Anguniagaq 16: Eqqissineq, naapertuilluarneq sullissiviillu qajannaatsut
Inunnut ataasiakkaanut ulorianarluinnartumik pinerluttuliortarneq
FN 16.10.1 Toqutsinermi timimillu pinerliinermi unnerluussinerit
# Import
KRDAN1 <-
statgl_url("KRXAN1", lang = language) |>
statgl_fetch(
`type of offence` = c("4", "20", "19", "2", "21", "1"),
time = px_top(10),
.col_code = T
) |>
as_tibble() |>
drop_na()
# Plot
KRDAN1 |>
ggplot(aes(
x = time,
y = value,
fill = `type of offence`
)) +
geom_col() +
theme_statgl() +
scale_fill_statgl(reverse = T, guide = guide_legend(nrow = 3, reverse = T)) +
theme(plot.margin = margin(10, 10, 10, 10)) +
labs(
title = sdg16$figs$fig1$title[language],
x = " ",
y = colnames(KRDAN1)[1],
fill = " ",
caption = sdg16$figs$fig1$cap[language]
)
Periaaseq
Kinguaassiuutitigut kannguttaatsuliornerit
FN 16.2.3 Arnat angutillu 18-it 29-llu akornanni ukiullit 18-iliinnginnermi kinguaassiuutitigut pinerluttuliorfigineqarsimasut annertussusaat
# Import
SIF_raw <-
data.frame(overgreb = c(32.8, 32.8, 27.0),
tid = c("2005-2010", "2014", "2018")) %>%
as_tibble()
# Transform
SIF <-
SIF_raw %>%
rename(`Andel 18-29-årige, der har vøret udsat for seksuelle overgreb inden 18-årsalderen` = overgreb) %>%
gather(indikatorer, værdi, -tid)
# Plot
SIF_overgreb_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, accuracy = 1, big.mark = ".",
decimal.mark = ",")) +
theme_statgl() + scale_fill_statgl(reverse = TRUE) +
theme(legend.position = "None") +
labs(
title = sdg16$figs$fig2$title[language],
x = " ",
y = " ",
caption = sdg16$figs$fig2$cap[language]
)
SIF_overgreb_plot
Toqutsineq
FN 16.1.1 Piaarinaatsoornerunngitsumik toqutaasimasut amerlassusaat, suiaassuseq malillugu
# Import
SUDLDM3_raw <-
read_csv(
paste0("https://bank.stat.gl:443/sq/50013c7c-14d5-4d6a-96e0-df61cb3044f3", "?lang=", language),
locale = locale(encoding = "latin1")
)
# Transform
SUDLDM3 <-
SUDLDM3_raw %>%
rename(
"causes" = 1,
"sex" = 2,
"time" = 3,
"value" = 4
)
# Plot
SUDLDM3 %>%
ggplot(aes(
x = time,
y = value,
fill = sex
)) +
geom_col() +
theme_statgl() + scale_fill_statgl(reverse = TRUE) +
scale_y_continuous(breaks = c(0, 2, 4, 6, 8, 10)) +
labs(
title = SUDLDM3[[1]][1],
y = sdg16$figs$fig3$y_lab[language],
fill = " ",
x = " ",
caption = sdg16$figs$fig3$cap[language]
)
Toqqissisimaneq pillugu politiit misissuinerat
GS Toqqissisimaneq pillugu politiit misissuinerannit inernerit
# Import
police1_raw <-
data.frame(
Tryg = c(82.9, 81.6),
hverken = c(5.5, 7.3),
Utryg = c(10.1, 10.4),
ved_ikke = c(1.5, 0.7),
tid = c(2018 , 2019)
) %>%
as_tibble()
# Transform
police1 <-
police1_raw %>%
rename(`Hverken/eller` = hverken,
`Ved ikke/ ønsker ikke at svare` = ved_ikke) %>%
gather(svar, procent, -tid) %>%
mutate(tid = as.factor(tid))
# Plot
police1_plot <-
police1 %>%
ggplot(aes(x = svar,
y = procent,
fill = tid)) +
geom_col(position = "dodge2") +
expand_limits(y = 100) +
theme_statgl() + scale_fill_statgl() +
scale_y_continuous(labels = scales::percent_format(scale = 1, accuracy = 1, big.mark = ".",
decimal.mark = ",")) +
labs(
title = sdg16$figs$fig4$title[language],
x = " ",
y = " ",
fill = " ",
caption = sdg16$figs$fig4$cap[language]
)
police1_plot
Apeqqut: 1-7-imut eqqarsaatigigukku, tassani 1 isumaqarpoq ‘najugaqarfinni najugaqarnera eqqissisimalluinnartumik misigiffigaara’ 7-ilu ‘najugaqarfinni najugaqarneq toqqissisimananngilaq’, taava qanoq toqqissisimatigaat? Najugaqarfiit tassaavoq angerlarsimaffiit eqqaamiusilu. Titartakkami akissutit eqimattakkuutaarlugit takutinneqarput, 1-3-mik akisimasut tassaapput najugaqarfimminni toqqissisimallutik inuusut, 4-imik akisimasut tassaapput najugaqarfimminni eqqissisinngillat aamma toqqissisimannginnermik misigisimanngillat, 5-7-imillu akisimasut tassaapput najugaqarfimminni toqqissisimanngitsut.
