Tilbage


Delmål 5: Ligestilling mellem kønnene

Gennemsnitsindkomst


GS Gennemsnitsindkomst efter køn, bosted og uddannelse
INXPI104_raw <- 
  "INXPI104" %>% 
  statgl_url(lang = language) %>% 
  statgl_fetch(
    "level of education" = px_all(),
    unit                 = 3,
    gender               = 1:2,
    age                  = c(0, 4),
    "type of income"     = 1,
    time                 = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()


INXPI104 <- 
  INXPI104_raw %>%
  filter(
    value != "NA",
    age   == unique(INXPI104_raw %>% pull(age))[1]
    ) %>% 
  rename(
    "edu"  = `level of education`,
    "type" = `type of income`
    ) %>% 
  mutate(
    edu  = edu %>% fct_inorder(),
    type = type %>%  str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws()
    )

INXPI104 %>% 
  ggplot(aes(
    x     = time %>% as.numeric(),
    y     = value,
    color = gender %>% fct_rev()
  )) +
  geom_line(size = 2) +
  facet_wrap(~ edu) +
  theme_statgl() +
  scale_color_statgl() +
  scale_y_continuous(labels = function(x) format(x, big.mark = ".", scientific = FALSE)) +
  scale_x_continuous(breaks = scales:: pretty_breaks()) + 
  labs(
    title    = INXPI104 %>% pull(type) %>% unique(),
    subtitle = INXPI104 %>% pull(unit) %>% unique(),
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg5$figs$fig1$cap[language]
  )

Statistikbanken

Metode


tab <- 
  INXPI104 %>% 
  filter(time >= Sys.time() %>% year() - 7) %>% 
  mutate(time = time %>% fct_rev()) %>% 
  spread(time, value) %>% 
  select(-age)
  

tab %>% 
  select(-c(edu, unit, type)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["type"]] %>%  table()) %>% 
  pack_rows(index = tab[["edu"]] %>% table()) %>% 
  add_footnote(tab %>% pull(unit) %>% unique(), notation = "symbol")
2020 2019 2018 2017 2016 2015
Indkomst i alt før skat mv
Grundskole
Kvinder 162.877 159.525 155.730 152.097 148.952 144.543
Mænd 221.784 220.169 219.905 212.799 218.026 201.875
Gymnasial uddannelse
Kvinder 200.704 195.960 188.470 186.094 177.640 177.606
Mænd 306.561 307.562 306.545 310.075 304.290 282.303
Erhvervsuddannelse
Kvinder 274.232 272.702 265.223 257.950 249.013 244.584
Mænd 402.390 386.557 381.615 370.215 372.599 353.406
Kort videregående uddannelse
Kvinder 302.875 302.445 294.940 291.796 284.501 274.645
Mænd 317.910 331.182 307.996 296.458 284.432 273.779
Mellemlang videregående uddannelse
Kvinder 415.736 410.521 405.208 417.047 384.120 379.001
Mænd 527.720 534.416 531.466 555.195 520.320 524.025
Videregående uddannelse
Kvinder 591.396 578.418 561.255 613.203 559.033 553.385
Mænd 757.895 765.664 713.391 780.811 767.929 735.965
* Gennemsnit for personer med indkomsttypen (kr)
INXPI104 <- 
  INXPI104_raw %>%
  filter(
    value != "NA",
    age   == unique(INXPI104_raw %>% pull(age))[2]
  ) %>% 
  rename(
    "edu"  = `level of education`,
    "type" = `type of income`
  ) %>% 
  mutate(
    edu  = edu %>% fct_inorder(),
    type = type %>%  str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws()
  )

