Tilbage

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Delmål 9: Industri, innovation og infrastruktur

Forskere

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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

Vis tabel

# 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
Phd. og forskeruddannelse
Kvinder	27	32	35	38
Mænd	27	30	27	26

Erhvervsstruktur

---

GS Fordeling af virksomheder efter lønsumsintervaller

# Import
ESD6A_raw <-
  statgl_url("ESX6A", lang = language) %>%
  statgl_fetch(
    unit                                        = "N",
    time                                        = px_all(),
    "interval of aggregate salaries and shares" = c(LETTERS[1:8], "K"),
    .col_code                                   = TRUE
  ) %>% 
    as_tibble()

# transform
ESD6A <- 
  ESD6A_raw %>%
  mutate(
    `interval of aggregate salaries and shares` = `interval of aggregate salaries and shares` %>% str_remove_all("[A-K]|\\.") %>% trimws() %>% fct_inorder() %>% fct_rev(),
    time = time %>% make_date()
  )


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

# Plot
ESD6A %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = `interval of aggregate salaries and shares`
  )) +
  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

Vis tabel

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


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

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

Lønsumsinterval	2021	2020
00-10	708	620
10-50	1.360	1.020
50-100	1.114	922
100-250	1.636	1.502
250-500	1.226	1.282
500-1000	744	736
1000-5000	954	958
5000-10000	162	168
Over 10000	222	200
* Antal virksomheder

# Import
ESX5A_raw <-
  statgl_url("ESX5A", lang = language) %>%
  statgl_fetch(
    municipality                                = 1:5,
    "interval of aggregate salaries and shares" = c(LETTERS[1:8], "K"),
    unit                                        = "N",
    time                                        = px_all(),
    .col_code                                   = TRUE
    ) %>% 
  as_tibble()

ESX5A <- 
  ESX5A_raw %>% 
  rename(lonsum = `interval of aggregate salaries and shares`) %>% 
  mutate(
    time   = time %>% make_date(),
    lonsum = lonsum %>% str_remove_all("[A-K]|\\.") %>% trimws() %>% fct_inorder() %>% fct_rev()
  )


# Plot
ESX5A %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = lonsum
  )) +
  geom_area() +
  facet_wrap(~ municipality, 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

Vis tabel

ESX5A <- 
  ESX5A_raw %>% 
  rename(lonsum = `interval of aggregate salaries and shares`) %>% 
  filter(time >= Sys.time() %>% year() - 5) %>% 
  mutate(
    time         = time %>% fct_inorder() %>% fct_rev(),
    municipality = municipality %>% fct_inorder(),
    lonsum       = lonsum %>% str_remove_all("[A-K]|\\.") %>% trimws() %>% fct_inorder() %>% fct_rev()
    ) %>% 
  spread(time, value) %>% 
  arrange(municipality, desc(lonsum))

ESX5A %>% 
  select(-c(municipality, unit)) %>% 
  rename(col_lab) %>% 
  statgl_table() %>% 
  pack_rows(index = ESX5A[["municipality"]] %>% table()) %>% 
  add_footnote(ESX5A[["unit"]][1], notation = "symbol")

Lønsumsinterval	2021	2020
Kommune Kujalleq
00-10	37	36
10-50	70	45
50-100	58	48
100-250	62	71
250-500	45	33
500-1000	26	25
1000-5000	35	35
5000-10000	7	5
Over 10000	4	5
Kommuneqarfik Sermersooq
00-10	84	94
10-50	173	134
50-100	145	116
100-250	213	189
250-500	174	165
500-1000	150	141
1000-5000	243	247
5000-10000	42	45
Over 10000	78	69
Qeqqata Kommunia
00-10	67	51
10-50	105	66
50-100	86	66
100-250	97	98
250-500	64	70
500-1000	39	43
1000-5000	76	71
5000-10000	16	17
Over 10000	9	9
Kommune Qeqertalik
00-10	76	43
10-50	93	95
50-100	60	55
100-250	86	84
250-500	50	44
500-1000	17	20
1000-5000	22	23
5000-10000	5	5
Over 10000	3	2
Avannaata Kommunia
00-10	70	65
10-50	178	152
50-100	176	169
100-250	312	288
250-500	251	309
500-1000	120	122
1000-5000	72	75
5000-10000	7	7
Over 10000	11	10
* Antal virksomheder

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

Vis tabel

# 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

Vis tabel

# 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

Vis tabel

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

Vis tabel

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