Tilbage

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Delmål 17: Partnerskaber for handling

Resultat af offentlige finanser

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GS Resultat af offentlige finanser

# Import 
indtagter_raw <-
  statgl_url("OFXREAI", lang = language) %>% 
  statgl_fetch(
    "sector"      = px_all(),
    "transaction" = 43,
    "time"        = px_all(),
    .col_code     = TRUE) %>% 
  as_tibble()

udgifter_raw <-
  statgl_url("OFXREAU", lang = language) %>% 
  statgl_fetch(
    "sector"      = px_all(),
    "transaction" = 44,
    "time"        = px_all(),
    .col_code     = TRUE) %>% 
  as_tibble()

indtagter <- 
  indtagter_raw %>% 
  mutate(transaction = transaction %>% str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws()) %>% 
  spread(2, 4)

udgifter <- 
  udgifter_raw %>% 
  mutate(transaction = transaction %>% str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws()) %>% 
  spread(2, 4)

drift <-
  indtagter %>% 
  left_join(udgifter) %>% 
  gather(transaction, value, -(1:2)) %>% 
  mutate(value = value / 10^6,
         time = time %>% make_date())
  
# Plot
drift %>%
  ggplot(aes(
    x     = time,
    y     = value,
    color = transaction
  )) +
  geom_line(size = 2) +
  facet_wrap(~ sector, scales = "free") +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title = sdg17$figs$fig1$title[language],
    subtitle = " ",
    x = " ",
    y = sdg17$figs$fig1$y_lab[language],
    color = " ",
    caption = sdg17$figs$fig1$cap[language]
  )

Statistikbanken, indtægter

Statistikbanken, udgifter

Metode

Vis tabel

# Transform
drift <-
  indtagter %>% 
  left_join(udgifter) %>% 
  gather(transaction, value, -(1:2)) %>% 
  mutate(value = value / 10^6,
         value = round(value, 2)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  spread(3, 4) %>% 
  arrange(desc(time))

# Table
drift %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(drift[[2]]) %>% rev()) %>% 
  add_footnote(sdg17$figs$fig1$foot[language], notation = "symbol")

	Drifts og kapitalindtægter i alt	Drifts og kapitaludgifter i alt
2024
Den kommunale sektor	7,18	6,82
Den samlede offentlige sektor	14,83	14,28
Den selvstyrede sektor	8,27	8,13
Den statslige sektor	1,80	1,80
2023
Den kommunale sektor	6,72	7,20
Den samlede offentlige sektor	14,14	14,03
Den selvstyrede sektor	8,16	7,61
Den statslige sektor	1,52	1,52
2022
Den kommunale sektor	6,57	6,28
Den samlede offentlige sektor	13,60	13,00
Den selvstyrede sektor	7,92	7,67
Den statslige sektor	1,35	1,35
2021
Den kommunale sektor	6,20	6,13
Den samlede offentlige sektor	12,78	12,67
Den selvstyrede sektor	7,33	7,31
Den statslige sektor	1,39	1,39
2020
Den kommunale sektor	6,13	6,04
Den samlede offentlige sektor	12,87	12,61
Den selvstyrede sektor	7,61	7,44
Den statslige sektor	1,36	1,36
* Millarder kroner

# Import 
indtagter_raw <-
  statgl_url("OFXREAI", lang = language) %>% 
  statgl_fetch(
    "sector"      = 0,
    "transaction" = 43,
    "time"        = px_all(),
    .col_code     = TRUE) %>% 
  as_tibble()

udgifter_raw <-
  statgl_url("OFXREAU", lang = language) %>% 
  statgl_fetch(
    "sector"      = 0,
    "transaction" = 44,
    "time"        = px_all(),
    .col_code     = TRUE) %>% 
  as_tibble()

bnp_raw <-
  statgl_url("NRX02", lang = language) %>% 
  statgl_fetch(
    "units"        = "L",
    "account name" = "LBNPTOT",
    "time"         = px_all(),
    .col_code      = TRUE) %>% 
  as_tibble()

# Transform
saldo <- 
  bnp_raw %>% 
  select(3, 4) %>% 
  rename("bnp" = 2) %>% 
  left_join(udgifter_raw  %>% select(3, 4) %>% rename("expenditure" = 2)) %>% 
  left_join(indtagter_raw %>% select(3, 4) %>% rename("revenue" = 2)) %>% 
  mutate(saldo = (revenue - expenditure) / bnp * 10^-3,
         time  = time %>% make_date(),
         type  = "saldo")