INXPI104 %>% 
  ggplot(aes(
    x     = time %>% as.numeric(),
    y     = value,
    color = gender %>% fct_rev()
  )) +
  geom_line(size = 2) +
  facet_wrap(~ edu) +
  theme_statgl() +
  scale_color_statgl() +
  scale_y_continuous(labels = function(x) format(x, big.mark = ".", scientific = FALSE)) +
  scale_x_continuous(breaks = scales:: pretty_breaks()) + 
  labs(
    title    = paste0(
      INXPI104 %>% pull(type) %>% unique(), ", ", 
      INXPI104 %>% pull(age) %>% unique()
    ),
    subtitle = INXPI104 %>% pull(unit) %>% unique(),
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg5$figs$fig1$cap[language]
  )

Statistikbanken

Metode


tab <- 
  INXPI104 %>% 
  filter(time >= Sys.time() %>% year() - 7) %>% 
  mutate(time = time %>% fct_rev()) %>% 
  spread(time, value) %>% 
  unite(type, type, age, sep = ", ")


tab %>% 
  select(-c(edu, unit, type)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["type"]] %>%  table()) %>% 
  pack_rows(index = tab[["edu"]] %>% table()) %>% 
  add_footnote(tab %>% pull(unit) %>% unique(), notation = "symbol")
2020 2019 2018 2017 2016 2015
Indkomst i alt før skat mv, 30-34 år
Grundskole
Kvinder 165.499 161.223 158.541 154.962 152.546 151.840
Mænd 226.409 229.455 220.974 208.084 218.329 204.017
Gymnasial uddannelse
Kvinder 192.447 188.873 187.006 176.472 178.359 187.828
Mænd 289.586 313.604 273.793 301.519 277.873 277.968
Erhvervsuddannelse
Kvinder 260.461 266.917 256.938 249.862 239.516 234.589
Mænd 357.335 347.180 331.054 325.784 320.800 297.976
Kort videregående uddannelse
Kvinder 222.469 208.038 226.267 233.077 267.414 260.336
Mænd 306.309 294.879 347.271 336.414 276.652 248.768
Mellemlang videregående uddannelse
Kvinder 358.794 359.464 360.720 376.074 341.061 340.543
Mænd 472.974 445.387 438.141 432.105 401.887 392.161
Videregående uddannelse
Kvinder 475.581 460.440 441.388 492.136 441.866 443.326
Mænd 522.714 483.043 472.560 501.613 456.438 505.380
* Gennemsnit for personer med indkomsttypen (kr)

Fordelingen af folkevalgte efter køn


FN 5.5.1 Andel af kvindelige parlamentarikere i det nationale parlament
# Fejl, Nordic Statistics
Nordic Statistics

Metode


# Fejl, Nordic Statistics

Økonomisk udsatte


GS Andel af økonomisk udsatte i befolkningen efter køn
# Import 
SOXOU01_raw <-
  statgl_url("SOXOU01", lang = language) %>%
  statgl_fetch(
    "inventory variable" = c("Andel50", "Andel60"),
    gender               = 1:2,
    year                 = px_all(),
    .col_code            = TRUE
    ) %>% 
    as_tibble()

# Transform
SOXOU01 <-
  SOXOU01_raw %>% 
  mutate(
    year   = year %>%  make_date(),
    gender = gender %>% fct_inorder()
    )

# Plot
SOXOU01 %>% 
  mutate(`inventory variable` = `inventory variable` %>% str_to_sentence()) %>% 
  ggplot(aes(
    x    = year,
    y    = value,
    fill = gender)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ",")
    ) +
  facet_wrap(~ `inventory variable`) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title    = sdg5$figs$fig3$title[language],
    subtitle = sdg5$figs$fig3$sub[language],
    x        = " ", 
    y        = " ", 
    fill     = " ",
    caption  = sdg5$figs$fig3$cap[language]
  )

Statistikbanken

Metode


# Transform
SOXOU01 <-
  SOXOU01_raw %>% 
  arrange(desc(year)) %>% 
  filter(year >= year(Sys.time()) - 5) %>% 
  mutate(year = year %>% fct_inorder()) %>% 
  unite(combi, 1, 2, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(1, 3)

vec      <- SOXOU01[-1] %>% colnames() %>% str_split(",") %>% unlist() %>% str_to_sentence()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]

