library(magrittr)
library(nlme)

setwd("/home/michael/Dropbox/BGU/1_Other/Ilan_Stavi/")

# Read data
dat = read.csv("data/Sde Zin_all data_170703+correlations.csv", stringsAsFactors = FALSE, check.names = FALSE)

# Variables
vars = setdiff(colnames(dat), c("#", "Block", "Treatment", "Spot", "Depth"))

# Histograms
for(i in vars) {
  
  hist(dat[[i]], main = i)  
  k = shapiro.test(dat[[i]])  
  print(k)
  
}

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.73486, p-value = 4.339e-10

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.9395, p-value = 0.001787

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.89779, p-value = 0.002971

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.82537, p-value = 8.83e-08

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.96247, p-value = 0.0305

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.96247, p-value = 0.0305

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.98297, p-value = 0.4394

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.93378, p-value = 0.0009348

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.90729, p-value = 0.005434

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.97201, p-value = 0.1084

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.79855, p-value = 3.761e-07

## 
##  Shapiro-Wilk normality test
## 
## data:  dat[[i]]
## W = 0.96596, p-value = 0.1275