1. Introducción

Este cuaderno tiene como objetivo clasificar e identificar los dos cultivos con mayor producción en el departamento de Boyacá y sus respectivos municipios productores, utilizando una base de datos de la Unidad de Planificación Agropecuaria (UPRA).

2. Configuración

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(readxl)

3. Leer un archivo de excel con estadisticas agropecuarias municipales

(archivos = list.files(pattern='xls'))
## [1] "20210624_BaseSIPRA2020.xlsx"
(hojas = readxl::excel_sheets("20210624_BaseSIPRA2020.xlsx"))
## [1] "Agrícola_SIPRA_AGRONET"       "InventarioBovino"            
## [3] "InventarioPorcino"            "InvBufalosCaprinoOvinoEquino"
## [5] "InvAves"
eva2020 = readxl::read_excel("20210624_BaseSIPRA2020.xlsx", sheet = "Agrícola_SIPRA_AGRONET")
eva2020

4. Subconjunto de datos relevantes para nuestro departamento

J.ber2020 = dplyr::filter(eva2020, Departamento == "Boyacá")
J.ber2020

5. Limpiar los datos filtrados

J.ber2020.tmp <-  J.ber2020 %>% select('Código del Municipio':'Ciclo del cultivo')
J.ber2020.tmp
J.ber2020.tmp %>% rename(Cod_Mun = 'Código del Municipio', 
                         Grupo = 'Grupo cultivo según especie', 
                         Subgrupo = 'Subgrupo cultivo según especie',
                         AreaSiembra = 'Area Sembrada (ha)',
                         AreaCosecha = 'Area Cosechada (ha)',
                         Produccion = 'Producción (t)',                              
                         Rendimiento = 'Rendimiento (t/ha)', Ciclo='Ciclo del cultivo') ->                                  nJ.ber2020
nJ.ber2020
nJ.ber2020 %>% mutate(AreaSiembra = as.numeric(AreaSiembra),
                       AreaCosecha = as.numeric(AreaCosecha),
                       Produccion = as.numeric(Produccion),
                       Rendimiento = as.numeric(Rendimiento)) -> nJ.ber2020 
nJ.ber2020

6. Procesamiento y análisis de datos

nJ.ber2020 %>%
  filter(Produccion > 0) %>%
  group_by(Cultivo) %>%
  summarize(total_produccion = sum(Produccion)) %>% 
  arrange(desc(total_produccion))
nJ.ber2020 %>%
  group_by(Cultivo, Municipio) %>%
  summarize(max_prod = max(Produccion, na.rm = TRUE)) %>%
  slice(which.max(max_prod))  %>%
  arrange(desc(max_prod))
## `summarise()` has grouped output by 'Cultivo'. You can override using the
## `.groups` argument.
nJ.ber2020 %>%
  group_by(Grupo,Municipio) %>%
  summarize(max_prod = max(Produccion, na.rm = TRUE)) %>%
  slice(which.max(max_prod))  %>%
  arrange(desc(max_prod))
## `summarise()` has grouped output by 'Grupo'. You can override using the
## `.groups` argument.
nJ.ber2020 %>%
  group_by(Cod_Mun, Municipio, Grupo) %>%
  filter(Grupo=='Tubérculos Y Plátanos') %>% 
  summarize(max_prod = max(Produccion, na.rm = TRUE)) %>%
  arrange(desc(max_prod)) -> Tuberculos2020
## `summarise()` has grouped output by 'Cod_Mun', 'Municipio'. You can override
## using the `.groups` argument.
Tuberculos2020
nJ.ber2020 %>%
  group_by(Cod_Mun, Municipio, Grupo) %>%
  filter(Grupo=='Hortalizas') %>% 
  summarize(max_prod = max(Produccion, na.rm = TRUE)) %>%
  arrange(desc(max_prod)) -> Hortalizas2020
## `summarise()` has grouped output by 'Cod_Mun', 'Municipio'. You can override
## using the `.groups` argument.
Hortalizas2020

7. Escribir datos seleccionados en el archivo

write_csv(Hortalizas2020, "./J.ber_Hort_2020.csv")
write_csv(Tuberculos2020, "./J.ber_Tuber_2020.csv")

8. Referencias

“Lizarazo, I. Reading and processing municipal agricultural statistics for 2020. Available at: https://rpubs.com/ials2un/readingEVAv1.”

“Unidad de planificación agropecuaria UPRA: https://www.upra.gov.co/”

sessionInfo()
## R version 4.1.3 (2022-03-10)
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## 
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## other attached packages:
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##  [5] purrr_0.3.4     readr_2.1.2     tidyr_1.2.0     tibble_3.1.6   
##  [9] ggplot2_3.3.5   tidyverse_1.3.1
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