sdg2020

Delmål 9: Industri, innovation og infrastruktur

Forskere

FN 9.5.2 Antal personer med Ph.D og forskeruddannelse

# Import
UDXISCPROD_raw <-
  statgl_url("UDXISCPROD", lang = language) %>% 
  statgl_fetch(
    ISCED11_level = 80,
    Sex           = px_all(),
    Aar           = px_all(),
    .col_code     = TRUE
    ) %>% 
  as_tibble()
 
# Transform
UDXISCPROD <- 
  UDXISCPROD_raw %>% 
  mutate(
    Aar = Aar %>% make_date(),
    Sex = Sex %>% fct_inorder()
    )

# Plot
UDXISCPROD %>% 
  ggplot(aes(
    x    = Aar,
    y    = value,
    fill = Sex
  )) +
  geom_col() +
  #facet_wrap(~ gender) +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE) +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  labs(
    title   = sdg9$figs$fig1$title[language],
    x       = " ",
    y       = sdg9$figs$fig1$y_lab[language],
    fill    = " ",
    caption = sdg9$figs$fig1$cap[language]
  )

Statistikbanken

Metode

# Transform
UDXISCPROD <- 
  UDXISCPROD_raw %>% 
  filter(Aar >= year(Sys.time()) - 5) %>% 
  #arrange(desc(time)) %>% 
  mutate(Aar = Aar %>% factor(levels = unique(Aar))) %>% 
  spread(3, 4)

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

	2020	2021	2022	2023	2024
Phd. og forskeruddannelse
Kvinder	27	32	35	39	37
Mænd	27	30	27	26	23

Erhvervsstruktur

GS Fordeling af virksomheder efter lønsumsintervaller

# Import
ESD6A_raw <-
  statgl_url("ESX6A", lang = language) %>% 
  statgl_fetch(
    aar         = px_all(),
    ai2         = px_all(),
    section     = "total",
    .col_code   = TRUE
  ) %>% 
    as_tibble() |> 
  filter(ai2 != "Total")

# transform
ESD6A <- 
  ESD6A_raw %>%
  mutate(
    ai2 = ai2 %>% str_remove_all("[A-K]|\\.") %>% trimws() %>% fct_inorder() %>% fct_rev(),
    aar = aar %>% make_date()
  )


# legend ...
fill_lab <- colnames(statgl_url("ESX6A", lang = language) %>% statgl_fetch() %>% as_tibble())[1] %>% str_to_sentence()

# Plot
ESD6A %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = ai2
  )) +
  geom_area() +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE)) +
  labs(
    title    = sdg9$figs$fig2$title[language],
    subtitle = sdg9$figs$fig2$sub[language],
    x        = " ",
    y        = ESD6A[["unit"]][1],
    fill     = fill_lab,
    caption  = sdg9$figs$fig2$cap[language]
  )

Statistikbanken

Metode

# col lab
col_lab        <- 1
names(col_lab) <- fill_lab


# transform
ESD6A <- 
  ESD6A_raw %>%
  mutate(
    ai2 = ai2 %>% 
      str_remove_all("[A-K]|\\.") %>% 
      trimws() %>% 
      fct_inorder() %>% 
      fct_rev(),
    ) %>% 
  filter(aar >= Sys.time() %>% year() - 5) %>% 
  mutate(aar = aar %>% fct_inorder() %>% fct_rev()) %>% 
  spread(aar, value) %>% 
  arrange(desc(ai2)) %>% 
  rename(col_lab)

# table
ESD6A %>%  
  statgl_table() %>% 
  add_footnote(ESD6A[["unit"]][1], notation = "symbol")

Lønsumsinterval	section	2022	2021	2020
00-10	Total	359	354	310
10-50	Total	585	680	510
50-100	Total	473	557	461
100-250	Total	785	818	751
250-500	Total	590	613	641
500-1000	Total	407	372	368
1000-5000	Total	517	477	479
5000-10000	Total	88	81	84
Over 10000	Total	110	111	100

# Import
ESX5A_raw <-
  statgl_url("ESX5A", lang = language) %>% 
  statgl_fetch(
    nykom      = 1:5,
    ai2        = px_all(),
    aar        = px_all(),
    .col_code  = TRUE
    ) %>% 
  as_tibble() |> 
  filter(ai2 != "Total")

ESX5A <- 
  ESX5A_raw %>% 
  rename(lonsum = ai2) %>% 
  mutate(
    aar    = aar %>% make_date(),
    lonsum = lonsum %>% str_remove_all("[A-K]|\\.") %>% trimws() %>% fct_inorder() %>% fct_rev()
  )


# Plot
ESX5A %>% 
  ggplot(aes(
    x    = aar,
    y    = value,
    fill = lonsum
  )) +
  geom_area() +
  facet_wrap(~ nykom, scales = "free") +
  theme_statgl() + 
  scale_fill_statgl(reverse = TRUE, guide = guide_legend(reverse = TRUE)) +
  labs(
    title    = sdg9$figs$fig3$title[language],
    subtitle = sdg9$figs$fig3$sub[language],
    x        = " ",
    y        = ESX5A[[3]][1],
    fill     = fill_lab,
    caption  = sdg9$figs$fig3$cap[language]
  )

