Librerias
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
library(tidyr)
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
library(readxl)
library(knitr)
Parte 1
1.1 Importaciones 2018 - 2020
setwd("C:/Users/eazuc/OneDrive/Escritorio/Datos BC/Importaciones/")
IMPORTACIONES_2018_2020 <- list.files()
Lista_IMPORTACIONES <- lapply(IMPORTACIONES_2018_2020,
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
)
## Warning: The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
importaciones_men_2020<-bind_rows(Lista_IMPORTACIONES)
head(importaciones_men_2020)
## # A tibble: 6 × 6
## pais sac anio mes valor_cif Kilogramos
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Canada 0404900000 2018 2 68040 18810
## 2 Canada 0404900000 2018 3 92726. 25529.
## 3 Canada 0404900000 2018 4 92546. 25525
## 4 Canada 0404900000 2018 6 91531 25525
## 5 Canada 0405100000 2018 6 26.6 20
## 6 Canada 0511100000 2018 1 16365. 23.6
1.2 Exportaciones 2018 - 2020
setwd("C:/Users/eazuc/OneDrive/Escritorio/Datos BC/Exportaciones/")
EXPORTACIONES_2018_2020 <- list.files()
Lista_EXPORTACIONES <- lapply(EXPORTACIONES_2018_2020,
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
)
## Warning: The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
exportaciones_men_2020<-bind_rows(Lista_EXPORTACIONES)
head(exportaciones_men_2020)
## # A tibble: 6 × 6
## pais sac anio mes valor_fob Kilogramos
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Canada 0106900000 2018 2 4667. 15
## 2 Canada 0106900000 2018 3 4237. 14
## 3 Canada 0106900000 2018 4 3918 13.5
## 4 Canada 0106900000 2018 5 3155. 10
## 5 Canada 0106900000 2018 6 3097. 9
## 6 Canada 0305720000 2018 2 2702. 280
1.3 Tabla base de datos del comercio exterior (2018 - 2020)
importaciones_men_2020 %>% full_join(exportaciones_men_2020,
by = c("pais",
"sac",
"anio",
"mes"),
suffix = c("_importaciones"
,"_exportaciones")) -> df_comercio_exterior
df_comercio_exterior <- df_comercio_exterior %>% replace_na(
list(
valor_cif=0,
valor_fob=0,
Kilogramos_importaciones=0,
Kilogramos_exportaciones=0
)
) %>% arrange(pais,sac,mes,anio)
df_comercio_exterior %>% head(n = 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 = "black")
Base de Comercio Exterior 2018-2020
|
pais
|
sac
|
anio
|
mes
|
valor_cif
|
Kilogramos_importaciones
|
valor_fob
|
Kilogramos_exportaciones
|
|
Afganistan
|
0806200000
|
2018
|
6
|
6448.43
|
1463.92
|
0.00
|
0
|
|
Afganistan
|
0910200000
|
2019
|
10
|
1821.00
|
34.90
|
0.00
|
0
|
|
Afganistan
|
3004909100
|
2019
|
9
|
0.00
|
0.00
|
382.13
|
19
|
|
Afganistan
|
3924909000
|
2020
|
9
|
26.89
|
8.31
|
0.00
|
0
|
|
Afganistan
|
4202190000
|
2020
|
8
|
18.88
|
0.68
|
0.00
|
0
|
|
Afganistan
|
4202990000
|
2020
|
8
|
7.55
|
0.27
|
0.00
|
0
|
|
Afganistan
|
4901990000
|
2020
|
9
|
67.22
|
8.31
|
0.00
|
0
|
|
Afganistan
|
6103109000
|
2020
|
8
|
75.52
|
2.73
|
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
|
|
* Elaboración propia con base en datos del BCR
|
Parte 2
2.1 Actualizacion Importaciones hasta 2023
setwd("C:/Users/eazuc/OneDrive/Escritorio/Datos BC/Importaciones 2023/")
IMPORTACIONES_2018_2023 <- list.files()
Lista_IMPORTACIONES_2023 <- lapply(IMPORTACIONES_2018_2023,
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
)
## Warning: The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
## The following named parsers don't match the column names: kilogramos
importaciones_men_2023<-bind_rows(Lista_IMPORTACIONES_2023)
head(importaciones_men_2023)
## # A tibble: 6 × 6
## pais sac anio mes valor_cif Kilogramos
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Canada 0404900000 2018 2 68040 18810
