[1] “/Users/adrianelizondo/Desktop/icegif-1080.gif”

Introduccion

[fuente: WB] (https://data.worldbank.org/indicator)

# install.packages("WDI")
library(WDI)

# install.packages("wbstats")
library(wbstats)

# install.packages("tidyverse")
library(tidyverse)

# install.packages("ggplot2")
library(ggplot2)

Informacion de 1 pais

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: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_col()

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_point(fill="blue")+
  geom_col(fill="cyan")+
  labs(title="Producto Interno Bruto en México (US per Capita", x = "Año", y = "PIB")

Informacion 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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