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")
2022 2021 2020 2019 2018 2017
Indkomst i alt før skat mv
Grundskole
Kvinder 172.329 168.006 162.850 159.522 155.738 152.098
Mænd 234.135 221.379 221.751 220.133 219.867 212.780
Gymnasial uddannelse
Kvinder 233.118 206.330 200.436 195.745 188.368 186.094
Mænd 348.357 322.442 306.486 307.544 306.674 310.075
Erhvervsuddannelse
Kvinder 295.870 281.768 274.182 272.668 265.089 257.890
Mænd 417.861 398.507 402.334 386.489 381.560 370.171
Kort videregående uddannelse
Kvinder 320.252 306.207 302.860 302.482 294.940 291.796
Mænd 349.507 338.797 317.874 331.182 307.996 296.458
Mellemlang videregående uddannelse
Kvinder 446.885 436.757 415.822 410.472 405.119 416.966
Mænd 554.668 548.204 528.469 535.014 530.823 555.195
Videregående uddannelse
Kvinder 618.724 606.830 590.392 578.418 561.255 613.203
Mænd 768.439 767.868 757.895 765.664 713.391 780.811
* 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")
2022 2021 2020 2019 2018 2017
Indkomst i alt før skat mv, 30-34 år
Grundskole
Kvinder 176.076 171.912 165.499 161.197 158.658 155.093
Mænd 250.832 235.607 226.090 229.122 220.892 208.084
Gymnasial uddannelse
Kvinder 208.197 206.114 192.447 188.873 187.006 176.472
Mænd 299.808 294.967 289.586 313.604 273.332 301.519
Erhvervsuddannelse
Kvinder 280.832 268.671 260.461 266.763 256.601 249.524
Mænd 408.290 381.070 357.371 347.546 331.054 325.784
Kort videregående uddannelse
Kvinder 262.814 220.418 222.469 208.038 226.267 233.077
Mænd 348.301 342.909 306.309 294.879 347.271 336.414
Mellemlang videregående uddannelse
Kvinder 387.396 378.566 359.046 359.464 360.720 376.074
Mænd 505.500 505.478 473.930 446.026 436.353 432.105
Videregående uddannelse
Kvinder 485.566 473.687 472.983 460.440 441.388 492.136
Mænd 525.041 504.234 522.714 483.043 472.560 501.613
* Gennemsnit for personer med indkomsttypen (kr)

Fordelingen af folkevalgte efter køn

FN 5.5.1 Andel af kvindelige parlamentarikere i det nationale parlament
# Import
ELEC03_raw <- 
  "https://pxweb.nordicstatistics.org:443/sq/6c4d7add-c65a-43ab-a60a-0119c13f9bd6.csv" |> 
  read.csv() |> 
  as_tibble()

vec <- 1:21
names(vec) <- c("country", 2003:2022)

# Transform
ELEC03 <- 
  ELEC03_raw |> 
  rename(vec) |> 
  mutate(across(everything(), as.numeric),
         country = "greenland") |> 
  pivot_longer(cols = c("2003", "2004", "2005", "2006", "2007", "2008", "2009", 
                        "2010", "2011", "2012", "2013", "2014", "2015", "2016",
                        "2017", "2018", "2019", "2020", "2021", "2022"),
               names_to = "time",
               values_to = "value") |> 
  drop_na(value)

# Plot
ELEC03 |> 
  ggplot(aes(
    x = time,
    y = value,
    fill = country
  )) +
  geom_col() +
  theme_statgl() +
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title   = sdg5$figs$fig2$title[language],
    x       = " ",
    y       = sdg5$figs$fig2$y_lab[language],
    fill    = " ",
    caption = sdg5$figs$fig2$cap[language]
  )

Nordic Statistics

Metode 


col0 <- sdg5$figs$fig2$col0[language]

# Tabel
ELEC03 |> 
  spread(time, value) |> 
  mutate(country = col0) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg5$figs$fig2$foot[language], notation = "symbol")
X2023 2005 2009 2013 2014 2015 2016 2017 2018 2020 2021
Antal kvinder NA 42 29 41 43 33 33 31 42 47 32
* Antal ved årets udgang

