# 1.Carga de librerias
library(readr)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.6.1
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(kableExtra)
##
## Adjuntando el paquete: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
load("C:/Users/mendo/OneDrive/Desktop/MAE/comercio exter/data_comercio_exterior (1).RData")
# 2. Tabla con códigos ISO (si ya existen en el data frame)
data_comercio_exterior %>%
head(10) %>%
kable(caption = "Datos con códigos ISO") %>%
kable_styling(bootstrap_options = c("striped", "hover"))
Datos con códigos ISO
|
pais
|
sac
|
anio
|
mes
|
valor_cif
|
kilogramos_importaciones
|
valor_fob
|
kilogramos_exportaciones
|
|
AFGANISTAN
|
8708990000
|
2022
|
2
|
220.50
|
0.29
|
0
|
0
|
|
AFGANISTAN
|
8471900000
|
2022
|
5
|
665.90
|
4.00
|
0
|
0
|
|
AFGANISTAN
|
5702310000
|
2022
|
6
|
913.74
|
22.02
|
0
|
0
|
|
AFGANISTAN
|
5702502000
|
2022
|
6
|
566.35
|
11.36
|
0
|
0
|
|
AFGANISTAN
|
6111200000
|
2022
|
8
|
213.69
|
1.56
|
0
|
0
|
|
AFGANISTAN
|
8536509000
|
2022
|
9
|
42.28
|
2.00
|
0
|
0
|
|
AFGANISTAN
|
6210400000
|
2023
|
7
|
204.75
|
0.10
|
0
|
0
|
|
AFGANISTAN
|
0901210000
|
2024
|
4
|
0.00
|
0.00
|
440
|
21
|
|
AFGANISTAN
|
4909000000
|
2024
|
4
|
0.07
|
0.15
|
0
|
0
|
|
AFGANISTAN
|
8473300000
|
2024
|
6
|
60.18
|
1.00
|
0
|
0
|
# Carga de script de funciones
source(
file = "C:/Users/mendo/OneDrive/Desktop/MAE/funciones_comercio_exterior.R",
echo = TRUE,
max.deparse.length = 100,
encoding = "utf-8"
) #Usar en el Rdata en el "Funciones_comercio_exterior" la ruta de acceso que usaste en save 2 chunks arriba
##
## > options(scipen = 999)
##
## > library(dplyr)
##
## > library(readxl)
##
## > library(stringr)
## Warning: package 'stringr' was built under R version 4.6.1
##
## > load("C:/Users/mendo/OneDrive/Desktop/MAE/comercio exter/data_comercio_exterior (1).RData")
##
## > nombre_archivo <- ("C:/Users/mendo/OneDrive/Desktop/MAE/nombres_iso_paises.xlsx")
##
## > nombres_iso_paises <- read_excel(nombre_archivo)
##
## > nombres_iso_paises$nom_pais_esp <- toupper(nombres_iso_paises$nom_pais_esp)
##
## > data_comercio_exterior <- data_comercio_exterior %>%
## + left_join(nombres_iso_paises, by = c(p .... [TRUNCATED]
##
## > herramientas_mostrar_paises <- function() {
## + paises <- nombres_iso_paises %>% select(nom_pais .... [TRUNCATED]
##
## > herramientas_mostrar_regiones <- function() {
## + nombres_iso_paises %>% group_by(region, cod_re .... [TRUNCATED]
##
## > herramientas_mostrar_paises_en_tabla <- function() {
## + data_comercio_exterior %>% group_by(pai .... [TRUNCATED]
##
## > indicadores_IVCR_capitulo <- function(codigo_pais,
## + capitulo, anio) {
## + codigo_pais <- e .... [TRUNCATED]
##
## > indicadores_IVCR_partida <- function(codigo_pais,
## + partida, anio) {
## + codigo_pais <- enq .... [TRUNCATED]
##
## > indicadores_Balassa_capitulo <- function(codigo_pais,
## + capitulo, anio, normalizado = TRUE) { .... [TRUNCATED]
##
## > indicadores_Balassa_partida <- function(codigo_pais,
## + partida, anio, normalizado = TRUE) {
## + .... [TRUNCATED]
##
## > indicadores_IHH_Herfindahl_Hirschmann_anual_texto <- function(codigo_pais,
## + anio, normalizad .... [TRUNCATED]
##
## > indicadores_IHH_Herfindahl_Hirschmann_anual <- function(codigo_pais,
## + anio, normalizado = TR .... [TRUNCATED]
#Seleccionar Años
anios_ranking<-2022:2026
data_comercio_exterior %>%
filter(anio %in% anios_ranking) ->data_ranking
data_ranking %>%
group_by(anio,iso_3) %>%
summarise(total=sum(valor_fob)) %>% mutate(percent=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,"|",percent,sep = "")) %>%
select(anio,data,rank) %>% as.data.frame() -> insumo_reporte
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by anio and iso_3.
