Tilbage

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("level of education" = 80,
               gender               = px_all(),
               time                 = px_all(),
               .col_code            = TRUE) %>% 
    as_tibble()
 
# Transform
UDXISCPROD <- 
  UDXISCPROD_raw %>% 
  mutate(
    time   = time %>% make_date(),
    gender = gender %>% fct_inorder()
    )

# Plot
UDXISCPROD %>% 
  ggplot(aes(
    x    = time,
    y    = value,
    fill = gender
  )) +
  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(time >= year(Sys.time()) - 5) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  spread(3, 4)

# Table
UDXISCPROD %>% 
  select(-1) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = UDXISCPROD[[1]] %>% table())
2019 2020 2021 2022
Phd. og forskeruddannelse
Kvinder 24 27 30 30
Mænd 29 27 30 24

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 


# 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 2019
00-10 708 620 524
10-50 1.360 1.020 1.220
50-100 1.114 922 982
100-250 1.636 1.502 1.912
250-500 1.226 1.282 1.226
500-1000 744 736 762
1000-5000 954 958 964
5000-10000 162 168 186
Over 10000 222 200 198
* 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 


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 2019
Kommune Kujalleq
00-10 37 36 31
10-50 70 45 50
50-100 58 48 46
100-250 62 71 83
250-500 45 33 49
500-1000 26 25 31
1000-5000 35 35 35
5000-10000 7 5 4
Over 10000 4 5 5
Kommuneqarfik Sermersooq
00-10 84 94 93
10-50 173 134 206
50-100 145 116 136
100-250 213 189 227
250-500 174 165 168
500-1000 150 141 143
1000-5000 243 247 248
5000-10000 42 45 52
Over 10000 78 69 68
Qeqqata Kommunia
00-10 67 51 45
10-50 105 66 85
50-100 86 66 72
100-250 97 98 121
250-500 64 70 65
500-1000 39 43 39
1000-5000 76 71 73
5000-10000 16 17 14
Over 10000 9 9 10
Kommune Qeqertalik
00-10 76 43 32
10-50 93 95 95
50-100 60 55 73
100-250 86 84 126
250-500 50 44 50
500-1000 17 20 25
1000-5000 22 23 25
5000-10000 5 5 7
Over 10000 3 2 1
Avannaata Kommunia
00-10 70 65 52
10-50 178 152 163
50-100 176 169 152
100-250 312 288 381
250-500 251 309 267
500-1000 120 122 128
1000-5000 72 75 77
5000-10000 7 7 10
Over 10000 11 10 12
* 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 


# 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")
2019 2020 2021 2022 2023
Hele året
I alt 5,52 5,30 5,06 6,07 6,01
Fisk, krebsdyr, bløddyr og varer deraf 5,06 4,85 4,78 5,83 5,86
* 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")
2017 2018 2019 2020 2021 2022 2023
Eksport
Bearb. Varer, hovedsagelig halvfabrikata i alt 6,287 13,285 5,699 13,193 8,149 16,420 20,536
Bearbejdede varer i.a.n. i alt 18,718 11,818 11,720 5,117 4,683 16,185 26,685
Diverse varer og transaktioner i.a.n. i alt 123,768 124,994 108,924 91,546 74,050 9,617 19,494
Maskiner og transportmidler i alt 235,614 530,455 315,276 326,187 182,999 179,201 64,002
Mineral, brændsels og smørestoffer o.l. i alt 0,009 0,002 0,004 0,079 0,002 0,004 0,017
Råstoffer, ikke spiselige (undt. Brændsel) i alt 3,476 4,605 7,882 6,824 7,135 11,907 11,294
* 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",
               "Aar"    = px_all(),
               .col_code = TRUE
               ) %>% 
    as_tibble() %>% 
  rename("time" = Aar)

# 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")
2016 2017 2018 2019 2020 2021
Industri 0.26% 0.14% 0.29% 0.19% 0.19% 0.16%
* 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"    = c("TOT", "C"),
    "industry" = "BES",
    "time"     = px_all(),
    .col_code  = TRUE
    ) %>% 
    as_tibble()


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



NRX0518 %>% 
  ggplot(aes(
    x = time,
    y = value,
    color = industry
  )) +
  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")
2016 2017 2018 2019 2020 2021
5,8 5,7 5,8 5,4 5,4 5,5
* Procentvis andel af samlede beskæftigelse