1. Introduction

This is the third notebook which Geomatica Basica students have to write to get started with R & RStudio.

#install.packages(c("tidyverse","dplyr","ggplot2"))
library("tidyverse")
library("dplyr")
library("ggplot2")
EVA_VAUPES <- read_csv(file = "./EVA_VAUPES.TXT")
## 
## -- Column specification --------------------------------------------------------
## cols(
##   DPTO = col_character(),
##   COD_MUN = col_double(),
##   MUNICIPIO = col_character(),
##   GRUPO = col_character(),
##   SUBGRUPO = col_character(),
##   CULTIVO = col_character(),
##   DESAGREG = col_character(),
##   YEAR = col_double(),
##   PERIODO = col_character(),
##   HA_SEMBRADA = col_double(),
##   HA_COSECHA = col_double(),
##   TON_PROD = col_double(),
##   RENDIM = col_double(),
##   CICLO_CULTIVO = col_character()
## )
list.files("./")
##  [1] "97_VAUPES"                   "EVA_VAUPES.csv"             
##  [3] "EVA_VAUPES.txt"              "MGN2017_97_VAUPES.rar"      
##  [5] "rsconnect"                   "segundolibro.nb.html"       
##  [7] "tercerlibro.html"            "tercerlibro.Rmd"            
##  [9] "Tercerlibrocultivos.html"    "Tercerlibrocultivos.nb.html"
## [11] "Tercerlibrocultivos.Rmd"
class(EVA_VAUPES)
## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"
head(EVA_VAUPES)
evita  <-   select(EVA_VAUPES, MUNICIPIO, CULTIVO, YEAR, TON_PROD,RENDIM)
evita
evita_aji <- filter(evita, CULTIVO == "AJI")
evita_aji
evita_mitu <- filter(evita, MUNICIPIO == "MITU")
evita_mitu
evita_caruru_2018 <- evita %>%
  filter(MUNICIPIO == "CARURU") %>%
  filter(YEAR == 2018) %>%
  select(CULTIVO, RENDIM)
evita_caruru_2018
EVA_VAUPES %>%
  select(MUNICIPIO, CULTIVO, HA_SEMBRADA, TON_PROD, RENDIM) %>%
  filter(HA_SEMBRADA!=1) %>%
  mutate(RENDIM_SIEMBRA = TON_PROD/HA_SEMBRADA)
evita %>%
  group_by(CULTIVO) %>%
  summarize(mean_rend = mean(RENDIM, na.rm = TRUE))
EVA_VAUPES %>%
  group_by(CULTIVO, MUNICIPIO) %>%
  summarize(max_rend = max(RENDIM, na.rm = TRUE)) %>%
  slice(which.max(max_rend))
## `summarise()` has grouped output by 'CULTIVO'. You can override using the `.groups` argument.
EVA_VAUPES %>%
  group_by(GRUPO, MUNICIPIO) %>%
  summarize(max_rend = max(RENDIM, na.rm = TRUE)) %>%
  slice(which.max(max_rend))
## `summarise()` has grouped output by 'GRUPO'. You can override using the `.groups` argument.
EVA_VAUPES %>% 
  filter(YEAR==2018) %>% 
  group_by(GRUPO, MUNICIPIO) %>%
  summarize(max_area_cosecha = max(HA_COSECHA, na.rm = TRUE)) %>%
    slice(which.max(max_area_cosecha)) %>%
    arrange(desc(max_area_cosecha)) -> area_cosecha_max
## `summarise()` has grouped output by 'GRUPO'. You can override using the `.groups` argument.
area_cosecha_max
EVA_VAUPES %>% 
  group_by(GRUPO, MUNICIPIO, YEAR) %>%
  summarize(max_prod = max(TON_PROD, na.rm = TRUE)) %>%
    slice(which.max(max_prod)) %>%
    arrange(desc(max_prod)) -> ton_prod_max
## `summarise()` has grouped output by 'GRUPO', 'MUNICIPIO'. You can override using the `.groups` argument.
ton_prod_max
EVA_VAUPES %>% 
  filter(MUNICIPIO=="CARURU" & CULTIVO=="AJI") %>% 
  group_by(YEAR, CULTIVO) %>%
  select(MUNICIPIO, CULTIVO, TON_PROD, YEAR) ->  caruru_aji
caruru_aji
g <- ggplot(aes(x=YEAR, y=TON_PROD/1000), data = caruru_aji) + geom_bar(stat='identity') + labs(y='Produccion de Papa [Ton x 1000]')
g + ggtitle("Evolucion del aji 2008-2010 en caruru Vaupes") + labs(caption= "Based on EVA data (Minagricultura, 2020)")

data <- EVA_VAUPES %>%
       filter(CULTIVO=="MAIZ" & YEAR==2007 & TON_PROD>5) %>% 
       select(MUNICIPIO, TON_PROD)
ggplot(data, aes(x="", y=TON_PROD/10, fill=MUNICIPIO)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)