# Table
SOXOU01 %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  add_footnote(
    sdg5$figs$fig3$foot[language],
    notation = "symbol"
    )
Andel under 50%
Andel under 60%
Kvinder Mænd Kvinder Mænd
2020 3,7 4,5 7,3 8,1
2019 3,5 4,1 7,0 7,8
2018 3,5 4,2 6,7 7,7
2017 3,4 3,9 6,6 7,1
* Procentvis andel under 50 eller 60% af medianindkomsten.

Trintest-resultater


GS Trintest resultater efter køn
# Import
UDXTKK_raw <-
  statgl_url("UDXTKK", lang = language) %>%
  statgl_fetch(subject   = px_all(),
               grade     = px_all(),
               sex       = 1:2,
               unit      = "B",
               time      = px_all(),
               .col_code = TRUE
               ) %>% 
    as_tibble()

# Transform
UDXTKK <- 
  UDXTKK_raw %>% 
  mutate(
    time     = time %>% make_date(),
     subject =  subject %>% fct_inorder()
    )

fig_legend   <- statgl_url("UDXTKK", lang = language) %>% statgl_fetch() %>% select(1) %>% colnames()
fig_title    <- (statgl_url("UDXTKK", lang = language) %>% statgl_meta())$title
fig_subtitle <- UDXTKK_raw[["unit"]] %>% unique()
  
# Plot
UDXTKK %>% 
  ggplot(aes(
    x = time,
    y = value,
    color = subject
  )) +
  geom_line(size = 2) +
  facet_grid(grade ~ sex) +
  theme_statgl() + 
  scale_color_statgl() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = fig_title,
    subtitle = fig_subtitle,
    x        = " ",
    y        = " ",
    color    = fig_legend,
    caption  = sdg5$figs$fig4$cap[language]
  )

Statistikbanken

Metode


UDXTKK <- 
  UDXTKK_raw %>% 
  mutate(
    subject = subject %>% fct_inorder(),
    grade   = grade %>% fct_inorder(),
    sex     = sex %>% fct_inorder()
    ) %>% 
  arrange(subject, time) %>% 
  unite(combi, 2, 1, 3, sep = ",") %>% 
  spread(1, 4) %>% 
  arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5)

vec       <- UDXTKK %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec1 <- rep((vec[c(F, T, F)])[1:8] %>% table(), 2)
head_vec2 <- vec[c(T, F, F)] %>% table()
col_vec   <- vec[c(F, F, T)] 

# Table
UDXTKK %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  mutate_all(~replace(., is.na(.), 0)) %>% 
  statgl_table(col.names = c(" ", col_vec),
               replace_0s = TRUE) %>% 
  add_header_above(c(" ", head_vec1)) %>% 
  add_header_above(c(" ", head_vec2)) %>% 
  pack_rows(index = UDXTKK[["unit"]] %>% table())
  1. klasse
  1. klasse
Dansk
Engelsk
Grønlandsk
Matematik
Dansk
Engelsk
Grønlandsk
Matematik
Drenge Piger Drenge Piger Drenge Piger Drenge Piger Drenge Piger Drenge Piger Drenge Piger Drenge Piger
Løsningssikkerhed (pct. rigtige)
2021 46 48 - - 45 50 53 49 47 59 71 76 54 66 41 38
2020 49 50 - - 40 48 53 48 54 60 73 73 57 65 41 42
2019 50 59 - - 39 50 51 52 48 60 53 67 61 70 40 43
2018 47 64 - - 46 54 50 56 53 60 53 59 59 66 41 40
2017 50 60 - - 44 53 65 62 58 67 59 65 62 68 45 47

