R Script Panel y Ejercicio 2

R Script “Panel” para obtener Indicadores del Banco Mundial (5 puntos)

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

## -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
## v dplyr     1.1.0     v readr     2.1.2
## v forcats   1.0.0     v stringr   1.5.0
## v ggplot2   3.4.1     v tibble    3.1.8
## v lubridate 1.9.3     v tidyr     1.3.0
## v purrr     1.0.1     
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## i Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

gdp_data <- wb_data(country=c("MX","EC","CA"), indicator = "NY.GDP.PCAP.CD", start_date=2013, end_date=2023)
gdp_data

## # A tibble: 30 x 9
##    iso2c iso3c country  date NY.GDP.PCAP.CD unit  obs_status footnote
##    <chr> <chr> <chr>   <dbl>          <dbl> <chr> <chr>      <chr>   
##  1 CA    CAN   Canada   2013         52635. <NA>  <NA>       <NA>    
##  2 CA    CAN   Canada   2014         50956. <NA>  <NA>       <NA>    
##  3 CA    CAN   Canada   2015         43596. <NA>  <NA>       <NA>    
##  4 CA    CAN   Canada   2016         42316. <NA>  <NA>       <NA>    
##  5 CA    CAN   Canada   2017         45129. <NA>  <NA>       <NA>    
##  6 CA    CAN   Canada   2018         46549. <NA>  <NA>       <NA>    
##  7 CA    CAN   Canada   2019         46374. <NA>  <NA>       <NA>    
##  8 CA    CAN   Canada   2020         43562. <NA>  <NA>       <NA>    
##  9 CA    CAN   Canada   2021         52515. <NA>  <NA>       <NA>    
## 10 CA    CAN   Canada   2022         55522. <NA>  <NA>       <NA>    
## # i 20 more rows
## # i 1 more variable: last_updated <date>

panel <- select(gdp_data,country,date,NY.GDP.PCAP.CD)
panel

## # A tibble: 30 x 3
##    country  date NY.GDP.PCAP.CD
##    <chr>   <dbl>          <dbl>
##  1 Canada   2013         52635.
##  2 Canada   2014         50956.
##  3 Canada   2015         43596.
##  4 Canada   2016         42316.
##  5 Canada   2017         45129.
##  6 Canada   2018         46549.
##  7 Canada   2019         46374.
##  8 Canada   2020         43562.
##  9 Canada   2021         52515.
## 10 Canada   2022         55522.
## # i 20 more rows

Ejercicio 2. Conjunto de Datos de Panel con Indicadores del Banco Mundial (15 puntos)

climate_change <- wb_data(country=c("MX","NO","FI","SE","DK"), indicator=c("EN.ATM.CO2E.KT","EG.FEC.RNEW.ZS","SP.URB.TOTL.IN.ZS","AG.LND.AGRI.ZS"), start_date=2010, end_date = 2020)

climate_change

## # A tibble: 55 x 8
##    iso2c iso3c country  date AG.LND.AGRI.ZS EG.FEC.RNEW.ZS EN.ATM.CO2E.KT
##    <chr> <chr> <chr>   <dbl>          <dbl>          <dbl>          <dbl>
##  1 DK    DNK   Denmark  2010           65.6           21.2         48125.
##  2 DK    DNK   Denmark  2011           67.2           23.8         43098 
##  3 DK    DNK   Denmark  2012           65.6           26.9         38216.
##  4 DK    DNK   Denmark  2013           65.7           27.0         39961.
##  5 DK    DNK   Denmark  2014           66.3           30.2         35820 
##  6 DK    DNK   Denmark  2015           65.8           32.5         33705.
##  7 DK    DNK   Denmark  2016           65.6           32.0         35340.
##  8 DK    DNK   Denmark  2017           65.8           35.0         33184 
##  9 DK    DNK   Denmark  2018           65.8           34.6         33133.
## 10 DK    DNK   Denmark  2019           65.6           37.3         29696.
## # i 45 more rows
## # i 1 more variable: SP.URB.TOTL.IN.ZS <dbl>

climate_change_clean <- select(climate_change,country,date,EN.ATM.CO2E.KT,EG.FEC.RNEW.ZS,SP.URB.TOTL.IN.ZS,AG.LND.AGRI.ZS)
climate_change_clean

## # A tibble: 55 x 6
##    country  date EN.ATM.CO2E.KT EG.FEC.RNEW.ZS SP.URB.TOTL.IN.ZS AG.LND.AGRI.ZS
##    <chr>   <dbl>          <dbl>          <dbl>             <dbl>          <dbl>
##  1 Denmark  2010         48125.           21.2              86.8           65.6
##  2 Denmark  2011         43098            23.8              87.0           67.2
##  3 Denmark  2012         38216.           26.9              87.1           65.6
##  4 Denmark  2013         39961.           27.0              87.3           65.7
##  5 Denmark  2014         35820            30.2              87.4           66.3
##  6 Denmark  2015         33705.           32.5              87.5           65.8
##  7 Denmark  2016         35340.           32.0              87.6           65.6
##  8 Denmark  2017         33184            35.0              87.8           65.8
##  9 Denmark  2018         33133.           34.6              87.9           65.8
## 10 Denmark  2019         29696.           37.3              88.0           65.6
## # i 45 more rows