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Goal 5: Gender equality

Average income

GS Average income by gender, place of residence and education
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]
  )

StatBank

Method 


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")
2024 2023 2022 2021 2020 2019 2018
Pretax Income total
Basic scool 8-10 grade
Men 246.130 242.372 234.324 216.600 221.765 220.133 219.879
Woman 186.549 178.302 172.812 165.146 162.849 159.516 155.746
Upper secondary school
Men 349.248 338.409 327.912 321.539 306.222 307.544 306.674
Woman 228.977 215.094 213.660 202.973 200.076 195.379 188.188
Vocational education
Men 432.780 419.922 409.975 391.283 399.353 385.654 378.860
Woman 321.476 309.519 304.231 290.398 284.749 282.021 275.123
Short-cycle higher education
Men 353.615 327.698 310.500 306.983 275.872 268.076 263.876
Woman 272.260 242.656 228.568 203.095 198.828 212.133 186.694
Medium-cycle higher education, Bachelors
Men 596.076 571.541 550.200 539.642 528.675 535.483 532.088
Woman 462.415 444.865 439.964 427.006 416.231 410.856 405.089
Long-cycle higher education
Men 827.517 789.057 760.808 766.408 759.769 767.176 713.205
Woman 671.863 614.281 609.123 601.243 589.539 575.814 560.462
* Average income for persons with type of income (DKK) 
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]
  )

StatBank

Method 


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")
2024 2023 2022 2021 2020 2019 2018
Pretax Income total, 30-34 years
Basic scool 8-10 grade
Men 252.434 262.244 252.055 230.804 226.090 229.122 220.892
Woman 191.654 186.574 176.554 171.386 165.637 161.100 158.492
Upper secondary school
Men 320.323 314.355 293.683 296.680 289.586 313.604 273.332
Woman 238.633 209.734 202.595 207.268 191.671 188.423 186.541
Vocational education
Men 432.038 417.811 406.392 380.457 357.371 347.546 331.144
Woman 285.259 278.977 275.243 265.726 260.237 266.759 256.674
Short-cycle higher education
Men 388.152 359.467 350.995 349.079 306.309 294.879 347.271
Woman 278.932 246.090 251.569 208.128 222.469 208.038 226.267
Medium-cycle higher education, Bachelors
Men 497.088 485.544 496.798 498.912 473.930 446.026 436.701
Woman 403.970 386.579 383.420 377.972 359.267 359.695 360.720
Long-cycle higher education
Men 556.644 556.060 519.394 503.610 522.714 483.043 472.560
Woman 537.490 496.914 482.401 471.482 472.983 460.440 441.388
* Average income for persons with type of income (DKK)

Distribution of elected representatives by sex

FN 5.5.1 Proportion of female parlamentarians in the national parliament
# 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

Method 


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 X2024 2005 2009 2013 2014 2015 2016 2017 2018 2020 2021
Amount of women NA NA 42 29 41 43 33 33 31 42 47 32
* Situation by end of the year

Economically disadvantaged

GS Proportion of economically disadvantaged by gender
# 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]
  )

StatBank

Method 


# 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"
    )
Fraction below 50%
Fraction below 60%
fraction below 50%,Female fraction below 50%,Male fraction below 60%,Female fraction below 60%,Male
2023 4,1 5,0 7,8 8,7
2022 4,1 4,8 7,7 8,6
2021 3,8 4,3 7,4 7,9
2020 3,7 4,5 7,3 8,1
* Proportion below 50 or 60% of median income

Grade test results

GS Grade test results by gender
# 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]
  )

StatBank

Method 


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())
3rd grade
7th grade
Danish
English
Greenlandic
Mathematics
Danish
English
Greenlandic
Mathematics
3rd grade,Danish,Boys 3rd grade,Danish,Girls 3rd grade,English,Boys 3rd grade,English,Girls 3rd grade,Greenlandic,Boys 3rd grade,Greenlandic,Girls 3rd grade,Mathematics,Boys 3rd grade,Mathematics,Girls 7th grade,Danish,Boys 7th grade,Danish,Girls 7th grade,English,Boys 7th grade,English,Girls 7th grade,Greenlandic,Boys 7th grade,Greenlandic,Girls 7th grade,Mathematics,Boys 7th grade,Mathematics,Girls
Problem-solving proficiency (pct. correct)
2024 39 43 0 0 43 48 51 46 41 45 80 88 50 59 41 38
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

Grade point average in lower secondary education

GS Marks by gender
# Import
UDXFKK_raw <-
  statgl_url("UDXFKK", lang = language) %>% 
  statgl_fetch(unit             = "andel",
               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]
  )