Karaktergennemsnit


GS Prøvekarakterer efter køn
# Import
UDXFKK_raw <-
  statgl_url("UDXFKK", lang = language) %>% 
  statgl_fetch(unit             = "Avg",
               grade            = "FO",
               subject          = c("01", "02", "03", "04"),
               "type of grades" = 56:58,
               sex              = 1:2,
               time             = px_all(),
               .col_code = TRUE) %>% 
    as_tibble()

# Transform
UDXFKK <-
  UDXFKK_raw %>% 
  separate(`type of grades`, c("split1", "split2"),  " - ") %>% 
  mutate(split2 = split2 %>% str_to_title(),
         split1 = split1 %>% str_to_lower(),
         time = time %>% make_date()) %>% 
  unite(combi, 2, 4, sep = ", ")

fig_title    <- (statgl_url("UDXFKK", lang = language) %>% statgl_meta())$title
fig_y        <- UDXFKK[["unit"]] %>% unique() %>% str_to_title()
fig_subtitle <- UDXFKK[["combi"]] %>% unique()

# Plot
UDXFKK %>% 
  ggplot(aes(
    x     = time,
    y     = value, 
    color = sex
    )) +
  geom_line(size = 2) +
  facet_grid(split2 ~ subject) +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ",", 
    accuracy     = 1
    )) +
  theme_statgl() + 
  scale_color_statgl(guide = guide_legend(reverse = TRUE)) +
  labs(
    title    = fig_title,
    subtitle = fig_subtitle,
    x        = " ",
    y        = fig_y,
    color    = " ",
    caption  = sdg5$figs$fig5$cap[language]
  )

Statistikbanken

Metode


# Transform
UDXFKK <-
  UDXFKK_raw %>% 
  filter(time >= year(Sys.Date()) - 6,
         value != "NA") %>% 
  separate(`type of grades`, c("split1", "split2"),  " - ") %>% 
  mutate(split2 = split2 %>% str_to_title(),
         split1 = split1 %>% str_to_lower()) %>% 
  unite(combi1, 2, 4, sep = ", ") %>% 
  unite(combi2, 3, 4, sep = ",") %>% 
  spread(3, ncol(.)) %>% 
  arrange(desc(time))

vec      <- UDXFKK %>% select(-(1:4)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]

# Table
UDXFKK %>% 
  select(-(1:2), -4) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = UDXFKK[[1]] %>% str_to_title() %>% table()) %>% 
  pack_rows(index = UDXFKK[["time"]] %>% table() %>% rev()) %>% 
  add_footnote(UDXFKK[[2]] %>% unique(),
               notation = "symbol")
Dansk
Engelsk
Grønlandsk
Matematik
Færdighedsprøve Mundtlig Skriftlig Færdighedsprøve Mundtlig Skriftlig Færdighedsprøve Mundtlig Skriftlig Færdighedsprøve Mundtlig Skriftlig
Karaktergennemsnit
2021
Drenge 3,93 4,89 2,59 4,73 6,66 3,75 3,11 5,67 4,18 5,06 4,79 2,16
Piger 4,93 5,74 4,00 5,03 6,36 4,40 3,94 6,21 6,31 4,84 4,94 2,17
2019
Drenge 4,31 3,63 3,30 4,52 4,72 3,28 4,21 5,32 3,72 5,33 4,64 2,18
Piger 5,74 5,75 4,83 5,58 5,81 4,69 5,24 7,65 5,90 5,06 4,60 2,69
2018
Drenge 4,32 4,13 3,20 4,73 3,32 3,05 5,07 5,33 4,15 5,39 5,37 2,05
Piger 4,92 4,47 4,44 5,26 4,46 3,98 6,29 6,69 6,41 5,01 5,20 2,18
2017
Drenge 5,09 4,98 3,70 5,54 4,56 3,15 4,48 5,20 4,24 5,45 5,06 2,73
Piger 5,61 5,14 4,62 5,74 4,95 3,96 5,58 6,55 6,63 4,92 5,10 2,74
2016
Drenge 4,63 4,42 3,81 4,62 4,89 3,14 4,04 4,94 4,14 5,48 4,87 3,04
Piger 5,56 5,88 5,19 5,27 5,62 3,87 5,60 6,53 6,42 5,14 4,99 2,93
* Folkeskolens afgangselever, prøvekarakter

