Goal 2: Zero Hunger
Food insecurity
FN 2.1.2 Prevalence of food insecurity
# 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]
)
Population survey in Greenland
vec <- 1:2
names(vec) <- c(" ", sdg2$figs$fig1$y_lab[language])
food_insecurity |>
mutate(value = value * 100) |>
rename(vec) |>
statgl_table(year_col = " ")| Food insecurity (%) | |
|---|---|
| 2014 | 11,8 |
| 2018 | 8,2 |
Explanation
Food security means that you have access to nutritious foods. The National Institute of Public Health measures the food insecurity in their regular surveys of public health in Greenland. Food insecurity is defined as affirmative answers to three questions about lack food and lack of money to buy food.
Diet
GS Indicators regarding diet
# Import
SIF_raw <-
data.frame(
frugt_daglig = c(37.2, 44.9, 38.8),
grøntsag_daglig = c(23.9, 30.4, 29.6),
fisk_ugen = c(56.0, 50.2, 42.8),
saft_sodavand = c(24.4, 32.1, 43.9),
kød_havpattedyr = c(35.9, 35.7, 33.3),
tid = c("2005-2010", "2014", "2018")
) |>
as_tibble()
vec <- 1:5
names(vec) <-
c(
sdg2$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$cap[language]
)
Population survey in Greenland
vec <- 1
names(vec) <- sdg2$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")| Indicators | 2005-2010 | 2014 | 2018 |
|---|---|---|---|
| Weekly fish consumption | 56,0 | 50,2 | 42,8 |
| Daily fruit consumption | 37,2 | 44,9 | 38,8 |
| Consumption of marine mammals 1-3 times a week | 35,9 | 35,7 | 33,3 |
| Daily juice or soda consumption | 24,4 | 32,1 | 43,9 |
| Daily vegetable consumption | 23,9 | 30,4 | 29,6 |
| * Percentage share |
# 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$cap[language]
)
Population survey in Greenland
vec <- 1
names(vec) <- sdg2$figs$fig3$cols$col1[language]
# Import
SIF |>
spread(1, 3) |>
rename(vec) |>
statgl_table()| Proportion of Body Mass Index greater or equal to 30 | 2005-2010 | 2014 | 2018 |
|---|---|---|---|
| Proportion of Body Mass Index greater or equal to 30 | 22,9 | 27,3 | 27,8 |
Going to bed hungry
GS Going to bed hungry
key1 <- sdg2$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]
) 