Tarea_5
Ejercicio 1
Compile la información disponible en la “Base de Datos de Comercio Exterior” del BCR,para los años 2018-2020. Y genera una tabla tal como se mostró en las clases (aún no incluya los nombres ISO de los países). Muestre un head de 10 casos.
Tabla de importaciones 2018-2020
library(readr)
IMPORTACIONES_2018_ENEJUN <- read_delim("C:/Users/Daniel/OneDrive/Desktop/Importaciones/IMPORTACIONES_2018_ENEJUN.csv",
delim = "|", escape_double = FALSE, trim_ws = TRUE)
head(IMPORTACIONES_2018_ENEJUN,10)## # A tibble: 10 × 6
## pais sac anio mes `valor cif` kilogramos
## <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 Canada 0404900000 2018 02 68040 18810
## 2 Canada 0404900000 2018 03 92726. 25529.
## 3 Canada 0404900000 2018 04 92546. 25525
## 4 Canada 0404900000 2018 06 91531 25525
## 5 Canada 0405100000 2018 06 26.6 20
## 6 Canada 0511100000 2018 01 16365. 23.6
## 7 Canada 0511100000 2018 03 17885. 24
## 8 Canada 0706900000 2018 04 964. 6500
## 9 Canada 0706900000 2018 06 116. 1176
## 10 Canada 0709991000 2018 06 166. 800library(dplyr)
library(kableExtra)
library(tidyr)
setwd("C:/Users/Daniel/OneDrive/Desktop/Importaciones/")
Importaciones_20182020<-list.files()
lista_importaciones<-lapply(Importaciones_20182020,FUN = read_delim,
delim="|",
col_names=c("Pais","Sac","Anio","Mes",
"Valor CIF","Kilogramos"),
col_types=cols(pais="c",sac="c",anio="d",
mes="d",valor_cif="d",kilogramos="d"),
skip=1
)
importaciones_20182020_mensuales<-bind_rows(lista_importaciones)
importaciones_20182020_mensuales %>%
head(10)%>%
kable(caption="El Salvador: Base de Importaciones 2018-2020",
align="c") %>%
add_footnote(label="Elaboración propia con base en datos del BCR",
notation="symbol") %>%
kable_styling() %>%
row_spec(0,bold = TRUE,color = "orange")| Pais | Sac | Anio | Mes | Valor CIF | Kilogramos |
|---|---|---|---|---|---|
| Canada | 0404900000 | 2018 | 02 | 68040.00 | 18810.00 |
| Canada | 0404900000 | 2018 | 03 | 92725.74 | 25528.60 |
| Canada | 0404900000 | 2018 | 04 | 92546.45 | 25525.00 |
| Canada | 0404900000 | 2018 | 06 | 91531.00 | 25525.00 |
| Canada | 0405100000 | 2018 | 06 | 26.56 | 20.00 |
| Canada | 0511100000 | 2018 | 01 | 16365.04 | 23.65 |
| Canada | 0511100000 | 2018 | 03 | 17884.85 | 24.00 |
| Canada | 0706900000 | 2018 | 04 | 963.78 | 6500.00 |
| Canada | 0706900000 | 2018 | 06 | 116.45 | 1176.00 |
| Canada | 0709991000 | 2018 | 06 | 166.36 | 800.00 |
| * Elaboración propia con base en datos del BCR |
Tabla de exportaciones 2018-2020
setwd("C:/Users/Daniel/OneDrive/Desktop/Exportaciones/")
Exportaciones_20182020<-list.files()
lista_exportaciones<-lapply(Exportaciones_20182020,FUN = read_delim,
delim="|",
col_names=c("Pais","Sac","Anio","Mes",
"Valor FOB","Kilogramos"),
