sdg2020

Delmål 8: Anstændige jobs og økonomisk vækst

BNP per indbygger

FN 8.1.1 Årlig realvækst i BNP pr. indbygger

# Import
NRX10_raw <- 
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units     = "K",
    account   = "02",
    time       = px_all(),
    .col_code = TRUE
               ) %>% 
  as_tibble()

# transform 
NRX10 <- 
  NRX10_raw %>% 
  mutate(time = time %>% as.numeric()) %>% 
  mutate(value = (value - lag(value)) / lag(value))


NRX10 %>% 
  ggplot(aes(
    x = time,
    y = value,
    fill = account
  )) +
  geom_col() +
  scale_fill_statgl() + 
  theme_statgl() +
  theme(legend.position = "none") +
  scale_y_continuous(labels = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig1$title[language],
    subtitle = NRX10[[1]][1],
    x        = " ",
    y        = sdg8$fig$fig1$y_lab[language],
    fill     = " ",
    caption  = sdg8$fig$fig1$cap[language]
  )

Statistikbanken, BNP

Statistikbanken, beskæftigede

tab <- 
  NRX10 %>% 
  mutate(value = value*100) %>% 
  filter(time >= max(time) - 5) %>% 
  arrange(desc(time)) %>% 
  select(-account) %>% 
  mutate(time = time %>% as.character())


if(language != "en"){
  
  table <- 
    tab %>% 
    select(-units) %>% 
    mutate(value = value %>% round(1)) %>% 
    rename(" " = 1, "Realvækst" = value) %>% 
    statgl_table() %>% 
    pack_rows(index = tab[[1]] %>% table()) %>% 
    add_footnote(sdg8$fig$fig1$foot1[language], notation = "symbol")
  
} else {
  
   table <- 
    tab %>% 
    select(-units) %>% 
    mutate(value = value %>% round(1)) %>% 
    rename(" " = 1, "Real growth rate" = value) %>% 
    statgl_table() %>% 
    pack_rows(index = tab[[1]] %>% table()) %>% 
    add_footnote(sdg8$fig$fig1$foot1[language], notation = "symbol")

}


table

	Realvækst
2010-priser, kædede værdier
2023	0,8
2022	1,7
2021	1,0
2020	0,1
2019	2,6
2018	0,6
^* Realvækst i procent, BNP per indbygger

Beskæftigelse

GS Beskæftigelsesgrad i forhold til samlet befolkning

ARXBFB05_raw <-
  statgl_url("ARXBFB05", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    komdist   = px_all(),
    opg_var   = "H",
    .col_code = T
  ) |> 
  as_tibble()


# Transform
ARXBFB05 <- 
  ARXBFB05_raw %>% 
  mutate(aar = aar %>% make_date(),
         komdist = komdist %>% factor(levels = unique(komdist)))

# Plot
ARXBFB05 %>% 
  filter(komdist == ARXBFB05[[1]][1]) %>% 
  ggplot(aes(
    x     = aar,
    y     = value,
    color = komdist
    )) +
  geom_line(linewidth = 2) +
  facet_wrap(~ komdist, scales = "free") +
    scale_y_continuous(labels = scales::percent_format(
      scale      = 1, 
      accuracy   = 0.1, 
      big.mark   = ".",
      decimal.mark = ","
      )) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg8$fig$fig2$title[language],
    subtitle = ARXBFB05[[1]][1],
    x        = " ",
    y        = " ",
    caption  = sdg8$fig$fig2$cap[language]
  )

Statistikbanken

Metode

# Transform
ARXBFB05 <- 
  ARXBFB05_raw %>% 
  filter(
    komdist == ARXBFB05_raw[[1]][1],
    aar     >= year(Sys.time()) - 7
    ) %>% 
  arrange(desc(aar))

vec        <- 1:2
names(vec) <- c(" ", sdg8$fig$fig2$cols$col2[language])

# Table
ARXBFB05 %>% 
  select(2, ncol(.)) %>% 
  rename(vec) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXBFB05[[3]] %>% table()) %>% 
  add_footnote(ARXBFB05[[1]][1], notation = "symbol")

	Beskæftigelsesgrad
Beskæftigelse i gennemsnit pr. måned i forhold til samlet befolkning (pct.)
2023	67,6
2022	67,2
2021	66,6
2020	66,0
2019	66,6
2018	66,1
^* Hele landet (inkl. uden for kommunal inddeling)

