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]
    )

Statistikbanken

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# 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	2018	2017	2016
Aborter pr. 1000	64,7	66,4	62,3	59,5
Aldersstandardiseret rate pr. 1000 kvinder, 12-49 år	56,2	57,9	54,5	52,4
Aborter	902,0	931,0	883,0	855,0
Middelfolketal	13.941,0	14.019,0	14.169,0	14.364,0

Senest opdateret: 07. april 2022

Middellevetid

---

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

# Import
BEXDT5A_raw <-
  statgl_url("BEXDT5A", lang = language) %>% 
  statgl_fetch(
    type      = "E",
    age       = c(0, 1),
    .col_code = TRUE) %>% 
  as_tibble()

# Transform
BEXDT5A <- 
  BEXDT5A_raw %>% 
  separate(time, into = c("startaar", "slutaar"), sep = " - ") %>% 
  mutate(slutaar = slutaar %>% as.numeric %>% make_date()) %>% 
  select(-3)

# Plot
BEXDT5A %>% 
  ggplot(aes(
    x     = slutaar, 
    y     = value, 
    color = age
    )) +
  geom_line(size = 2) +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title    = sdg3$figs$fig2$title[language],
    subtitle = sdg3$figs$fig2$sub[language],
    x        = sdg3$figs$fig2$x_lab[language],
    y        = BEXDT5A[[1]][1],
    color    = sdg3$figs$fig2$color[language],
    caption  = sdg3$figs$fig2$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
BEXDT5A <-
  BEXDT5A_raw %>% 
  mutate(timetime = time) %>% 
  separate(timetime, c("tip", "top"), sep = " - ") %>% 
  arrange(desc(top)) %>% 
  filter(top >= year(Sys.time()) - 5) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  select(-(5:6)) %>% 
  spread(3, 4)

# Table  
BEXDT5A %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(BEXDT5A[[1]]))

	2015 - 2019	2014 - 2018	2013 - 2017
Middellevetid
0	70,46	70,70	70,63
1	70,09	70,27	70,22

# Import
BEXDT5A_raw <-
  statgl_url("BEXDT5A", lang = language) %>%
  statgl_fetch(
    type = "E",
    age = c(0, 1),
    residence = 1:2,
    .col_code = TRUE) %>% 
  as_tibble()

# Transform
BEXDT5A <- 
  BEXDT5A_raw %>% 
  separate(time, into = c("startaar", "slutaar"), sep = " - ") %>% 
  mutate(slutaar = as.numeric(slutaar) %>% make_date()) %>% 
  select(-4)

# Plot
BEXDT5A %>% 
  ggplot(aes(
    x      = slutaar,
    y      = value,
    color  = age
  )) +
  geom_line(size = 2) +
  facet_wrap(~ residence) +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  scale_color_statgl(
    reverse = TRUE, 
    guide = guide_legend(reverse = TRUE)
    ) +
  labs(
    title    = sdg3$figs$fig3$title[language],
    subtitle = sdg3$figs$fig3$sub[language],
    x        = sdg3$figs$fig3$x_lab[language],
    y        = BEXDT5A[[1]][1],
    color    = sdg3$figs$fig3$color[language],
    caption  = sdg3$figs$fig3$cap[language]
  )

Statistikbanken

Metode

Vis tabel

# Transform
BEXDT5A <-
  BEXDT5A_raw %>% 
  mutate(timetime = time) %>% 
  separate(timetime, c("tip", "top"), sep = " - ") %>% 
  arrange(desc(top), age) %>% 
  filter(top >= year(Sys.time()) - 5) %>% 
  unite(combi, 3, 2, sep = ",") %>% 
  mutate(
    combi = combi %>% factor(levels = unique(combi)),
    time  = time  %>% factor(levels = unique(time))
    ) %>% 
  select(-(5:6)) %>% 
  spread(2, 4)

vec      <- BEXDT5A %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]

# Table
BEXDT5A %>% 
  select(-1) %>%
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = table(BEXDT5A[[1]]))

	0		1
	By	Bygd	By	Bygd
Middellevetid
2015 - 2019	70,73	68,74	70,41	68,08
2014 - 2018	70,89	69,56	70,54	68,68
2013 - 2017	70,81	69,60	70,49	68,72

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]
  )

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()

	2021	2020	2019	2018	2017	2016
Crude Rate	9,404	9,267	9,784	8,712	8,937	8,724
Std. Rate	5,787	5,699	6,073	5,428	5,857	5,726

# 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(),
    gender    = c("M", "K"),
    .col_code = TRUE) %>% 
  as_tibble() %>% 
    rename(c(
      "alder"    = 1,
      "art"      = 2,
      "køn"      = 3,
      "tid"      = 4,
      "Dødsfald" = 5
      ))

BEDBBDM1 <- BEDBBDM1_raw %>% as_tibble() %>% spread(art, Dødsfald) %>% 
  mutate_at(c(1, 3), strtoi)

BEDBBDM1_2000 <- BEDBBDM1 %>% arrange(tid, køn, alder) %>% filter(tid == 2000) %>% pull(Middelfolketal)