col_types=cols(pais="c",sac="c",anio="d",
mes="d",valor_cif="d",kilogramos="d"),
skip=1
)
Exportaciones_20182020_mensuales<-bind_rows(lista_exportaciones)
Exportaciones_20182020_mensuales %>%
head(10)%>%
kable(caption="El Salvador: Base de exportaciones 2018-2020",
align="c") %>%
add_footnote(label="Elaboración propia con base en datos del BCR",
notation="symbol") %>%
kable_styling() %>%
row_spec(0,bold = TRUE,color = "orange")| Pais | Sac | Anio | Mes | Valor FOB | Kilogramos |
|---|---|---|---|---|---|
| Canada | 0106900000 | 2018 | 02 | 4667.20 | 15.00 |
| Canada | 0106900000 | 2018 | 03 | 4237.10 | 14.00 |
| Canada | 0106900000 | 2018 | 04 | 3918.00 | 13.50 |
| Canada | 0106900000 | 2018 | 05 | 3155.40 | 10.00 |
| Canada | 0106900000 | 2018 | 06 | 3097.40 | 9.00 |
| Canada | 0305720000 | 2018 | 02 | 2702.30 | 280.00 |
| Canada | 0602100000 | 2018 | 01 | 475519.57 | 7504.03 |
| Canada | 0602100000 | 2018 | 02 | 400232.82 | 6216.29 |
| Canada | 0602100000 | 2018 | 03 | 158138.42 | 2464.76 |
| Canada | 0602100000 | 2018 | 04 | 30370.08 | 510.83 |
| * Elaboración propia con base en datos del BCR |
Tabla de comercio exterior de El Salvador 2018-2020
importaciones_20182020_mensuales %>%
full_join(Exportaciones_20182020_mensuales,
by = c("Pais","Sac","Anio","Mes"),
suffix=c("_Importaciones","_Exportaciones"))->Comercio_Exterior
Comercio_Exterior %>%
replace_na(
list(
`Valor CIF`=0,
`Valor FOB`=0,
Kilogramos_Importaciones=0,
Kilogramos_Exportaciones=0
)
) %>%
arrange(Pais,Anio,Mes,Sac)->Comercio_Exterior
Comercio_Exterior %>%
head(10) %>%
kable(caption = "Base de Comercio Exterior 2018-2020",
align = "c") %>%
add_footnote(label = "Elaboración propia con base en datos del BCR",
notation = "symbol") %>%
kable_styling() %>%
row_spec(0,bold = TRUE,color = "orange")| Pais | Sac | Anio | Mes | Valor CIF | Kilogramos_Importaciones | Valor FOB | Kilogramos_Exportaciones |
|---|---|---|---|---|---|---|---|
| Afganistan | 0806200000 | 2018 | 06 | 6448.43 | 1463.92 | 0 | 0 |
| Afganistan | 6104220000 | 2018 | 10 | 3153.37 | 2407.61 | 0 | 0 |
| Afganistan | 6104620000 | 2018 | 10 | 946.01 | 722.28 | 0 | 0 |
| Afganistan | 6105100000 | 2018 | 10 | 9405.39 | 7181.03 | 0 | 0 |
| Afganistan | 6106100000 | 2018 | 10 | 1353.32 | 1725.55 | 0 | 0 |
| Afganistan | 6405900000 | 2018 | 10 | 2260.03 | 1725.55 | 0 | 0 |
| Afganistan | 8206000000 | 2018 | 10 | 6.56 | 5.02 | 0 | 0 |
| Afganistan | 6404110000 | 2019 | 02 | 7752.13 | 6748.03 | 0 | 0 |
| Afganistan | 6405100000 | 2019 | 02 | 508.03 | 442.24 | 0 | 0 |
| Afganistan | 6405900000 | 2019 | 02 | 12.45 | 10.85 | 0 | 0 |
| * Elaboración propia con base en datos del BCR |
Ejercicio 2
Realice la actualización de la tabla anterior con toda la información disponible para 2023.
