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---

Goal 17: Partnerships for the goals

Public finances

---

GS Public finances

# 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]
  )

Statbank, revenue

Statbank, expenditure

Method

Show table

# 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")

	Current surpluss gross saving	TOTAL OUTLAYS
2024
Central government	1,80	1,80
General government sector, total	14,83	14,28
Municipalities	7,18	6,82
Selfgovernment	8,27	8,13
2023
Central government	1,52	1,52
General government sector, total	14,14	14,03
Municipalities	6,72	7,20
Selfgovernment	8,16	7,61
2022
Central government	1,35	1,35
General government sector, total	13,60	13,00
Municipalities	6,57	6,28
Selfgovernment	7,92	7,67
2021
Central government	1,39	1,39
General government sector, total	12,78	12,67
Municipalities	6,20	6,13
Selfgovernment	7,33	7,31
2020
Central government	1,36	1,36
General government sector, total	12,87	12,61
Municipalities	6,13	6,04
Selfgovernment	7,61	7,44
* Billions (DKK)

# 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]
  )

Statbank, revenue

Statbank, expenditure

Statbank, GDP

Method, public finances

Show table

# 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
Actual government balance	6	6,2	1,3	0,5	2,7	0,5
* Share of GDP

Consumer prices

---

GS Consumer price index

# 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)

StatBank

Method

Show table

# 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
All goods and services	100	103,0	103,5	103,9	105,0
Food etc.	100	105,3	106,0	108,9	111,0
Bread and cereals	100	107,5	109,5	111,5	109,8
Meat	100	106,9	106,5	109,4	110,9
Fish	100	101,8	101,3	104,8	109,7
Milk, cream, cheese and eggs	100	104,9	104,5	103,8	106,6
Butter, margarine, oils and fats	100	104,7	115,7	117,6	122,2
Fruit	100	105,6	103,2	106,6	105,1
Vegetables	100	109,6	110,0	107,3	111,9
Sugar, jam, honey, chocolate and confectionery	100	107,9	108,8	110,1	113,0
Foodproducts n.e.c.	100	108,9	107,7	109,3	110,0
Coffee, tee and cocoa	100	101,5	106,4	106,1	107,2
Mineral waters, soft drinks, fruit and vegetable juices	100	104,1	107,3	114,0	114,4
Alcohol and tobacco	100	104,2	104,2	105,0	105,6
Alcohol	100	104,9	104,9	106,0	107,0
Tobacco	100	103,4	103,4	104,0	104,0
Clothing and footwear	100	102,6	103,1	100,2	101,8
Housing	100	103,9	104,9	105,1	105,9
Furnishing and household services	100	102,6	102,1	114,3	111,5
Medicine, pharmaceutical articles	100	102,0	102,0	104,4	106,2
Transportation	100	102,7	103,7	103,8	104,9
Telephone and postage	100	94,8	94,8	95,0	95,0
Leisure and culture	100	102,0	101,5	99,0	99,3
Restaurants and hotels	100	99,2	100,5	100,6	101,1
Other goods and services	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]
  ) 

StatBank

Method

Show table

# 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 January	1971 July	1972 January	1972 July	1973 January
Rate of change from the same period the previous year	NA	NA	5,7	6,3	7,7
* Consumer

Supply balance

---

GS Supply balance

# 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]
  )

StatBank

Show table

# 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
Annual percentage change
Gross Domestic Product	0,3	1,6	2,0	0,9
Private consumption	-0,3	3,2	0,6	0,1
Government consumption	-2,9	2,3	-1,7	2,2
Gross investment	7,0	13,7	1,8	-3,6
Exports of goods and services	-4,0	-6,0	13,9	3,0
Imports of goods and services	-2,1	6,0	5,7	-0,1
* 2010-prices, chained values

Government consumption and block grants

---

GS Government consumption and block grants

# 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]
  )

StatBank

Show table

# 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
Current prices
Government consumption	43,1	43,8	43,6	44,3	41,3	41,7
Block grants	19,9	19,3	19,4	19,0	17,9	18,1
* Share of GDP

High-speed internet

---

GS High-speed internet

# 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
  )

Show table

# 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
Access to high-speed internet	80%	92.6%	92.6%