sdg2020

Goal 8: Decent work and economic growth

GDP per capita

FN 8.1.1 Real growth in GDP per capita

# Import
NRX10_raw <- 
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units     = "K",
    account   = "02",
    time       = px_all(),
    .col_code = TRUE
               ) %>% 
  as_tibble()

# transform 
NRX10 <- 
  NRX10_raw %>% 
  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]
  )

Statbank, GDP

Statbank, employment

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

	Real growth rate
2010-prices, chained values
2023	0,8
2022	1,7
2021	1,0
2020	0,1
2019	2,6
2018	0,6
^* Real growth, GDP per capita

Employment

GS Employment rate

ARXBFB05_raw <-
  statgl_url("ARXBFB05", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    komdist   = px_all(),
    opg_var   = "H",
    .col_code = T
  ) |> 
  as_tibble()


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

# Plot
ARXBFB05 %>% 
  filter(komdist == ARXBFB05[[1]][1]) %>% 
  ggplot(aes(
    x     = aar,
    y     = value,
    color = komdist
    )) +
  geom_line(linewidth = 2) +
  facet_wrap(~ komdist, 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]
  )

StatBank

Method

# Transform
ARXBFB05 <- 
  ARXBFB05_raw %>% 
  filter(
    komdist == ARXBFB05_raw[[1]][1],
    aar     >= year(Sys.time()) - 7
    ) %>% 
  arrange(desc(aar))

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")

	Employment rate
Employed persons in average per month (pct.)
2023	67,6
2022	67,2
2021	66,6
2020	66,0
2019	66,6
2018	66,1
^* Total (incl. outside the municipalities)

Unemployment

FN 8.5.2 Unemployment rate by age

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    alder_grp = "1",
    opg_var   = "P",
    .col_code = TRUE
    ) %>% 
  as_tibble()

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

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar,
    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]
  )

StatBank

Method

# Transform

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

	2018	2019	2020	2021	2022	2023
Unemployment rate	5	4,3	4,5	3,7	3,2	2,9

ARXLED4_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    opg_var   = "P",
    alder_grp = px_all(),
    .col_code = T
    ) %>% 
  as_tibble()

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

# Plot
ARXLED4 %>% 
  ggplot(aes(
    x     = aar, 
    y     = value,
    color = alder_grp
    )) +
  geom_line(linewidth = 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]
  )

StatBank

Method

# Transform
ARXLED4 <- 
  ARXLED4_raw %>% 
  filter(
    alder_grp != ARXLED4_raw[[2]][1],
    aar >= year(Sys.time()) - 6
    ) %>% 
  arrange(desc(aar)) %>% 
  mutate(
    alder_grp = alder_grp %>% factor(levels = unique(alder_grp)),
    aar = aar %>% factor(levels = unique(aar))
    ) %>% 
  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")

	Unemployment rate (pct.)
Unemployment rate
Total, 18-19 years
2023	5,3
2022	5,0
2021	6,8
2020	8,5
2019	7,6
Total, 20-24 years
2023	3,7
2022	4,2
2021	4,8
2020	6,1
2019	6,2
Total, 25-29 years
2023	2,6
2022	3,1
2021	3,2
2020	4,5
2019	4,7
Total, 30-34 years
2023	2,8
2022	3,2
2021	3,3
2020	4,4
2019	4,0
Total, 35-39 years
2023	2,4
2022	2,8
2021	3,2
2020	4,2
2019	4,0
Total, 40-44 years
2023	2,8
2022	2,6
2021	3,4
2020	3,9
2019	3,7
Total, 45-49 years
2023	2,2
2022	2,4
2021	2,9
2020	3,9
2019	4,0
Total, 50-54 years
2023	2,7
2022	3,4
2021	4,0
2020	4,4
2019	3,9
Total, 55-59 years
2023	3,4
2022	3,2
2021	3,9
2020	4,3
2019	3,7
Total, 60 years-retirement age
2023	3,4
2022	3,5
2021	3,6
2020	4,2
2019	4,2
^* Total

GS Unemployment rate by place of residence

# Import

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

ARXLED3_raw <-
  statgl_url("ARXLED3", lang = language) |> 
  statgl_fetch(
    aar       = px_all(),
    distrikt  = "AA",
    alder_grp = "1",
    opg_var   = "P",
    bybygd    = px_all(),
    .col_code = TRUE
    ) %>% 
  as_tibble()

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

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = aar,
    y     = value, 
    color = bybygd
    )) +
  geom_line(linewidth = 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]
    )

StatBank

Method

# Transform
ARXLED3 <- 
  ARXLED3_raw %>% 
  filter(aar >= year(Sys.time()) - 6) %>% 
  arrange(aar) %>% 
  mutate(aar = aar %>% factor(levels = unique(aar))) %>% 
  unite(combi, distrikt, alder_grp, 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$bybygd))

	Total, Total
Unemployment rate
Settlements etc.
2019	6,1
2020	6,5
2021	5,4
2022	4,8
2023	4,7
Total
2019	4,3
2020	4,5
2021	3,7
2022	3,2
2023	2,9
Towns
2019	4,1
2020	4,3
2021	3,4
2022	3,0
2023	2,7
^* Settlements etc.

Research and development

GS Research and development expenditure (share of GDP)

# 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",
    time      = 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]
  )

Statbank, investments (research and development)

Statbank, GDP

# 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")

	2018	2019	2020	2021	2022	2023
Current prices, million dkk
Research and development expenditure	1,8	1,8	1,6	1,5	1,9	2,1
^* Share of GDP

International passengers

GS Number of international passengers on scheduled flights

# 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]
  )

StatBank

Method

# 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")

	International passengers on scheduled flights
2024	96.817
2023	96.362
2022	85.484
2021	39.293
2020	30.785
2019	86.989
2018	85.306
^* Number of persons.

NEET

GS NEET

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]
)

StatBank

Method

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")

	2023			2016
age	2023,In education	2023,In employment	2023,NEET	2016,In education	2016,In employment	2016,NEET
16 years	310	86	310	390	70	295
17 years	304	114	293	275	127	362
18 years	272	212	265	298	176	325
19 years	229	296	206	274	300	318
20 years	142	338	221	171	374	264
21 years	138	403	216	205	389	242
22 years	158	376	206	219	411	233
23 years	150	362	174	223	472	221
24 years	149	424	181	227	476	197
25 years	188	437	163	230	487	186
^* Number of persons