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
2022
Den kommunale sektor	6,53	6,20
Den samlede offentlige sektor	13,53	12,80
Den selvstyrede sektor	7,93	7,57
Den statslige sektor	1,28	1,28
2021
Den kommunale sektor	6,19	6,11
Den samlede offentlige sektor	12,78	12,64
Den selvstyrede sektor	7,33	7,29
Den statslige sektor	1,39	1,39
2020
Den kommunale sektor	6,12	6,04
Den samlede offentlige sektor	12,87	12,61
Den selvstyrede sektor	7,61	7,44
Den statslige sektor	1,36	1,36
2019
Den kommunale sektor	6,04	5,79
Den samlede offentlige sektor	12,99	11,78
Den selvstyrede sektor	7,89	6,93
Den statslige sektor	1,38	1,38
* 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")

	2017	2018	2019	2020	2021
Faktisk offentlig saldo	2,7	6,1	6,1	1,3	0,7
* 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(size = 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)

	2022 jan	2022 jul	2023 jan	2023 jul	2024 Jan
Samtlige varer og tjenester	123,6	125,9	127,0	129,0	129,8
Fødevarer mv.	142,0	146,6	147,5	150,1	151,1
Brød og kornprodukter	135,4	137,8	139,5	146,4	146,1
Kød, kødvarer	143,8	151,2	149,6	150,9	152,1
Fisk	122,8	123,8	126,6	127,3	127,2
Mælk, fløde, ost, æg	131,5	137,6	140,4	142,2	142,0
Smør, margarine m.v.	178,7	190,7	205,6	205,7	211,8
Frugt	131,4	131,9	135,9	135,9	145,8
Grønsager	147,0	150,5	151,6	153,0	150,5
Sukker og slik mv.	171,1	171,5	176,3	178,6	180,5
Andre fødevarer	135,5	137,8	139,7	144,5	143,4
Kaffe, te m.v.	140,4	142,3	143,5	145,0	145,6
Sodavand og juice	155,3	162,7	161,2	165,7	168,1
Alkohol og tobak	129,4	132,3	133,0	134,2	135,5
Alkohol	126,9	130,3	130,6	132,1	133,5
Tobak	131,5	133,7	134,9	135,9	137,0
Beklædning og fodtøj	89,6	90,4	90,6	91,3	92,2
Bolig	125,5	125,9	127,7	129,4	130,1
Boligudstyr, husholdningstj	114,4	116,9	122,7	123,1	117,8
Medicin, pharm. artikler	128,3	132,0	131,5	132,8	132,5
Transport	125,8	126,4	128,5	129,1	132,6
Telefon, porto	85,3	85,3	85,5	85,5	85,6
Fritid og kultur	108,6	116,6	114,2	120,3	119,6
Restauranter og hoteller	134,5	135,7	137,9	141,1	142,4
Andre varer og tjenester	117,6	117,8	119,5	121,3	123,3

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

# 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")

	2022 januar	2022 juli	2023 januar	2023 juli	2024 januar
Ændring i procent pr. løbende 12 måneder	0,2	2,2	2,7	2,5	2,2
* 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")

	2019	2020	2021
Årlig procentvis ændring
Bruttonationalprodukt	2,8	0,2	1,3
Privat forbrug	0,5	0,4	3,2
Offentlig forbrug	4,0	-1,8	1,4
Bruttoinvestering	46,9	0,4	6,5
Eksport af varer og tjenester	-5,4	-5,6	0,7
Import af varer og tjenester	15,1	-5,8	5,9
* 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")

	2017	2018	2019	2020	2021
Løbende priser
Offentlig forbrug	42,4	43,1	43,8	43,7	45,1
Bloktilskud m.v.	20,0	19,9	19,3	19,4	19,4
* 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%