## 2 Canada 0404900000 2018 3 92726. 25529.
## 3 Canada 0404900000 2018 4 92546. 25525
## 4 Canada 0404900000 2018 6 91531 25525
## 5 Canada 0405100000 2018 6 26.6 20
## 6 Canada 0511100000 2018 1 16365. 23.6
2.2 Actualizacion EXportaciones hasta 2023
setwd("C:/Users/eazuc/OneDrive/Escritorio/Datos BC/Exportaciones 2023/")
EXPORTACIONES_2018_2023 <- list.files()
Lista_EXPORTACIONES_2023 <- lapply(EXPORTACIONES_2018_2023,
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
)
## Warning: The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
## The following named parsers don't match the column names: valor_cif, kilogramos
Exportaciones_men_2023<-bind_rows(Lista_EXPORTACIONES_2023)
head(Exportaciones_men_2023)
## # A tibble: 6 × 6
## pais sac anio mes valor_fob Kilogramos
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Canada 0106900000 2018 2 4667. 15
## 2 Canada 0106900000 2018 3 4237. 14
## 3 Canada 0106900000 2018 4 3918 13.5
## 4 Canada 0106900000 2018 5 3155. 10
## 5 Canada 0106900000 2018 6 3097. 9
## 6 Canada 0305720000 2018 2 2702. 280
2.3 Tabla de comercio exterior de 2018 a 2023
importaciones_men_2023 %>% full_join(Exportaciones_men_2023,
by = c("pais",
"sac",
"anio",
"mes"),
suffix = c("_importaciones"
,"_exportaciones")) -> df_comercio_exterior23
df_comercio_exterior23 <- df_comercio_exterior23 %>% replace_na(
list(
valor_cif=0,
valor_fob=0,
Kilogramos_importaciones=0,
Kilogramos_exportaciones=0
)
) %>% arrange(pais,sac,mes,anio)
df_comercio_exterior23 %>% head(n = 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 = "black")
Base de Comercio Exterior 2018-2023
|
pais
|
sac
|
anio
|
mes
|
valor_cif
|
Kilogramos_importaciones
|
valor_fob
|
Kilogramos_exportaciones
|
|
Afganistan
|
0806200000
|
2018
|
6
|
6448.43
|
1463.92
|
0.00
|
0
|
|
Afganistan
|
0910200000
|
2019
|
10
|
1821.00
|
34.90
|
0.00
|
0
|
|
Afganistan
|
3004909100
|
2019
|
9
|
0.00
|
0.00
|
382.13
|
19
|
|
Afganistan
|
3924909000
|
2020
|
9
|
26.89
|
8.31
|
0.00
|
0
|
|
Afganistan
|
4202190000
|
2020
|
8
|
18.88
|
0.68
|
0.00
|
0
|
|
Afganistan
|
4202990000
|
2020
|
8
|
7.55
|
0.27
|
0.00
|
0
|
|
Afganistan
|
4901990000
|
2020
|
9
|
67.22
|
8.31
|
0.00
|
0
|
|
Afganistan
|
5702310000
|
2022
|
6
|
913.74
|
22.02
|
0.00
|
0
|
|
Afganistan
|
5702502000
|
2022
|
6
|
566.35
|
11.36
|
0.00
|
0
|
|
Afganistan
|
6103109000
|
2020
|
8
|
75.52
|
2.73
|
0.00
|
0
|
|
* Elaboración propia con base en datos del BCR
|
Parte 3
3.1 Cargar base de datos Iso, region, etc.
df_iso <- read_excel ("C:/Users/eazuc/OneDrive/Escritorio/Datos BC/codigo Iso/master_paises_iso.xlsx")
## New names:
## • `codigo_pais` -> `codigo_pais...5`
## • `codigo_pais` -> `codigo_pais...6`
## • `` -> `...13`
df_ISO <- df_iso %>% select(pais, iso_3, cod_region, cod_region_intermedia)
3.2 agregar el df_ISO a nuestro df_comercio_exterior
df_comercio_exterior23 <- df_comercio_exterior23 %>%
left_join(df_ISO, by = "pais")
3.3 Tabla actualizada del comercio exterior incluido el codigo ISO
de cada pais y su region.