Højest fuldførte uddannelse


GS Højst fuldførte uddannelse blandt 35-39 årige efter køn
# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch("level of education" = px_all(),
               gender               = px_all(),
               time                 = px_all(),
               age                  = "35-39",
               .col_code = TRUE) %>% 
  as_tibble()

# Transform
UDXISCPROD <-
  UDXISCPROD_raw %>% 
  filter(`level of education` != UDXISCPROD_raw[[1]][1]) %>% 
  mutate(
    `level of education` = `level of education` %>% factor(level = unique(`level of education`) %>% rev()),
    time                 = time %>% make_date()
    )

# Plot
UDXISCPROD %>% 
  arrange(`level of education`) %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `level of education`
  )) +
  geom_area(position = "fill") +
  facet_wrap(~ gender) +
  scale_y_continuous(labels = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE, nrow = 3)) +
  labs(
    title    = sdg5$figs$fig6$title[language],
    subtitle = unique(UDXISCPROD[["age"]]),
    x        = " ",
    y        = " ",
    fill     = NULL,
    caption  = sdg5$figs$fig6$cap[language]
  )

Statistikbanken

Metode


UDXISCPROD <- 
  UDXISCPROD_raw %>% 
  filter(
    `level of education` != UDXISCPROD_raw[[1]][1],
    time > year(Sys.Date()) - 7
    ) %>% 
  mutate(
    `level of education` = `level of education` %>% factor(levels = unique(`level of education`))
    ) %>% 
  arrange(`level of education`, desc(time)) %>% 
  unite(combi, 2, 4, sep = ",") %>% 
  mutate(combi = combi %>% factor(level = unique(combi))) %>% 
  spread(2, 4, sep = ",")

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

UDXISCPROD %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE, col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = UDXISCPROD[["age"]] %>% table())
2020
2019
2018
2017
2016
Kvinder Mænd Kvinder Mænd Kvinder Mænd Kvinder Mænd Kvinder Mænd
35-39 år
Grundskole 10. klasses niveau 634 1.037 615 986 627 936 591 907 572 877
Gymnasial uddannelse 91 63 82 73 78 76 68 73 62 73
Erhvervsuddannelse 555 667 516 657 470 633 433 589 410 541
Suppleringskurser 75 43 97 48 113 65 148 75 159 87
Kort videregående uddannelse 87 77 77 68 76 80 72 71 72 73
Bacheloruddannelse 35 13 32 18 34 17 30 15 33 16
Professionsbacheloruddannelse 299 93 294 92 285 82 271 81 238 89
Kandidatuddannelse 86 70 81 76 96 72 100 72 96 75
Phd. og forskeruddannelse 6 1 2 - 4 3 4 5 5 7

Beskæftigelse


GS Hovedbeskæftigelse blandt fastboende, efter brancher og køn
# Import
ARXBFB1_raw <-
  statgl_url("ARXBFB1", lang = language) %>% 
  statgl_fetch(
    time                 = px_all(),
    industry             = 1:16,
    gender               = 1:2,
    "inventory variable" = 1,
    .col_code            = TRUE
    ) %>% 
  as_tibble() 

# Transform
ARXBFB1 <-
  ARXBFB1_raw %>% 
  mutate(
    time     = time %>% make_date(),
    industry = industry %>% fct_reorder(value) %>% fct_rev()
    ) %>% 
  arrange(industry)

