sithjaisong

# ref:https://www.r-statistics.com/tag/friedman-test/
friedman.test.with.post.hoc <- function(formu, data, to.print.friedman = T, to.post.hoc.if.signif = T,  to.plot.parallel = T, to.plot.boxplot = T, signif.P = .05, color.blocks.in.cor.plot = T, jitter.Y.in.cor.plot =F)
{
  # formu is a formula of the shape: 	Y ~ X | block
  # data is a long data.frame with three columns:    [[ Y (numeric), X (factor), block (factor) ]]
  
  # Note: This function doesn't handle NA's! In case of NA in Y in one of the blocks, then that entire block should be removed.
  
  
  # Loading needed packages

sithjaisong

# ref:https://sites.google.com/a/lakeheadu.ca/yong-luo/blog/convert-utm-to-longlat-in-r
library(rgdal)
# prepare UTM coordinates matrix
utmcoor<-SpatialPoints(cbind(utmdata$X,utmdata$Y), proj4string=CRS("+proj=utm +zone=30")) # UTM zone Central Thailand: 47P
#utmdata$X and utmdata$Y are corresponding to UTM Easting and Northing, respectively.
#zone= UTM zone
# converting
longlatcoor<-spTransform(utmcoor,CRS("+proj=longlat"))

sithjaisong

# ref:https://fabiomarroni.wordpress.com/2017/03/25/perform-pairwise-wilcoxon-test-classify-groups-by-significance-and-plot-results/
library(multcompView)
 
first.set<-cbind(rnorm(100,mean=1.8),1)
second.set<-cbind(rnorm(100,mean=0.9),2)
third.set<-cbind(rnorm(100,mean=1),3)
 
full<-rbind(first.set,second.set,third.set)
 
pp<-pairwise.wilcox.test(full[,1], full[,2], p.adjust.method = "none", paired = FALSE)

sithjaisong

# List of useful packages
pkg <- c("tidyr", "dplyr", "ggplot2", "knitr", "rmarkdown")

# Check if packages are not installed and assign the
# names of the uninstalled packages to the variable new.pkg
new.pkg <- pkg[!(pkg %in% installed.packages())]

# If there are any packages in the list that aren't installed,
# install them
if (length(new.pkg)) {

sithjaisong

#title: code sample for ploting polynomial model  

# load packages
library(readxl) # load readxl package
library(ggplot2) # load ggplot2 paackage
library(dplyr) # load dplyr package
library(polynom) # load polynom
library(gsheet) # load gsheet 

# load data 

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library(desplot)
library(agricolae)
library(RColorBrewer)

plot.RCBD.layout <- function(trt_name, num_block){
  
  repeticion <- rep(num_block, length(trt_name))
  
  RCBD <- design.rcbd(trt_name, r = repeticion, serie = 0)
  

sithjaisong

library(agricolae)
library(desplot)

plot.CRD.layout <- function(trt_name, num_rep){
  
  repeticion <- rep(num_rep, length(trt))
  
  CRD <- design.crd(trt, r = repeticion, serie = 0)
  
  CRD_layout <- CRD$book

sithjaisong

dat <- read_csv("/Users/sithjaisong/Desktop/ycom.csv") # โหลดไฟล์ที่ต้องการ

# ตอนนี้ข้อมูลที่ผมมีนั้น  จะเห็นว่า trt ที่เรียงนั้น มันถูกเรียงตาม rep 
head(dat)
Source: local data frame [6 x 15]

    trt   rep    sm  sm y   moi  hill panicle  sunk mc_sunk float_sunk mc_float tgw sunk mc_tgw_sunk tgw float mc_tgw_float
  (chr) (int) (int) (dbl) (dbl) (int)   (int) (dbl)   (dbl)      (dbl)    (dbl)    (dbl)       (dbl)     (dbl)        (dbl)
1    PR     1     1 2.320  14.0   108     214 261.8    13.1       64.1     14.0   24.548       12.80     3.425         14.0
2    PR     1     2 2.570  15.6   108     230 283.8    13.7       62.1     14.5   23.958       12.20     3.524         14.2

sithjaisong

lapply(data, shapiro.test)

$DH
Shapiro-Wilk normality test
data:  X[[i]]
W = 0.4481, p-value < 2.2e-16

sithjaisong

###########################Header##############################################
# title         : multiggplot.R;
# purpose       : create muti histogram of all varibles in the dataset;
# producer      : prepared by S. Jaisong ([email protected]);
# last update   : in Los Baños, Laguna, PHL, April 2015;
# inputs        : "airquality" data from R;
# outputs       : exaple.pdf;
# remarks 1     : ;
# remarks 2     : ;
###############################End#############################################