Anguniagaq 2: Kaattoqassanngilaq
Nerisassaqarsinnaannginneq
FN 2.1.2 Naammattunik eqqortunillu inuussutissartalinnik nerisassaqarsinnaannginneq
# FROM SIF
food_insecurity <-
tibble(
time = c("2014", "2018"),
value = c(0.118, 0.082)
)
food_insecurity |>
ggplot(aes(x = time, y = value)) +
geom_col(fill = statgl:::statgl_cols(1)) +
theme_statgl() +
scale_y_continuous(labels = scales::percent_format(decimal.mark = ",")) +
labs(
x = " ",
y = sdg2$figs$fig1$y_lab[language],
caption = sdg2$figs$fig2$cap[language]
)
Kalaallit Nunaanni innuttaasut peqqissusaannik
misisuititsineq
vec <- 1:2
names(vec) <- c(" ", sdg2$figs$fig1$y_lab[language])
food_insecurity |>
mutate(value = value * 100) |>
rename(vec) |>
statgl_table(year_col = " ")| Naammattunik eqqortunillu inuussutissartalinnik nerisassaqarsinnaannginneq | |
|---|---|
| 2014 | 11,8 |
| 2018 | 8,2 |
Nassuiaat
Nerisassat isumannaatsuunerat isumaqarpoq naleqquttunik inuussutissaqarluartunillu isumannaatsumik nerisassaqartitaaneq. Statens Institut for Folkesundhed Kalaallit Nunaanni innuttaasut peqqissusaannut misissuisarpoq, tassanilu aamma innuttaasut nerisaqarnerisa isumannaatsuunerat uuttortarneqartarpoq. Tamanna misissuinermi apeqqutinut pingasunut, nerisassaaleqisarnermut kiisalu nerisassanik pisinissamut aningaasaateqarnermut tunngasunut akissutit uppernarsaatitut atorlugit nassuiarneqartarpoq.
Nerisaqarneq
GS Naleqqersuutit nerisaqarnermut tunngasut
# Import
SIF_raw <-
data.frame(
frugt_daglig = c(37.2, 44.9, 38.8),
grøntsag_daglig = c(23.9, 30.4, 29.6),
fisk_ugen = c(56.0, 50.2, 42.8),
saft_sodavand = c(24.4, 32.1, 43.9),
kød_havpattedyr = c(35.9, 35.7, 33.3),
tid = c("2005-2010", "2014", "2018")
) |>
as_tibble()
vec <- 1:5
names(vec) <-
c(
sdg2$figs$fig2$cols$col1[language],
sdg2$figs$fig2$cols$col2[language],
sdg2$figs$fig2$cols$col3[language],
sdg2$figs$fig2$cols$col4[language],
sdg2$figs$fig2$cols$col5[language]
)
# Transform
SIF <-
SIF_raw |>
rename(vec) |>
gather(indikatorer, værdi, -tid) |>
mutate(indikatorer = indikatorer |> fct_reorder(værdi) |> fct_rev())
# Plot
SIF |>
ggplot(aes(x = tid, y = værdi, fill = indikatorer)) +
geom_col() +
facet_wrap(~ indikatorer, nrow = 2) +
scale_y_continuous(labels = scales::percent_format(scale = 1, big.mark = ".",
decimal.mark = ",")) +
theme_statgl(base_size = 10) +
scale_fill_statgl(reverse = TRUE) +
theme(legend.position = "None") +
labs(
title = sdg2$figs$fig2$title[language],
x = " ",
y = " ",
caption = sdg2$figs$fig2$cap[language]
)
Kalaallit Nunaanni innuttaasut peqqissusaannik
misisuititsineq
vec <- 1
names(vec) <- sdg2$figs$fig2$cols$col1_tab[language]
# Table
SIF |>
mutate(værdi = format(værdi, digits = 3, decimal.mark = ",")) |>
spread(tid, værdi) |>
rename(vec) |>
#set_names(str_to_title(names(.))) |>
statgl_table() |>
add_footnote(sdg2$figs$fig2$foot[language], notation = "symbol")| Naleqqersuutit | 2005-2010 | 2014 | 2018 |
|---|---|---|---|
| Sap. ak. minnerpaamik ataasiarlutik aalisagartortartut | 56,0 | 50,2 | 42,8 |
| Ullormut paarnanik naatitartortartut | 37,2 | 44,9 | 38,8 |
| Immami miluumasut neqaannik sap. ak. ataasiarlutik pingasoriarlutilluunniit nerisartut | 35,9 | 35,7 | 33,3 |
| Ullut tamaasa saftitunngikkunik sodavanditorartut | 24,4 | 32,1 | 43,9 |
| Ullormut naatitartortartut | 23,9 | 30,4 | 29,6 |
| * Annertussusaat procentinngorlugit |
# Import
SIF_raw <-
data.frame(
BMI_overlig_30 = c(22.9, 27.3, 27.8),
tid = c("2005-2010", "2014", "2018")
) |>
as_tibble()
vec <- 1
names(vec) <- sdg2$figs$fig3$cols$col1[language]
# Transform
SIF <-
SIF_raw |>
rename(vec) |>
gather(indikatorer, værdi,-tid)
# Plot
SIF |>
mutate(tid = as.character(tid)) |>
ggplot(aes(x = tid, y = værdi, fill = indikatorer)) +
geom_col() +
scale_y_continuous(labels = scales::percent_format(scale = 1, big.mark = ".",
decimal.mark = ",")) +
theme_statgl() + scale_fill_statgl(reverse = TRUE) +
theme(legend.position = "None") +
labs(
title = sdg2$figs$fig3$cols$col1[language],
x = " ",
y = " ",
caption = sdg2$figs$fig3$cap[language]
)
Kalaallit Nunaanni innuttaasut peqqissusaannik
misisuititsineq
vec <- 1
names(vec) <- sdg2$figs$fig3$cols$col1[language]
# Import
SIF |>
spread(1, 3) |>
rename(vec) |>
statgl_table()| Body Mass Index malillugu 30-mi imaluunniit qaangerlugu inissisimasut annertussusaat | 2005-2010 | 2014 | 2018 |
|---|---|---|---|
| Body Mass Index malillugu 30-mi imaluunniit qaangerlugu inissisimasut annertussusaat | 22,9 | 27,3 | 27,8 |
Kaallutik innartartut
GS Kaallutik innartartut
key1 <- sdg2$figs$fig4$keys$key1[language] |> unlist()
key2 <- sdg2$figs$fig4$keys$key2[language] |> unlist()
key3 <- sdg2$figs$fig4$keys$key3[language] |> unlist()
# Import
a <- c(17,13,12,12,24,19,17,17,59,68,71,71)
b <- c(rep(key1, 4), rep(key2, 4), rep(key3, 4))
c <- rep(c(2006,2010,2014,2018), 3)
# Transform
hbsc <-
data.frame(c, b, a) |>
as_tibble() |>
rename(
"time" = 1,
"key" = 2,
"value" = 3
) |>
mutate(key = key |> factor(levels = unique(key)))
# Plot
hbsc |>
ggplot(aes(
x = time,
y = value,
color = key
)) +
geom_line(size = 2) +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_color_statgl() +
coord_cartesian(ylim = c(0, 100)) +
labs(
title = sdg2$figs$fig4$title[language],
y = " ",
x = " ",
color = " ",
caption = sdg2$figs$fig4$cap[language]
) 