head(df_comercio_exterior23 , 10)
## # A tibble: 10 × 11
## pais sac anio mes valor_cif Kilogramos_importacio…¹ valor_fob
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Afganistan 0806200000 2018 6 6448. 1464. 0
## 2 Afganistan 0910200000 2019 10 1821 34.9 0
## 3 Afganistan 3004909100 2019 9 0 0 382.
## 4 Afganistan 3924909000 2020 9 26.9 8.31 0
## 5 Afganistan 4202190000 2020 8 18.9 0.68 0
## 6 Afganistan 4202990000 2020 8 7.55 0.27 0
## 7 Afganistan 4901990000 2020 9 67.2 8.31 0
## 8 Afganistan 5702310000 2022 6 914. 22.0 0
## 9 Afganistan 5702502000 2022 6 566. 11.4 0
## 10 Afganistan 6103109000 2020 8 75.5 2.73 0
## # ℹ abbreviated name: ¹Kilogramos_importaciones
## # ℹ 4 more variables: Kilogramos_exportaciones <dbl>, iso_3 <chr>,
## # cod_region <chr>, cod_region_intermedia <chr>
Parte 4
años_ranking <- 2018:2023
df_comercio_exterior23 %>%
filter(anio %in% años_ranking) ->df_ranking
4.1 Vista de la tabla de los top 5 de exportadores a El Salvador
(2018 - 2023).
tabla_ranking <- df_ranking %>%
group_by(anio, iso_3) %>%
summarise(total= sum(valor_fob)) %>%
mutate(porcentaje = round(prop.table(total) * 100, 2)) %>%
slice_max(n = 5, order_by = total) %>%
as.data.frame() %>%
group_by(anio) %>%
mutate(rank = row_number(), data = paste(iso_3, "|", porcentaje, sep = "")) %>%
select(anio, data, rank) %>%
rename(año = anio)
## `summarise()` has grouped output by 'anio'. You can override using the
## `.groups` argument.
tabla_especial <- tabla_ranking %>%
pivot_wider(names_from = rank, values_from = data)
tabla_especial <- tabla_especial %>%
mutate(across(where(is.numeric), ~format(., big.mark = ",",
decimal.mark = ".",
nsmall = 2)))
tabla_especial %>%
kable(caption = paste("Top", 5, "de Exportaciones periodo",
min(años_ranking), max(años_ranking))) %>%
row_spec(1, bold = TRUE, color = "blue") %>%
add_footnote(label = "Elaboracion propia con base en datos del BCR")
Top 5 de Exportaciones periodo 2018 2023
|
año
|
1
|
2
|
3
|
4
|
5
|
|
2018
|
NA|47.09
|
HND|15.34
|
GTM|14.36
|
NIC|6.87
|
CRI|4.39
|
|
2019
|
NA|44.41
|
GTM|15.92
|
HND|15.9
|
NIC|6.66
|
CRI|4.46
|
|
2020
|
NA|43.91
|
GTM|16.42
|
HND|15.56
|
NIC|7.1
|
CRI|4.49
|
|
2021
|
NA|43.83
|
GTM|16.78
|
HND|16.49
|
NIC|7.2
|
CRI|4.11
|
|
2022
|
NA|41.17
|
GTM|17.05
|
HND|16.65
|
NIC|6.98
|
CRI|4.27
|
|
2023
|
NA|38.49
|
GTM|17.93
|
HND|15.94
|
NIC|7.39
|
CRI|4.62
|
|
a Elaboracion propia con base en datos del BCR
|