Utertiguk

---

Anguniagaq 3: Peqqissuseq atugarissaarnerlu

Naartuersittarnerit

---

GS Arnat 12-it 49-llu akornanni ukiullit annertussusaat

# 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]
    )

Kisitsisaataasivik

Tabel nuisiguk

# 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()

	2018	2019	2020	2021	2022
Arnat 12-49 ukiullit 1000-iugaangata agguaqatigiisillugit naartumik katatsinerit	66,4	64,7	63,7	60,0	61,7
Ukiui malillugit annertussusaat	57,9	56,2	55,8	52,8	54,5
Naartumik katatsinerit	931,0	902,0	889,0	842,0	870,0
Agguaqatigiisillugu inuttussuseq	14.019,0	13.941,0	13.953,0	14.029,0	14.097,0

Kingullermik nutarterneqarfia 12. april 2024

Inuuneqqortussuseq

---

GS 0-iniit 1-inut ukiullit Kalaallit Nunaanni inunngorsimasut inuuneqqortussusaat

# 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]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# 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

Toqusoqartarnera

---

GS Ukiut malillugit toqusoqartarnerata annertussusaa

# 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]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# 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()

	2023	2022	2021	2020	2019	2018
Crude Rate	9,43	9,28	9,40	9,27	9,78	8,71
Std. Rate	5,38	5,60	5,79	5,70	6,07	5,43

# 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]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# 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))

	2020		2021		2022		2023
	2020,Angutit	2020,Arnat	2021,Angutit	2021,Arnat	2022,Angutit	2022,Arnat	2023,Angutit	2023,Arnat
Crude Rate	99.832	118.714	99.923	114.631	95.631	125.559	88.453	136.464
Std. Rate	74.432	70.433	94.469	96.049	101.568	77.195	87.731	75.868

Tuberkulose

---

FN 3.3.2 Innuttaasut 100.000-iugaangata tuberkulosemik tunillatsissimasut nutaat

# 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

Tabel nuisiguk

# 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"
  )

	2017	2018	2019	2020	2021	2022
Tuberkulosemik napparsimasut
Angutit	132.5	119.5	187.3	..	..	..
Arnat	91.8	96.3	86.5	..	..	..
* Inuit 100.000-iugaangata

Imminut toquttarneq

---

FN 3.4.3 Imminut toquttarnerup annertussusaa

# 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]
  )

Kisitsisaataasivik

Tabel nuisiguk

# 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"
    )

	2017	2018	2019	2020	2021	2022
Imminut toquttut/imminut toqoriartut	69,8	80,5	80,4	73,1	83,3	72,5
* Inuit 100.000-iugaangata

Meeraaqqat toqusarnerat

---

FN 3.2.1 Meeqqat tallimat inorlugit ukiullit akornanni toqusoqartarnera

# 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]
  )

Kisitsisaataasivik

Tabel nuisiguk

# 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"
    )

	2019	2020	2021	2022	2023
Meeraaqqat toqusarnerat	14,1	12	15,8	13,4	12,6
* Uumallutik inunngortut tuusintiugaangata toqusartut annertussusaat

Imigassaq tupalu

---

FN 3.5.2 14-ileereersimasunut imigassamik pilersuineq

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]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# 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]]))

	2019	2020	2021	2022	2023
Liter inummut 15-ileereersimasumut ataatsimut
Immiaaqqat	5,54	6,31	6,28	5,86	6,26
Kimittuut	0,64	0,77	0,86	0,87	0,81
Viinni	1,43	1,68	1,65	1,52	1,43

FN 3.a.1 14-ileereersimasumut ullormut tupanik eqqussuineq

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]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

# 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]]))

	2019	2020	2021	2022	2023
Inummut 15-leereersimasumut agg. ullormut stk.
Immussat	2,91	2,46	2,40	2,13	1,78
Sikaritsit nalinginnaasut	2,75	3,28	2,82	3,02	3,20

Pisortanit peqqinnissaqarfimmi atorfillit

---

FN 3.c.1 Pisortanit peqqinnissaqarfimmi atorfillit

# Import
OFXOA1_raw <-
  statgl_url("OFXOA1", lang = language) |> 
  statgl_fetch(
    `inventory variable` = px_all(),
    .col_code            = TRUE
    ) |> 
  as_tibble()

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

# Plot
OFXOA1 |> 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `inventory variable`
  )) +
  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]
  )

Kisitsisaataasivik

Periaaseq

Tabel nuisiguk

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

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

	2019	2020	2021	2022
Atorfillit	18.976	18.691	18.909	18.990
Piffissaq tamakkerlugu sulisut	10.872	11.066	11.279	11.224
* Peqqinnissaqarfimmiittut amerlassusaat

Inunngornermi oqimaassuseq

---

GS Inunngornermi oqimaassuseq

# 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]
  )

Kisitsisaataasivik

Tabel nuisiguk

# 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
* Inuit amerlassusaat

Aanngajaarniutit

---

GS Aanngajaarniutit

# 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-mik misissuineq

Tabel nuisiguk

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")

Ukiut	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
* 11-16-inik ukiullit akornanni ullut tamaasa pujortartartut procentinngorlugit (2018-imi N=2.085)

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-mik misissuineq

Tabel nuisiguk

# 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
Minnerpaamik ataasiarluni naamaarsimavoq	6	3
Qaammatini kingullerni 12-ini naamaartarsimavoq	4	2
Ulluni kingullerni 30-ni naamaartarsimavoq	3	2
* 11-17-inik ukiullit akornanni inuunertik naallugu, qaammatini kingullerni 12-ini aamma ulluni kingullerni 30-ni naamaartarsimasut annertussusaat (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-mik misissuineq

