Goal 1: No Poverty
Economically disadvantaged
FN 1.2.1 Proportion of the population living below the poverty line, by gender and age
# Import
SOXOU01_raw <-
statgl_url("SOXOU01", lang = language) |>
statgl_fetch(
"inventory variable" = px_all("Andel*"),
.col_code = T
) |>
as_tibble()
# Transform
SOXOU01 <-
SOXOU01_raw |>
mutate(year = year |> make_date())
# Plot
SOXOU01 |>
ggplot(aes(
x = year,
y = value,
fill = `inventory variable`
)) +
geom_col(position = "dodge") +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
accuracy = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_fill_statgl(guide = guide_legend(nrow = 2)) +
labs(
title = sdg1$figs$fig1$title[language],
subtitle = sdg1$figs$fig1$sub[language],
x = " ",
y = " ",
fill = sdg1$figs$fig1$fill[language],
caption = sdg1$figs$fig1$cap[language]
)
# Transform
SOXOU01 <-
SOXOU01_raw |>
filter(year >= year(Sys.time()) - 5) |>
mutate(year = year %>% fct_inorder()) |>
spread(2, 3) |>
mutate(`inventory variable` = `inventory variable` |> str_to_sentence())
# Table
SOXOU01 |>
rename(" " = 1) |>
statgl_table() |>
add_footnote(
sdg1$figs$fig1$sub[language],
notation = "symbol")| 2020 | 2021 | 2022 | 2023 | |
|---|---|---|---|---|
| Fraction below 50% | 4,1 | 4,1 | 4,4 | 4,6 |
| Fraction below 60% | 7,7 | 7,7 | 8,2 | 8,2 |
|
* The proportion of the population with an income below 50%
or 60% of the median income for 3 consecutive years |
# Import
SOXOU01_raw <-
statgl_url("SOXOU01", lang = language) |>
statgl_fetch(
"inventory variable" = px_all("Andel*"),
"gender" = 1:2,
.col_code = T
) |>
as_tibble()
# Transform
SOXOU01 <-
SOXOU01_raw |>
mutate(
year = year |> make_date(),
gender = gender |> fct_inorder()
)
# Plot
SOXOU01 |>
mutate(`inventory variable` = `inventory variable` |> str_to_sentence()) |>
ggplot(aes(
x = year,
y = value,
fill = gender
)) +
geom_col(position = "dodge") +
scale_y_continuous(labels = scales::percent_format(
scale = 1
)) +
facet_wrap(~ `inventory variable`) +
theme_statgl() +
scale_fill_statgl(reverse = TRUE) +
labs(
title = sdg1$figs$fig2$title[language],
subtitle = sdg1$figs$fig1$sub[language],
x = " ",
y = " ",
fill = " ",
caption = sdg1$figs$fig1$cap[language]
)
# Transform
SOXOU01 <-
SOXOU01_raw |>
filter(year >= year(Sys.time()) - 5) |>
mutate(year = year |> fct_inorder()) |>
spread(3, 4) |>
mutate(`inventory variable` = `inventory variable` |> str_to_sentence())
# Table
SOXOU01 |>
select(-1) |>
rename(" " = 1) |>
statgl_table() |>
pack_rows(index = table(SOXOU01[[1]])) |>
add_footnote(
sdg1$figs$fig1$sub[language],
notation = "symbol")| 2020 | 2021 | 2022 | 2023 | |
|---|---|---|---|---|
| Fraction below 50% | ||||
| Female | 3,7 | 3,8 | 4,1 | 4,1 |
| Male | 4,5 | 4,3 | 4,8 | 5,0 |
| Fraction below 60% | ||||
| Female | 7,3 | 7,4 | 7,7 | 7,8 |
| Male | 8,1 | 7,9 | 8,6 | 8,7 |
|
* The proportion of the population with an income below 50%
or 60% of the median income for 3 consecutive years |
||||
# Import
SOXOU04_raw <-
statgl_url("SOXOU04", lang = language) |>
statgl_fetch(
"inventory variable" = px_all("Andel*"),
"age" = 2:6,
.col_code = T
) |>
as_tibble()
# Transform
SOXOU04 <-
SOXOU04_raw |>
mutate(year = year |> make_date())
# Plot
SOXOU04 |>
mutate(`inventory variable` = `inventory variable` |> str_to_sentence()) |>
ggplot(aes(
x = year,
y = value,
color = age
)) +
geom_line(size = 2) +
facet_wrap(~ `inventory variable`, scales = "free_y") +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_color_statgl(reverse = TRUE) +
labs(
title = sdg1$figs$fig3$title[language],
subtitle = sdg1$figs$fig3$sub[language],
x = " ",
y = " ",
color = sdg1$figs$fig3$color[language],
caption = sdg1$figs$fig3$cap[language]
)
# Transform
SOXOU04 <-
SOXOU04_raw |>
filter(year >= year(Sys.time()) - 5) |>
mutate(year = year |> fct_inorder()) |>
spread(3, 4) |>
mutate(`inventory variable` = `inventory variable` |> str_to_sentence())
# Table
SOXOU04 |>
select(-1) |>
rename(" " = 1) |>
statgl_table() |>
pack_rows(index = table(SOXOU04[[1]])) |>
add_footnote(
sdg1$figs$fig3$foot[language],
notation = "symbol")| 2020 | 2021 | 2022 | 2023 | |
|---|---|---|---|---|
| Fraction below 50% | ||||
| -29 | 10,9 | 10,8 | 12,3 | 13,1 |
| 30-39 | 5,0 | 4,9 | 5,3 | 5,3 |
| 40-49 | 3,1 | 3,1 | 3,0 | 3,1 |
| 50-60 | 2,4 | 2,4 | 2,6 | 2,7 |
| 60+ | 1,5 | 1,5 | 1,6 | 1,7 |
| Fraction below 60% | ||||
| -29 | 17,5 | 17,6 | 19,2 | 20,1 |
| 30-39 | 9,0 | 8,6 | 9,3 | 9,3 |
| 40-49 | 5,3 | 5,4 | 5,5 | 5,8 |
| 50-60 | 5,0 | 4,7 | 4,9 | 5,2 |
| 60+ | 3,5 | 3,5 | 3,9 | 3,8 |
|
* Percentage of the population with an income below 50% or
60% of the median income for 3 consecutive years. The table includes only peoply over 18 years of age. |
||||
# Tak til podcasten VELKOMMEN TIL INTERNETTET for god lytter!Explanation
Note that the population used here is different from the total population. As an example, students and residents of social institutions are not included in the analysis of the economically disadvantaged.
