sdg2020

Anguniagaq 16: Eqqissineq, naapertuilluarneq sullissiviillu qajannaatsut

Inunnut ataasiakkaanut ulorianarluinnartumik pinerluttuliortarneq

FN 16.10.1 Toqutsinermi timimillu pinerliinermi unnerluussinerit

# Import
KRDAN_raw <-
  read_csv(paste0("https://bank.stat.gl:443/sq/004c1708-6db1-4553-a019-41e76ba8a8f6", "?lang=", language),
           locale = locale(encoding = "latin1"))

# Transform
KRDAN <-
  KRDAN_raw %>% 
  rename(
    "offence" = 1,
    "time"    = 2,
    "value"   = 3
  ) %>% 
  mutate(offence = offence %>% str_remove_all("[:digit:]|\\-") %>% trimws(),
         offence = offence %>% fct_reorder(value))

# Plot
KRDAN %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = offence
  )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(nrow = 3, reverse = TRUE)) +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  labs(
    title = sdg16$figs$fig1$title[language],
    x = " ",
    y = colnames(KRDAN_raw)[3],
    fill = " ",
    caption = sdg16$figs$fig1$cap[language]
  )

Kisitsisaataasivik

Periaaseq

# Transform
KRDAN <- 
  KRDAN_raw %>% 
  rename(
    "offence" = 1,
    "time"    = 2,
    "value"   = 3
  ) %>% 
  mutate(offence = offence %>% str_remove_all("[:digit:]|\\-") %>% trimws(),
         offence = offence %>% fct_reorder(value)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time)),
         var = colnames(KRDAN_raw)[3]) %>% 
  spread(2, 3)

# Table
KRDAN %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(KRDAN[[2]]))

	2019	2020	2021	2022
Inatsisinik unioqqutitsinerit unnerluutigineqartut
Toqutsineq	6	7	3	10
Inatsisit unioqqutillugit pinngitsaaliineq kiffaanngissusiiaanerlu	6	13	15	12
Toqutseriarneq	8	8	10	14
Toqutsilluni timimillu pinerliilluni pinerluttuliornerit allat	19	20	39	28
Angerlarsimafimmi eqqissiviilliortitsineq	42	51	44	53
Inuunermik, peqqissutsimik, atugarissaarnermilu ajoqusiinissamik siorasaarineq	151	188	163	198
Nakuuserneq	805	981	995	880

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

Befolkningsundersøgelse

# 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 været udsat for seksuelle overgreb inden 18-årsalderen` = overgreb) %>%
  gather(indikatorer, værdi, -tid)

# Table
SIF_overgreb_table <-
  SIF %>%
  mutate(værdi = format(værdi, digits = 3, decimal.mark = ",")) %>%
  spread(tid, værdi) %>%
  set_names(str_to_title(names(.))) %>%
  kable(align = "lrrrrrrrrrrrrrrrr") %>%
  kable_styling(bootstrap_options = c("condensed", "reactive"),
                full_width = FALSE) %>%
  add_footnote(
    sdg16$figs$fig2$foot[language],
    notation = "symbol"
  )

SIF_overgreb_table

Indikatorer	2005-2010	2014	2018
Andel 18-29-årige, der har været udsat for seksuelle overgreb inden 18-årsalderen	32,8	32,8	27,0
^* 18-iniit 29-nut ukiullit 18-iliinnginnerminni kinguaassiuutitigut kannguttaatsuliorfigineqarsimasut annertussusaat procentinngorlugit.

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]
  )

Kisitsisaataasivik

# Transform
SUDLDM3 <- 
  SUDLDM3_raw %>% 
  rename(
    "causes" = 1,
    "sex"    = 2,
    "time"   = 3,
    "value"  = 4
  ) %>% 
  spread(2, 4) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  arrange(desc(time))

SUDLDM3 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table(year_col = " ") %>% 
  pack_rows(index = table(SUDLDM3[[1]])) %>% 
  add_footnote(sdg16$figs$fig3$foot[language], notation = "symbol")

	Angutit	Arnat
Toqutsinerit/saassussinerit
2022	5	1
2021	2	1
2020	2	3
2019	3	1
^* Inuit amerlassusaat

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

Toqqissisimaneq pillugu misissuineq

# 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)

# Table
police1_table <-
  police1 %>%
  mutate(procent = format(procent, digits = 3, decimal.mark = ",")) %>%
  spread(tid, procent) %>%
  set_names(str_to_title(names(.))) %>%
  kable(align = "lrr") %>%
  kable_styling(bootstrap_options = c("condensed", "reactive"),
                full_width = TRUE) %>%
  add_footnote(sdg16$figs$fig4$foot[language],
               notation = "symbol")

police1_table

Svar	2018	2019
Hverken/eller	5,5	7,3
Tryg	82,9	81,6
Utryg	10,1	10,4
Ved ikke/ ønsker ikke at svare	1,5	0,7
^* Procentinngorlugu, Kalaallit Nunaanni innuttaasut toqqissisimanerat.