Statistikbanken

Metode

ESX5A <- 
  ESX5A_raw %>% 
  rename(lonsum = ai2) %>% 
  filter(aar >= Sys.time() %>% year() - 5) %>% 
  mutate(
    aar    = aar %>% fct_inorder() %>% fct_rev(),
    nykom  = nykom %>% fct_inorder(),
    lonsum = lonsum %>% str_remove_all("[A-K]|\\.") %>% trimws() %>% fct_inorder() %>% fct_rev()
    ) %>% 
  spread(aar, value) %>% 
  arrange(nykom, desc(lonsum))

ESX5A %>% 
  select(nykom) %>% 
  rename(col_lab) %>% 
  statgl_table() %>% 
  pack_rows(index = ESX5A[["nykom"]] %>% table())

Lønsumsinterval
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Kommuneqarfik Sermersooq
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Qeqqata Kommunia
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Kujalleq
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Kommune Qeqertalik
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia
Avannaata Kommunia

Eksport af andet end fisk

GS Fiskeriets andel af samlet eksport

# Import
IEXSITC_raw <-
  statgl_url("IEXSITC", lang = language) %>% 
  statgl_fetch(quarter     = 0,
               processing  = c("G11", "3"),
               transaction = 2,
               time        = px_all(),
               .col_code   = TRUE
               ) %>% 
    as_tibble()

# Transform
step <-
  IEXSITC_raw %>% 
  mutate(processing = processing %>% str_remove_all("[:digit:]|\\-") %>% trimws(which = "left")) %>% 
  arrange(time, processing)

IEXSITC <- 
  step %>% 
  mutate(export = fct_reorder(processing, -value, sum, na.rm = TRUE),
         time = make_date(time),
         value = value / 10^9) 
# Plot
IEXSITC %>% 
  ggplot(aes(
    x = time,
    y = value, 
    fill = export
  )) +
  geom_area(position = "identity") +
  theme_statgl() +
  scale_fill_statgl(reverse = TRUE) +
  labs(
    title = sdg9$figs$fig4$title[language],
    y     = sdg9$figs$fig4$y_lab[language],
    x     = " ",
    fill  = " ",
    caption = sdg9$figs$fig4$cap[language]
  )

Statistikbanken

Metode

# Transform
IEXSITC <- 
  step %>% 
  mutate(
    export = fct_reorder(processing, -value, sum, na.rm = TRUE),
    value = value / 10^9
    ) 

tab <- 
  IEXSITC %>% 
  select(-2, -3) %>% 
  #arrange(desc(time)) %>% 
  filter(time >= year(Sys.time()) - 5) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(time, value)

# Table
tab %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[[1]] %>% table()) %>% 
  row_spec(1, bold = TRUE) %>% 
  add_footnote(sdg9$figs$fig4$foot[language], notation = "symbol")

	2020	2021	2022	2023	2024	2025
Hele året
I alt	5,30	5,06	5,88	6,21	5,21	NA
Fisk, krebsdyr, bløddyr og varer deraf	4,85	4,78	5,64	5,86	5,08	NA
^* Millarder kroner

# Import
IEXSITC_raw <-
  statgl_url("IEXSITC", lang = language) %>%
  statgl_fetch(processing  = px_all(),
               transaction = 2,
               time        = px_all(),
               .col_code   = TRUE
               ) %>% 
    as_tibble()

# Transform
IEXSITC <- 
  IEXSITC_raw %>% 
  filter(processing %in% unique(IEXSITC_raw[[1]])[c(16, 26, 45, 55, 65, 74)]) %>% 
  mutate(
    time       = time %>% make_date(),
    value      = value / 10^6,
    processing = processing %>% str_remove_all("[:digit:]|\\-") %>% trimws()
    )

# Plot
IEXSITC %>% 
  ggplot(aes(
    x = time, 
    y = value,
    fill = processing
    )) +
  geom_area() + 
  facet_wrap(~ processing, labeller = label_wrap_gen(30)) +
  scale_fill_statgl() +
  theme_statgl() +
  theme(plot.margin = margin(10, 10, 10, 10)) +
  theme(legend.position = "none") +
  labs(
    title = sdg9$figs$fig5$title[language],
    x = " ", 
    y = sdg9$figs$fig5$y_lab[language],
    fill = " ",
    caption = sdg9$figs$fig4$cap[language]
 )

Statistikbanken

Metode

# Transform
IEXSITC <- 
  IEXSITC_raw %>% 
  filter(processing %in% unique(IEXSITC_raw[[1]])[c(16, 26, 45, 55, 65, 74)],
         time >= year(Sys.time()) -7,
         value != "NA") %>% 
  #arrange(desc(time)) %>% 
  mutate(
    value      = round(value / 10^6, 3),
    processing = processing %>% str_remove_all("[:digit:]|\\-") %>% trimws(),
    time       = time %>% factor(levels = unique(time))
    ) %>% 
  spread(3,4)

