Delmål 17: Partnerskaber for handling
Resultat af offentlige finanser
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$cap[language]
)
Statistikbanken, udgifter
Metode
# Import
indtagter_raw <-
statgl_url("OFXREAI", lang = language) %>%
statgl_fetch(
"sector" = 0,
"transaction" = 43,
"time" = px_all(),
.col_code = TRUE) %>%
as_tibble()
udgifter_raw <-
statgl_url("OFXREAU", lang = language) %>%
statgl_fetch(
"sector" = 0,
"transaction" = 44,
"time" = px_all(),
.col_code = TRUE) %>%
as_tibble()
bnp_raw <-
statgl_url("NRX02", lang = language) %>%
statgl_fetch(
"units" = "L",
"account name" = "LBNPTOT",
"time" = px_all(),
.col_code = TRUE) %>%
as_tibble()
# Transform
saldo <-
bnp_raw %>%
select(3, 4) %>%
rename("bnp" = 2) %>%
left_join(udgifter_raw %>% select(3, 4) %>% rename("expenditure" = 2)) %>%
left_join(indtagter_raw %>% select(3, 4) %>% rename("revenue" = 2)) %>%
mutate(saldo = (revenue - expenditure) / bnp * 10^-3,
time = time %>% make_date(),
type = "saldo")
# Plot
saldo %>%
ggplot(aes(
x = time,
y = saldo,
fill = type
)) +
geom_col() +
scale_y_continuous(labels = scales::percent_format(
scale = 100,
accuracy = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_fill_statgl() +
theme(legend.position = "None") +
labs(
title = sdg17$figs$fig2$title[language],
subtitle = sdg17$figs$fig2$sub[language],
x = " ",
y = sdg17$figs$fig2$y_lab[language],
color = " ",
caption = sdg17$figs$fig2$cap[language]
)
Statistikbanken, udgifter
Statistikbanken, BNP
Metode, offentlige finanser
Forbrugerpriser
GS Forbrugerprisindeks
# Import
PRXPRISV_raw <-
statgl_url("PRXPRISV", lang = language) %>%
statgl_fetch(
"commodity group" = px_all(),
"time" = px_all(),
.col_code = TRUE) %>%
as_tibble()
# Transform
PRXPRISV <-
PRXPRISV_raw %>%
mutate(time = time %>% as.character() %>% readr::parse_date(format = "%Y %b"),
`commodity group` = `commodity group` %>% factor(levels = unique(`commodity group`)))
# Plot
cpi <-
PRXPRISV %>%
ggplot(aes(
x = time,
y = value,
color = `commodity group`
)) +
geom_line(linewidth = 1) +
theme_statgl() +
scale_color_statgl() +
labs(
title = (statgl_url("PRXPRISV", lang = language) %>% statgl_meta())$title,
subtitle = " ",
x = " ",
y = sdg17$figs$fig3$y_lab[language],
color = sdg17$figs$fig3$color[language],
caption = sdg17$figs$fig3$cap[language]
)
plotly::ggplotly(cpi)Metode
# Import
PRXPRISH_raw <-
statgl_url("PRXPRISH", lang = "da") %>%
statgl_fetch(
"time" = px_all(),
"type" = 0,
.col_code = TRUE) %>%
as_tibble()
time <- statgl_url("PRXPRISH", lang = "en") %>%
statgl_fetch(
"time" = px_all()) %>%
select(time_eng = time)
fig_title <- unlist(statgl_meta(statgl_url("PRXPRISH", lang = language))$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]
) 
Metode
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$cap[language]
)
Offentligt forbrug og bloktilskud mv.
GS Offentligt forbrug og bloktilskud mv.
# Import
NRD11_raw <-
statgl_url("NRX11", lang = language) %>%
statgl_fetch(
units = "L",
account = c("0000", "3200"),
time = px_all(),
.col_code = TRUE
) %>%
as_tibble()
OFXREAI_raw <-
statgl_url("OFXREAI", lang = language) %>%
statgl_fetch(
sector = c(0),
transaction = c(27),
time = px_all(),
.col_code = TRUE
) %>%
as_tibble()
# Transform
bnp <-
NRD11_raw %>%
mutate(account = account %>% factor(levels = unique(account))) %>%
spread(account, value) %>%
left_join(OFXREAI_raw %>%
mutate(
transaction = transaction %>%
trimws() %>%
str_remove_all("[:digit:]") %>%
str_remove("...") %>%
trimws(),
value = value / 1000
) %>%
spread(transaction, value) %>%
select(-1)
)
labels <- bnp %>% colnames()
vec <- 1:length(labels)
names(vec) <- labels
bnp_relativ <-
bnp %>%
rename(
Y = 3,
O = 4,
B = 5
) %>%
mutate(
O = O / Y * 100,
B = B / Y * 100
) %>% rename(vec) %>%
select(-3) %>%
gather(key, value, -units, -time) %>%
mutate(time = time %>% as.numeric(),
key = key %>% factor(levels = unique(key)))
sub_lab <-
bnp_relativ %>%
select(1) %>%
separate(units, c("units", "drop"), ",") %>%
pull(units) %>%
unique()
# Plot
bnp_relativ %>%
ggplot(aes(
x = time,
y = value,
fill = key
)) +
geom_col(size = 2) +
facet_wrap(~ key, scales = "free", ncol = 1) +
theme_statgl() +
theme(legend.position = "none") +
scale_y_continuous(labels = scales::percent_format(
scale = 1
)) +
scale_fill_statgl() +
labs(
title = sdg17$figs$fig6$title[language],
subtitle = sub_lab,
y = " ",
x = " ",
caption = sdg17$figs$fig6$cap[language]
)
Højhastighedsinternet
GS Højhastighedsinternet
# Import
time <- seq(2018, 2020)
value <- c(80, 92.6, 92.6)
type <- "internet"
title <- sdg17$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
)