ejercicio

Bibliotecas

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(skimr)
library(readxl)

Tuberia en R

La tuberia en r se obtiene con CTRL + Shift + M: |>

Importado excel

excel_sheets("estadistica-202402/estadistica-202402/datos-banco-mundial/agricultura_excel.xls")

[1] "Data"                "Metadata-Countries"  "Metadata-Indicators"

excel <- read_excel("estadistica-202402/estadistica-202402/datos-banco-mundial/agricultura_excel.xls",
                    sheet = "Data",
                    skip = 3 ) |>
  pivot_longer(cols = -c("Country Name", "Country Code", "Indicator Name", "Indicator Code"), 
               names_to = "year_es", 
               values_to = "Agricultura_silvicultura_pesca")|>
  select(-c("Indicator Name", "Indicator Code"))

excel2 <- read_excel("estadistica-202402/estadistica-202402/datos-banco-mundial/tierras agrícolas_excel.xls",
                    sheet = "Data",
                    skip = 3 ) |>
  pivot_longer(cols = -c("Country Name", "Country Code", "Indicator Name", "Indicator Code"), 
               names_to = "year_es", 
               values_to = "tierras_agricolas")|>
  select(-c("Indicator Name", "Indicator Code"))

excel3 <- read_excel("estadistica-202402/estadistica-202402/datos-banco-mundial/tierras cultivables hectáreas por persona_excel.xls",
                    sheet = "Data",
                    skip = 3 ) |>
  pivot_longer(cols = -c("Country Name", "Country Code", "Indicator Name", "Indicator Code"), 
               names_to = "year_es", 
               values_to = "tierras cultivables hectáreas por persona")|>
  select(-c("Indicator Name", "Indicator Code"))

excel4 <- read_excel("estadistica-202402/estadistica-202402/datos-banco-mundial/tierras cultivables_excel.xls",
                    sheet = "Data",
                    skip = 3 ) |>
  pivot_longer(cols = -c("Country Name", "Country Code", "Indicator Name", "Indicator Code"), 
               names_to = "year_es", 
               values_to = "Tierras cultivables del área de tierra")|>
  select(-c("Indicator Name", "Indicator Code"))

excel5 <- read_excel("estadistica-202402/estadistica-202402/datos-banco-mundial/tierras destinadas al cultivo de manera permanente_excel.xls",
                    sheet = "Data",
                    skip = 3 ) |>
  pivot_longer(cols = -c("Country Name", "Country Code", "Indicator Name", "Indicator Code"), 
               names_to = "year_es", 
               values_to = "Tierras destinadas al cultivo de manera permanente")|>
  select(-c("Indicator Name", "Indicator Code"))

tabla_intermedia <- inner_join(excel, excel2, by = c("Country Name", "Country Code", "year_es"))

tabla_intermedia2 <- inner_join(tabla_intermedia, excel3, by = c("Country Name", "Country Code", "year_es"))

tabla_intermedia3 <- inner_join(tabla_intermedia2, excel4, by = c("Country Name", "Country Code", "year_es"))

tabla_total <- inner_join(tabla_intermedia3, excel5, by = c("Country Name", "Country Code", "year_es"))