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---

Goal 14: Life below water

Consumption of marine resources

---

GS Consumption of marine resources

FIX020_raw <- 
  statgl_url("FIX021", lang = language) %>% 
  statgl_fetch(
    species   = px_all(),
    area      = px_all(),
    form      = px_all(),
    time      = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

vec        <- 7:8
names(vec) <- c(sdg14$figs$fig0$cols$col1[language], sdg14$figs$fig0$cols$col2[language])

  
FIX020 <- 
  FIX020_raw %>% 
  mutate(
    species = species %>% fct_inorder(),
    form = form %>% fct_inorder(),
    time = time %>% as.numeric()
    ) %>% 
  spread(form, value) %>% 
  rename(
    "Rådgivning"  = 4,
    "Kvotex"      = 5,
    "Fangstx"     = 6
  ) %>% 
  filter(Kvotex > 0) %>% 
  mutate(
    Kvoteafvigelse  = Kvotex - Rådgivning,
    Fangstafvigelse = Fangstx - Rådgivning
  ) %>% 
  rename(vec) %>% 
  select(-c(4:6)) %>% 
  gather(key, value, -(1:3)) %>% 
  mutate(value = value / 1000)

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[1]) %>% 
  ggplot(aes(
    x = time, 
    y = value,
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  scale_y_continuous(breaks= scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  facet_wrap(~ area) +
  labs(
    title    = unique(FIX020[[1]])[1],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$units$`1000_ton`[language] %>% unlist(),
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap_fish[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    time > year(Sys.time()) - 6,
    species == unique(FIX020_raw[[1]])[1],
    area %in% unique(FIX020_raw[[2]])[1:2]
    ) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab["species"] %>% table()) %>% 
  pack_rows(index = tab["area"] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$ton[language] %>% unlist(), notation = "symbol")

	2019	2020	2021	2022
Prawns
Eastgreenland
Advice	2.000	2.000	3.000	3.000
Catch	1.574	3.172	3.071	5.510
Kvota	4.000	4.750	7.000	6.850
Westgreenland
Advice	103.383	108.383	113.777	113.777
Catch	98.115	107.860	108.352	112.659
Kvota	103.383	108.383	113.777	113.777
* Ton

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[2]) %>% 
  ggplot(aes(
    x = time, 
    y = value,
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  facet_wrap(~ area) +
  labs(
    title    = unique(FIX020[[1]])[2],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$units$`1000_ton`[language] %>% unlist(),
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap_fish[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    time > year(Sys.time()) - 6,
    species == unique(FIX020_raw[[1]])[2],
    area != unique(FIX020_raw[[2]])[3]
    ) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab["species"] %>% table()) %>% 
  pack_rows(index = tab["area"] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$ton[language] %>% unlist(), notation = "symbol")

	2019	2020	2021	2022
Grenland halibut
Eastgreenland
Advice	9.084	8.010	8.847	10.020
Catch	9.087	7.046	8.255	8.933
Kvota	9.080	8.031	8.847	10.020
Westgreenland
Advice	18.184	18.184	18.184	18.185
Catch	18.324	18.146	17.989	18.112
Kvota	18.184	18.184	18.184	18.185
* Ton

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[3]) %>% 
  ggplot(aes(
    x = time, 
    y = value,
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  facet_wrap(~ area) +
  labs(
    title    = unique(FIX020[[1]])[3],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$units$`1000_ton`[language] %>% unlist(),
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap_fish[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    time > year(Sys.time()) - 6,
    species == unique(FIX020_raw[[1]])[3],
    area %in% unique(FIX020_raw[[2]])[c(1, 3)]
    ) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab["species"] %>% table()) %>% 
  pack_rows(index = tab["area"] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$ton[language] %>% unlist(), notation = "symbol")

	2019	2020	2021	2022
Atlantic cod
West- Eastgreenland
Advice	5.363	3.409	6.091	8.708
Catch	18.412	15.807	16.146	21.487
Kvota	22.000	18.824	26.091	21.630
Westgreenland
Advice	NA	NA	NA	NA
Catch	NA	NA	NA	NA
Kvota	NA	NA	NA	NA
* Ton

FIX020_raw <- 
  statgl_url("FIX020", lang = language) %>% 
  statgl_fetch(
    species   = px_all(),
    area      = px_all(),
    form      = px_all(),
    time      = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

vec        <- 7:8
names(vec) <- c(sdg14$figs$fig0$cols$col1[language], sdg14$figs$fig0$cols$col2[language])

