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",
    Aar       = px_all(),
    .col_code = TRUE
               ) %>% 
  as_tibble()

# transform 
NRX10 <- 
  NRX10_raw %>% 
  rename("time" = "Aar") %>% 
  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
2021	0,7
2020	0,0
2019	2,6
2018	0,6
2017	0,0
2016	4,9
^* Realvækst i procent, BNP per indbygger

Beskæftigelse

GS Beskæftigelsesgrad i forhold til samlet befolkning

# Import

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR30/ARXBFB05.px")

ARXBFB05_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    municipality         = px_all(),
    "inventory variable" = "H",
    .col_code            = T
  ) |> 
  as_tibble()


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

# Plot
ARXBFB05 %>% 
  filter(municipality == ARXBFB05[[1]][1]) %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = municipality
    )) +
  geom_line(size = 2) +
  facet_wrap(~ municipality, 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(
    municipality == ARXBFB05_raw[[1]][1],
    time         >= year(Sys.time()) - 7
    ) %>% 
  arrange(desc(time))

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.)
2022	66,9
2021	66,7
2020	66,0
2019	66,6
2018	66,1
2017	65,3
^* Hele landet (inkl. uden for kommunal inddeling)

Ledighed

FN 8.5.2 Ledighedsgrad fordelt på alder

# Import

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

ARXLED3_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    district             = "AA",
    age                  = "1",
    "inventory variable" = "P",
    .col_code            = TRUE
    ) %>% 
  as_tibble()

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

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

	2017	2018	2019	2020	2021	2022
Ledighedsprocent	5,9	5	4,3	4,5	3,7	3,2

# Import

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

ARXLED4_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    district             = "AA",
    "inventory variable" = "P",
    age                  = px_all(),
    .col_code            = T
    ) %>% 
  as_tibble()

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

# Plot
ARXLED4 %>% 
  ggplot(aes(
    x     = time, 
    y     = value,
    color = age
    )) +
  geom_line(size = 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(
    age != ARXLED4_raw[[2]][1],
    time >= year(Sys.time()) - 6
    ) %>% 
  arrange(desc(time)) %>% 
  mutate(
    age = age %>% factor(levels = unique(age)),
    time = time %>% factor(levels = unique(time))
    ) %>% 
  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
2022	5,0
2021	6,8
2020	8,5
2019	7,6
2018	12,2
Hele landet, 20-24 år
2022	4,2
2021	4,8
2020	6,1
2019	6,2
2018	7,8
Hele landet, 25-29 år
2022	3,1
2021	3,2
2020	4,5
2019	4,7
2018	5,3
Hele landet, 30-34 år
2022	3,2
2021	3,3
2020	4,4
2019	4,0
2018	4,6
Hele landet, 35-39 år
2022	2,8
2021	3,2
2020	4,2
2019	4,0
2018	4,6
Hele landet, 40-44 år
2022	2,7
2021	3,4
2020	3,9
2019	3,7
2018	4,0
Hele landet, 45-49 år
2022	2,4
2021	2,9
2020	3,9
2019	4,0
2018	4,3
Hele landet, 50-54 år
2022	3,4
2021	4,0
2020	4,4
2019	3,9
2018	4,3
Hele landet, 55-59 år
2022	3,2
2021	3,9
2020	4,3
2019	3,7
2018	4,3
Hele landet, 60 år-pensionalderen
2022	3,5
2021	3,6
2020	4,2
2019	4,2
2018	4,6
^* Hele landet

GS Ledighedsprocent efter bosted

# Import

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

ARXLED3_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    district             = "AA",
    age                  = "1",
    "inventory variable" = "P",
    "place of residence" = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()

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

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = time,
    y     = value, 
    color = `place of residence`
    )) +
  geom_line(size = 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(time >= year(Sys.time()) - 6) %>% 
  arrange(time) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  unite(combi, district, age, 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$`place of residence`))

	Hele landet, Alle
Ledighedsprocent
Alle
2018	5,0
2019	4,3
2020	4,5
2021	3,7
2022	3,2
Byer
2018	4,8
2019	4,1
2020	4,3
2021	3,4
2022	3,0
Bygder m.m.
2018	6,9
2019	6,1
2020	6,5
2021	5,3
2022	4,9
^* 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",
    "Aar"      = 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")

	2017	2018	2019	2020	2021
Løbende priser
Udgifter til forskning og udvikling	2,1	1,8	1,8	1,6	1,6
^* 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
2023	96.362
2022	85.484
2021	39.293
2020	30.785
2019	86.989
2018	85.306
2017	83.487
^* 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")

	2022			2019			2016
age	2022,Hverken	2022,I beskæftigelse	2022,Under uddannelse	2019,Hverken	2019,I beskæftigelse	2019,Under uddannelse	2016,Hverken	2016,I beskæftigelse	2016,Under uddannelse
16 år	292	68	347	317	84	314	295	70	390
17 år	325	136	299	358	141	290	362	127	275
18 år	260	174	303	271	220	278	325	176	298
19 år	218	266	238	205	288	230	318	300	274
20 år	221	388	167	221	372	150	264	374	171
21 år	215	360	176	225	397	159	242	389	205
22 år	177	363	149	236	462	174	233	411	219
23 år	198	390	161	189	452	169	221	472	223
24 år	199	404	187	181	482	195	197	476	227
25 år	199	519	158	173	496	194	186	487	230
^* Antal personer