Banco Mundial

Libraries

library(WDI)
library(wbstats)
library(tidyverse)
library(ggplot2)

Informacion de Mexico

gdp_data <- wb_data(country = "MX",indicator="NY.GDP.PCAP.CD",start_date = 1973,end_date = 2022)

summary(gdp_data)

##     iso2c              iso3c             country               date     
##  Length:49          Length:49          Length:49          Min.   :1973  
##  Class :character   Class :character   Class :character   1st Qu.:1985  
##  Mode  :character   Mode  :character   Mode  :character   Median :1997  
##                                                           Mean   :1997  
##                                                           3rd Qu.:2009  
##                                                           Max.   :2021  
##  NY.GDP.PCAP.CD        unit            obs_status          footnote        
##  Min.   :  981.5   Length:49          Length:49          Length:49         
##  1st Qu.: 2569.2   Class :character   Class :character   Class :character  
##  Median : 5650.0   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 5751.7                                                           
##  3rd Qu.: 9068.3                                                           
##  Max.   :10928.9                                                           
##   last_updated       
##  Min.   :2022-09-16  
##  1st Qu.:2022-09-16  
##  Median :2022-09-16  
##  Mean   :2022-09-16  
##  3rd Qu.:2022-09-16  
##  Max.   :2022-09-16

head(gdp_data)

## # A tibble: 6 x 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   1973           981. <NA>  <NA>       <NA>    
## 2 MX    MEX   Mexico   1974          1242. <NA>  <NA>       <NA>    
## 3 MX    MEX   Mexico   1975          1476. <NA>  <NA>       <NA>    
## 4 MX    MEX   Mexico   1976          1454. <NA>  <NA>       <NA>    
## 5 MX    MEX   Mexico   1977          1301. <NA>  <NA>       <NA>    
## 6 MX    MEX   Mexico   1978          1589. <NA>  <NA>       <NA>    
## # ... with 1 more variable: last_updated <date>

tail(gdp_data)

## # A tibble: 6 x 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   2016          8745. <NA>  <NA>       <NA>    
## 2 MX    MEX   Mexico   2017          9288. <NA>  <NA>       <NA>    
## 3 MX    MEX   Mexico   2018          9687. <NA>  <NA>       <NA>    
## 4 MX    MEX   Mexico   2019          9950. <NA>  <NA>       <NA>    
## 5 MX    MEX   Mexico   2020          8432. <NA>  <NA>       <NA>    
## 6 MX    MEX   Mexico   2021          9926. <NA>  <NA>       <NA>    
## # ... with 1 more variable: last_updated <date>

PIB de Mexico

ggplot(gdp_data,aes(x=date,y=NY.GDP.PCAP.CD))+geom_point()

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

ggplot(gdp_data,aes(x=date,y=NY.GDP.PCAP.CD))+geom_col(fill="red") +geom_point(color="blue")

Informacion de varios paises

more_gdp_data<-wb_data(country = c("NG","HT","KE"),indicator="NY.GDP.PCAP.CD",start_date = 1981,end_date = 2015)

ggplot(more_gdp_data,aes(x=date, y=NY.GDP.PCAP.CD,color=country,shape=country))+geom_point()

Conclusiones

Conocer las bases de datos a las cuales podemos acceder directamente de r agrega valor a nosotros como científicos de datos puesto que tenemos a la mano grandes cantidades de información disponibles. Así mismo, es importante que sepamos como transformarla y sacarle provecho.