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Delmål 10: Mindre ulighed

Arbejdskraftens andel af BNP


FN 10.4.1 Arbejdskraftens andel af BNP
# Import
NRX02_raw <-
  statgl_url("NRX02", lang = language) %>%
  statgl_fetch(
    "account name" = c("LBNPTOT", "LLONTOT"),
    units          = "L",
    time           = px_all(),
    .col_code      = TRUE
    ) %>% 
  as_tibble()



NRX02 <- 
  NRX02_raw %>% 
  spread(`account name`, value) %>% 
  rename(BNP = 3, lon = 4) %>% 
  mutate(
    value = lon / BNP,
    time = time %>% as.numeric()
    )


NRX02 %>% 
  ggplot(aes(
    x = time,
    y = value
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels  = scales::percent_format(
     scale = 100, 
     accuracy = 1, 
     big.mark = ".",
     decimal.mark = ","
    )) +
  theme_statgl() +
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg10$figs$fig1$title[language],
    x        = " ",
    y        = " ",
    caption  = sdg10$figs$fig1$cap[language]
  )

Statistikbanken


table <- 
  NRX02 %>% 
  select(-c(BNP, lon)) %>% 
  mutate(value = value * 100) %>% 
  arrange(desc(time)) %>% 
  filter(time >= max(time) - 5) %>% 
  mutate(time = time %>% as.character() %>% fct_inorder()) %>%
  mutate(value = value %>% round(1)) %>% 
  mutate(value = paste0(value, "%")) %>% 
  spread(time, value) %>% 
  mutate(var = sdg10$figs$fig1$var[language] %>% unlist()) %>% 
  select(units, var, everything())



table %>% 
  select(-units) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>%
  add_footnote(sdg10$figs$fig1$foot[language],notation = "symbol") 
2020 2019 2018 2017 2016 2015
Arbejdskraftens andel af BNP 54% 53.2% 53.2% 52.6% 52.3% 53.8%
* Aflønning af ansatte over BNP

Gini-koefficient


GS Gini-koefficient
# Import
INXIU101_raw <-
  statgl_url("INXIU101", lang = language) %>% 
  statgl_fetch(indicator = 0:1,
               time      = px_all(),
               .col_code = TRUE) %>% 
  as_tibble()

INXIU101 <-
  INXIU101_raw %>% 
  filter(indicator == unique(INXIU101_raw[[1]])[1]) %>% 
  mutate(time = time %>% make_date())

INXIU101 %>% 
  ggplot(aes(
    x = time,
    y = value
  )) +
  geom_line(size = 2) +
  scale_color_statgl() +
  theme_statgl() +
  theme(legend.position = "none") +
  labs(
    title = unique(INXIU101_raw[[1]])[1],
    subtitle = sdg10$figs$fig2$sub[language],
    x        = " ",
    y        = sdg10$figs$fig2$y_lab[language],
    color    = sdg10$figs$fig2$color[language],
    caption  = sdg10$figs$fig2$cap[language]
  )

Statistikbanken

Metode


# Transform
INXIU101 <-
  INXIU101_raw %>% 
  filter(indicator == unique(INXIU101_raw[[1]])[1],
         time >= year(Sys.time()) - 7) %>% 
  arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(2, 3)

# Table
INXIU101 %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(sdg10$figs$fig2$foot[language], notation = "symbol")
2020 2019 2018 2017 2016 2015
Gini-koefficient 34,9 34,6 34,9 36 35,6 35,4
* Gini-point

Ratio 80/20


GS Forhold mellem 20% højeste og 20% laveste indkomster
# Transform
INXIU101 <-
  INXIU101_raw %>% 
  filter(indicator == unique(INXIU101_raw[[1]])[2]) %>% 
  mutate(time = time %>% make_date())

# Plot
INXIU101 %>% 
  ggplot(aes(
    x = time,
    y = value
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels = scales::unit_format(
    suffix = " ",
    big.mark = ".",
    decimal.mark = ","
  )) +
  scale_color_statgl() +
  theme_statgl() +
  theme(legend.position = "none") +
  labs(
    title = unique(INXIU101_raw[[1]])[2],
    subtitle = sdg10$figs$fig3$sub[language],
    x = " ",
    y = sdg10$figs$fig3$y_lab[language],
    caption = sdg10$figs$fig3$cap[language]
  )

Statistikbanken

Metode


# Transform
INXIU101 <-
  INXIU101_raw %>% 
  filter(indicator == unique(INXIU101_raw[[1]])[2],
         time >= year(Sys.time()) - 7) %>% 
  arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(2, 3)

# Table
INXIU101 %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(sdg10$figs$fig3$foot[language], notation = "symbol")
2020 2019 2018 2017 2016 2015
S80/20 5,5 5,3 5,4 5,6 5,4 5,4
* S80/20-ratio

Modtagere af offentlig hjælp


GS Modtagere af offentlig hjælp
# NÅET HERTIL!!!

