Banco Mundial

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

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

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.8
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

getwd()

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 × 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 × 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>

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")

Información 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

En esta actividad, observamos como realizar gráficas de barras y dispersión. Estas gráficas nos permiten analizar los datos de una manera más sencilla, además, de volver más facil la comparación con otros datos. En este caso se utilizaron datos obtenidos del Banco Mundial.