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%
andel under 50%,Kvinder andel under 50%,Mænd andel under 60%,Kvinder andel under 60%,Mænd
2022 4,3 5,0 8,4 9,2
2021 4,0 4,5 7,9 8,3
2020 3,7 4,5 7,3 8,1
2019 3,5 4,1 7,0 7,8
* 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
  1. klasse,Dansk,Drenge
  1. klasse,Dansk,Piger
  1. klasse,Engelsk,Drenge
  1. klasse,Engelsk,Piger
  1. klasse,Grønlandsk,Drenge
  1. klasse,Grønlandsk,Piger
  1. klasse,Matematik,Drenge
  1. klasse,Matematik,Piger
  1. klasse,Dansk,Drenge
  1. klasse,Dansk,Piger
  1. klasse,Engelsk,Drenge
  1. klasse,Engelsk,Piger
  1. klasse,Grønlandsk,Drenge
  1. klasse,Grønlandsk,Piger
  1. klasse,Matematik,Drenge
  1. klasse,Matematik,Piger
Løsningssikkerhed (pct. rigtige)
2023 45 48 0 0 48 48 56 48 42 50 82 90 54 64 41 42
2022 39 44 0 0 40 43 48 48 44 59 75 86 57 66 41 41
2021 46 48 0 0 45 50 53 49 47 59 71 76 54 66 41 38
2020 49 50 0 0 40 48 53 48 54 60 73 73 57 65 41 42
2019 50 59 0 0 39 50 51 52 48 60 53 67 61 70 40 43

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 = 1.5) +
  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
Dansk,Færdighedsprøve Dansk,Mundtlig Dansk,Skriftlig Engelsk,Færdighedsprøve Engelsk,Mundtlig Engelsk,Skriftlig Grønlandsk,Færdighedsprøve Grønlandsk,Mundtlig Grønlandsk,Skriftlig Matematik,Færdighedsprøve Matematik,Mundtlig Matematik,Skriftlig
Karaktergennemsnit
2023
Drenge 3,92 6,16 3,39 5,60 6,60 4,39 3,50 6,30 3,84 5,14 5,81 2,89
Piger 4,18 6,13 4,28 5,52 7,34 4,73 4,49 6,74 5,65 4,49 5,37 3,07
2022
Drenge 3,43 3,86 2,78 4,76 6,27 3,82 3,37 5,87 4,60 4,95 5,26 2,41
Piger 4,71 5,55 4,22 5,55 6,74 5,07 3,98 7,49 6,18 4,84 5,22 2,61
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
* Folkeskolens afgangselever, prøvekarakter


På grund af Covid-19 har der ikke været afholdt afgangseksamen i 2020.

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[[2]][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(base_size = 11) +
  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[[2]][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, 3, 4, sep = "-") %>% 
  mutate(combi = combi %>% factor(level = unique(combi))) %>% 
  spread(3, 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(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE, col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = UDXISCPROD[["age"]] %>% table())
2022
2021
2020
2019
2018
combi-Kvinder-2022 combi-Mænd-2022 combi-Kvinder-2021 combi-Mænd-2021 combi-Kvinder-2020 combi-Mænd-2020 combi-Kvinder-2019 combi-Mænd-2019 combi-Kvinder-2018 combi-Mænd-2018
35-39 år
Grundskole 10. klasses niveau 681 1.153 627 1.068 632 1.037 614 986 626 936
Gymnasial uddannelse 112 83 106 71 91 63 82 73 78 76
Erhvervsuddannelse 577 648 561 685 556 667 516 657 470 633
Suppleringskurser 45 31 54 37 75 43 97 48 113 65
Kort videregående uddannelse 82 74 89 69 87 76 77 67 76 80
Bacheloruddannelse 37 15 35 13 35 13 32 18 34 17
Professionsbacheloruddannelse 299 102 319 98 300 94 295 93 286 82
Kandidatuddannelse 103 63 105 68 86 70 81 76 96 72
Phd. og forskeruddannelse 5 1 6 3 6 1 2 0 4 3

Beskæftigelse

GS Hovedbeskæftigelse blandt fastboende, efter brancher og køn
# Import
url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR30/ARXBFB01.px")

ARXBFB01_raw <-
  url |> 
  statgl_fetch(
    industry             = c("01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16"),
    gender               = c("M","K"),
    "inventory variable" = "G",
    time                 = px_all(),
    .col_code            = T
  ) |> 
  as_tibble()

# Transform
ARXBFB01 <-
  ARXBFB01_raw %>% 
  mutate(
    time     = time %>% make_date(),
    industry = industry %>% fct_reorder(value) %>% fct_rev()
    ) %>% 
  arrange(industry)

# Plot
ARXBFB01 %>% 
  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(ARXBFB01[[4]]),
      subtitle = sdg5$figs$fig7$title[language],
      x        = " ",
      y        = sdg5$figs$fig7$y_lab[language],
      fill     = " ",
      caption  = sdg5$figs$fig7$cap[language]
      )

