Foreign trade
IEX2PROD_raw <-
statgl_url("IEX2PROD", lang = language) %>%
statgl_fetch(
branch = px_all(),
quarter = 1:4,
time = px_top(1),
.col_code = TRUE
) %>%
as_tibble()
IEX2PROD <-
IEX2PROD_raw %>%
mutate(branch = branch %>% fct_inorder()) %>%
filter(branch %>% str_detect("-[:digit:]")) %>%
mutate(
quarter = quarter %>% fct_inorder(),
branch = branch %>% str_remove_all("[:digit:]|[:punct:]") %>% trimws(),
branch = branch %>% fct_inorder()
) %>%
filter(value != "Na") %>%
spread(quarter, value)
IEX2PROD %>%
select(-time) %>%
rename(" " = 1) %>%
statgl_table() %>%
pack_rows(index = IEX2PROD[["time"]] %>% table()) %>%
row_spec(1, bold = TRUE)
|
quarter 1
|
quarter 2
|
quarter 3
|
quarter 4
|
2023
|
Exports total
|
1.070.761.570
|
1.491.901.632
|
1.963.571.249
|
1.485.223.302
|
Agricultural products of animals origin total
|
25.123
|
76.303
|
103.020
|
56.754
|
Agricultural products of vegetable origin total
|
NA
|
NA
|
5.029
|
NA
|
Manufactures goods total
|
206.595.114
|
330.165.655
|
353.558.676
|
275.617.223
|
Ships of more than GT aircraft and drilling rigs and production
platforms total
|
NA
|
NA
|
260.500
|
NA
|
Fish crustaceans and molluscs not prepared or preserved total
|
863.792.549
|
1.144.397.182
|
1.608.211.599
|
1.208.980.012
|
Fuels lubricant and current total
|
2.179
|
1.605
|
10.160
|
2.753
|
Other goods total
|
346.605
|
17.260.887
|
1.422.265
|
566.560
|
See the table in our Statbank: IEX2PROD
IEXANV_raw <-
statgl_url("IEXANV", lang = language) %>%
statgl_fetch(
quarter = 1:4,
time = px_top(1),
"end-use" = px_all(),
.col_code = TRUE
) %>%
as_tibble()
IEXANV <-
IEXANV_raw %>%
filter(`end-use` %>% word(1) %>% str_detect("-")) %>%
mutate(
`end-use` = `end-use` %>% str_remove_all("[:digit:]|[:punct:]") %>% trimws(),
`end-use` = `end-use` %>% fct_inorder()
) %>%
filter(value != "Na") %>%
spread(quarter, value)
IEXANV %>%
select(-time) %>%
rename(" " = 1) %>%
statgl_table() %>%
pack_rows(index = IEXANV[["time"]] %>% table()) %>%
row_spec(1, bold = TRUE)
|
quarter 1
|
quarter 2
|
quarter 3
|
quarter 4
|
2023
|
Imports total
|
1.098.101.623
|
1.993.478.311
|
1.460.991.973
|
1.686.377.300
|
Commodities for use in aggriculture and farming total
|
11.479.086
|
12.860.143
|
23.605.406
|
22.101.887
|
Commodities for use in other businesses total
|
173.161.848
|
282.152.923
|
200.127.923
|
161.592.051
|
Commodities for use in building and construction total
|
263.428.312
|
380.866.390
|
371.512.583
|
329.639.197
|
Fuels and lubricants total
|
27.068.937
|
461.614.960
|
14.647.444
|
237.513.718
|
Machinery total
|
121.218.387
|
179.381.568
|
137.565.023
|
188.830.941
|
Transport equipments total
|
33.119.002
|
76.145.552
|
45.741.542
|
83.019.274
|
Commodities for final use total
|
444.686.187
|
558.153.857
|
643.938.547
|
648.029.145
|
Goods not elsewhere specified total
|
23.939.865
|
42.302.919
|
23.853.504
|
15.651.087
|
See the table in our Statbank: IEXANV
IEXBALMND_raw <-
statgl_url("IEXBALMND", lang = language) %>%
statgl_fetch(
month = px_all(),
transaction = px_all(),
time = px_top(1),
.col_code = TRUE
) %>%
as_tibble()
IEXBALMND <-
IEXBALMND_raw %>%
mutate(
month = month %>% str_to_sentence(),
month = month %>% fct_inorder(),
transaction = transaction %>% fct_inorder()
) %>%
filter(value != "Na") %>%
spread(transaction, value)
IEXBALMND %>%
select(-time) %>%
rename(" " = 1) %>%
statgl_table() %>%
pack_rows(index = IEXBALMND[["time"]] %>% table()) %>%
row_spec(1, bold = TRUE)
|
Balance
|
Export
|
Import
|
2023
|
Whole year
|
-227.491
|
6.011.458
|
6.238.949
|
January
|
15.380
|
338.317
|
322.936
|
February
|
2.895
|
346.400
|
343.504
|
March
|
-45.616
|
386.045
|
431.661
|
April
|
-308.853
|
302.821
|
611.673
|
May
|
45.493
|
665.180
|
619.688
|
June
|
-238.217
|
523.901
|
762.117
|
July
|
124.410
|
643.644
|
519.234
|
August
|
317.304
|
767.949
|
450.645
|
September
|
60.865
|
551.979
|
491.113
|
October
|
-354.770
|
493.649
|
848.418
|
November
|
64.395
|
559.330
|
494.935
|
December
|
89.220
|
432.245
|
343.024
|
See the table in our Statbank: IEXBALMND
IEXSITC_raw <-
statgl_url("IEXSITC", lang = language) %>%
statgl_fetch(
processing = px_all(),
transaction = 1:2,
time = px_top(2),
.col_code = TRUE
) %>%
as_tibble() %>%
filter(time != max(time))
IEXSITC <-
IEXSITC_raw %>%
filter(processing %>% str_detect("I alt|i alt|Katillugit|katillugit|total|Total")) %>%
mutate(
processing = processing %>%
str_remove_all("[:digit:]|\\-") %>%
trimws() %>%
fct_inorder(),
value = value |> prettyNum(big.mark = ".", decimal.mark = ",")
) %>%
spread(transaction, value) %>%
mutate_if(is.numeric, ~replace(., is.na(.), 0)) %>%
gather(var, val, -c(processing, time)) %>%
mutate(var = var %>% str_to_title()) %>%
spread(var, val)
IEXSITC %>%
select(-time) %>%
rename(" " = 1) %>%
statgl_table(replace_0s = TRUE) %>%
pack_rows(index = table(paste0("Kroner, ", IEXSITC %>% pull(time)))) %>%
row_spec(1, bold = TRUE)
|
Export
|
Import
|
Kroner, 2022
|
Total
|
6.074.965.766
|
7.340.662.485
|
Provisions and livestock, total
|
5.836.940.972
|
1.056.407.397
|
Alcoholic beverages and tobacco, total
|
308.746
|
203.629.659
|
Raw materials, inedible, total
|
11.906.983
|
55.249.242
|
Mineral fuels and lubricants etc., total
|
4.120
|
1.450.555.164
|
Animal or vegetable fats and oils, total
|
3.298.396
|
8.080.838
|
Chemicals and chemical products, total
|
1.082.966
|
440.611.951
|
Manufactured products mainlysemimanufactured products, total
|
16.419.591
|
1.065.683.956
|
Machinery and transport equipment, total
|
179.201.166
|
2.346.499.594
|
Manufactured products, total
|
16.185.340
|
635.428.213
|
Miscellaneous articles and transactions, total
|
9.617.486
|
78.516.470
|
See the table in our Statbank: IEXSITC