	50	47	53
Qaqortoq	34	40	31
Ilulissat	32	37	36
Maniitsoq	37	36	25
Sisimiut	29	32	35
Nanortalik	25	31	25
Aasiaat	15	17	20
Narsaq	19	18	14
Upernavik	17	13	12
Paamiut	10	11	19
Qasigiannguit	11	12	12
Kangaatsiaq	9	9	12
Qeqertarsuaq	6	11	12
Qaanaaq	4	9	14
Uummannaq	6	5	5
Ittoqqortoormiit	1	NA	2
* Antal børn

# Import
SOXPV001_raw <- 
  statgl_url("SOXPV001", lang = language) |> 
  statgl_fetch(
    unit               = c("Aarsvaerk"),
    "age of the child" = 0:19,
    year               = px_top(3),
    .col_code          = TRUE
    ) |> 
  as_tibble()

# Transform
SOXPV001 <- 
  SOXPV001_raw |> 
  mutate(
    year = year |>  as.numeric(),
    `age of the child` = `age of the child` |>  factor(levels = unique(`age of the child`))
    )

# Plot
SOXPV001 |> 
  ggplot(aes(
    x = `age of the child`,
    y = value,
    fill = year
  )) +
  geom_col() +
  facet_wrap(~ year) +
  coord_flip() +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = statgl_meta(statgl_url("SOXPV001", lang = language))[1],
    subtitle = str_to_sentence(SOXPV001_raw[[1]][1]),
    x        =  str_to_sentence(statgl_meta(statgl_url("SOXPV001", lang = language))[2]$variables[[2]]$text),
    y        = sdg3$figs$fig23$y_lab[language],
    caption  = sdg3$figs$fig23$cap[language]
  )

Statistikbanken

Vis tabel

# Table
age_lab        <- 1
names(age_lab) <- str_to_sentence(statgl_meta(statgl_url("SOXPV001", lang = language))[2]$variables[[2]]$text)

foot <- 

tab <- 
  SOXPV001 |> 
  mutate(year = year |>  factor(levels = unique(year)) |>  fct_rev()) |>  
  spread(3, 4)

tab |> 
  select(-1) |>  
  rename(age_lab) |>  
  statgl_table() |> 
  add_footnote(paste0(sdg3$figs$fig23$foot[language], SOXPV001_raw[[1]][1]), notation = "symbol")

Barnets alder	2024	2023	2022
0	3,7	1,5	1,5
1	7,0	9,7	9,2
2	13,3	13,4	19,3
3	14,4	20,8	22,6
4	26,5	25,8	24,5
5	29,6	27,3	27,8
6	28,9	25,9	25,2
7	26,2	28,5	22,4
8	25,7	23,3	14,4
9	23,8	15,3	23,5
10	15,6	25,0	24,2
11	26,7	28,6	24,9
12	28,3	28,1	19,5
13	28,8	22,5	25,7
14	23,7	23,0	17,1
15	20,8	19,0	22,0
16	19,8	15,3	11,8
17	14,0	9,7	20,5
18	8,6	19,0	17,2
19+	27,6	23,8	24,3
* Antal børn, omregnet til hele år

Diagnoser

---

GS Diagnoser

diag_cat <- 
  c(
    'F00: Uspec. fysisk handicap',
    'F01: Synstab',
    'F02: Høretab',
    'F03: Epilepsi',
    'F04: Stofskifte',
    'F05: Andre progressive lidelser',
    'F06: Gigt',
    'F07: Genetiske & medfødte',
    'F08: Indre organer',
    'F09: Hudlidelse,vansiring',
    'F10: Åndedræt',
    'F11: Kredsløb',
    'F12: Bevægeapparat',
    'F13: Hjerneskade',
    'F14: Immunforsvar',
    'F15: Andre handicaps',
    'F16: Talehandicap',
    'P00: Uspec. psykisk handicap',
    'P01: Mental retardering',
    'P02: Organiske psykiske lidelser',
    'P03: Misbrug',
    'P04: Udviklingsforstyrrelse',
    'P05: Personlighedsforstyrrelse',
    'P06: Psykotiske lidelser',
    'P07: Andre psykiske lidelser'
  )

SOXFO11_raw <- 
  statgl_url("SOXFO11", lang = language) |> 
  statgl_fetch(
    time                 = px_all(),
    "diagnosis Category" = diag_cat,
    .col_code            = TRUE
  ) |> 
  as_tibble()


if (language == "da") {
  
  f <- "Fysisk handicap"
  p <- "Psykisk handicap"
  