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

Toqqissisimaneq pillugu misissuineq

# 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)

# Table
police4_table <-
  police4 %>%
  mutate(procent = format(procent, digits = 3, decimal.mark = ",")) %>%
  spread(tid, procent) %>%
  set_names(str_to_title(names(.))) %>%
  kable(align = "lrr") %>%
  kable_styling(bootstrap_options = c("condensed", "reactive"),
                full_width = TRUE) %>%
  add_footnote(
    sdg16$figs$fig5$foot[language],
    notation = "symbol"
  )

police4_table

Svar	2018	2019
Hverken/eller	1,5	4,7
Tryg	92,0	86,8
Utryg	4,4	7,2
Ved ikke/ ønsker ikke at svare	2,2	1,4
^* Procentinngorlugu, inoqarfinni politeeqarfeqanngitsuni innuttaasut toqqissisimanerat.

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

Toqqissisimaneq pillugu misissuineq

# 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)

# Table
police5_table <-
  police5 %>%
  mutate(procent = format(procent, digits = 3, decimal.mark = ",")) %>%
  spread(tid, procent) %>%
  set_names(str_to_title(names(.))) %>%
  kable(align = "lrr") %>%
  kable_styling(bootstrap_options = c("condensed", "reactive"),
                full_width = TRUE) %>%
  add_footnote(sdg16$figs$fig6$foot[language],
               notation = "symbol")

police5_table

Svar	2018	2019
Ikke tillid til politiet	12,5	7,7
Tillid til politiet	85,0	89,3
Ved ikke/ ønsker ikke at svare	2,5	3,0
^* Procentinngorlugu, Kalaallit Nunaanni innuttaasut politiinut tatiginninnerat.

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]
    )

Kisitsisaataasivik

# Table
SAXLANST %>% 
  select(year, vote) %>% 
  #arrange(desc(year)) %>% 
  filter(year >= year(Sys.time()) - 20) %>% 
  mutate(
    year = year %>% factor(levels = unique(year)),
    vote = vote %>% round(1)
  ) %>% 
  spread(year, vote) %>% 
  statgl_table() %>% 
  add_footnote(sdg16$figs$fig9$foot[language], notation = "symbol")

2005	2009	2013	2014	2018	2021
74,2	72,6	73,3	72,2	71,1	64,4
^* Taasinerit procentinngorlugit, Inatsisartunut qinersineq

# 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]
    )

Kisitsisaataasivik

# Table
SAXKOMST %>% 
  select(year, vote) %>% 
  #arrange(desc(year)) %>% 
  filter(year >= year(Sys.time()) - 20) %>% 
  mutate(
    year = year %>% factor(levels = unique(year)),
    vote = vote %>% round(1)
  ) %>% 
  spread(year, vote) %>% 
  statgl_table() %>% 
  add_footnote(sdg16$figs$fig8$foot[language], notation = "symbol")

2005	2008	2013	2017	2021
66,4	61,2	57,6	60,1	62,6
^* Taasinerit procentinngorlugit, Kommunimut qinersineq

# 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]
    )

Kisitsisaataasivik

# Table
SAXFOLK %>% 
  select(year, vote) %>% 
  #arrange(desc(year)) %>% 
  filter(year >= year(Sys.time()) - 20) %>% 
  mutate(
    year = year %>% factor(levels = unique(year)),
    vote = vote %>% round(1)
    ) %>% 
  spread(year, vote) %>% 
  statgl_table() %>% 
  add_footnote(sdg16$figs$fig7$foot[language], notation = "symbol")

2005	2007	2011	2015	2019	2022
58,6	63,2	55	49,2	48,4	46,6
^* Taasinerit procentinngorlugit, Folketing-imut qinersineq