# Plot
ARXBFB1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gender
    )) +
  geom_area() +
  facet_wrap(~ industry, scales = "free", labeller = label_wrap_gen()) +
  theme_statgl(base_size = 8) + 
  scale_fill_statgl(reverse = TRUE) +
  scale_y_continuous(labels = scales::unit_format(
    suffix       = " ",
    big.mark     = ".",
    decimal.mark = ","
    )) +
    labs(
      title = unique(ARXBFB1[[4]]),
      subtitle = sdg5$figs$fig7$title[language],
      x        = " ",
      y        = sdg5$figs$fig7$y_lab[language],
      fill     = " ",
      caption  = sdg5$figs$fig7$cap[language]
      )

Statistikbanken

Metode


ARXBFB1 <- 
  ARXBFB1_raw %>% 
  filter(time >= year(Sys.time()) - 6) %>% 
  mutate(industry = industry %>% fct_reorder(value) %>% fct_rev()) %>% 
  arrange(industry, desc(time)) %>% 
  unite(combi, 1, 3, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(1, ncol(.))

vec      <- ARXBFB1 %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(T, F)] %>% table() %>% rev()
col_vec  <- vec[c(F, T)]

ARXBFB1 %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = ARXBFB1[[2]] %>% table())
2020
2019
2018
2017
2016
Mænd Kvinder Mænd Kvinder Mænd Kvinder Mænd Kvinder Mænd Kvinder
Hovedbeskæftigelse i gennemsnit pr. måned
Offentlig forvaltning og service 3.642 7.885 3.516 7.721 3.497 7.557 3.517 7.459 3.423 7.201
Fiskeri og fiskerirelateret industri og handel 3.424 712 3.604 723 3.585 716 3.639 680 3.739 725
Engroshandel og detailhandel 1.542 1.469 1.577 1.500 1.575 1.476 1.511 1.458 1.504 1.447
Bygge- og anlægsvirksomhed 1.858 181 1.844 181 1.854 183 1.799 166 1.791 163
Transport og godshåndtering 1.548 456 1.582 497 1.536 468 1.479 440 1.463 417
Overnatningsfaciliteter og restaurationsvirksomhed 343 365 380 421 385 436 360 448 341 419
Information og kommunikation 420 185 444 199 468 210 493 227 516 234
Administrative tjenesteydelser og hjælpetjenester 316 174 316 210 346 238 326 227 324 227
Energi- og vandforsyning 366 78 375 76 366 69 360 65 361 59
Øvrige serviceerhverv 171 181 196 193 194 182 193 183 178 184
Liberale, videnskabelige og tekniske tjenesteydelser 188 114 180 121 167 133 178 132 167 115
Fast ejendom 176 106 166 106 141 101 210 120 220 113
Fremstillingsvirksomhed 181 66 174 55 173 49 191 52 200 50
Pengeinstitut og finansvirksomhed 78 140 63 133 60 120 75 117 91 125
Råstofindvinding 67 26 68 25 66 28 59 24 58 24
Landbrug, skovbrug og landbrugsrelateret industri og handel 55 20 56 20 63 20 49 17 60 16

Ledighed


GS Ledighedsprocent efter køn
# Import
ARXLED4_raw <-
  statgl_url("ARXLED4", lang = language) %>%
  statgl_fetch(
    time      = px_all(),
    gender    = 1:2,
    age       = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
ARXLED4 <-
  ARXLED4_raw %>% 
  mutate(
    time = time %>% make_date(),
    age  = age %>% factor(levels = unique(age))
    )

# Plot
ARXLED4 %>% 
  ggplot(aes(
    x     = time, 
    y     = value,
    color = gender
    )) +
  geom_line(size = 2) +
  facet_wrap(~ age, scales = "free") +
  theme_statgl() + scale_color_statgl(reverse = TRUE) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    accuracy     = 1,
    big.mark     = ".",
    decimal.mark = ",")) +
  labs(
    title    = sdg5$figs$fig8$title[language],
    subtitle = sdg5$figs$fig8$sub[language],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg5$figs$fig8$cap[language]
    )