# Table
IEXSITC %>% 
  select(-2) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = IEXSITC[[2]] %>% str_to_title() %>% table()) %>% 
  add_footnote(sdg9$figs$fig5$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023	2024
Eksport
Bearb. Varer, hovedsagelig halvfabrikata i alt	13,285	5,699	13,193	8,149	16,420	20,536	11,039
Bearbejdede varer i.a.n. i alt	11,818	11,720	5,117	4,683	16,185	26,685	24,564
Diverse varer og transaktioner i.a.n. i alt	124,994	108,924	91,546	74,050	9,617	19,494	4,548
Maskiner og transportmidler i alt	530,455	315,276	326,187	182,999	179,201	260,847	76,197
Mineral, brændsels og smørestoffer o.l. i alt	0,002	0,004	0,079	0,002	0,004	0,017	0,141
Råstoffer, ikke spiselige (undt. Brændsel) i alt	4,605	7,882	6,824	7,135	11,907	11,294	13,291
^* Millioner kroner

Industriens bruttoværditilvækst

FN 9.2.1 Industriens bruttoværditilvækst

# Import
BVT <-
  statgl_url("NRX0418", lang = language) %>% 
  statgl_fetch(units    = "L",
               industry = px_all(),
               time     = px_all(),
               .col_code  = TRUE
               ) %>% 
    as_tibble() %>% 
  mutate(value = value/1000)


BVT <- BVT %>%  filter(industry %in% unique(BVT %>% pull(2))[9])


BNP <-
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units   = "L",
    account = "02",
    time    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

# Transform
industry <- 
  BVT %>% 
  rename("BVT" = 4) %>% 
  left_join(BNP %>% rename("BNP" = 4)) %>% 
  mutate(value = BVT / BNP,
         time = time %>% as.numeric())

# Plot
industry %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = units
  )) +
  geom_line(size = 2) +
  scale_y_continuous(labels = scales:: percent) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title    = sdg9$figs$fig6$title[language],
    subtitle = industry[[1]][1],
    x        = " ",
    y        = sdg9$figs$fig6$y_lab[language],
    caption  = sdg9$figs$fig6$cap[language]
  )

Statistikbanken, bruttoværditilvækst

Statistikbanken, BNP per indbygger

tab <- 
  industry %>% 
  select(industry, time, value) %>% 
  mutate(
    industry = industry %>% str_remove_all("C "),
    value    = value * 100
    ) %>% 
  #arrange(desc(time)) %>% 
  filter(time >= max(time) - 5) %>% 
  mutate(time = time %>% as.character() %>%  fct_inorder()) %>% 
  mutate(value = value %>% round(2)) %>% 
  mutate(value = paste0(value, "%")) %>% 
  spread(time, value)

tab %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(sdg9$figs$fig6$foot[language], notation = "symbol")

	2018	2019	2020	2021	2022	2023
Industri	0.29%	0.19%	0.16%	0.11%	0.23%	0.15%
^* Procentvis andel af BNP per indbygger

Industriens beskæftigelse

FN 9.2.2 Industriens beskæftigelse

# Import
NRX0518_raw <-
  statgl_url("NRX0518", lang = language) %>% 
  statgl_fetch(
    units    = "BES",
    industry = c("C", "TOT"),
    time     = px_all(),
    .col_code  = TRUE
    ) %>% 
    as_tibble()


NRX0518 <- 
  NRX0518_raw %>% 
  spread(industry, value) %>% 
  rename("indu" = 3, "total" = 4) %>% 
  mutate(
    value = indu / total,
    time = time %>% as.numeric()
    )



NRX0518 %>% 
  ggplot(aes(
    x = time,
    y = value,
    color = units
  )) +
  geom_line(size = 2) +
  expand_limits(y = 0) +
  scale_y_continuous(labels  = scales::percent_format(scale = 100, accuracy = 1, big.mark = ".",
  decimal.mark = ",")) +
  theme_statgl() + 
  scale_color_statgl() +
  theme(legend.position = "none") +
  labs(
    title = sdg9$figs$fig7$title[language],
    x = " ",
    y = sdg9$figs$fig7$y_lab[language],
    caption = sdg9$figs$fig7$cap[language]
  )

Statistikbanken

NRX0518 %>% 
  select(time, value) %>% 
  #arrange(desc(time)) %>% 
  filter(time >= max(time) - 5) %>% 
  mutate(time = time %>% as.character() %>%  fct_inorder()) %>% 
  mutate(
    value = value * 100,
    value = value %>% round(1)
    ) %>% 
  spread(time, value) %>% 
  statgl_table() %>% 
  add_footnote(sdg9$figs$fig7$foot[language], notation = "symbol")

2018	2019	2020	2021	2022	2023
5,8	5,4	5,6	5,4	5,4	5,4
^* Procentvis andel af samlede beskæftigelse