Ledighed

FN 8.5.2 Ledighedsgrad fordelt på alder

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    alder_grp = "1",
    opg_var   = "P",
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
ARXLED3 <-
  ARXLED3_raw %>% 
  mutate(aar = aar %>% make_date()) %>% 
  unite(combi, 1, 2, 4, sep = ", ")

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar,
    y     = value,
    color = combi
    )) +
  geom_line(size = 2) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 0.1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig3$title[language],
    subtitle = ARXLED3[[1]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg8$fig$fig3$cap[language]
  )

Statistikbanken

Metode

# Transform

ARXLED3 %>% 
  mutate(aar = aar %>% year()) %>% 
  filter(aar > year(Sys.time()) - 8) %>% 
  spread(aar, value) %>% 
  mutate(combi = sdg8$fig$fig3$cols$ncol[language][[1]]) %>% 
  rename(" " = 1) %>% 
  statgl_table()

	2018	2019	2020	2021	2022	2023
Ledighedsprocent	5	4,3	4,5	3,7	3,2	2,9

ARXLED4_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    opg_var   = "P",
    alder_grp = px_all(),
    .col_code = T
    ) %>% 
  as_tibble()

# Transform
ARXLED4 <- 
  ARXLED4_raw %>% 
  filter(alder_grp != ARXLED4_raw[[2]][1]) %>% 
  arrange(desc(aar)) %>% 
  mutate(alder_grp = alder_grp %>% factor(levels = unique(alder_grp)),
         aar = aar %>% make_date()) %>% 
  unite(combi, 1, 4, sep = ", ")

# Plot
ARXLED4 %>% 
  ggplot(aes(
    x     = aar, 
    y     = value,
    color = alder_grp
    )) +
  geom_line(linewidth = 1.5) +
  theme_statgl() + 
  scale_color_statgl() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig4$title[language],
    subtitle = ARXLED4[[1]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg8$fig$fig4$cap[language]
  )

Statistikbanken

Metode

# Transform
ARXLED4 <- 
  ARXLED4_raw %>% 
  filter(
    alder_grp != ARXLED4_raw[[2]][1],
    aar >= year(Sys.time()) - 6
    ) %>% 
  arrange(desc(aar)) %>% 
  mutate(
    alder_grp = alder_grp %>% factor(levels = unique(alder_grp)),
    aar = aar %>% factor(levels = unique(aar))
    ) %>% 
  unite(combi, 1, 2, sep = ", ") %>% 
  spread(3, ncol(.))

vec        <- ARXLED4[[2]] %>% length()
names(vec) <- sdg8$fig$fig4$index[language]


# Table
ARXLED4 %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = vec) %>% 
  pack_rows(index = table(ARXLED4$combi)) |> 
  add_footnote(ARXLED4_raw[[1]][1], notation = "symbol")

	Ledighedsprocent i gennemsnit pr. måned
Ledighedsprocent
Hele landet, 18-19 år
2023	5,3
2022	5,0
2021	6,8
2020	8,5
2019	7,6
Hele landet, 20-24 år
2023	3,7
2022	4,2
2021	4,8
2020	6,1
2019	6,2
Hele landet, 25-29 år
2023	2,6
2022	3,1
2021	3,2
2020	4,5
2019	4,7
Hele landet, 30-34 år
2023	2,8
2022	3,2
2021	3,3
2020	4,4
2019	4,0
Hele landet, 35-39 år
2023	2,4
2022	2,8
2021	3,2
2020	4,2
2019	4,0
Hele landet, 40-44 år
2023	2,8
2022	2,6
2021	3,4
2020	3,9
2019	3,7
Hele landet, 45-49 år
2023	2,2
2022	2,4
2021	2,9
2020	3,9
2019	4,0
Hele landet, 50-54 år
2023	2,7
2022	3,4
2021	4,0
2020	4,4
2019	3,9
Hele landet, 55-59 år
2023	3,4
2022	3,2
2021	3,9
2020	4,3
2019	3,7
Hele landet, 60 år-pensionalderen
2023	3,4
2022	3,5
2021	3,6
2020	4,2
2019	4,2
^* Hele landet

GS Ledighedsprocent efter bosted

# Import

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR40/ARXLED3.px")

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    alder_grp = "1",
    opg_var   = "P",
    bybygd    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
ARXLED3 <- 
  ARXLED3_raw %>% 
  mutate(aar = aar %>% make_date())

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar,
    y     = value, 
    color = bybygd
    )) +
  geom_line(linewidth = 2) +
  theme_statgl() + 
  scale_color_statgl() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 1, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  labs(
    title    = sdg8$fig$fig5$title[language],
    subtitle = ARXLED4[[2]][1],
    x        = " ",
    y        = " ",
    color    = " ",
    caption  = sdg8$fig$fig5$cap[language]
    )