BEDBBDM1_std <- BEDBBDM1 %>% group_by(tid, køn) %>% arrange(alder) %>% 
  summarise(age_adjust(Døde, Middelfolketal, stdpop = BEDBBDM1_2000) * 1000) %>% 
  ungroup()

BEDBBDM1_std %>% 
  ggplot(aes(
    x     = tid, 
    y     = std_rate, 
    color = køn
  )) +
  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]
  )

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(),
    gender    = c("M", "K"),
    .col_code = TRUE) %>% 
  as_tibble() %>% 
    rename(c(
      "alder"    = 1,
      "art"      = 2,
      "køn"      = 3,
      "tid"      = 4,
      "Dødsfald" = 5
      ))

BEDBBDM1 <- 
  BEDBBDM1_raw %>% 
  spread(art, Dødsfald) %>% 
  mutate_at(c(1, 3), strtoi)

BEDBBDM1_2000 <- BEDBBDM1 %>% arrange(tid, køn, alder) %>% filter(tid == 2000) %>% pull(Middelfolketal)

BEDBBDM1_std <- BEDBBDM1 %>% group_by(tid, køn) %>% arrange(alder) %>% 
  summarise(age_adjust(Døde, Middelfolketal, 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 %>% 
  arrange(desc(tid)) %>% 
  filter(tid > 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() %>% rev()
col_vec  <- vecvec[c(F, T)]

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

Tuberkulose

---

FN 3.3.2 Nye tuberkulosetilfælde pr. 100.000 indbyggere

# Import
TUB01_raw <- 
  read_csv(
  "http://pxweb.fujitsu.dk/sq/1417044b-c578-4c3a-a97d-5056b117ad52",
  locale = locale(encoding = "latin1")) %>%
  as_tibble()

# Transform
TUB01 <- 
  TUB01_raw %>% 
  mutate(
    sex  = sex %>% str_replace("Men",   sdg3$figs$fig5$groups$group1[language] %>% unlist()),
    sex  = sex %>% str_replace("Women", sdg3$figs$fig5$groups$group2[language] %>% unlist()),
    year = year %>% make_date()
    )

# Plot
TUB01 %>% 
  ggplot(aes(
    x     = year, 
    y     = Greenland, 
    color = sex
    )) +
  geom_line(size = 2) +
  expand_limits(y = 0) +
  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
TUB01 <- 
  TUB01_raw %>% 
  mutate(
    sex = sex %>% str_replace("Men",   sdg3$figs$fig5$groups$group1[language] %>% unlist()),
    sex = sex %>% str_replace("Women", sdg3$figs$fig5$groups$group2[language] %>% unlist())
    ) %>% 
  arrange(desc(year)) %>% 
  filter(year >= year(Sys.time()) - 7) %>% 
  mutate(year = year %>% factor(levels = unique(year))) %>% 
  spread(2, 3)

# Table
TUB01 %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = rep(sdg3$figs$fig6$index[language] %>% unlist(), length(TUB01[[1]])) %>% table()) %>% 
  add_footnote(
    sdg3$figs$fig6$foot[language], 
    notation = "symbol"
    )

	2016	2015
Diagnosticerede tilfælde af tuberkulose
Kvinder	64,2	105,9
Mænd	148,1	165,8
* 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())

# 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(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"
    )

	2020	2019	2018	2017	2016	2015
Selvmord og selvmordsforsøg	0	78,58	80,53	71,61	85,95	57,16
* 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"
    )

	2021	2020	2019	2018	2017
Børnedødelighed	15,769	11,976	14,134	8,547	8,206
* Dødelighedsrate per tusinde levendefødte

Alkohol og tobak

---

FN 3.5.2 Tilførsel af alkohol per person over 14 år.

# Import
ALXALK1_raw <-
  statgl_url("ALXALK1", lang = language) %>%
  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]]))

	2021	2020	2019	2018	2017
Liter pr. person over 14 år
Spiritus	0,87	0,77	0,64	0,65	0,73
Vin	1,66	1,68	1,43	1,33	1,66
Øl	6,15	6,31	5,54	5,45	6,02

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

# Import
ALXTOB2_raw <-
  statgl_url("ALXTOB2", lang = language) %>% 
  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]]))

	2021	2020	2019	2018	2017
Antal stk. pr. dag pr. person over 14 år
Almindelige cigaretter	2,82	3,28	2,75	2,98	2,88
Rulletobak	2,35	2,44	2,91	1,86	2,46

Offentligt ansatte i sundhedsvæsenet

---

FN 3.c.1 Offentligt ansatte i sundhedsvæsenet

# 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]
  )

Statistikbanken

Metode

Vis tabel

# 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"
    )

	2020	2019	2018	2017
Ansatte	18.691	18.976	18.517	18.428
Fuldtidsbeskæftigede	11.066	10.872	10.699	10.536
* 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
  )) +
  geom_col() +
  facet_wrap(~ time) + 
  coord_flip() +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title   = sdg3$figs$fig12$title[language],
    x       = sdg3$figs$fig12$x_lab[language],
    y       = sdg3$figs$fig12$y_lab[language],
    caption = sdg3$figs$fig12$cap[language]
  )