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",
    Aar       = px_all(),
    .col_code = TRUE
               ) %>% 
  as_tibble()

# transform 
NRX10 <- 
  NRX10_raw %>% 
  rename("time" = "Aar") %>% 
  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
2021	0,7
2020	0,0
2019	2,6
2018	0,6
2017	0,0
2016	4,9
^* Real growth, GDP per capita

Employment

GS Employment rate

# Import

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR30/ARXBFB05.px")

ARXBFB05_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    municipality         = px_all(),
    "inventory variable" = "H",
    .col_code            = T
  ) |> 
  as_tibble()


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

# Plot
ARXBFB05 %>% 
  filter(municipality == ARXBFB05[[1]][1]) %>% 
  ggplot(aes(
    x     = time,
    y     = value,
    color = municipality
    )) +
  geom_line(size = 2) +
  facet_wrap(~ municipality, 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(
    municipality == ARXBFB05_raw[[1]][1],
    time         >= year(Sys.time()) - 7
    ) %>% 
  arrange(desc(time))

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.)
2022	66,9
2021	66,7
2020	66,0
2019	66,6
2018	66,1
2017	65,3
^* Total (incl. outside the municipalities)

Unemployment

FN 8.5.2 Unemployment rate by age

# Import

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

ARXLED3_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    district             = "AA",
    age                  = "1",
    "inventory variable" = "P",
    .col_code            = TRUE
    ) %>% 
  as_tibble()

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

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

	2017	2018	2019	2020	2021	2022
Unemployment rate	5,9	5	4,3	4,5	3,7	3,2

# Import

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

ARXLED4_raw <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    district             = "AA",
    "inventory variable" = "P",
    age                  = px_all(),
    .col_code            = T
    ) %>% 
  as_tibble()

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

# Plot
ARXLED4 %>% 
  ggplot(aes(
    x     = time, 
    y     = value,
    color = age
    )) +
  geom_line(size = 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(
    age != ARXLED4_raw[[2]][1],
    time >= year(Sys.time()) - 6
    ) %>% 
  arrange(desc(time)) %>% 
  mutate(
    age = age %>% factor(levels = unique(age)),
    time = time %>% factor(levels = unique(time))
    ) %>% 
  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
2022	5,0
2021	6,8
2020	8,5
2019	7,6
2018	12,2
Total, 20-24 years
2022	4,2
2021	4,8
2020	6,1
2019	6,2
2018	7,8
Total, 25-29 years
2022	3,1
2021	3,2
2020	4,5
2019	4,7
2018	5,3
Total, 30-34 years
2022	3,2
2021	3,3
2020	4,4
2019	4,0
2018	4,6
Total, 35-39 years
2022	2,8
2021	3,2
2020	4,2
2019	4,0
2018	4,6
Total, 40-44 years
2022	2,7
2021	3,4
2020	3,9
2019	3,7
2018	4,0
Total, 45-49 years
2022	2,4
2021	2,9
2020	3,9
2019	4,0
2018	4,3
Total, 50-54 years
2022	3,4
2021	4,0
2020	4,4
2019	3,9
2018	4,3
Total, 55-59 years
2022	3,2
2021	3,9
2020	4,3
2019	3,7
2018	4,3
Total, 60 years-retirement age
2022	3,5
2021	3,6
2020	4,2
2019	4,2
2018	4,6
^* 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 <-
  url |> 
  statgl_fetch(
    time                 = px_all(),
    district             = "AA",
    age                  = "1",
    "inventory variable" = "P",
    "place of residence" = px_all(),
    .col_code            = TRUE
    ) %>% 
  as_tibble()

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

# Plot
ARXLED3 %>% 
  ggplot(aes(
    x     = time,
    y     = value, 
    color = `place of residence`
    )) +
  geom_line(size = 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(time >= year(Sys.time()) - 6) %>% 
  arrange(time) %>% 
  mutate(time = time %>% factor(levels = unique(time))) %>% 
  unite(combi, district, age, 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$`place of residence`))

	Total, Total
Unemployment rate
Settlements etc.
2018	6,9
2019	6,1
2020	6,5
2021	5,3
2022	4,9
Total
2018	5,0
2019	4,3
2020	4,5
2021	3,7
2022	3,2
Towns
2018	4,8
2019	4,1
2020	4,3
2021	3,4
2022	3,0
^* 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",
    "Aar"      = 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")

	2017	2018	2019	2020	2021
Current prices, million dkk
Research and development expenditure	2,1	1,8	1,8	1,6	1,6
^* 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
2023	96.362
2022	85.484
2021	39.293
2020	30.785
2019	86.989
2018	85.306
2017	83.487
^* 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")

	2022			2019			2016
age	2022,In education	2022,In employment	2022,NEET	2019,In education	2019,In employment	2019,NEET	2016,In education	2016,In employment	2016,NEET
16 years	347	68	292	314	84	317	390	70	295
17 years	299	136	325	290	141	358	275	127	362
18 years	303	174	260	278	220	271	298	176	325
19 years	238	266	218	230	288	205	274	300	318
20 years	167	388	221	150	372	221	171	374	264
21 years	176	360	215	159	397	225	205	389	242
22 years	149	363	177	174	462	236	219	411	233
23 years	161	390	198	169	452	189	223	472	221
24 years	187	404	199	195	482	181	227	476	197
25 years	158	519	199	194	496	173	230	487	186
^* Number of persons