# Introducción Los datos del World Bank Indicators (WBI) son una fuente completa de indicadores económicos, sociales y medioambientales de mÔs de 200 países. Fuente: WB

Instalar paquetes y llamar librerĆ­as

# install.packages("WDI")
library(WDI)
# install.packages("wbstats")
library(wbstats)
# install.packages("tidyverse")
library(tidyverse)
# install.packages("ggplot2")
library(ggplot2)

Información de 1 país

gdp_mexico <- wb_data(country = "MX", indicator = "NY.GDP.PCAP.CD",
start_date= 1900, end_date=2024)
# (ISO3166-2 country codes)
summary(gdp_mexico)
##     iso2c              iso3c             country               date     
##  Length:65          Length:65          Length:65          Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2008  
##                                                           Max.   :2024  
##  NY.GDP.PCAP.CD        unit            obs_status          footnote        
##  Min.   :  355.1   Length:65          Length:65          Length:65         
##  1st Qu.: 1465.5   Class :character   Class :character   Class :character  
##  Median : 4183.9   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 5238.3                                                           
##  3rd Qu.: 9097.9                                                           
##  Max.   :14185.8                                                           
##   last_updated       
##  Min.   :2026-02-24  
##  1st Qu.:2026-02-24  
##  Median :2026-02-24  
##  Mean   :2026-02-24  
##  3rd Qu.:2026-02-24  
##  Max.   :2026-02-24
head(gdp_mexico)
## # A tibble: 6 Ɨ 9
##   iso2c iso3c country  date NY.GDP.PCAP.CD unit  obs_status footnote
##   <chr> <chr> <chr>   <dbl>          <dbl> <chr> <chr>      <chr>   
## 1 MX    MEX   Mexico   1960           355. <NA>  <NA>       <NA>    
## 2 MX    MEX   Mexico   1961           374. <NA>  <NA>       <NA>    
## 3 MX    MEX   Mexico   1962           388. <NA>  <NA>       <NA>    
## 4 MX    MEX   Mexico   1963           420. <NA>  <NA>       <NA>    
## 5 MX    MEX   Mexico   1964           481. <NA>  <NA>       <NA>    
## 6 MX    MEX   Mexico   1965           506. <NA>  <NA>       <NA>    
## # ℹ 1 more variable: last_updated <date>
tail(gdp_mexico)
## # A tibble: 6 Ɨ 9
##   iso2c iso3c country  date NY.GDP.PCAP.CD unit  obs_status footnote
##   <chr> <chr> <chr>   <dbl>          <dbl> <chr> <chr>      <chr>   
## 1 MX    MEX   Mexico   2019         10370. <NA>  <NA>       <NA>    
## 2 MX    MEX   Mexico   2020          8841. <NA>  <NA>       <NA>    
## 3 MX    MEX   Mexico   2021         10314. <NA>  <NA>       <NA>    
## 4 MX    MEX   Mexico   2022         11406. <NA>  <NA>       <NA>    
## 5 MX    MEX   Mexico   2023         13861. <NA>  <NA>       <NA>    
## 6 MX    MEX   Mexico   2024         14186. <NA>  <NA>       <NA>    
## # ℹ 1 more variable: last_updated <date>
ggplot(gdp_mexico, aes(x = date, y = NY.GDP.PCAP.CD)) +
geom_point()

ggplot(gdp_mexico, aes(x = date, y = NY.GDP.PCAP.CD)) +
geom_col()

ggplot(gdp_mexico, aes(x = date, y = NY.GDP.PCAP.CD)) +
geom_col(fill = "#FF1493") +
geom_point(color = "#8A2BE2") +
labs(title="Producto Interno Bruto en MƩxico (US per Capita)", x =
"AƱo", y = "PIB")

Información de varios paises

gdp_varios <- wb_data(country = c("MX","EC","CL"), indicator =
"NY.GDP.PCAP.CD", start_date= 1900, end_date=2024)
ggplot(gdp_varios, aes(x=date, y=NY.GDP.PCAP.CD, color=country))+
geom_point()

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