# Plot
saldo %>% 
  ggplot(aes(
    x    = time,
    y    = saldo,
    fill = type
  )) +
  geom_col() +
  scale_y_continuous(labels  = scales::percent_format(
    scale = 100, 
    accuracy = 1,
    big.mark = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "None") +
  labs(
    title = sdg17$figs$fig2$title[language],
    subtitle = sdg17$figs$fig2$sub[language],
    x = " ",
    y = sdg17$figs$fig2$y_lab[language],
    color = " ",
    caption = sdg17$figs$fig2$cap[language]
  )

Statistikbanken, indtægter

Statistikbanken, udgifter

Statistikbanken, BNP

Metode, offentlige finanser

Vis tabel

# transform
  saldo <-
  bnp_raw %>% 
  select(3, 4) %>% 
  rename("bnp" = 2) %>% 
  left_join(udgifter_raw  %>% select(3, 4) %>% rename("expenditure" = 2)) %>% 
  left_join(indtagter_raw %>% select(3, 4) %>% rename("revenue" = 2)) %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  #arrange(desc(time)) %>% 
  mutate(value = (revenue - expenditure) / bnp * 10^-3 * 100,
         value = round(value, 1),
         time  = time %>% factor(levels = unique(time)),
         saldo = sdg17$figs$fig2$saldo[language]) %>% 
  select(-(2:4)) %>% 
  spread(1, 2)



# table
saldo %>%
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(sdg17$figs$fig2$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023
Faktisk offentlig saldo	6	6,2	1,3	0,5	2,7	0,5
* Procentvis andel af BNP i løbende priser

Forbrugerpriser

---

GS Forbrugerprisindeks

# Import
PRXPRISV_raw <-
  statgl_url("PRXPRISV", lang = language) %>% 
  statgl_fetch(
      "commodity group" = px_all(),
      "time"            = px_all(),
    .col_code           = TRUE) %>% 
  as_tibble()

# Transform
PRXPRISV <- 
  PRXPRISV_raw %>% 
  mutate(time              = time %>% as.character() %>% readr::parse_date(format = "%Y %b"),
         `commodity group` = `commodity group` %>% factor(levels = unique(`commodity group`)))

# Plot  
cpi <-
  PRXPRISV %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = `commodity group` 
  )) +
  geom_line(linewidth = 1) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title    = (statgl_url("PRXPRISV", lang = language) %>% statgl_meta())$title,
    subtitle = " ",
    x        = " ",
    y        = sdg17$figs$fig3$y_lab[language],
    color    = sdg17$figs$fig3$color[language],
    caption  = sdg17$figs$fig3$cap[language]
  )
  
plotly::ggplotly(cpi)

Statistikbanken

Metode

Vis tabel

# Import
PRXPRISV_raw <-
  statgl_url("PRXPRISV", lang = language) %>% 
  statgl_fetch(
      "commodity group" = px_all(),
      "time"            = px_top(5),
    .col_code           = TRUE) %>% 
  as_tibble()

# Transform
PRXPRISV <-
  PRXPRISV_raw %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time)),
         `commodity group` = `commodity group` %>% factor(levels = unique(`commodity group`))) %>% 
  spread(2, 3)

# Table
PRXPRISV %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  row_spec(1, bold = TRUE)

	2008 jan	2008 jul	2009 jan	2009 jul	2010 jan
Samtlige varer og tjenester	100	103,0	103,5	103,9	105,0
Fødevarer mv.	100	105,3	106,0	108,9	111,0
Brød og kornprodukter	100	107,5	109,5	111,5	109,8
Kød, kødvarer	100	106,9	106,5	109,4	110,9
Fisk	100	101,8	101,3	104,8	109,7
Mælk, fløde, ost, æg	100	104,9	104,5	103,8	106,6
Smør, margarine m.v.	100	104,7	115,7	117,6	122,2
Frugt	100	105,6	103,2	106,6	105,1
Grønsager	100	109,6	110,0	107,3	111,9
Sukker og slik mv.	100	107,9	108,8	110,1	113,0
Andre fødevarer	100	108,9	107,7	109,3	110,0
Kaffe, te m.v.	100	101,5	106,4	106,1	107,2
Sodavand og juice	100	104,1	107,3	114,0	114,4
Alkohol og tobak	100	104,2	104,2	105,0	105,6
Alkohol	100	104,9	104,9	106,0	107,0
Tobak	100	103,4	103,4	104,0	104,0
Beklædning og fodtøj	100	102,6	103,1	100,2	101,8
Bolig	100	103,9	104,9	105,1	105,9
Boligudstyr, husholdningstj	100	102,6	102,1	114,3	111,5
Medicin, pharm. artikler	100	102,0	102,0	104,4	106,2
Transport	100	102,7	103,7	103,8	104,9
Telefon, porto	100	94,8	94,8	95,0	95,0
Fritid og kultur	100	102,0	101,5	99,0	99,3
Restauranter og hoteller	100	99,2	100,5	100,6	101,1
Andre varer og tjenester	100	100,8	105,0	96,2	98,4

