GDX1GDO_raw <-
statgl_url("GDX1GDO", lang = language) %>%
statgl_fetch(
municipality = px_all(),
affiliation = px_all(),
time = px_top(1),
.col_code = TRUE
) %>%
as_tibble()
regions <-
c("Danmark","Byen København", "Københavns omegn", "Nordsjælland",
"Bornholm i alt", "Østsjælland", "Vest- og Sydsjælland",
"Fyn", "Sydjylland", "Østjylland", "Vestjylland", "Nordjylland")
GDX1GDO <-
GDX1GDO_raw %>%
filter(municipality %in% regions) %>%
mutate(
affiliation = affiliation %>% fct_inorder(),
municipality = municipality %>% fct_reorder(value, .fun = sum, .desc = TRUE)
) %>%
spread(affiliation, value) %>%
mutate_if(is.integer, ~ replace(., is.na(.), 0))
GDX1GDO %>%
select(-time) %>%
rename(" " = 1) %>%
statgl_table(replace_0s = TRUE) %>%
pack_rows(index = GDX1GDO[["time"]] %>% table()) %>%
row_spec(1, bold = TRUE)
|
Total
|
Both parents born in Greenland
|
One parent born in Greenland and one outside
|
One parent born in Greenland and one unknown
|
Both parents born outside Greenland
|
One parent born outside Greenland and one unknown
|
Both parents birthplace unknown
|
2023
|
Danmark
|
17.079
|
5.308
|
3.618
|
1.104
|
4.091
|
616
|
2.342
|
Nordjylland
|
2.929
|
1.352
|
548
|
218
|
377
|
61
|
373
|
Østjylland
|
2.667
|
792
|
606
|
149
|
732
|
81
|
307
|
Sydjylland
|
2.366
|
854
|
507
|
161
|
452
|
68
|
324
|
Byen København
|
2.206
|
552
|
455
|
159
|
633
|
110
|
297
|
Fyn
|
1.787
|
612
|
387
|
112
|
384
|
50
|
242
|
Vest- og Sydsjælland
|
1.456
|
278
|
331
|
91
|
396
|
78
|
282
|
Vestjylland
|
1.273
|
504
|
247
|
86
|
238
|
38
|
160
|
Nordsjælland
|
885
|
102
|
180
|
46
|
355
|
50
|
152
|
Københavns omegn
|
860
|
166
|
199
|
39
|
299
|
45
|
112
|
Østsjælland
|
486
|
62
|
120
|
25
|
186
|
28
|
65
|
Bornholm i alt
|
164
|
34
|
38
|
18
|
39
|
7
|
28
|
See the table in our Statbank: GDX1GDO
GDXRA_raw <-
statgl_url("GDXRA", lang = language) %>%
statgl_fetch(
"socioeconomic status" = px_all(),
gender = px_all(),
time = px_top(1),
.col_code = TRUE
) %>%
as_tibble()
GDXRA <-
GDXRA_raw %>%
mutate(
gender = gender %>% fct_inorder(),
`socioeconomic status` = `socioeconomic status` %>% fct_inorder()
) %>%
spread(gender, value) %>%
mutate_if(is.integer, ~ replace(., is.na(.), 0))
GDXRA %>%
select(-time) %>%
rename(" " = 1) %>%
statgl_table(replace_0s = TRUE) %>%
pack_rows(index = GDXRA[["time"]] %>% table()) %>%
row_spec(1, bold = TRUE)
|
Total
|
Men
|
Women
|
2021
|
Total
|
16.814
|
7.213
|
9.601
|
Self-employed
|
337
|
165
|
172
|
Assisting spouses
|
9
|
0
|
0
|
Employees, managers
|
212
|
135
|
77
|
Employees - upper level
|
1.869
|
699
|
1.170
|
Employees - medium level
|
640
|
290
|
350
|
Employees - basic level
|
2.952
|
1.395
|
1.557
|
Other employees
|
677
|
330
|
347
|
Employees, not specified
|
556
|
278
|
278
|
Unemployed
|
345
|
147
|
198
|
Subsidized employment without salary
|
113
|
58
|
55
|
Persons receiving holiday benefits
|
0
|
0
|
0
|
Guidance and activities upgrading skills
|
140
|
77
|
63
|
Unemployment benefit
|
70
|
18
|
52
|
Maternity absence from unemployment
|
18
|
0
|
0
|
Sickness absence from unemployment
|
120
|
46
|
74
|
Cash benefit (passive)/cash benefit for foreigners
|
1.853
|
844
|
1.009
|
Rehabilitation
|
0
|
0
|
0
|
Specially arranged scheme
|
245
|
95
|
150
|
Job clarification program
|
75
|
10
|
65
|
Disability pension
|
1.734
|
672
|
1.062
|
Early retirement pay
|
80
|
19
|
61
|
Flex benefit
|
9
|
0
|
0
|
Old-age pension
|
1.554
|
420
|
1.134
|
Other pensions
|
152
|
60
|
92
|
Enrolled in education
|
2.007
|
942
|
1.065
|
Children and youth (not enrolled in education)
|
254
|
124
|
130
|
Others outside the labour force
|
754
|
358
|
396
|
Unknown
|
35
|
22
|
13
|
See the table in our Statbank: GDXRA