Tabel nuisiguk

# 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
Minnerpaamik sapaatip akunneranut ataasiarluni hashitortarsimavoq	27	15
Qaammatini kingullerni 12-ini hashitortarsimavoq	17	12
Ulluni kingullerni 30-ni hashitortarsimavoq	9	8
* 15-17-inik ukiullit akornanni hashimik misiliisimanermik, qaammatini kingullerni 12-ini aamma ulluni kingullerni 30-ni hashitortarsimanerminnik nalunaarsimasut annertussusaat (2018-imi N=329)

# 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-mik misissuineq

Tabel nuisiguk

  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
* Aalakoorimanngisaanartut 2006-imiit 2018-imut annertussusaasa iniartornerat

Timimik aalatitsineq

---

GS Timimik aalatitsineq

# 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-mik misissuineq

Tabel nuisiguk

# 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
Nukappiaqqat	33	30	27	24
Niviarsiaqqat	24	24	15	18
* 2006-2018-umut 60 minutsinik ullormut akunnattumik timimik atuisarneq suiaassutsinut immikkoortillugit procentinngortillugit (2018-imi N=2.075)

Timimik paasinninneq

---

GS Timimik paasinninneq

# 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-mik misissuineq

Tabel nuisiguk

# 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
Saluppallaartoq	3	3
Saluppallaalaartoq	9	11
Naammaginartoq	58	57
Pualavallaalaartoq	26	26
Pualavallaartoq	4	3
* Timimik iluarisimaarinninneq procentinngorlugu (2018-imi N=1.871).

Sodavandi

---

GS Sodavandi

# 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-mik misissuineq

Tabel nuisiguk

# 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
Illoqarfinni allani	35	38	34	9	31
Nunaqarfinni	44	41	42	13	35
* 2002-miit 2018-imut atuartut sodavandinik sukkutalinnik ullormut imertartut annertussusaat.

Kigutigissarneq

---

GS Kigutigissarneq

# 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-mik misissuineq

Tabel nuisiguk

# 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
Sapaatip akunneranut ataatsimit amerlanerusut	50	64	63	61
Ullormut ataaseq	24	31	28	27
Ullormut ataasianermit akuttunerusoq	10	16	19	8

Oqarasuaatikkut siunnersuisarfiit

---

GS Oqarasuaatikkut siunnersuisarfiit

# 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]
  )

Tabel nuisiguk

# 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
Inersimasoq	1.732	1.887	636
Meeqqat	272	382	106
SMS	547	668	182
Sianerluni	1.485	1.609	560
* 2021-imut kisitsisit januarip aallaqqaataaniit 23. apriilimut atuupput.

Ilaqutariinni paarsisartuniinneq

---

GS Ilaqutariinni paarsisartuniinneq

# 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]
  )

Kisitsisaataasivik

Tabel nuisiguk

# 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")

Kalaallit Nunaanni Adresse Siulleq	2023	2022	2021
Tasiilaq	81	73	65
Nuuk	47	53	61
Ilulissat	37	36	39
Sisimiut	32	35	40
Qaqortoq	40	31	31
Maniitsoq	36	25	24
Nanortalik	31	25	24
Aasiaat	17	20	17
Paamiut	11	19	18
Narsaq	18	14	13
Qasigiannguit	12	12	16
Upernavik	13	12	13
Qaanaaq	9	14	13
Kangaatsiaq	9	12	10
Qeqertarsuaq	11	12	6
Uummannaq	5	5	5
Ittoqqortoormiit	NA	2	3
* Meeqqat amerlassusaat

# 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]
  )

Kisitsisaataasivik

Tabel nuisiguk

# 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")

Meeqqap ukiui	2023	2022	2021
0	1,5	1,5	2,8
1	9,7	9,2	15,3
2	13,4	19,3	18,8
3	20,8	22,6	21,3
4	25,8	24,5	24,4
5	27,3	27,8	22,9
6	25,9	25,2	20,2
7	28,5	22,4	12,1
8	23,3	14,4	24,3
9	15,3	23,5	20,7
10	25,0	24,2	28,1
11	28,6	24,9	19,3
12	28,1	19,5	24,8
13	22,5	25,7	23,5
14	23,0	17,1	26,4
15	19,0	22,0	15,3
16	15,3	11,8	17,3
17	9,7	20,5	22,3
18	19,0	17,2	22,1
19+	23,8	24,3	14,7
* Meeqqat amerlassusaatukiumut ilivitsumut iluarsisat

Nappaatip suussusaa

---

GS Nappaatip suussusaa

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]
  )

Kisitsisaataasivik

Tabel nuisiguk

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
Tarnikkut innarluuteqarneq	825
Timikkut innarluuteqarneq	561
* Inuit amerlassusaat

Kinguaassiuutitigut nappaatit

FN 3.7.1 Kinguaassiutitigut nappaatit 15-it 24-llu akornanni ukiullit amerlassusaat

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]
  )

Kisitsisaataasivik

Tabel nuisiguk

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
Angutit
Gonorré Angutit	240	322	334	413	499
Klamydia Angutit	606	548	557	543	524
Syfilis Angutit	NA	NA	NA	NA	NA
Arnat
Gonorré Arnat	341	564	668	688	771
Klamydia Arnat	1.256	1.133	1.094	1.095	1.051
Syfilis Arnat	42	30	45	NA	NA
Katillugit
Gonorré Katillugit	581	886	1.002	1.101	1.270
Klamydia Katillugit	1.862	1.681	1.651	1.638	1.575
Syfilis Katillugit	62	48	66	NA	NA
* Inuit amerlassusaat