# Import
PRXPRISH_raw <-
  statgl_url("PRXPRISH", lang = language) %>%
  statgl_fetch(
      "time"  = px_all(),
      "type"  = 0,
    .col_code = TRUE) %>% 
  as_tibble()



time <- statgl_url("PRXPRISH", lang = "en") %>%
  statgl_fetch(
      "time"  = px_all()) %>% 
  select(time_eng = time)

fig_title <- unlist(statgl_meta(statgl_url("PRXPRISH", lang = language))$title %>% str_split(", "))[2]
fig_sub   <- unlist(statgl_meta(statgl_url("PRXPRISH", lang = language))$title %>% str_split(", "))[1]

# Transform
PRXPRISH <-
  PRXPRISH_raw %>% 
  cbind(time) %>% 
  mutate(time = time_eng %>% parse_date(format = "%Y %B")) %>% 
  select(-time_eng)


fig_title <- unlist(statgl_meta(statgl_url("PRXPRISH", lang = language))$title |> str_split(" "))[1]
fig_sub   <- unlist(statgl_meta(statgl_url("PRXPRISH", lang = language))$variable)[[226]]

# Plot
PRXPRISH %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = type
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels  = scales::percent_format(
    scale = 1, 
    accuracy = 1, 
    big.mark = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = fig_title,
    subtitle = fig_sub,
    x        = " ",
    y        = " ",
    caption  = sdg17$figs$fig4$cap[language]
  ) 

Statistikbanken

Metode

Vis tabel

# Import
PRXPRISH_raw <-
  statgl_url("PRXPRISH", lang = language) %>%
  statgl_fetch(
      "time"  = px_top(5),
      "type"  = 0,
    .col_code = TRUE) %>% 
  as_tibble()

# Transform
PRXPRISH <-
  PRXPRISH_raw %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(1, ncol(.))

PRXPRISH %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(fig_title, notation = "symbol")

	1971 januar	1971 juli	1972 januar	1972 juli	1973 januar
Ændring i procent pr. løbende 12 måneder	NA	NA	5,7	6,3	7,7
* Forbrugerpristal

Forsyningsbalancen

---

GS Forsyningsbalance

# Import
NRX11_raw <-
  statgl_url("NRX11", lang = language) %>% 
  statgl_fetch(
      "units"   = "K",
      "account" = px_all(),
      "time"    = px_all(),
    .col_code   = TRUE) %>% 
  as_tibble()

var <- unique(NRX11_raw[[2]])
vec <- c(var[1], var[4], var[5], var[6], var[7], var[2])

# Transform
NRX11 <-
  NRX11_raw %>% 
  filter(account %in% vec) %>% 
  mutate(account = account %>% factor(levels = unique(vec)),
         time    = time    %>% make_date()) %>% 
  arrange(account, time) %>% 
  group_by(account) %>% 
  mutate(pct = (value - lag(value)) / lag(value)) %>% 
  ungroup()

# Plot
NRX11 %>% 
  ggplot(aes(
    x     = time,
    y     = pct,
    color = account
  )) +
  geom_line(size = 2) +
  geom_hline(yintercept = 0, linetype = "dashed") + 
  facet_wrap(~ account, scales = "free") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
  )) +
  theme_statgl() + 
  scale_color_statgl() +
  labs(
    title    = sdg17$figs$fig5$title[language],
    subtitle = NRX11[[1]][1],
    x        = " ",
    y        = sdg17$figs$fig5$y_lab[language],
    color    = " ",
    caption  = sdg17$figs$fig5$cap[language]
  )

Statistikbanken

Vis tabel

# Transform
NRX11 <-
  NRX11_raw %>% 
  filter(account %in% vec) %>% 
  mutate(account = account %>% factor(levels = unique(vec))) %>% 
  arrange(account, time) %>% 
  group_by(account) %>% 
  mutate(pct = (value - lag(value)) / lag(value) * 100,
         pct = round(pct, 1)) %>% 
  ungroup() %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  select(-4) %>% 
  spread(3, 4)

# Table
NRX11 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(sdg17$figs$fig5$index[language], 1, length(NRX11[[2]])) %>% 
  add_footnote(NRX11[[1]][1], notation = "symbol")

	2020	2021	2022	2023
Årlig procentvis ændring
Bruttonationalprodukt	0,3	1,6	2,0	0,9
Privat forbrug	-0,3	3,2	0,6	0,1
Offentlig forbrug	-2,9	2,3	-1,7	2,2
Bruttoinvestering	7,0	13,7	1,8	-3,6
Eksport af varer og tjenester	-4,0	-6,0	13,9	3,0
Import af varer og tjenester	-2,1	6,0	5,7	-0,1
* 2010-priser, kædede værdier

Offentligt forbrug og bloktilskud mv.