# Import
SOX004_raw <-
  statgl_url("SOX004", lang = language) %>% 
  statgl_fetch(
    "unit"    = "Antal",
    "type"    = 10,
    "time"    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
SOX004 <- 
  SOX004_raw %>% 
  mutate(
    time  = time %>% make_date(),
    value = value / 1000
  )

# Plot
SOX004 %>% 
  ggplot(aes(
    x = time,
    y = value
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title = SOX004[[2]][1],
    subtitle = sdg10$figs$fig4$sub[language],
    x = " ",
    y = sdg10$figs$fig4$y_lab[language],
    fill = " ",
    caption = sdg10$figs$fig4$cap[language]
  )

Statistikbanken

Metode


# Transform
SOX004 <- 
  SOX004_raw %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(3, 4)

# Table
SOX004 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SOX004[[1]] %>% table())
2020 2019 2018 2017 2016 2015
Antal
Offentlig hjælp i alt 4.702 4.760 5.086 5.787 6.063 7.111



# Import
sox010_raw <-
  statgl_url("sox010", lang = language) %>%
  statgl_fetch(
    "no of children" = 0:4,
    "no of adults"   = 1:2,
    "unit"           = "Antal",
    "type"           = 10,
    "time"           = px_all(),
    .col_code        = TRUE
    ) %>% 
  as_tibble()

sox010 <- 
  sox010_raw %>% 
  mutate(time = time %>% make_date(),
         `no of children` = `no of children` %>% factor(levels = unique(`no of children`) %>% rev()))

# Plot
sox010 %>% 
  ggplot(aes(
    x = time,
    y = value,
    fill = `no of children`
  )) +
  geom_col(position = "fill") +
  facet_wrap(~ `no of adults`) +
   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)) +
  labs(
    title = sdg10$figs$fig5$title[language],
    subtitle = sdg10$figs$fig5$sub[language],
    x = " ",
    y = " ",
    fill = " ",
    caption = sdg10$figs$fig5$cap[language]
  )

Statistikbanken

Metode


# Transform
sox010 <- 
  sox010_raw %>%
  unite(combi, 1, 2, sep = ",") %>% 
  unite(index, 3, 2, sep = ", ") %>% 
  mutate(
    combi = combi %>% factor(levels = unique(combi)),
    ) %>% 
  spread(1, ncol(.)) %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  arrange(desc(time))

vec      <- sox010 %>% select(-(1:2)) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(F, T)] %>% table()
col_vec  <- rep(vec[c(T, F)][seq(1, length(vec[c(T, F)]), 2)], 2)

# Table
sox010 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  pack_rows(index = sox010[[1]] %>% table()) %>% 
  add_header_above(c(" ", head_vec))
1 voksen
2 voksne
0 børn 1 børn 2 børn 3 børn 4 eller flere børn 0 børn 1 børn 2 børn 3 børn 4 eller flere børn
Offentlig hjælp i alt, Antal
2020 2.113 422 467 284 215 223 97 147 40 118
2019 2.152 445 507 271 204 207 96 143 56 130
2018 2.317 425 525 329 259 230 94 153 51 143
2017 2.541 483 580 361 268 301 113 203 54 164
2016 2.654 453 584 387 302 349 119 223 61 172
2015 3.001 564 672 481 317 402 141 261 65 213

Modtagere af førtidspension


GS Modtagere af førtidspension
# Import
SOXFPE1_raw <-
  statgl_url("SOXFPE1", lang = language) %>% 
  statgl_fetch(
      "unit"  = "Andel",
      "time"  = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
SOXFPE1 <- 
  SOXFPE1_raw %>% 
  mutate(time = time %>% make_date())

# Plot
SOXFPE1 %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = unit
  )) +
  geom_col() +
  scale_y_continuous(labels  = scales::percent_format(
    scale = 1, 
    accuracy = 1, 
    big.mark = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg10$figs$fig6$title[language],
    subtitle = SOXFPE1[[1]][1],
    x        = " ",
    y        = " ",
    caption  = sdg10$figs$fig6$cap[language]
  )

Statistikbanken

Metode


# Transform
SOXFPE1 <-
  SOXFPE1_raw %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time)),
         var = sdg10$figs$fig6$var[language]) %>% 
  spread(2, 3)

# Table
SOXFPE1 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SOXFPE1[[1]] %>% table())
2020 2019 2018 2017 2016 2015
% af aldersgruppe
Førtidspensionister i alt 6 6 6 6 6 6



# Import
SOXFPE1_raw <-
  statgl_url("SOXFPE1", lang = language) %>% 
  statgl_fetch(
    "unit"      = "Andel",
    "age group" = px_all(),
    "time"      = px_all(),
    .col_code   = TRUE
    ) %>% 
  as_tibble()

# Transform
SOXFPE1 <-
  SOXFPE1_raw %>% 
  filter(`age group` %in% unique(SOXFPE1_raw[[1]])[2:6]) %>% 
  mutate(
    `age group` = `age group` %>% factor(levels = unique(`age group`)),
    time        = time %>% make_date()
    )

# Plot
SOXFPE1 %>% 
  ggplot(aes(
    x    = time,
    y    = value, 
    fill = `age group`
  )) +
  geom_col() +
  facet_wrap(~ `age group`) +
  theme_statgl() + 
  scale_fill_statgl() +
  labs(
    title = sdg10$figs$fig7$title[language],
    subtitle = SOXFPE1[[2]][1],
    x = " ",
    y = " ",
    fill = " ",
    caption = sdg10$figs$fig7$cap[language]
  )

Statistikbanken

Metode


# Transform
SOXFPE1 <-
  SOXFPE1_raw %>% 
  filter(`age group` %in% unique(SOXFPE1_raw[[1]])[2:6],
         time >= year(Sys.time()) - 7) %>% 
  mutate(
    `age group` = `age group` %>% factor(levels = unique(`age group`))) %>% 
  spread(1, ncol(.)) %>% 
  arrange(desc(time))

# Table
SOXFPE1 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SOXFPE1[[1]] %>% table()) %>% 
  add_footnote(sdg10$figs$fig7$foot[language], notation = "symbol")
18-24 år 25-34 år 35-44 år 45-54 år 55-64 år
% af aldersgruppe
2020 1 2 3 7 15
2019 2 2 3 7 15
2018 1 2 3 7 15
2017 1 2 3 7 14
2016 1 2 3 7 15
2015 1 2 4 8 17
* Førtidspensionister i alt