Statistikbanken

Metode 


ARXBFB01 <- 
  ARXBFB01_raw %>% 
  filter(time >= year(Sys.time()) - 6) %>% 
  mutate(industry = industry %>% fct_reorder(value) %>% fct_rev()) %>% 
  arrange(industry, time) %>% 
  unite(combi, 2, 3, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(2, ncol(.))

vec      <- ARXBFB01 %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(F, T)] %>% table()
col_vec  <- vec[c(T, F)]

ARXBFB01 %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = ARXBFB01[[2]] %>% table())
2018
2019
2020
2021
2022
Mænd,2018 Kvinder,2018 Mænd,2019 Kvinder,2019 Mænd,2020 Kvinder,2020 Mænd,2021 Kvinder,2021 Mænd,2022 Kvinder,2022
Hovedbeskæftigelse i gennemsnit pr. måned
Offentlig forvaltning og service 3.782 8.540 3.810 8.721 3.889 8.859 3.968 8.928 3.941 8.932
Fiskeri og fiskerirelateret industri og handel 3.979 708 4.009 716 3.880 719 3.680 683 3.672 671
Engroshandel og detailhandel 1.482 1.387 1.488 1.424 1.498 1.417 1.540 1.485 1.556 1.519
Bygge- og anlægsvirksomhed 1.781 168 1.773 175 1.850 179 2.089 206 2.102 205
Transport og godshåndtering 1.477 464 1.525 488 1.521 457 1.505 446 1.560 483
Overnatningsfaciliteter og restaurationsvirksomhed 341 406 336 382 310 351 351 425 363 466
Information og kommunikation 451 205 431 197 419 196 413 195 379 184
Administrative tjenesteydelser og hjælpetjenester 313 212 296 187 297 165 248 152 243 159
Energi- og vandforsyning 355 68 362 75 360 77 358 77 348 69
Øvrige serviceerhverv 169 151 171 165 153 152 155 151 158 161
Liberale, videnskabelige og tekniske tjenesteydelser 144 117 151 111 158 109 174 116 176 123
Fast ejendom 136 98 159 103 170 106 166 103 180 118
Fremstillingsvirksomhed 159 42 159 49 166 47 173 50 176 52
Pengeinstitut og finansvirksomhed 58 110 63 122 75 127 72 128 64 137
Råstofindvinding 67 29 66 25 65 25 85 34 73 34
Landbrug, skovbrug og landbrugsrelateret industri og handel 82 17 81 19 85 19 92 18 80 18

Ledighed

GS Ledighedsprocent efter køn
# Import

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR40/ARXLED3.px")

ARXLED3_raw <-
  url |> 
  statgl_fetch(
    gender               = c("M", "K"),
    age                  = px_all(),
    time                 = px_all(),
    "inventory variable" = "P",
    .col_code            = T
  ) |> 
  as_tibble()

# Transform
ARXLED3 <-
  ARXLED3_raw %>% 
  mutate(
    time = time %>% make_date(),
    age  = age %>% factor(levels = unique(age))
    )

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = time, 
    y     = value,
    color = gender
    )) +
  geom_line(size = 1.5) +
  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]
    )

Statistikbanken

Metode 


ARXLED3 <- 
  ARXLED3_raw %>% 
  select(-`inventory variable`) |> 
  mutate(
    age = age %>% fct_inorder(),
    time = time %>% as.numeric()
    ) %>% 
  filter(time > max(time) - 5) %>% 
  arrange(age, time) %>% 
  unite(combi, time, gender, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(combi, value)
  
vec      <- ARXLED3 %>% select(-1) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(T, F)] %>% table()
col_vec  <- vec[c(F, T)]