Malugiuk: Kisitsisinik paasissutissiornermi nalornissutit isigissanngikkaani innuttaasut najukkaminni toqqissisimasut amerlassusaat taamaaginnarput.
# Import
police4_raw <-
data.frame(
Tryg = c(92.0, 86.8),
hverken = c(1.5, 4.7),
Utryg = c(4.4, 7.2),
ved_ikke = c(2.2, 1.4),
tid = c(2018 , 2019)
) %>%
as_tibble()
# Transform
police4 <-
police4_raw %>%
rename(`Hverken/eller` = hverken,
`Ved ikke/ ønsker ikke at svare` = ved_ikke) %>%
gather(svar, procent,-tid) %>%
mutate(tid = as.factor(tid))
# Plot
police4_plot <-
police4 %>%
ggplot(aes(x = svar,
y = procent,
fill = tid)) +
geom_col(position = "dodge2") +
theme_statgl() + scale_fill_statgl() +
expand_limits(y = 100) +
scale_y_continuous(labels = scales::percent_format(scale = 1, accuracy = 1, big.mark = ".",
decimal.mark = ",")) +
labs(
title = sdg16$figs$fig5$title[language],
x = " ",
y = " ",
fill = " ",
caption = sdg16$figs$fig5$cap[language]
)
police4_plot
Apeqqut: 1-7-imut eqqarsaatigigukku, tassani 1 isumaqarpoq ‘najugaqarfinni najugaqarnera eqqissisimalluinnartumik misigiffigaara’ 7-ilu ‘najugaqarfinni najugaqarneq toqqissisimananngilaq’, taava qanoq toqqissisimatigaat? Najugaqarfiit tassaavoq angerlarsimaffiit eqqaamiusilu. Titartakkami akissutit eqimattakkuutaarlugit takutinneqarput, 1-3-mik akisimasut tassaapput najugaqarfimminni toqqissisimallutik inuusut, 4-imik akisimasut tassaapput najugaqarfimminni eqqissisinngillat aamma toqqissisimannginnermik misigisimanngillat, 5-7-imillu akisimasut tassaapput najugaqarfimminni toqqissisimanngitsut.
Malugiuk: Kisitsisinik paasissutissiornermi nalornissutit isigissanngikkaani innuttaasut najukkaminni toqqissisimasut amerlassusaat ikilisimapput.
# Import
police5_raw <-
data.frame(
tillid = c(85.0, 89.3),
ikke_tillid = c(12.5, 7.7),
ved_ikke = c(2.5, 3.0),
tid = c(2018 , 2019)
) %>%
as_tibble()
# Transform
police5 <-
police5_raw %>%
rename(
`Tillid til politiet` = tillid,
`Ikke tillid til politiet` = ikke_tillid,
`Ved ikke/ ønsker ikke at svare` = ved_ikke
) %>%
gather(svar, procent,-tid) %>%
mutate(tid = as.factor(tid))
# Plot
police5_plot <-
police5 %>%
ggplot(aes(x = svar,
y = procent,
fill = tid)) +
geom_col(position = "dodge2") +
theme_statgl() + scale_fill_statgl() +
expand_limits(y = 100) +
scale_y_continuous(labels = scales::percent_format(scale = 1, accuracy = 1, big.mark = ".",
decimal.mark = ",")) +
labs(
title = sdg16$figs$fig6$title[language],
x = " ",
y = " ",
fill = " ",
caption = sdg16$figs$fig6$cap[language]
)
police5_plot
Apeqqut: Oqaaseqaammi uani isumaqataavit? Ikiorneqarnissannik pisariaqartitsissagaluaruma politiit tatigaakka. Titartakkami innuttaasut politiinut tatiginninnermut apeqqummut angersimasut kiisalu naameersimasut immikkoortinneqarput.
Maluigiuk: Kisitsisinik paasissutissiornermi nalornissutit isigissanngikkaani, innuttaasut politiinut tatiginninnerat annertunerulersimavoq. Sanilliussinermili ukiuni pineqartuni marlunni apeqqutip sammisani assigiinngitsuni apequtigineqarsimasinnaanera eqqumaffigineqassaaq.
