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

#Instalar paquetes y llamar librerías

#install.packages("WDI")
library(WDI)
#install.packages("wbstats")
library(wbstats)
#install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#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), es para buscar el código de letras de tus países
summary(gdp_mexico)
##     iso2c              iso3c             country               date     
##  Length:64          Length:64          Length:64          Min.   :1960  
##  Class :character   Class :character   Class :character   1st Qu.:1976  
##  Mode  :character   Mode  :character   Mode  :character   Median :1992  
##                                                           Mean   :1992  
##                                                           3rd Qu.:2007  
##                                                           Max.   :2023  
##  NY.GDP.PCAP.CD        unit            obs_status          footnote        
##  Min.   :  359.5   Length:64          Length:64          Length:64         
##  1st Qu.: 1431.5   Class :character   Class :character   Class :character  
##  Median : 4017.8   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 5132.1                                                           
##  3rd Qu.: 8959.9                                                           
##  Max.   :13926.1                                                           
##   last_updated       
##  Min.   :2024-06-28  
##  1st Qu.:2024-06-28  
##  Median :2024-06-28  
##  Mean   :2024-06-28  
##  3rd Qu.:2024-06-28  
##  Max.   :2024-06-28
head(gdp_mexico)
## # A tibble: 6 × 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           360. <NA>  <NA>       <NA>    
## 2 MX    MEX   Mexico   1961           378. <NA>  <NA>       <NA>    
## 3 MX    MEX   Mexico   1962           393. <NA>  <NA>       <NA>    
## 4 MX    MEX   Mexico   1963           424. <NA>  <NA>       <NA>    
## 5 MX    MEX   Mexico   1964           486. <NA>  <NA>       <NA>    
## 6 MX    MEX   Mexico   1965           511. <NA>  <NA>       <NA>    
## # ℹ 1 more variable: last_updated <date>
tail(gdp_mexico)
## # A tibble: 6 × 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   2018         10130. <NA>  <NA>       <NA>    
## 2 MX    MEX   Mexico   2019         10435. <NA>  <NA>       <NA>    
## 3 MX    MEX   Mexico   2020          8896. <NA>  <NA>       <NA>    
## 4 MX    MEX   Mexico   2021         10363. <NA>  <NA>       <NA>    
## 5 MX    MEX   Mexico   2022         11477. <NA>  <NA>       <NA>    
## 6 MX    MEX   Mexico   2023         13926. <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()+
  geom_point()

ggplot(gdp_mexico, aes(x=date, y= NY.GDP.PCAP.CD))+
  geom_col(fill="black")+
  geom_point(color="pink")+
  labs(title="Producto Interno Bruto en México (US per Capita)", x = "Año", y="PIB")

#Información de varios países

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()

