Tilbage


Delmål 5: Ligestilling mellem kønnene

Gennemsnitsindkomst


GS Gennemsnitsindkomst efter køn, bosted og uddannelse
# Import 
INXPI104_raw <-
  statgl_url("INXPI104", lang = language) %>%
  statgl_fetch(
    age              = 4,
    "type of income" = 26,
    gender           = 1:2,
    time             = px_all(),
    unit             = 3,
    .col_code        = TRUE
    ) %>% 
  as_tibble()


# Transformation
INXPI104 <- 
  INXPI104_raw %>% 
  mutate_if(is.factor, as.character) %>%
  mutate_if(~!is.numeric(.x), fct_inorder) %>% 
  mutate(time = time %>% strtoi() %>% make_date()) %>% 
  drop_na()

# Plot
INXPI104 %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = gender
    )) +
  geom_line(size = 2) +
  facet_wrap(~ `level of education`, labeller = label_wrap_gen()) +
  scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
  theme_statgl() + 
  scale_color_statgl() +
    labs(
    title    = INXPI104[["type of income"]] %>% str_to_title() %>% unique(),
    subtitle = paste(unique(INXPI104[["unit"]]), ",", unique(INXPI104[["age"]])),
    x        = " ",
    y        = sdg5$figs$fig1$y_lab[language],
    color    = " ",
    caption  = sdg5$figs$fig1$cap[language]
    )

Statistikbanken

Metode


# Transform
INXPI104 <-
  INXPI104_raw %>% 
  arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(
    time                 = time %>% fct_inorder(),
    `level of education` = `level of education` %>% fct_inorder()
         ) %>% 
  spread(6, 7) %>% 
  unite(combi, 5, 4, sep = ", ")

# Tabel
INXPI104 %>% 
  select(-c(1:2, 4)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(INXPI104[[4]])) %>% 
  pack_rows(index = table(INXPI104[[1]])) %>% 
  add_footnote(INXPI104[[2]][1], notation = "symbol")
2020 2019 2018 2017 2016
Ækvivaleret disponibel indkomst, 30-34 år
Grundskole
Kvinder 132.988 129.137 125.709 126.115 129.765
Mænd 146.138 145.773 140.293 136.251 139.265
Gymnasial uddannelse
Kvinder 150.350 152.381 146.339 143.878 147.632
Mænd 182.118 187.484 171.218 188.852 182.224
Erhvervsuddannelse
Kvinder 178.645 176.319 169.220 167.629 165.145
Mænd 206.703 194.776 190.257 192.075 187.230
Kort videregående uddannelse
Kvinder 168.310 177.001 163.892 163.271 169.778
Mænd 207.898 198.944 228.613 219.499 182.789
Mellemlang videregående uddannelse
Kvinder 223.355 221.949 219.997 233.709 219.301
Mænd 262.751 241.715 239.783 253.865 244.992
Videregående uddannelse
Kvinder 290.989 269.810 257.555 319.092 290.838
Mænd 309.589 277.323 266.116 306.933 278.557
* 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 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 %>% 
  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()
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
2019 3,5 4,1 7,1 7,8
2018 3,5 4,2 6,6 7,7
2017 3,4 3,9 6,6 7,1
2016 2,9 3,5 5,7 6,4
* 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
2016 52 62 - - 45 53 65 67 53 68 59 67 59 69 43 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 3,89 4,89 2,54 4,68 6,68 3,64 3,09 5,67 4,14 5,07 4,77 2,13
Piger 4,75 5,74 3,84 4,86 6,36 4,13 3,95 6,21 6,30 4,83 4,94 2,13
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
2015
Drenge 5,49 4,22 3,68 4,38 3,30 2,97 4,94 5,27 4,14 4,75 4,14 2,56
Piger 5,72 4,00 4,34 4,49 3,98 3,19 6,43 6,47 6,54 4,52 4,01 2,84
* 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]
  )