StatBank

Method 


# 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")
Danish
English
Greenlandic
Mathematics
Danish,Oral Danish,Proficiency Test Danish,Written English,Oral English,Proficiency Test English,Written Greenlandic,Oral Greenlandic,Proficiency Test Greenlandic,Written Mathematics,Oral Mathematics,Proficiency Test Mathematics,Written
Grade Point Average
2024
Boys 4,30 3,36 2,79 7,90 5,54 5,10 6,11 4,15 4,45 5,47 5,11 2,55
Girls 5,00 4,05 3,58 7,16 5,73 5,58 7,47 5,57 6,14 5,74 4,51 2,41
2023
Boys 6,16 3,92 3,39 6,60 5,60 4,39 6,30 3,50 3,84 5,81 5,14 2,89
Girls 6,13 4,18 4,28 7,34 5,52 4,73 6,74 4,49 5,65 5,37 4,49 3,07
2022
Boys 3,86 3,43 2,78 6,27 4,76 3,82 5,87 3,37 4,60 5,26 4,95 2,41
Girls 5,55 4,71 4,22 6,74 5,55 5,07 7,49 3,98 6,18 5,22 4,84 2,61
2021
Boys 4,89 3,93 2,59 6,66 4,73 3,75 5,67 3,11 4,18 4,79 5,06 2,16
Girls 5,74 4,93 4,00 6,36 5,03 4,40 6,21 3,94 6,31 4,94 4,84 2,17
2019
Boys 3,63 4,31 3,30 4,72 4,52 3,28 5,32 4,21 3,72 4,64 5,33 2,18
Girls 5,75 5,74 4,83 5,81 5,58 4,69 7,65 5,24 5,90 4,60 5,06 2,69
* School leavers - lower secondary education, mark


Due to Covid-19 there has not been held final exams in 2020.

Highest educational attainment

GS Highest educational attainment among 35-39 year olds by gender
# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch(
    ISCED11_level = px_all(),
    Sex           = px_all(),
    Aar           = px_all(),
    alder_grp     = "35-39",
    .col_code     = TRUE
    ) %>% 
  as_tibble()

# Transform
UDXISCPROD <-
  UDXISCPROD_raw %>% 
  filter(ISCED11_level != UDXISCPROD_raw[[2]][1]) %>% 
  mutate(
    ISCED11_level = ISCED11_level %>% factor(level = unique(ISCED11_level) %>% rev()),
    Aar           = Aar %>% make_date()
    )

# Plot
UDXISCPROD %>% 
  arrange(ISCED11_level) %>% 
  ggplot(aes(
    x    = Aar,
    y    = value,
    fill = ISCED11_level
  )) +
  geom_area(position = "fill") +
  facet_wrap(~ Sex) +
  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]
  )

StatBank

Method 


UDXISCPROD <- 
  UDXISCPROD_raw %>% 
  filter(
    ISCED11_level != UDXISCPROD_raw[[2]][1],
    Aar > year(Sys.Date()) - 7
    ) %>% 
  mutate(
    ISCED11_level = ISCED11_level %>% factor(levels = unique(ISCED11_level))
    ) %>% 
  arrange(ISCED11_level, desc(Aar)) %>% 
  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[["alder_grp"]] %>% table())
2024
2023
2022
2021
2020
2019
combi-Women-2024 combi-Men-2024 combi-Women-2023 combi-Men-2023 combi-Women-2022 combi-Men-2022 combi-Women-2021 combi-Men-2021 combi-Women-2020 combi-Men-2020 combi-Women-2019 combi-Men-2019
35-39 years
Lower secondary education 783 1.228 705 1.199 655 1.125 627 1.068 631 1.037 613 986
Upper secondary education 117 99 112 84 104 79 105 70 91 63 82 73
Vocational education and training 619 686 611 678 583 658 584 697 601 688 589 686
Supplementary examination courses 41 48 37 43 37 32 32 26 30 23 24 20
Short-cycle higher education 106 98 96 81 84 75 88 70 87 76 77 67
Bachelors programme 43 17 41 15 41 17 35 13 35 13 32 18
Professional bachelors programme 347 109 332 111 320 114 320 98 301 94 296 93
Masters programme 119 69 109 70 111 68 105 68 86 70 81 76
Phd. Programmes 8 3 8 4 6 3 6 3 6 1 2 0

Employment

GS Main employment for permanent residents by industry and gender
ARXBFB01_raw <-
  statgl_url("ARXBFB01", lang = language) |> 
  statgl_fetch(
    beskbrch  = c("01","02","03","04","05","06","07","08","09","10","11","12","13","14","15","16"),
    sex       = c("M","K"),
    opg_var   = "G",
    aar       = px_all(),
    .col_code = T
  ) |> 
  as_tibble()

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

# Plot
ARXBFB01 %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = sex
    )) +
  geom_area() +
  facet_wrap(~ beskbrch, 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]
      )