Tabla Comercio Exterior 2018-2023
# Importaciones 2021-2023
Data_2023_Importaciones<-read_delim("C:/Users/Daniel/OneDrive/Desktop/Importaciones23/IMPORTACIONES_2021_ENEJUN.csv",
delim = "|", escape_double = FALSE, trim_ws = TRUE)
setwd("C:/Users/Daniel/OneDrive/Desktop/Importaciones23/")
Data2023_Importaciones<-list.files()
listImportaciones23<-lapply(Data2023_Importaciones,FUN = read_delim,
delim="|",
col_names=c("Pais","Sac","Anio","Mes",
"Valor CIF","Kilogramos"),
col_types=cols(pais="c",sac="c",anio="d",
mes="d",valor_cif="d",kilogramos="d"),
skip=1
)
importaciones_23<-bind_rows(listImportaciones23)
# Exportaciones 2021-2023
Data_2023_Exportaciones<-read_delim("C:/Users/Daniel/OneDrive/Desktop/Exportaciones23/EXPORTACIONES_2021_ENEJUN.csv",delim = "|", escape_double = FALSE, trim_ws = TRUE)
setwd("C:/Users/Daniel/OneDrive/Desktop/Exportaciones23/")
Data2023_Exportaciones<-list.files()
listExportaciones23<-lapply(Data2023_Exportaciones,FUN = read_delim,
delim="|",
col_names=c("Pais","Sac","Anio","Mes",
"Valor FOB","Kilogramos"),
col_types=cols(pais="c",sac="c",anio="d",
mes="d",valor_cif="d",kilogramos="d"),
skip=1
)
Exportaciones_23<-bind_rows(listExportaciones23)
# Comercio Exterior 2021-2023
importaciones_23 %>%
full_join(Exportaciones_23,
by = c("Pais","Sac","Anio","Mes"),
suffix=c("_Importaciones","_Exportaciones"))->ComercioExterior_23
ComercioExterior_23 %>%
replace_na(
list(
`Valor CIF`=0,
`Valor FOB`=0,
Kilogramos_Importaciones=0,
Kilogramos_Exportaciones=0
)
)## # A tibble: 713,687 × 8
## Pais Sac Anio Mes `Valor CIF` Kilogramos_Importacio…¹ `Valor FOB`
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 Canada 0511100000 2021 03 7162. 39 0
## 2 Canada 0712909000 2021 01 1541. 420 0
## 3 Canada 0712909000 2021 06 4519. 1270. 0
## 4 Canada 0713200000 2021 03 827. 382. 0
## 5 Canada 0713200000 2021 04 808. 382. 0
## 6 Canada 0713331000 2021 06 199. 2162 0
## 7 Canada 0713332000 2021 05 1072. 3632 0
## 8 Canada 0713332000 2021 06 3425. 12712 0
## 9 Canada 0713334000 2021 01 518. 3632 0
## 10 Canada 0713334000 2021 02 903. 3632 0
## # ℹ 713,677 more rows
## # ℹ abbreviated name: ¹Kilogramos_Importaciones
## # ℹ 1 more variable: Kilogramos_Exportaciones <dbl># Unión de ambos Comercios Exteriores 2018 a 2023
Comercio_Exterior %>%
bind_rows(ComercioExterior_23) %>%
arrange(Pais,Anio,Sac,Mes)->Comercio_Exterior
Comercio_Exterior %>%
head(10) %>%
kable(caption = "Base de Comercio Exterior 2018-2023",
align = "c") %>%
add_footnote(label = "Elaboración propia con base en datos del BCR",
notation = "symbol") %>%
kable_styling() %>%
row_spec(0,bold = TRUE,color = "orange")| Pais | Sac | Anio | Mes | Valor CIF | Kilogramos_Importaciones | Valor FOB | Kilogramos_Exportaciones |
|---|---|---|---|---|---|---|---|
| Afganistan | 0806200000 | 2018 | 06 | 6448.43 | 1463.92 | 0.00 | 0 |
| Afganistan | 6104220000 | 2018 | 10 | 3153.37 | 2407.61 | 0.00 | 0 |
| Afganistan | 6104620000 | 2018 | 10 | 946.01 | 722.28 | 0.00 | 0 |
| Afganistan | 6105100000 | 2018 | 10 | 9405.39 | 7181.03 | 0.00 | 0 |
| Afganistan | 6106100000 | 2018 | 10 | 1353.32 | 1725.55 | 0.00 | 0 |
| Afganistan | 6405900000 | 2018 | 10 | 2260.03 | 1725.55 | 0.00 | 0 |
| Afganistan | 8206000000 | 2018 | 10 | 6.56 | 5.02 | 0.00 | 0 |
| Afganistan | 0910200000 | 2019 | 10 | 1821.00 | 34.90 | 0.00 | 0 |
| Afganistan | 3004909100 | 2019 | 09 | 0.00 | 0.00 | 382.13 | 19 |
| Afganistan | 6404110000 | 2019 | 02 | 7752.13 | 6748.03 | 0.00 | 0 |
| * Elaboración propia con base en datos del BCR |
Ejercicio 3
Agregue la información estandarizada de los países (nombres iso de los países y regiones, etc, tal como se vio en clases). Muestre un head de 10 casos.