LS0tCnRpdGxlOiAiV29ybGQgQmFuayIKYXV0aG9yOiBWaXZla2EgQ8OhcmRlbmFzIEVsaXpvbmRvIEEwMTI4NTY1NwpkYXRlOiAiMjAyNC0wOS0xMSIKb3V0cHV0OiAKICBodG1sX2RvY3VtZW50OiAKICAgIHRvYzogVFJVRQogICAgdG9jX2Zsb2F0OiBUUlVFCiAgICBjb2RlX2Rvd25sb2FkOiBUUlVFCiAgICB0aGVtZTogeWV0aQplZGl0b3Jfb3B0aW9uczogCiAgY2h1bmtfb3V0cHV0X3R5cGU6IGNvbnNvbGUKLS0tCgohW10oaHR0cHM6Ly9jZG4uZHJpYmJibGUuY29tL3VzZXJzLzEzODIzNjkvc2NyZWVuc2hvdHMvNTc2ODE3OC93b3JsZF9iYW5rX2xvb3AuZ2lmKQoKIzxzcGFuIHN0eWxlID0gImNvbG9yOnJlZDsiPkludHJvZHVjY2nDs24gPC9zcGFuPgpMb3MgZGF0b3MgZGVsICpXb3JsZCBCYW5rIEluZGljYXRvcnMgKFdCSSkqIHNvbiB1bmEgZnVlbnRlIGNvbXBsZXRhIGRlIGluZGljYWRvcmVzIGVjb27Ds21pY29zcywgc29jaWFsZXMsIHkgbWVkaW9hbWJpZW50YWxlcyBkZSBtw6FzIGRlIDIwMCBwYcOtc2VzLiAgCltGdWVudGU6IFdCXShodHRwczovL2RhdGEud29ybGRiYW5rLm9yZy9pbmRpY2F0b3IpCgojPHNwYW4gc3R5bGUgPSAiY29sb3I6cmVkOyI+SW5zdGFsYXIgcGFxdWV0ZXMgeSBsbGFtYXIgbGlicmVyw61hcyA8L3NwYW4+CmBgYHtyfQojaW5zdGFsbC5wYWNrYWdlcygiV0RJIikKbGlicmFyeShXREkpCiNpbnN0YWxsLnBhY2thZ2VzKCJ3YnN0YXRzIikKbGlicmFyeSh3YnN0YXRzKQojaW5zdGFsbC5wYWNrYWdlcygidGlkeXZlcnNlIikKbGlicmFyeSh0aWR5dmVyc2UpCiNpbnN0YWxsLnBhY2thZ2VzKCJnZ3Bsb3QyIikKbGlicmFyeShnZ3Bsb3QyKQpgYGAKCiM8c3BhbiBzdHlsZSA9ICJjb2xvcjpyZWQ7Ij5JbmZvcm1hY2nDs24gZGUgMSBwYcOtcyA8L3NwYW4+CmBgYHtyfQpnZHBfbWV4aWNvIDwtIHdiX2RhdGEoY291bnRyeT0iTVgiLCBpbmRpY2F0b3IgPSAiTlkuR0RQLlBDQVAuQ0QiLCBzdGFydF9kYXRlPTE5MDAsIGVuZF9kYXRlPTIwMjQpCiMoSVNPMzE2Ni0yIGNvdW50cnkgY29kZXMpLCBlcyBwYXJhIGJ1c2NhciBlbCBjw7NkaWdvIGRlIGxldHJhcyBkZSB0dXMgcGHDrXNlcwpzdW1tYXJ5KGdkcF9tZXhpY28pCmhlYWQoZ2RwX21leGljbykKdGFpbChnZHBfbWV4aWNvKQoKZ2dwbG90KGdkcF9tZXhpY28sIGFlcyh4PWRhdGUsIHk9IE5ZLkdEUC5QQ0FQLkNEKSkrCiAgZ2VvbV9wb2ludCgpCgpnZ3Bsb3QoZ2RwX21leGljbywgYWVzKHg9ZGF0ZSwgeT0gTlkuR0RQLlBDQVAuQ0QpKSsKICBnZW9tX2NvbCgpCgpnZ3Bsb3QoZ2RwX21leGljbywgYWVzKHg9ZGF0ZSwgeT0gTlkuR0RQLlBDQVAuQ0QpKSsKICBnZW9tX2NvbCgpKwogIGdlb21fcG9pbnQoKQoKZ2dwbG90KGdkcF9tZXhpY28sIGFlcyh4PWRhdGUsIHk9IE5ZLkdEUC5QQ0FQLkNEKSkrCiAgZ2VvbV9jb2woZmlsbD0iYmxhY2siKSsKICBnZW9tX3BvaW50KGNvbG9yPSJwaW5rIikrCiAgbGFicyh0aXRsZT0iUHJvZHVjdG8gSW50ZXJubyBCcnV0byBlbiBNw6l4aWNvIChVUyBwZXIgQ2FwaXRhKSIsIHggPSAiQcOxbyIsIHk9IlBJQiIpCmBgYAoKIzxzcGFuIHN0eWxlID0gImNvbG9yOnJlZDsiPkluZm9ybWFjacOzbiBkZSB2YXJpb3MgcGHDrXNlcyA8L3NwYW4+CmBgYHtyfQpnZHBfdmFyaW9zIDwtIHdiX2RhdGEoY291bnRyeT1jKCJNWCIsICJFQyIsIkNMIiksIGluZGljYXRvciA9ICJOWS5HRFAuUENBUC5DRCIsIHN0YXJ0X2RhdGU9MTkwMCwgZW5kX2RhdGU9MjAyNCkKZ2dwbG90KGdkcF92YXJpb3MsIGFlcyh4ID0gZGF0ZSwgeT0gTlkuR0RQLlBDQVAuQ0QsIGNvbG9yPWNvdW50cnkpKSsKICBnZW9tX3BvaW50KCkKYGBgCgo=