StatBank

Method 


ARXBFB01 <- 
  ARXBFB01_raw %>% 
  filter(aar >= year(Sys.time()) - 6) %>% 
  mutate(beskbrch = beskbrch %>% fct_reorder(value) %>% fct_rev()) %>% 
  arrange(beskbrch, aar) %>% 
  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())
2019
2020
2021
2022
2023
Men,2019 Women,2019 Men,2020 Women,2020 Men,2021 Women,2021 Men,2022 Women,2022 Men,2023 Women,2023
Number of main employed persons in average per month
Public administration and service 3.810 8.721 3.889 8.859 3.971 8.931 3.933 8.927 3.819 8.911
Fishing and other related industries 4.009 716 3.883 719 3.682 680 3.673 670 3.820 698
Wholesale and retail trade 1.488 1.424 1.495 1.417 1.534 1.486 1.553 1.519 1.564 1.490
Transportation and storage 1.525 488 1.521 457 1.508 447 1.558 483 1.594 493
Construction 1.773 175 1.850 179 2.090 207 2.101 204 2.044 195
Accommodation and food service activities 336 382 310 351 351 427 364 469 408 495
Information and communication 431 197 419 196 413 195 379 184 373 182
Administrative and support service activities 297 187 297 165 250 152 244 159 261 188
Energy and watersupply 362 75 360 77 360 77 348 69 353 71
Other service industries 171 165 153 152 155 151 156 160 159 171
Real estate activities 159 103 170 106 166 103 179 118 199 127
Professional, scientific and technical activities 151 111 158 109 174 116 176 123 179 119
Manufacturing 159 49 166 47 173 50 177 52 172 58
Financial and insurance activities 63 122 75 127 72 128 64 137 64 143
Mining and quarrying 66 25 65 25 85 34 73 34 56 29
Agriculture, forestry and related industries 81 19 85 19 92 18 80 18 68 15

Unemployment

GS Unemployment rate by gender
# Import

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

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    sex       = c("M", "K"),
    alder_grp = px_all(),
    aar       = px_all(),
    opg_var   = "P",
    .col_code = T
  ) |> 
  as_tibble()

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

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar, 
    y     = value,
    color = sex
    )) +
  geom_line(size = 1.5) +
  facet_wrap(~ alder_grp, 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]
    )

StatBank

Method 


ARXLED3 <- 
  ARXLED3_raw %>% 
  select(-opg_var) |> 
  mutate(
    alder_grp = alder_grp %>% fct_inorder(),
    aar = aar %>% as.numeric()
    ) %>% 
  filter(aar > max(aar) - 5) %>% 
  arrange(alder_grp, aar) %>% 
  unite(combi, aar, sex, 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")
2019
2020
2021
2022
2023
2019,Men 2019,Women 2020,Men 2020,Women 2021,Men 2021,Women 2022,Men 2022,Women 2023,Men 2023,Women
Ledighedsprocent
Total 4,6 4,0 4,9 4,2 3,9 3,4 3,4 3,0 3,1 2,7
18-19 years 7,1 8,2 8,4 8,6 7,1 6,4 4,8 5,3 5,1 5,5
20-24 years 6,4 6,1 6,3 6,0 4,8 4,9 4,1 4,4 3,8 3,5
25-29 years 4,8 4,6 4,7 4,2 3,4 3,0 3,4 2,8 2,9 2,3
30-34 years 3,9 4,1 4,2 4,5 3,2 3,4 3,4 3,1 2,9 2,8
35-39 years 4,4 3,6 4,4 4,1 3,2 3,2 2,7 2,8 2,3 2,5
40-44 years 4,1 3,2 4,4 3,4 3,5 3,2 2,8 2,4 3,0 2,5
45-49 years 4,3 3,7 4,0 3,8 3,1 2,7 2,6 2,1 2,3 2,1
50-54 years 4,5 3,3 5,1 3,7 4,3 3,7 3,8 3,0 2,9 2,5
55-59 years 4,1 3,3 4,9 3,6 4,4 3,2 3,6 2,8 3,9 2,8
60 years-retirement age 5,0 3,0 5,3 2,7 4,1 2,9 3,9 3,0 3,8 2,9
* Percentage, average unemployment per month among residents aged 18-65

Life expectancy

GS Life expectancy for 0 and 1-year-olds by gender
# 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]
    )

StatBank 


# 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
Men,0 Women,0 Men,1 Women,1
Life Expectancy
2015 - 2019 68,3 73 68,1 72,5
* Life expectancy for 0 and 1-year-olds, persons born in Greenland.


Maternity benefits

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]
  )

StatBank 


SOX007 |> 
  filter(time >= year(Sys.time()) - 6) |> 
  spread(time, value) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  add_footnote(sdg5$figs$fig10$foot[language], notation = "symbol")
2019 2020 2021 2022 2023
Man 190 165 148 128 94
Woman 897 867 834 718 625
* Number of persons