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
  )) +
  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