## ℹ Output is grouped by anio.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(anio, iso_3))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.
print(insumo_reporte)
## anio data rank
## 1 2022 USA|38.78 1
## 2 2022 GTM|17.05 2
## 3 2022 HND|16.65 3
## 4 2022 NIC|6.98 4
## 5 2022 CRI|4.27 5
## 6 2023 USA|35.6 1
## 7 2023 GTM|18.45 2
## 8 2023 HND|16.3 3
## 9 2023 NIC|7.85 4
## 10 2023 CRI|4.79 5
## 11 2024 USA|33.84 1
## 12 2024 GTM|19.53 2
## 13 2024 HND|16.14 3
## 14 2024 NIC|8.94 4
## 15 2024 CRI|4.71 5
## 16 2025 USA|32.45 1
## 17 2025 GTM|19.64 2
## 18 2025 HND|16.74 3
## 19 2025 NIC|9.12 4
## 20 2025 CRI|4.45 5
## 21 2026 USA|32.53 1
## 22 2026 GTM|19.46 2
## 23 2026 HND|15.44 3
## 24 2026 NIC|8.7 4
## 25 2026 CRI|4.51 5
library(tidyr)
insumo_reporte %>%
pivot_wider(names_from = rank,values_from = data)->mi_tabla
print(mi_tabla)
## # A tibble: 5 × 6
## anio `1` `2` `3` `4` `5`
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 2022 USA|38.78 GTM|17.05 HND|16.65 NIC|6.98 CRI|4.27
## 2 2023 USA|35.6 GTM|18.45 HND|16.3 NIC|7.85 CRI|4.79
## 3 2024 USA|33.84 GTM|19.53 HND|16.14 NIC|8.94 CRI|4.71
## 4 2025 USA|32.45 GTM|19.64 HND|16.74 NIC|9.12 CRI|4.45
## 5 2026 USA|32.53 GTM|19.46 HND|15.44 NIC|8.7 CRI|4.51
mi_tabla %>%
kable(caption = paste("Top",5,"de Exportaciones periodo",
min(anios_ranking),"-",max(anios_ranking))) %>%
add_footnote(label = "Elaboración propia con base en datos del BCR")
Top 5 de Exportaciones periodo 2022 - 2026
| 2022 |
USA|38.78 |
GTM|17.05 |
HND|16.65 |
NIC|6.98 |
CRI|4.27 |
| 2023 |
USA|35.6 |
GTM|18.45 |
HND|16.3 |
NIC|7.85 |
CRI|4.79 |
| 2024 |
USA|33.84 |
GTM|19.53 |
HND|16.14 |
NIC|8.94 |
CRI|4.71 |
| 2025 |
USA|32.45 |
GTM|19.64 |
HND|16.74 |
NIC|9.12 |
CRI|4.45 |
| 2026 |
USA|32.53 |
GTM|19.46 |
HND|15.44 |
NIC|8.7 |
CRI|4.51 |
Note: aElaboración propia con base en
datos del BCR