library(readxl)
## Warning: package 'readxl' was built under R version 4.1.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(clhs)
## Warning: package 'clhs' was built under R version 4.1.3
library(ClustGeo)
## Warning: package 'ClustGeo' was built under R version 4.1.3
df <- read_excel("C:/Users/usuagro/Downloads/BD_MODELADO.xlsx")
df %>% 
  ggplot(aes(Avg_X_MCB,Avg_Y_MCE,
             color=Avg_CEa_07))+
  geom_point(size=3)+
  coord_equal()

D0 = dist(df$Avg_CEa_07)
tree = hclustgeo(D0)
groups= cutree(tree,2)

df$Clusters = groups
df %>% 
  ggplot(aes(Avg_X_MCB,Avg_Y_MCE,
      color=as.factor(Clusters)))+
  geom_point(size=3)+
  coord_equal()

# Estadísticas por grupo

df %>% 
  group_by(Clusters) %>% 
  summarise(mediat=mean(Avg_CEa_07,
                       trim=0.05),
            media = mean(Avg_CEa_07),
            mediana=median(Avg_CEa_07),
            n=n())
## # A tibble: 2 x 5
##   Clusters mediat media mediana     n
##      <int>  <dbl> <dbl>   <dbl> <int>
## 1        1   8.86  8.80    9.00   193
## 2        2  11.3  11.3    11.2    120

Muestreo espacial por zona

Hipercubo latino condicional

df_1 = df%>% 
  select(Avg_X_MCB,
         Avg_Y_MCE,
         Avg_CEa_07)

res = clhs(x = df_1,size = 60)

df_1[res,] %>% 
  ggplot(aes(x=Avg_X_MCB,
             y=Avg_Y_MCE,
             color=Avg_CEa_07))+
  geom_point(size=3)

table(df[res,]$Clusters)
## 
##  1  2 
## 37 23
tapply(df[res,]$Avg_CEa_07,
       df[res,]$Clusters,
       mean)
##         1         2 
##  8.817569 11.333361
ggplot(data = df,
       aes(x=Avg_X_MCB,
          y=Avg_Y_MCE,
        color=as.factor(Clusters)))+
  geom_point(size=2)+
  geom_point(data = df[res,],
             aes(x=Avg_X_MCB,
                 y=Avg_Y_MCE),
             color="black",
             shape=5,
             size=5)

Asignación

Calcular los indices

https://revistas.inia.es/index.php/sjar/article/view/18631/5902

Cluster ¿cuántos?

NDVI

df %>% 
  ggplot(aes(x=Avg_X_MCB,y=Avg_Y_MCE,
             color=NDVI))+
  geom_point(size=4)+
  scale_color_viridis_c()+
  theme_minimal()+
  coord_equal()

#NDVI

d= dist(df[,c(3)])
tree <- hclustgeo(d)
groups <- cutree(tree,2)

df$grupo1 = groups

q6=df %>% 
  ggplot(aes(x=Avg_X_MCB,
             y=Avg_Y_MCE,
             color=as.factor(grupo1)))+
  geom_point(size=4)+
  theme_minimal()+
  coord_equal()+
  labs(title="CEa_7")