Taasinerit procentinngorlugit
GS Taasinerit procentinngorlugit
# Import
SAXLANST_raw <-
statgl_url("SAXLANST", lang = language) %>%
statgl_fetch(
"constituencies" = c(0),
"votes cast" = c(16, 20),
.col_code = TRUE
) %>%
as_tibble()
# Transform
SAXLANST <-
SAXLANST_raw %>%
separate(time, c("day", "month", "year")) %>%
select(-c("day", "month")) %>%
mutate(
year = year %>% as.numeric(),
year = year + 1900,
plus = case_when(
year < 1950 ~ 100,
year > 1950 ~ 0),
year = year + plus,
`votes cast` = `votes cast` %>% fct_reorder(value, sum)
) %>%
select(-ncol(.)) %>%
spread(3, 4) %>%
rename(
valid = 3,
total = 4
) %>%
mutate(
vote = valid / total * 100,
mean = mean(vote)
)
# Plot
SAXLANST %>%
ggplot(aes(
x = year,
y = vote
)) +
geom_point(size = 2) +
geom_segment(aes(
x = year,
xend = year,
y = 0,
yend = vote
)) +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
accuracy = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_fill_statgl() +
expand_limits(y = 0) +
expand_limits(y = 100) +
geom_hline(
size = 15,
alpha = 0.1,
color = "green",
yintercept = SAXLANST[["mean"]][1]
) +
labs(
title = sdg16$figs$fig9$title[language],
subtitle = SAXLANST[[2]][1],
y = " ",
x = " ",
caption = sdg16$figs$fig9$cap[language]
)
# Import
SAXKOMST_raw <-
statgl_url("SAXKOMST", lang = language) %>%
statgl_fetch(
municipality = c(0),
"votes cast" = c(15, 19),
.col_code = TRUE
) %>%
as_tibble()
# Transform
SAXKOMST <-
SAXKOMST_raw %>%
separate(time, c("day", "month", "year")) %>%
select(-c("day", "month")) %>%
mutate(
year = year %>% as.numeric(),
year = year + 1900,
plus = case_when(
year < 1950 ~ 100,
year > 1950 ~ 0),
year = year + plus,
`votes cast` = `votes cast` %>% fct_reorder(value, sum)
) %>%
select(-ncol(.)) %>%
spread(3, 4) %>%
rename(
valid = 3,
total = 4
) %>%
mutate(
vote = valid / total * 100,
mean = mean(vote)
)
# Plot
SAXKOMST %>%
ggplot(aes(
x = year,
y = vote
)) +
geom_point(size = 2) +
geom_segment(aes(
x = year,
xend = year,
y = 0,
yend = vote
)) +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
accuracy = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_fill_statgl() +
expand_limits(y = 0) +
expand_limits(y = 100) +
geom_hline(
size = 15,
alpha = 0.1,
color = "red",
yintercept = SAXKOMST[["mean"]][1],
) +
labs(
title = sdg16$figs$fig8$title[language],
subtitle = SAXKOMST[[2]][1],
y = " ",
x = " ",
caption = sdg16$figs$fig8$cap[language]
)
# Import
SAXFOLK_raw <-
statgl_url("SAXFOLK", lang = language) %>%
statgl_fetch(
municipality = c(0),
"votes cast" = c(12, 16),
.col_code = TRUE
) %>%
as_tibble()
# Transform
SAXFOLK <-
SAXFOLK_raw %>%
separate(time, c("day", "month", "year")) %>%
select(-c("day", "month")) %>%
mutate(
year = year %>% as.numeric(),
year = year + 1900,
plus = case_when(
year < 1980 ~ 100,
year > 1980 ~ 0
),
year = year + plus,
`votes cast` = `votes cast` %>% fct_reorder(value, sum)) %>%
select(-ncol(.)) %>%
spread(3, 4) %>%
rename(
valid = 3,
total = 4
) %>%
mutate(vote = valid / total * 100,
mean = mean(vote))
# Plot
SAXFOLK %>%
ggplot(aes(
x = year,
y = vote
)) +
geom_point(size = 2) +
geom_segment(aes(
x = year,
xend = year,
y = 0,
yend = vote
)) +
scale_y_continuous(labels = scales::percent_format(
scale = 1,
accuracy = 1,
big.mark = ".",
decimal.mark = ","
)) +
theme_statgl() +
scale_fill_statgl() +
expand_limits(y = 0) +
expand_limits(y = 100) +
geom_hline(
size = 15,
alpha = 0.1,
color = "blue",
yintercept = SAXFOLK[["mean"]][1]
) +
labs(
title = sdg16$figs$fig7$title[language],
subtitle = SAXFOLK[[2]][1],
y = " ",
x = " ",
caption = sdg16$figs$fig7$cap[language]
)