sdg2020

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,
    color = units
  )) +
  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(units = sdg10$figs$fig1$var[language] %>% unlist()) %>% 
  select(units, everything())

table %>% 
  #select(-units) %>% 
  rename(" " = 1) |> 
  statgl_table() %>%
  add_footnote(sdg10$figs$fig1$foot[language],notation = "symbol")

	2023	2022	2021	2020	2019	2018
Arbejdskraftens andel af BNP	54.5%	53.7%	54.7%	53.2%	54%	53.7%
^* 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,
    color = indicator
  )) +
  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")

	2018	2019	2020	2021	2022	2023
Gini-koefficient	34,9	34,6	34,9	34,3	34,2	33,9
^* Gini-point

Palmakoefficienten

FN 10.1.1 Palmakoefficienten

INXPI403_raw <- 
  statgl_url("INXPI403", lang = language) |> 
  statgl_fetch(
    "Decile: Palma ratio" = 0,
    time                  = px_all(),
    .col_code             = T
  ) |> 
  as_tibble()

INXPI403 <- 
  INXPI403_raw |> 
  mutate(time = time |> make_date())


INXPI403 |> 
  ggplot(aes(
    x     = time,
    y     = value,
    color = "Decile: Palma ratio"
  )) +
  geom_line(size = 2) +
  scale_color_statgl() +
  theme_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = unique(INXPI403_raw[[1]])[1],
    subtitle = sdg10$figs$fig3$sub[language],
    x        = " ",
    y        = sdg10$figs$fig3$y_lab[language],
    caption  = sdg10$figs$fig3$cap[language]
  )

Statistikbanken

Metode

INXPI403 <- 
  INXPI403_raw |> 
  filter(time >= year(Sys.time()) - 7) |> 
  mutate(time = time |> factor(levels = unique(time))) |> 
  spread(time, value)


INXPI403 |> 
  rename(" " = 1) |> 
  statgl_table()

	2018	2019	2020	2021	2022	2023
Palmakoefficient	1,34	1,33	1,36	1,32	1,31	1,3

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,
    color = indicator
  )) +
  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$figs$fig4$sub[language],
    x = " ",
    y = sdg10$figs$fig4$y_lab[language],
    caption = sdg10$figs$fig4$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$figs$fig4$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023
S80/20	5,4	5,3	5,5	5,8	5,5	5,3
^* 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,
    fill = unit
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = SOX004[[2]][1],
    subtitle = sdg10$figs$fig5$sub[language],
    x        = " ",
    y        = sdg10$figs$fig5$y_lab[language],
    fill     = " ",
    caption  = sdg10$figs$fig5$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())

	2018	2019	2020	2021	2022	2023
Antal
Offentlig hjælp i alt	5.051	4.733	4.296	3.749	3.323	3.022

Modtagere af førtidspension

GS Modtagere af førtidspension

SOXFPE1_raw <- 
  "SOXFPE1" %>% 
  statgl_url() %>% 
  statgl_fetch() %>% 
  as_tibble()

SOXFPE1_raw %>% 
  ggplot(aes(
    x = time,
    y = value,
    fill = time
  )) +
  geom_col() +
  theme_statgl() +
  scale_y_continuous(breaks = scales::pretty_breaks()) +
  scale_x_discrete(breaks = scales::pretty_breaks()) +
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg10$figs$fig6$title[language],
    x        = " ",
    y        = " ",
    caption  = sdg10$figs$fig6$cap[language]
  )

Statistikbanken

Metode

SOXFPE1_raw %>% 
  spread(time, value) %>% 
  statgl_table()

2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
2.728	2.691	2.619	2.565	2.457	2.243	2.286	2.392	2.396	2.406	2.526	2.463	2.457	2.511