Statistikbanken

Metode

# Transform
ARXLED3 <- 
  ARXLED3_raw %>% 
  filter(aar >= year(Sys.time()) - 6) %>% 
  arrange(aar) %>% 
  mutate(aar = aar %>% factor(levels = unique(aar))) %>% 
  unite(combi, distrikt, alder_grp, sep = ", ") %>% 
  spread(1, ncol(.))

vec        <- ARXLED3[[1]] %>% length()
names(vec) <- sdg8$fig$fig5$index[language]

# Table
ARXLED3 %>% 
  select(-c(1, 3)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(ARXLED3[[1]][1], notation = "symbol") %>% 
  row_spec(1, bold = TRUE) %>% 
  pack_rows(index = vec) |> 
  pack_rows(index = table(ARXLED3$bybygd))

	Hele landet, Alle
Ledighedsprocent
Alle
2019	4,3
2020	4,5
2021	3,7
2022	3,2
2023	2,9
Byer
2019	4,1
2020	4,3
2021	3,4
2022	3,0
2023	2,7
Bygder m.m.
2019	6,1
2020	6,5
2021	5,4
2022	4,8
2023	4,7
^* Alle

Forskning og udvikling

GS Udgifter til forskning og udvikling som andel af BNP

# Import
NRX09_raw <-
  statgl_url("NRX09", lang = language) %>% 
  statgl_fetch(
    units   = "L",
    account = px_all(),
    time    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

NRX09_raw <- NRX09_raw %>% filter(account %in% unique(NRX09_raw %>% pull(account))[7])

NRX10_raw <-
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units    = "L",
    account  = "01",
    time      = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
RD_GDP <- 
  NRX10_raw %>% 
  select(3:4) %>% 
  rename(
    "time" = 1,
    "GDP"  = 2
    ) %>% 
  left_join(
    NRX09_raw %>% 
      mutate(account = account %>% str_remove_all("[:digit:]") %>% trimws()) %>% 
      select(3:4) %>% 
      rename("RD" = 2)
    ) %>% 
  mutate(pct = RD / GDP) %>% 
  filter(pct != "NA")

# Plot
RD_GDP %>% 
  ggplot(aes(
    x    = time,
    y    = pct,
    fill = time
    )) +
  geom_col() +
  scale_y_continuous(labels  = scales::percent_format(
    scale        = 100, 
    accuracy     = 1, 
    big.mark     = ".",
    decimal.mark = ","
    )) +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg8$fig$fig6$title[language],
    subtitle = NRX09_raw[[1]][1],
    x        = " ",
    y        = sdg8$fig$fig6$y_lab[language],
    caption  = sdg8$fig$fig6$cap[language]
  )

Statistikbanken, investeringer (forskning og udvikling)

Statistikbanken, BNP

# Transform
tab <- 
  RD_GDP %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 7) %>% 
  mutate(
    time = time %>% fct_inorder(),
    pct  = pct * 100,
    pct  = pct %>% round(1),
    var  = sdg8$fig$fig6$cols$col1[language]
    ) %>% 
  select(-(2:3)) %>% 
  spread(1, 2)

# Table
tab %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table(NRX09_raw[[1]][1])) %>% 
  add_footnote(sdg8$fig$fig6$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023
Løbende priser
Udgifter til forskning og udvikling	1,8	1,8	1,6	1,5	1,9	2,1
^* Procentvis andel af BNP

Udenrigspassagerer

GS Antal udenrigspassagerer på rutefly

# Import
TUXUPAX_raw <-
  statgl_url("TUXUPAX", lang = language) %>%
  statgl_fetch(airport   = 0,
               month     = 0,
               time      = px_all(),
               .col_code = TRUE) %>% 
  as_tibble()

# Transform
TUXUPAX <-
  TUXUPAX_raw %>% 
  mutate(value = value / 1000,
         time = time %>% make_date())

# Plot
TUXUPAX %>% 
  ggplot(aes(
    x    = time,
    y    = value, 
    fill = airport
    )) +
  geom_col() +
  theme_statgl() + 
  scale_fill_statgl() +
  theme(plot.margin = margin(10, 10, 10, 10),
        legend.position = "none") +
  labs(
    title = sdg8$fig$fig7$title[language],
    x = " ",
    y = sdg8$fig$fig7$y_lab[language],
    caption = sdg8$fig$fig7$cap[language]
  )

