Tarea 4

#Cargar Datos

options (scipen = 999) #para mostrar todos los decimales
library(dplyr)

## 
## 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(readxl)
 load("C:/Users/Admin/Downloads/data_comercio_exterior.RData")

#Pegar nombres paises
nombre_archivo <- "C:/Users/Admin/Downloads/master_paises_iso.xlsx"
nombres_iso_paises <- read_excel(nombre_archivo)

## New names:
## • `codigo_pais` -> `codigo_pais...5`
## • `codigo_pais` -> `codigo_pais...6`
## • `` -> `...13`

data_comercio_exterior %>%
  left_join(nombres_iso_paises,
            by = c("pais" = "nom_pais_esp")) -> data_comercio_exterior

#Seleccionar Años
anios_ranking<-2018:2020
data_comercio_exterior %>% 
  filter(anio %in% anios_ranking) ->data_ranking

#Cálculo de ranking & porcentajes

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 grouped output by 'anio'. You can override using the
## `.groups` argument.

print(insumo_reporte)

##    anio      data rank
## 1  2018 USA|44.07    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 USA|41.88    1
## 7  2019 GTM|15.95    2
## 8  2019 HND|15.91    3
## 9  2019  NIC|6.68    4
## 10 2019   CRI|4.5    5
## 11 2020 USA|35.73    1
## 12 2020  GTM|16.9    2
## 13 2020 HND|15.21    3
## 14 2020  NIC|7.65    4
## 15 2020  CRI|5.21    5