ARXLED3 %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  row_spec(1, bold = TRUE) %>% 
  pack_rows(index = c("Ledighedsprocent" = ARXLED3[[1]] %>% length())) %>% 
  add_footnote(
    sdg5$figs$fig8$foot[language],
    notation = "symbol")
2018
2019
2020
2021
2022
2018,Mænd 2018,Kvinder 2019,Mænd 2019,Kvinder 2020,Mænd 2020,Kvinder 2021,Mænd 2021,Kvinder 2022,Mænd 2022,Kvinder
Ledighedsprocent
Alle 5,2 4,8 4,6 4,0 4,9 4,2 3,9 3,4 3,4 3,0
18-19 år 11,9 12,5 7,1 8,2 8,4 8,6 7,1 6,4 4,8 5,3
20-24 år 7,1 8,6 6,4 6,1 6,3 6,0 4,8 4,9 4,1 4,4
25-29 år 5,3 5,2 4,8 4,6 4,7 4,2 3,4 3,0 3,4 2,8
30-34 år 4,7 4,5 3,9 4,1 4,2 4,5 3,2 3,4 3,4 3,1
35-39 år 4,9 4,3 4,4 3,6 4,4 4,1 3,1 3,2 2,8 2,8
40-44 år 4,2 3,7 4,1 3,2 4,4 3,4 3,5 3,2 2,9 2,4
45-49 år 4,7 3,8 4,3 3,7 4,0 3,8 3,1 2,7 2,6 2,1
50-54 år 4,4 4,2 4,5 3,3 5,1 3,7 4,3 3,7 3,8 3,0
55-59 år 4,9 3,5 4,1 3,3 4,9 3,6 4,4 3,2 3,6 2,8
60 år-pensionalderen 5,7 3,0 5,0 3,0 5,3 2,7 4,1 2,9 3,9 3,0
* Procent, Ledighed i gennemsnit pr. måned blandt fastboende 18-65-årige.

Middellevetid

GS Middellevetid for 0 og 1-årige efter køn
# Import
BEXDT5A_raw <-
  statgl_url("BEXDT5A", lang = language) %>% 
  statgl_fetch(type   = "E",
               gender = c("M", "K"),
               time   = px_all(),
               age    = 0:1,
               .col_code = TRUE) %>% 
  as_tibble()

# Transform
BEXDT5A <- 
  BEXDT5A_raw %>% 
    separate(time, c("startar", "slutar"),  " - ") %>% 
  mutate(slutar = slutar %>% make_date())

# Plot
BEXDT5A %>% 
  ggplot(aes(
    x     = slutar,
    y     = value,
    color = gender
    )) +
  geom_line(size = 2) +
  facet_wrap(~ age) +
    theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  labs(
    title    = sdg5$figs$fig9$title[language],
    subtitle = sdg5$figs$fig9$sub[language],
    x        = sdg5$figs$fig9$x_lab[language],
    y        = sdg5$figs$fig9$y_lab[language],
    color    = " ",
    caption  = sdg5$figs$fig9$cap[language]
    )

Statistikbanken 


# Transform
BEXDT5A <-
  BEXDT5A_raw %>% 
  arrange(desc(time), age) %>% 
  unite(combi, 2, 3, sep = ",") %>% 
  mutate(combi = combi %>% factor(levels = unique(combi))) %>% 
  spread(2, 4) %>% 
  arrange(desc(time)) %>% 
  mutate(timetime = time) %>% 
  separate(timetime, c("time1", "time2"), " - ") %>% 
  filter(time >= year(Sys.time()) - 10) %>% 
  select(-c("time1", "time2"))

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

# Table
BEXDT5A %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  pack_rows(index = BEXDT5A[[1]] %>% table()) %>% 
  add_footnote(
    sdg5$figs$fig9$foot[language], 
    notation = "symbol"
    )
0
1
Mænd,0 Kvinder,0 Mænd,1 Kvinder,1
Middellevetid
2015 - 2019 68,3 73 68,1 72,5
2014 - 2018 68,8 73 68,6 72,3
* Middellevetid for 0 og 1-årige, personer født i Grønland.


Barselsdagpenge

FN 5.4.1
# Import
SOX007_raw <- 
  statgl_url("SOX007", lang = language) |> 
  statgl_fetch(
    gender    = 1:2,
    type      = 30,
    time      = px_all(),
    .col_code = T
  ) |> 
  as_tibble()

# Transform
SOX007 <- 
  SOX007_raw |> 
  mutate(value = as.numeric(value)) |> 
  select(-2)


# Plot
SOX007 |> 
  ggplot(aes(
    x     = as.integer(time),
    y     = value,
    color = gender
  )) +
  geom_line(size = 2) +
  theme_statgl() +
  scale_color_statgl() +
  labs(
    title   = sdg5$figs$fig10$title[language],
    x       = " ",
    y       = " ",
    color   = " ",
    caption = sdg5$figs$fig10$cap[language]
  )

Statistikbanken 


SOX007 |> 
  filter(time >= year(Sys.time()) - 6) |> 
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg5$figs$fig10$foot[language], notation = "symbol")
2018 2019 2020 2021 2022
Kvinde 838 897 867 834 718
Mand 185 190 165 148 128
* Antal personer