FIX020 <- 
  FIX020_raw %>% 
  mutate(
    form = form %>% fct_inorder(),
    area = area %>% fct_inorder(),
    time = time %>% as.numeric()
    ) %>% 
  spread(form, value) %>% 
  rename(
    "Rådgivning" = 4,
    "Kvote"      = 5,
    "Fangst"     = 6
    ) %>% 
  mutate(
    Kvoteafvigelse  = Kvote - Rådgivning,
    Fangstafvigelse = Fangst - Rådgivning
  ) %>% 
  rename(vec) %>% 
  filter(Kvote > 0) %>% 
  select(-(4:6)) %>% 
  gather(key, value, -c(species, area, time))


FIX020 %>% 
  filter(species == unique(FIX020[[1]])[6]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[6],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[6],
    area != unique(FIX020_raw[[2]])[3]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Peluga
Westgreenland
Advice	320	320	265
Catch	137	182	139
Kvota	320	320	265
Qaanaaq
Advice	20	20	37
Catch	109	12	9
Kvota	20	20	29
Eastgreenland
Advice	0	0	0
Catch	0	0	0
Kvota	0	0	0
Melville Bay
Advice	0	0	0
Catch	0	0	0
Kvota	0	0	0
* Quantity

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[5]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[5],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[5],
    area %in% unique(FIX020_raw[[2]])[1:2]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Narwhale
Westgreenland
Advice	251	251	251
Catch	181	84	235
Kvota	251	251	251
Eastgreenland
Advice	0	0	0
Catch	76	57	20
Kvota	66	50	60
* Quantity

Consumption of marine resources -2

GS Consumption of marine resources -2

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[4]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[4],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[4],
    area %in% unique(FIX020_raw[[2]])[c(1, 4)]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Minke whale
Westgreenland
Advice	212	164	164
Catch	160	138	177
Kvota	164	164	164
Qaanaaq
Advice	0	0	0
Catch	0	0	0
Kvota	0	0	0
* Quantity

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[1]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[1],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[1],
    area %in% unique(FIX020_raw[[2]])[c(1)]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Bowhead whale
Westgreenland
Advice	2	2	2
Catch	0	0	0
Kvota	2	2	2
* Quantity

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[2]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[2],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[2],
    area %in% unique(FIX020_raw[[2]])[c(1)]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Fin whale
Westgreenland
Advice	19	19	19
Catch	8	3	2
Kvota	19	19	19
* Quantity

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[3]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[3],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter() %>% 
  filter(
    species == unique(FIX020[[1]])[3],
    area %in% unique(FIX020_raw[[2]])[1:3]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Humpback whale
Westgreenland
Advice	10	10	10
Catch	4	4	7
Kvota	10	10	10
Eastgreenland
Advice	0	0	0
Catch	0	0	0
Kvota	0	0	0
Northern water
Advice	0	0	0
Catch	0	0	0
Kvota	0	0	0
* Quantity

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[7]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[7],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[7],
        area %in% unique(FIX020_raw[[2]])[c(1)]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Polar bear
Westgreenland
Advice	92	92	92
Catch	80	80	86
Kvota	92	92	92
* Quantity

FIX020 %>% 
  filter(species == unique(FIX020[[1]])[8]) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  ggplot(aes(
    x = time,
    y = value, 
    color = key
  )) +
  geom_hline(yintercept = 0, color = "red", size = 3, linetype = "dotted") +
  geom_line(size = 2) +
  facet_wrap( ~ area) +
  scale_y_continuous(breaks = scales:: pretty_breaks()) +
  theme_statgl() + 
  scale_color_statgl(reverse = TRUE) +
  labs(
    title    = unique(FIX020[[1]])[8],
    subtitle = sdg14$figs$fig0$sub[language],
    y        = sdg14$figs$fig0$y_lab[language],
    x        = " ",
    color    = " ",
    caption  = sdg14$figs$fig0$cap[language]
  )

StatBank

Show table

tab <- 
  FIX020_raw %>% 
  filter(
    species == unique(FIX020[[1]])[8],
    area %in% unique(FIX020_raw[[2]])[1:2]
    ) %>% 
  mutate(area = fct_reorder(area, value, .fun = sum, .desc = TRUE)) %>% 
  filter(time > year(Sys.time()) - 6) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>%  fct_inorder()) %>% 
  spread(time, value)

tab %>% 
  select(-c(species, area)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = tab[["species"]] %>% table) %>% 
  pack_rows(index = tab[["area"]] %>% table()) %>% 
  add_footnote(sdg14$figs$fig0$units$antal[language] %>% unlist(), notation = "symbol")