library(readxl)
ISO_Paises<- read_excel("C:/Users/Daniel/Downloads/master_paises_iso.xlsx")
Comercio_Exterior %>%
left_join(ISO_Paises,
by = c("Pais"="nom_pais_esp"))->Comercio_Extrerior_ISO
Comercio_Extrerior_ISO %>%
head(10) %>%
kable(caption = "Base de Comercio Exterior 2018-2023",
align = "c") %>%
add_footnote(label = "Elaboración propia con base en datos del BCR",
notation = "symbol") %>%
kable_styling() %>%
row_spec(0,bold = TRUE,color = "orange")| Pais | Sac | Anio | Mes | Valor CIF | Kilogramos_Importaciones | Valor FOB | Kilogramos_Exportaciones | nom_pais_ingles | iso_2 | iso_3 | codigo_pais…5 | codigo_pais…6 | region | cod_region | sub_region | cod_sub_region | region_intermedia | cod_region_intermedia | …13 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afganistan | 0806200000 | 2018 | 06 | 6448.43 | 1463.92 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 6104220000 | 2018 | 10 | 3153.37 | 2407.61 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 6104620000 | 2018 | 10 | 946.01 | 722.28 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 6105100000 | 2018 | 10 | 9405.39 | 7181.03 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 6106100000 | 2018 | 10 | 1353.32 | 1725.55 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 6405900000 | 2018 | 10 | 2260.03 | 1725.55 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 8206000000 | 2018 | 10 | 6.56 | 5.02 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 0910200000 | 2019 | 10 | 1821.00 | 34.90 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 3004909100 | 2019 | 09 | 0.00 | 0.00 | 382.13 | 19 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| Afganistan | 6404110000 | 2019 | 02 | 7752.13 | 6748.03 | 0.00 | 0 | Afghanistan | AF | AFG | 4 | Asia | 142 | Asia Meridional | 34 | NA | NA | Asia | 142 |
| * Elaboración propia con base en datos del BCR |
Ejercicio 4
Ranking y Porcentajes
Obtenga un ranking, anual, de los 5 principales socios comerciales de El Salvador, para el periodo 2018-2022.
Años_Ranking<-2018:2023
Comercio_Extrerior_ISO%>%
filter(Anio %in% Años_Ranking)->rankingAños_SociosComerciales
Principales_Socios_Comerciales_ESA<-rankingAños_SociosComerciales %>%
group_by(Anio,iso_3) %>%
summarise(TotalFOB=sum(`Valor FOB`)) %>%
mutate(Porcentaje_TotalFOB=round(prop.table(TotalFOB)*100,2)) %>%
slice_max(n=5,order_by = TotalFOB) %>%
as.data.frame() %>%
group_by(Anio) %>%
mutate(rank=row_number(),data=paste(iso_3,"|",Porcentaje_TotalFOB,sep = "")) %>%
select(Anio,data,rank) %>%
as.data.frame() ->Principales_Socios_Comerciales_ESA
head(Principales_Socios_Comerciales_ESA,20)## Anio data rank
