title: “Marganai project - remote sensing” author: - affiliation: NuoroForestrySchool name: “Antonio Ganga” date: “03 novembre 2018” output: html_document: default html_notebook: default pdf_document: default keywords: data wrangling # subtitle: Just first checks abstract: TO BE COMPLETED —

library(tidyverse)
library(googlesheets)
suppressMessages(library(dplyr))

# gs_ls()
Gsheets <- 
  "https://docs.google.com/spreadsheets/d/1pvEgtHwW4caUYfmZWt5r89A7L7gFGOKR0_vtF7aSMgQ" %>%
  gs_url()

## Sheet-identifying info appears to be a browser URL.
## googlesheets will attempt to extract sheet key from the URL.

## Putative key: 1pvEgtHwW4caUYfmZWt5r89A7L7gFGOKR0_vtF7aSMgQ

## Sheet successfully identified: "table"

Gsheets %>%
  gs_ws_ls()

## [1] "2016"  "2018"  "morph" "tot"

tot.table <- Gsheets %>% gs_read("tot")

## Accessing worksheet titled 'tot'.

## Parsed with column specification:
## cols(
##   id = col_integer(),
##   X = col_number(),
##   Y = col_number(),
##   Aspect = col_number(),
##   TWI = col_number(),
##   ELEV = col_number(),
##   SLOPE = col_number(),
##   RI_2016 = col_character(),
##   VARI_2016 = col_character(),
##   TGI_2016 = col_character(),
##   NDVI_2016 = col_character(),
##   RI_2018 = col_character(),
##   VARI_2018 = col_character(),
##   TGI_2018 = col_character(),
##   NDVI_2018 = col_character(),
##   `<U+0394>NDVI` = col_character(),
##   `<U+0394>VARI` = col_character(),
##   `<U+0394>RI` = col_number(),
##   `<U+0394>TGI` = col_number()
## )

library(rpart)
# https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf

NDVI_mod <- rpart(`<U+0394>NDVI` ~  X + Y + Aspect + TWI + ELEV + SLOPE, tot.table)
summary(NDVI_mod, cp = 0.1)

## Call:
## rpart(formula = `<U+0394>NDVI` ~ X + Y + Aspect + TWI + ELEV + 
##     SLOPE, data = tot.table)
##   n= 29 
## 
##           CP nsplit rel error   xerror xstd
## 1 0.03571429      0 1.0000000 1.035714    0
## 2 0.01000000      2 0.9285714 1.035714    0
## 
## Variable importance
##     X  ELEV   TWI SLOPE     Y 
##    39    29    21     8     2 
## 
## Node number 1: 29 observations
##   predicted class=0,03314  expected loss=0.9655172  P(node) =1
##     class counts:     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1
##    probabilities: 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034

plot(NDVI_mod, uniform = TRUE, branch = 0.4, compress = TRUE)
text(NDVI_mod, use.n = TRUE)

rmarkdown::render("provaCART.Rmd")