---

GS Offentligt forbrug og bloktilskud mv.

# Import 
NRD11_raw <- 
  statgl_url("NRX11", lang = language) %>% 
  statgl_fetch(
    units   = "L",
    account = c("0000", "3200"),
    time    = px_all(),
    .col_code   = TRUE
  ) %>% 
  as_tibble()

OFXREAI_raw <- 
  statgl_url("OFXREAI", lang = language) %>% 
  statgl_fetch(
    sector      = c(0),
    transaction = c(27),
    time        = px_all(),
    .col_code   = TRUE
  ) %>% 
  as_tibble()

# Transform
bnp <- 
  NRD11_raw %>% 
  mutate(account = account %>% factor(levels = unique(account))) %>% 
  spread(account, value) %>% 
  left_join(OFXREAI_raw %>%
              mutate(
                transaction = transaction %>% 
                  trimws() %>% 
                  str_remove_all("[:digit:]") %>% 
                  str_remove("...") %>%
                  trimws(),
                value = value / 1000
                ) %>% 
              spread(transaction, value) %>% 
              select(-1)
            )
  
labels     <- bnp %>% colnames()
vec        <- 1:length(labels)
names(vec) <- labels

bnp_relativ <-
  bnp %>% 
  rename(
    Y = 3,
    O = 4,
    B = 5
  ) %>% 
  mutate(
    O = O / Y * 100,
    B = B / Y * 100
  ) %>% rename(vec) %>% 
  select(-3) %>% 
  gather(key, value, -units, -time) %>% 
  mutate(time = time %>% as.numeric(),
         key  = key %>% factor(levels = unique(key)))


sub_lab <- 
  bnp_relativ %>% 
  select(1) %>% 
  separate(units, c("units", "drop"), ",") %>% 
  pull(units) %>% 
  unique()


# Plot
bnp_relativ %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    fill  = key
  )) +
  geom_col(size = 2) +
  facet_wrap(~ key, scales = "free", ncol = 1) +
  theme_statgl() + 
  theme(legend.position = "none") +
  scale_y_continuous(labels  = scales::percent_format(
    scale = 1
  )) +
  scale_fill_statgl() +
  labs(
    title    = sdg17$figs$fig6$title[language],
    subtitle = sub_lab,
    y        = " ",
    x        = " ",
    caption  = sdg17$figs$fig6$cap[language]
  )

Statistikbanken

Vis tabel

# Table
tab <- 
  bnp_relativ %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  mutate(
    time = time %>% factor(levels = unique(time)),
    value = value %>% round(1)
    ) %>% 
  spread(time, value)

foot_lab <- 
  tab %>% 
  select(1) %>% 
  separate(units, c("units", "drop"), ",") %>% 
  pull(units) %>%
  table()

tab %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = foot_lab) %>% 
  add_footnote(sdg17$figs$fig6$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023
Løbende priser
Offentlig forbrug	43,1	43,8	43,6	44,3	41,3	41,7
Bloktilskud m.v.	19,9	19,3	19,4	19,0	17,9	18,1
* Procent i forhold til bruttonationalprodukt

Højhastighedsinternet

---

GS Højhastighedsinternet

# Import 
time  <- seq(2018, 2020)
value <- c(80, 92.6, 92.6) 
type  <- "internet"

title   <- sdg17$figs$fig7$title[language]
caption <- sdg17$figs$fig7$cap[language]
unit    <- sdg17$figs$fig7$unit[language]


# Plot
data.frame(time, value, type) %>% 
  as_tibble() %>% 
  ggplot(aes(
    x = time, 
    y = value,
    fill = type
    )) + 
  geom_col() +
  expand_limits(y = 100) +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1,
    accuracy     = 1,
    big.mark     = ".",
    decimal.mark = ","
  )) +
  scale_fill_statgl() +
  theme_statgl() + 
  theme(legend.position = "none") +
  labs(
    title    = title,
    subtitle = unit,
    x        = " ",
    y        = " ",
    caption  = caption
  )

Vis tabel

# Table 
value <- c("80%", "92.6%", "92.6%") 

data.frame(time, value) %>% 
  as_tibble() %>% 
  mutate(col = title) %>% 
  spread(time, value) %>% 
  rename(" " = 1) %>% 
  statgl_table()

	2018	2019	2020
Adgang til højhastighedsinternet	80%	92.6%	92.6%