## 1 2018 NA|47.09 1
## 2 2018 HND|15.34 2
## 3 2018 GTM|14.36 3
## 4 2018 NIC|6.87 4
## 5 2018 CRI|4.39 5
## 6 2019 NA|44.41 1
## 7 2019 GTM|15.92 2
## 8 2019 HND|15.9 3
## 9 2019 NIC|6.66 4
## 10 2019 CRI|4.46 5
## 11 2020 NA|43.91 1
## 12 2020 GTM|16.42 2
## 13 2020 HND|15.56 3
## 14 2020 NIC|7.1 4
## 15 2020 CRI|4.49 5
## 16 2021 LCA|NA 1
## 17 2021 AGO|NA 2
## 18 2021 ALB|NA 3
## 19 2021 AND|NA 4
## 20 2021 ANT|NA 5Tabla formato RAW
Principales_Socios_Comerciales_ESA %>%
pivot_wider(names_from = rank,values_from = data)->Tabla_RAW
print(Tabla_RAW)## # A tibble: 6 × 124
## Anio `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` `12`
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 2018 NA|47… HND|… GTM|… NIC|… CRI|… <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 2019 NA|44… GTM|… HND|… NIC|… CRI|… <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 2020 NA|43… GTM|… HND|… NIC|… CRI|… <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 2021 LCA|NA AGO|… ALB|… AND|… ANT|… ARE|… ARG|… ARM|… ATA|… AUS|… AUT|… BEL|…
## 5 2022 TGO|NA AFG|… ALB|… AND|… ANT|… ARE|… ARG|… ARM|… AUS|… AUT|… BEL|… BEN|…
## 6 2023 LCA|NA MNG|… AFG|… AGO|… ALB|… AND|… ANT|… ARE|… ARG|… ARM|… AUS|… AUT|…
## # ℹ 111 more variables: `13` <chr>, `14` <chr>, `15` <chr>, `16` <chr>,
## # `17` <chr>, `18` <chr>, `19` <chr>, `20` <chr>, `21` <chr>, `22` <chr>,
## # `23` <chr>, `24` <chr>, `25` <chr>, `26` <chr>, `27` <chr>, `28` <chr>,
## # `29` <chr>, `30` <chr>, `31` <chr>, `32` <chr>, `33` <chr>, `34` <chr>,
## # `35` <chr>, `36` <chr>, `37` <chr>, `38` <chr>, `39` <chr>, `40` <chr>,
## # `41` <chr>, `42` <chr>, `43` <chr>, `44` <chr>, `45` <chr>, `46` <chr>,
## # `47` <chr>, `48` <chr>, `49` <chr>, `50` <chr>, `51` <chr>, `52` <chr>, …library(kableExtra)
Tabla_RAW %>%
kable(caption = paste("Top",5,"de exportaciones período",
min(Años_Ranking),"-",max(Años_Ranking))) %>%
add_footnote(label = "Elaboración propia con base en datos del BCR")| Anio | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018 | NA|47.09 | HND|15.34 | GTM|14.36 | NIC|6.87 | CRI|4.39 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2019 | NA|44.41 | GTM|15.92 | HND|15.9 | NIC|6.66 | CRI|4.46 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2020 | NA|43.91 | GTM|16.42 | HND|15.56 | NIC|7.1 | CRI|4.49 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2021 | LCA|NA | AGO|NA | ALB|NA | AND|NA | ANT|NA | ARE|NA | ARG|NA | ARM|NA | ATA|NA | AUS|NA | AUT|NA | BEL|NA | BEN|NA | BGR|NA | BHS|NA | BLZ|NA | BOL|NA | BRA|NA | BRB|NA | CAN|NA | CHE|NA | CHL|NA | CMR|NA | COL|NA | CRI|NA | CUB|NA | CYP|NA | DEU|NA | DMA|NA | DNK|NA | DOM|NA | DZA|NA | ECU|NA | EGY|NA | ESP|NA | EST|NA | FIN|NA | FRA|NA | GAB|NA | GEO|NA | GHA|NA | GRC|NA | GRD|NA | GTM|NA | HND|NA | HRV|NA | HTI|NA | HUN|NA | IDN|NA | IND|NA | IRL|NA | IRN|NA | ISL|NA | ISR|NA | ITA|NA | JAM|NA | JOR|NA | JPN|NA | KEN|NA | KHM|NA | KIR|NA | LAO|NA | LBN|NA | LBR|NA | LIE|NA | LTU|NA | LUX|NA | MAR|NA | MCO|NA | MDA|NA | MDG|NA | MEX|NA | MLI|NA | MLT|NA | MNE|NA | MNG|NA | MOZ|NA | MUS|NA | MYS|NA | NAM|NA | NER|NA | NGA|NA | NIC|NA | NOR|NA | NPL|NA | PAN|NA | PER|NA | PHL|NA | PLW|NA | PNG|NA | POL|NA | PRI|NA | PRT|NA | PRY|NA | QAT|NA | ROU|NA | SDN|NA | SEN|NA | SGP|NA | SLE|NA | SLV|NA | SMR|NA | SRB|NA | SVK|NA | SVN|NA | SWE|NA | TCD|NA | TGO|NA | THA|NA | TKM|NA | TUN|NA | TUR|NA | TWN|NA | UGA|NA | UKR|NA | URY|NA | VCT|NA | VEN|NA | ZWE|NA | NA|NA | NA | NA | NA |
| 2022 | TGO|NA | AFG|NA | ALB|NA | AND|NA | ANT|NA | ARE|NA | ARG|NA | ARM|NA | AUS|NA | AUT|NA | BEL|NA | BEN|NA | BFA|NA | BGR|NA | BHS|NA | BLZ|NA | BOL|NA | BRA|NA | BRB|NA | CAF|NA | CAN|NA | CHE|NA | CHL|NA | CIV|NA | CMR|NA | COL|NA | CRI|NA | CUB|NA | CYP|NA | DEU|NA | DMA|NA | DNK|NA | DOM|NA | DZA|NA | ECU|NA | EGY|NA | ERI|NA | ESP|NA | EST|NA | FIN|NA | FRA|NA | GAB|NA | GEO|NA | GHA|NA | GMB|NA | GRC|NA | GRD|NA | GTM|NA | HND|NA | HRV|NA | HTI|NA | HUN|NA | IDN|NA | IND|NA | IRL|NA | IRN|NA | ISL|NA | ISR|NA | ITA|NA | JAM|NA | JOR|NA | JPN|NA | KEN|NA | KHM|NA | LAO|NA | LBN|NA | LBR|NA | LCA|NA | LIE|NA | LTU|NA | LUX|NA | MAR|NA | MCO|NA | MDA|NA | MDG|NA | MEX|NA | MLI|NA | MLT|NA | MNE|NA | MNG|NA | MOZ|NA | MRT|NA | MUS|NA | MWI|NA | MYS|NA | NAM|NA | NER|NA | NGA|NA | NIC|NA | NOR|NA | NPL|NA | PAN|NA | PER|NA | PHL|NA | PLW|NA | POL|NA | PRI|NA | PRT|NA | PRY|NA | QAT|NA | ROU|NA | SEN|NA | SGP|NA | SLE|NA | SLV|NA | SMR|NA | SRB|NA | SVK|NA | SVN|NA | SWE|NA | TCD|NA | THA|NA | TUN|NA | TUR|NA | TWN|NA | UGA|NA | UKR|NA | URY|NA | UZB|NA | VCT|NA | VEN|NA | ZWE|NA | NA|NA |
| 2023 | LCA|NA | MNG|NA | AFG|NA | AGO|NA | ALB|NA | AND|NA | ANT|NA | ARE|NA | ARG|NA | ARM|NA | AUS|NA | AUT|NA | BEL|NA | BFA|NA | BGR|NA | BHS|NA | BLZ|NA | BOL|NA | BRA|NA | BRB|NA | CAN|NA | CHE|NA | CHL|NA | CIV|NA | CMR|NA | COG|NA | COL|NA | CRI|NA | CUB|NA | CYP|NA | DEU|NA | DMA|NA | DNK|NA | DOM|NA | DZA|NA | ECU|NA | EGY|NA | ERI|NA | ESP|NA | EST|NA | FIN|NA | FRA|NA | GEO|NA | GHA|NA | GMB|NA | GRC|NA | GRD|NA | GTM|NA | HND|NA | HRV|NA | HTI|NA | HUN|NA | IDN|NA | IND|NA | IRL|NA | IRN|NA | ISL|NA | ISR|NA | ITA|NA | JAM|NA | JOR|NA | JPN|NA | KEN|NA | KHM|NA | LAO|NA | LBN|NA | LBR|NA | LIE|NA | LTU|NA | LUX|NA | MAR|NA | MCO|NA | MDA|NA | MDG|NA | MEX|NA | MLI|NA | MLT|NA | MNE|NA | MRT|NA | MUS|NA | MYS|NA | NAM|NA | NER|NA | NGA|NA | NIC|NA | NOR|NA | NPL|NA | PAN|NA | PER|NA | PHL|NA | PLW|NA | POL|NA | PRI|NA | PRT|NA | PRY|NA | PSE|NA | QAT|NA | ROU|NA | SEN|NA | SGP|NA | SLE|NA | SLV|NA | SMR|NA | SRB|NA | SVK|NA | SVN|NA | SWE|NA | TGO|NA | THA|NA | TUN|NA | TUR|NA | TUV|NA | TWN|NA | UGA|NA | UKR|NA | URY|NA | UZB|NA | VCT|NA | VEN|NA | ZWE|NA | NA|NA | NA | NA |
| a Elaboración propia con base en datos del BCR |