Banco Mundial Markdown

Obtener datos del Banco Mundial

Obtener datos del GDP

install.packages("WDI", repos = "http://cran.us.r-project.org")

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install.packages("wbstats", repos = "http://cran.us.r-project.org")

## Installing package into 'C:/Users/paulr/AppData/Local/R/win-library/4.2'
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install.packages("tidyverse", repos = "http://cran.us.r-project.org")

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#Informacion de 1 Pais
library(wbstats)
gdp_data <- wb_data(country = "MX", indicator = "NY.GDP.PCAP.CD", start_date = 1973, end_date = 2022) #El indicador de dos digitos del pais googlealo y el indicador esta en la URL de la pagina web del World Bank
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,10)

## # A tibble: 10 × 9
##    iso2c iso3c country  date NY.GDP.PCAP.CD unit  obs_status footnote last_upd…¹
##    <chr> <chr> <chr>   <dbl>          <dbl> <chr> <chr>      <chr>    <date>    
##  1 MX    MEX   Mexico   1973           981. <NA>  <NA>       <NA>     2022-09-16
##  2 MX    MEX   Mexico   1974          1242. <NA>  <NA>       <NA>     2022-09-16
##  3 MX    MEX   Mexico   1975          1476. <NA>  <NA>       <NA>     2022-09-16
##  4 MX    MEX   Mexico   1976          1454. <NA>  <NA>       <NA>     2022-09-16
##  5 MX    MEX   Mexico   1977          1301. <NA>  <NA>       <NA>     2022-09-16
##  6 MX    MEX   Mexico   1978          1589. <NA>  <NA>       <NA>     2022-09-16
##  7 MX    MEX   Mexico   1979          2035. <NA>  <NA>       <NA>     2022-09-16
##  8 MX    MEX   Mexico   1980          3027. <NA>  <NA>       <NA>     2022-09-16
##  9 MX    MEX   Mexico   1981          3803. <NA>  <NA>       <NA>     2022-09-16
## 10 MX    MEX   Mexico   1982          2598. <NA>  <NA>       <NA>     2022-09-16
## # … with abbreviated variable name ¹​last_updated

tail(gdp_data,10)

## # A tibble: 10 × 9
##    iso2c iso3c country  date NY.GDP.PCAP.CD unit  obs_status footnote last_upd…¹
##    <chr> <chr> <chr>   <dbl>          <dbl> <chr> <chr>      <chr>    <date>    
##  1 MX    MEX   Mexico   2012         10242. <NA>  <NA>       <NA>     2022-09-16
##  2 MX    MEX   Mexico   2013         10725. <NA>  <NA>       <NA>     2022-09-16
##  3 MX    MEX   Mexico   2014         10929. <NA>  <NA>       <NA>     2022-09-16
##  4 MX    MEX   Mexico   2015          9617. <NA>  <NA>       <NA>     2022-09-16
##  5 MX    MEX   Mexico   2016          8745. <NA>  <NA>       <NA>     2022-09-16
##  6 MX    MEX   Mexico   2017          9288. <NA>  <NA>       <NA>     2022-09-16
##  7 MX    MEX   Mexico   2018          9687. <NA>  <NA>       <NA>     2022-09-16
##  8 MX    MEX   Mexico   2019          9950. <NA>  <NA>       <NA>     2022-09-16
##  9 MX    MEX   Mexico   2020          8432. <NA>  <NA>       <NA>     2022-09-16
## 10 MX    MEX   Mexico   2021          9926. <NA>  <NA>       <NA>     2022-09-16
## # … with abbreviated variable name ¹​last_updated

library(ggplot2)

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")

#Mas Datos de GDP

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()

Conclusión Esta técnica nos permite recopilar datos factuales de las diferentes bases de datos del banco mundial; la cual es una fuente extremadamente confiable; siendo esta una herramienta de gran uso para encontrar ágilmente datos para las investigaciones que estes haciendo.