} else if (language == "kl") {

  f <- "Timikkut innarluuteqarneq"
  p <- "Tarnikkut innarluuteqarneq"
  
} else {

  f <- "Physical disability"
  p <- "Mental disability"
  
}

vec        <- c("F", "P")
names(vec) <- c(f, p)

SOXFO11 <- 
  SOXFO11_raw |> 
  separate(`diagnosis Category`, into = c("type", "cat")) |> 
  select(-cat) |> 
  mutate(type = type |>  str_remove_all("[:digit:]")) |> 
  group_by(type, time) |> 
  summarise(value = sum(value, na.rm = TRUE)) |> 
  ungroup() |> 
  spread(type, value) |> 
  rename(vec) |> 
  gather(key, value, -time) |> 
  mutate(key = key |>  fct_reorder(value, .fun = sum, .desc = FALSE))

SOXFO11 |> 
  ggplot(aes(
    x    = time, 
    y    = value,
    fill = key
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl() +
  labs(
    title    = sdg3$figs$fig24$title[language],
    subtitle = sdg3$figs$fig24$sub[language],
    y        = " ",
    x        = " ",
    fill     = " ",
    caption  = sdg3$figs$fig24$cap[language]
  )

Statistikbanken

Vis tabel

SOXFO11 |> 
  mutate(
    time = time |>  fct_rev(),
    key = key |>  fct_reorder(value, sum) |>  fct_rev()
    ) |> 
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  kableExtra:: add_footnote(sdg3$figs$fig24$foot[language], notation = "symbol")

	2018
Psykisk handicap	825
Fysisk handicap	561
* Antal personer

Forekomst af sexsygdomme

FN 3.7.1 Forekomst af sexsygdomme blandt 15-24-årige fordelt på type og køn

url <- paste0("https://bank.stat.gl:443/api/v1/", language, "/Greenland/SU/SU01/SU0120/SUXLSKS1.px")

# Import
SUXLSKS1_raw <- 
  url |>
  statgl_fetch(
    age       = 15:24,
    disease   = 1:3,
    sex       = px_all(),
    time      = px_top(5),
    .col_code = T
  ) |> 
  as_tibble()

# Transform

SUXLSKS1 <- 
  SUXLSKS1_raw |> 
  group_by(disease, sex, time) |> 
  summarise(value = sum(value)) |> 
  ungroup() |> 
  arrange(disease, time) |> 
  unite(combi, disease, sex, sep = " ") |> 
  mutate(combi = fct_inorder(combi))

# Plot
SUXLSKS1 |> 
  ggplot() +
    geom_col(aes(
      x     = time,
      y     = value,
      fill  = combi
     ),
     position = "dodge"
    ) +
  scale_fill_statgl(guide = guide_legend(ncol = 3)) +
  theme_statgl() +
  labs(
    title    = sdg3$figs$fig25$title[language],
    subtitle = sdg3$figs$fig25$sub[language],
    x       = " ",
    y       = " ",
    fill    = " ",
    caption = sdg3$figs$fig25$cap[language]
  )

Statistikbanken

Vis tabel

tabel <- 
  SUXLSKS1_raw |> 
  filter(time >= year(Sys.time()) - 7) |> 
  group_by(disease, sex, time) |> 
  summarise(value = sum(value)) |> 
  ungroup() |> 
  arrange(sex) |>  
  mutate(vec = sex) |> 
  unite(combi, disease, sex, sep = " ") |> 
  mutate(combi = fct_inorder(combi)) |> 
  spread(time, value)
  

tabel |> 
  rename(" " = 1) |> 
  select(-vec) |> 
  statgl_table() |> 
  pack_rows(index = table(tabel[[2]])) |> 
  add_footnote(sdg3$figs$fig25$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022
I alt
Gonorré I alt	581	886	1.002	1.101	1.270
Klamydia I alt	1.862	1.681	1.651	1.638	1.575
Syfilis I alt	62	48	66	NA	NA
Kvinder
Gonorré Kvinder	341	564	668	688	771
Klamydia Kvinder	1.256	1.133	1.094	1.095	1.051
Syfilis Kvinder	42	30	45	NA	NA
Mænd
Gonorré Mænd	240	322	334	413	499
Klamydia Mænd	606	548	557	543	524
Syfilis Mænd	NA	NA	NA	NA	NA
* Antal personer