At-risk-of-poverty rate
FN 1.2.1 Proportion of the population living in relative poverty
# Import
INXIU101_raw <-
statgl_url("INXIU101", lang = language) |>
statgl_fetch(
indicator = 2:4,
time = px_all(),
.col_code = T
) |>
as_tibble()
# Transform
INXIU101 <-
INXIU101_raw |>
mutate(
time = time |> make_date(),
indicator = indicator |> as.factor() |> fct_rev()
)
# Plot
INXIU101 |>
ggplot(aes(
x = time,
y = value,
fill = indicator
)) +
geom_area(position = "identity") +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
accuracy = 1.1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_fill_statgl(reverse = TRUE, guide = guide_legend(nrow = 3)) +
labs(
title = "At-risk-of-povery rate",
x = " ",
y = " ",
fill = sdg1$figs$fig1$fill[language],
caption = sdg1$figs$fig4$cap[language]
)
Explanation
Greenland has no official poverty line, however, the at-risk-of-poverty rate (ROP) is often used as an indicator of relative povery in an international context.
The measurement is calculated as the proportion of the population living in a household, in which the disposable household income is below a fixed percentage (40, 50 or 60 pct.) of the median income
Public funds
FN 1.a.2 Proportion of total goverment spending on essential services (education, health and social protection)
# Import
OFXFUNK_raw <-
statgl_url("OFXFUNK", lang = language) |>
statgl_fetch(
"function" = c(0, 33, 44, 50),
sector = c(0, 1, 2),
.col_code = T
) |>
as_tibble()
# Transform
vec <- 4:6
names(vec) <- trimws(unique(OFXFUNK_raw[[2]]) |> str_remove_all("[:digit:]\\.") |> str_remove_all("1 "))[-1]
OFXFUNK <-
OFXFUNK_raw |>
mutate(value = value |> replace_na(0)) |>
spread(2, 4) |>
select(1:2, unique(OFXFUNK_raw[[2]])) |>
rename(
total = 3,
sund = 4,
under = 5,
social = 6
) |>
mutate(other = total - (sund + under + social)) |>
rename(vec) |>
select(-3) |>
gather(key, value, -(1:2)) |>
mutate(
key = key |> str_replace("other", sdg1$figs$fig5$other[language] |> unlist()),
key = key |> fct_inorder(),
sector = sector |> fct_inorder(),
time = time |> make_date()
)
# Plot
OFXFUNK |>
ggplot(aes(
x = time,
y = value,
fill = key
)) +
geom_area(position = "fill") +
facet_wrap(~ sector) +
scale_fill_statgl(palette = "spring", reverse = TRUE) +
theme_statgl() +
scale_y_continuous(labels = scales::percent_format()) +
labs(
title = sdg1$figs$fig5$title[language],
subtitle = sdg1$figs$fig5$sub[language],
x = " ",
y = " ",
caption = sdg1$figs$fig5$cap[language],
fill = sdg1$figs$fig5$fill[language]
)
Longterm social security benefits
FN 1.3 Proportion of the population of working age on longterm social security benefits
# Import
ARXLEDVAR_raw <-
statgl_url("ARXLEDVAR", lang = language) |>
statgl_fetch(
taar_kvar = px_all(),
kvar_led = 1:4,
opg_var = "procent",
.col_code = T
) |>
as_tibble()
# Transform
ARXLEDVAR <-
ARXLEDVAR_raw |>
mutate(kvar_led = fct_inorder(kvar_led)) |>
mutate(taar_kvar = str_replace_all(taar_kvar, "Q", "-")) |>
mutate(taar_kvar = yq(taar_kvar))
# Plot
ARXLEDVAR |>
ggplot(aes(
x = taar_kvar,
y = value,
fill = kvar_led
)) +
geom_area(position = "fill") +
scale_x_date() +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_statgl(reverse = T, guide = guide_legend(nrow = 2, byrow = T)) +
theme_statgl() +
labs(
title = sdg1$figs$fig6$title[language],
subtitle = sdg1$figs$fig6$sub[language],
x = " ",
y = " ",
fill = sdg1$figs$fig6$fill[language],
caption = sdg1$figs$fig6$cap[language]
)