Statistikbanken

Metode

# Transform
TUXUPAX <-
  TUXUPAX_raw %>% 
  mutate(value = value) %>% 
  filter(time >= year(Sys.time()) - 7,
         value != "NA") %>% 
  arrange(desc(time))

vec <- 1:2
names(vec) <- c(" ",  sdg8$fig$fig7$cols$col2[language])


# Table
TUXUPAX %>% 
  select(-(1:2)) %>% 
  rename(vec) %>% 
  statgl_table() %>% 
  add_footnote(sdg8$fig$fig7$foot[language],
               notation = "symbol")

	Udenrigspassagerer på rutefly
2024	96.817
2023	96.362
2022	85.484
2021	39.293
2020	30.785
2019	86.989
2018	85.306
^* Antal personer.

Ungemålgruppe

GS Ungemålgruppe

UDXUMG3_raw <- 
  statgl_url("UDXUMG3", lang = language) %>%
  statgl_fetch(
    alder        = px_all(),
    registrering = 5:7,
    aar          = px_all(),
    .col_code    = TRUE
  ) %>% 
  as_tibble()

#sdg8 <- read_yaml("S:/STATGS/VM/SDG_dokument/input/text/txt_08.yml")

lab_vec        <- 1:5
names(lab_vec) <- 
  c(
    "age",
    "time",
    sdg8$fig$fig8$tags$tag1[language] %>% unlist(),
    sdg8$fig$fig8$tags$tag2[language] %>% unlist(),
    sdg8$fig$fig8$tags$tag3[language] %>% unlist()
)


UDXUMG3 <-
  UDXUMG3_raw %>% 
  rename("status" = registrering, "age" = alder, "time" = aar) |> 
  mutate(status = status %>% fct_inorder()) %>% 
  spread(status, value) %>% 
  rename(
    "age"   = 1,
    "time"  = 2,
    "work"  = 3,
    "none"  = 4,
    "total" = 5
  ) %>% 
  mutate(edu = total - work - none) %>% 
  select(-total) %>% 
  rename(lab_vec) %>% 
  gather(status, value, -c(age, time)) %>% 
  mutate(time = time %>% as.numeric()) %>% 
  filter(time %in% c(min(time), mean(time), max(time))) %>%
  mutate(time = time %>% as.character() %>% fct_rev())
  

UDXUMG3 %>%
  ggplot(aes(
    x    = parse_number(age),
    y    = value,
    fill = status
  )) +
  geom_col(position = "fill") +
  facet_wrap(~ time) + 
  scale_x_continuous(labels = function(x) round(x)) +
  scale_y_continuous(labels = scales:: percent) + 
  scale_fill_statgl(reverse = TRUE) +
  theme_statgl() + 
  labs(
    title    = sdg8$fig$fig8$title[language],
    subtitle = sdg8$fig$fig8$sub[language],
    x        = sdg8$fig$fig8$x_lab[language],
    y        = " ",
    color    = colnames(UDXUMG3_raw)[2] %>% str_to_title(),
    caption  = sdg8$fig$fig8$cap[language]
)

Statistikbanken

Metode

tab <- 
  UDXUMG3 %>% 
  mutate(age = age %>% fct_inorder()) %>% 
  arrange(age, time, status) %>% 
  unite(combi, time, status, sep = ",") %>% 
  mutate(combi = combi %>% fct_inorder()) %>% 
  spread(combi, value)

vec      <- tab %>% select(-1) %>% colnames() %>% str_split(",") %>% unlist()
head_vec <- vec[c(TRUE, FALSE)] %>% table() %>% rev()
col_vec  <- vec[c(FALSE, TRUE)]

tab %>% 
  statgl_table(col.names = c(" ", col_vec)) %>% 
  add_header_above(c(" ", head_vec)) %>% 
  add_footnote(sdg8$fig$fig8$foot[language], notation = "symbol")

	2023			2016
age	2023,Hverken	2023,I beskæftigelse	2023,Under uddannelse	2016,Hverken	2016,I beskæftigelse	2016,Under uddannelse
16 år	310	86	310	295	70	390
17 år	293	114	304	362	127	275
18 år	265	212	272	325	176	298
19 år	206	296	229	318	300	274
20 år	221	338	142	264	374	171
21 år	216	403	138	242	389	205
22 år	206	376	158	233	411	219
23 år	174	362	150	221	472	223
24 år	181	424	149	197	476	227
25 år	163	437	188	186	487	230
^* Antal personer