	2019	2020	2021
Walrus
Westgreenland
Advice	100	86	86
Catch	61	75	67
Kvota	69	74	74
Eastgreenland
Advice	18	19	19
Catch	7	8	12
Kvota	18	17	17
* Quantity

Gross value added for fishing industries

---

GS Gross value added for fishing industries

# Import 
NRX0418_raw <- 
  statgl_url("NRX0418", lang = language) %>%  
  statgl_fetch(
    units     = "K",
    industry  = c("BVTTOT", "BVT0301", "BVT0302", "BVT0303"), 
    time      = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()
  


NRX0418_raw %>% 
  mutate(time = time %>% as.numeric()) %>% 
  mutate(industry = industry %>% str_remove_all("[:digit:]") %>% trimws()) %>% 
  ggplot(aes(
    x = time,
    y = value/1e3,
    color = industry
  )) +
  geom_line(size = 2) +
  facet_wrap(~ industry, scales = "free") +
  theme_statgl() + 
  theme(legend.position = "none") +
  scale_color_statgl() +
  labs(
    title    = sdg14$figs$fig9$title[language],
    subtitle = NRX0418_raw %>% pull(units) %>% unique(),
    y        = sdg14$figs$fig9$y_lab[language],
    x        = " ",
    caption  = sdg14$figs$fig9$cap[language]
  )

StatBank

Show table

table <- 
  NRX0418_raw %>% 
  mutate(industry = industry %>% str_remove_all("[:digit:]") %>% trimws() %>% fct_inorder()) %>% 
  mutate(time = time %>% as.numeric()) %>% 
  filter(!time %in% 2019:2020) %>% 
  filter(time >= max(time) - 7) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% as.character() %>% fct_inorder()) %>% 
  mutate(value = round(value / 1000, 1)) %>% 
  spread(time, value)

table %>% 
  select(-units) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = table[1] %>% table()) %>% 
  add_footnote(sdg14$figs$fig9$y_lab[language], notation = "symbol")

	2014	2015	2016	2017	2018	2021
2010-prices, chained values
Gross value added total	14,3	14,0	14,7	14,8	15,0	15,6
Inshore fishing	0,4	0,3	0,3	0,3	0,4	NA
Offshore fisheries	1,1	0,9	1,0	1,1	1,1	NA
Fishing, other	0,2	0,2	0,3	0,2	0,2	NA
* Billions (DKK)

NRX13_raw <- 
  statgl_url("NRX13", lang = language) %>% 
  statgl_fetch(
    Kode  = c("VBVT0301", "VBVT0302", "VBVT0303"),
    Aar      = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble() %>% 
  rename("industry" = 1, "time" = 2)

NRX13_raw %>% 
  drop_na() %>% 
  mutate(industry = industry %>% str_remove_all("[:digit:]") %>% trimws() %>% fct_rev()) %>% 
  mutate(time = time %>% as.numeric()) %>% 
  ggplot(aes(
    x = time,
    y = value,
    color = industry
  )) +
  geom_line(size = 2) +
  geom_hline(yintercept = 0, linetype = "dashed") + 
  facet_wrap(~ industry, scales = "free", ncol = 1) +
    scale_y_continuous(labels  = scales::percent_format(
    scale = 1
  )) +
  theme_statgl() + 
  theme(legend.position = "none") +
  scale_color_statgl() +
  labs(
    title    = sdg14$figs$fig11$title[language],
    y        = sdg14$figs$fig11$y_lab[language],
    x        = " ",
    caption  = sdg14$figs$fig11$cap[language]
  )

StatBank

Show table

NRX13_raw %>% 
  drop_na() %>% 
  mutate(industry = industry %>% str_remove_all("[:digit:]") %>% trimws() %>% fct_rev()) %>% 
  mutate(time = time %>% as.numeric()) %>% 
  filter(time >= max(time) -5) %>% 
  #arrange(desc(time)) %>% 
  mutate(time = time %>% as.character() %>% fct_inorder()) %>% 
  spread(time, value) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  add_footnote(sdg14$figs$fig11$y_lab[language], notation = "symbol")

	2014	2015	2016	2017	2018	2019
Offshore fisheries	2,43	-1,59	1,43	1,05	-0,39	0,45
Inshore fishing	0,13	-1,84	1,28	-0,02	0,55	0,26
Fishing, other	0,38	0,04	0,43	-0,43	-0,11	0,22
* Percentage point