Phenology by species and growth forms

library(coin)

## Warning: package 'coin' was built under R version 4.0.5

## Loading required package: survival

library(readr)

## Warning: package 'readr' was built under R version 4.0.3

library(knitr)
library(kableExtra)

## Warning: package 'kableExtra' was built under R version 4.0.4

Data prep

setwd("C:/Users/fsanc/OneDrive - Colorado College/Research/Open/Roxaneh")

# Upload data: use "phenology2019.csv" for 2019 and "phenology2020.csv" for 2020
phenology1 <- read.csv("phenology2020.csv") 

# Order data by Day
phenology1 <- phenology1[order(phenology1$Day),]

# Merge SPC and SPH into SP
phenology1$Species[phenology1$Species == "SPC"] <- "SP"
phenology1$Species[phenology1$Species == "SPH"] <- "SP"

# For 2019, exclude the following species (tiny sample sizes):
#exclude <- c("AL", "BT", "DL", "LC", "PA", "PF", "PK", "PV", "SR")

# For 2020, exclude the following species (tiny sample sizes):
exclude <- c("DL", "EV",  "PB", "ON")

# First 10 rows of the phenology dataset
head(phenology1, 10)

##    Camera.number  Date Day Location  Site Treatment Plot Anthesis Species Group
## 1             14 5-Jun 157   Toolik   Dry         E    7        1      AA     F
## 2             15 5-Jun 157   Toolik   Dry         E    8        1      AA     F
## 3             17 5-Jun 157   Toolik   Dry         C    4        1      AA     F
## 4             18 5-Jun 157   Toolik   Dry         C    6        1      AA     F
## 5             19 5-Jun 157   Toolik Moist         E    2        1      BN    DS
## 6             20 5-Jun 157   Toolik Moist         E    5        1      BN    DS
## 7             21 5-Jun 157   Toolik Moist         E    8        1      BN    DS
## 8             23 5-Jun 157   Toolik Moist         C    4        1      EV    GS
## 9             13 5-Jun 157   Toolik   Dry         E    3        0    <NA>  <NA>
## 10            16 5-Jun 157   Toolik   Dry         C    1        0    <NA>  <NA>

Flowering calculations

print_kable = function(x) {
  print(kable_print_output <<- x)
  cat('\n')
}
set.seed(673)
for(growth in c("F", "ES", "DS", "GS", "All")){
  if(growth == "All"){
    phenology0 <- phenology1
    all_sp <- levels(factor(phenology0$Species))
    sp <- setdiff(all_sp, exclude)
  } else {
    phenology0 <- subset(phenology1, phenology1$Group == growth)
    all_sp <- levels(factor(phenology0$Species))
    sp <- setdiff(all_sp, exclude)
  }
  firstC_median <- NA
  firstC_mean <- NA
  firstE_median <- NA
  firstE_mean <- NA
  first_pval <- numeric(length(sp))
  first_Z <- numeric(length(sp))
  firstC <- list(length(sp))
  firstE <- list(length(sp))
  nC <- integer(length(sp))
  nE <- integer(length(sp))
  
  lastC_median <- NA
  lastC_mean <- NA
  lastE_median <- NA
  lastE_mean <- NA
  last_pval <- numeric(length(sp))
  last_Z <- numeric(length(sp))
  lastC <- list(length(sp))
  lastE <- list(length(sp))
  
  durC_median <- NA
  durC_mean <- NA
  durE_median <- NA
  durE_mean <- NA
  dur_pval <- numeric(length(sp))
  dur_Z <- numeric(length(sp))
  durC <- list(length(sp))
  durE <- list(length(sp))
  
  for(j in 1:length(sp)){
    
    data <- subset(phenology0, phenology0$Species == sp[j])
    
    ########### Control plots ##########
    
    firstC[[j]] <- NA
    lastC[[j]] <- NA
    durC[[j]] <- NA
    aux <- subset(data, 
                  data$Location == "Imnavait"
                  & data$Site == "Dry"
                  & data$Treatment == "C")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstC[[j]] <- c(firstC[[j]], aux$Day[w[1]])
        lastC[[j]] <- c(lastC[[j]], aux$Day[w[length(w)]])
      }
    }
    
    aux <- subset(data, 
                  data$Location == "Imnavait"
                  & data$Site == "Moist"
                  & data$Treatment == "C")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstC[[j]] <- c(firstC[[j]], aux$Day[w[1]])
        lastC[[j]] <- c(lastC[[j]], aux$Day[w[length(w)]])
      }
    }
    
    aux <- subset(data, 
                  data$Location == "Toolik"
                  & data$Site == "Dry"
                  & data$Treatment == "C")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstC[[j]] <- c(firstC[[j]], aux$Day[w[1]])
        lastC[[j]] <- c(lastC[[j]], aux$Day[w[length(w)]])
      }
    }
    
    aux <- subset(data, 
                  data$Location == "Toolik"
                  & data$Site == "Moist"
                  & data$Treatment == "C")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstC[[j]] <- c(firstC[[j]], aux$Day[w[1]])
        lastC[[j]] <- c(lastC[[j]], aux$Day[w[length(w)]])
      }
    }
    
    firstC[[j]] <- firstC[[j]][-1]
    lastC[[j]] <- lastC[[j]][-1]
    durC[[j]] <- lastC[[j]] - firstC[[j]]
    
    ########### Experimental plots ##########
    
    firstE[[j]] <- NA
    lastE[[j]] <- NA
    durE[[j]] <- NA
    aux <- subset(data, 
                  data$Location == "Imnavait"
                  & data$Site == "Dry"
                  & data$Treatment == "E")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstE[[j]] <- c(firstE[[j]], aux$Day[w[1]])
        lastE[[j]] <- c(lastE[[j]], aux$Day[w[length(w)]])
      }
    }
    
    aux <- subset(data, 
                  data$Location == "Imnavait"
                  & data$Site == "Moist"
                  & data$Treatment == "E")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstE[[j]] <- c(firstE[[j]], aux$Day[w[1]])
        lastE[[j]] <- c(lastE[[j]], aux$Day[w[length(w)]])
      }
    }
    
    aux <- subset(data, 
                  data$Location == "Toolik"
                  & data$Site == "Dry"
                  & data$Treatment == "E")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstE[[j]] <- c(firstE[[j]], aux$Day[w[1]])
        lastE[[j]] <- c(lastE[[j]], aux$Day[w[length(w)]])
      }
    }
    
    aux <- subset(data, 
                  data$Location == "Toolik"
                  & data$Site == "Moist"
                  & data$Treatment == "E")
    if(nrow(aux) > 0){
      u <- unique(aux$Plot)
      for(i in 1:length(u)){
        w <- which(aux$Plot == u[i])
        firstE[[j]] <- c(firstE[[j]], aux$Day[w[1]])
        lastE[[j]] <- c(lastE[[j]], aux$Day[w[length(w)]])
      }
    }
    
    firstE[[j]] <- firstE[[j]][-1]
    lastE[[j]] <- lastE[[j]][-1]
    durE[[j]] <- lastE[[j]] - firstE[[j]]
    
    firstC_median <- c(firstC_median, median(firstC[[j]], na.rm = T))
    firstC_mean <- c(firstC_mean, mean(firstC[[j]], na.rm = T))
    firstE_median <- c(firstE_median, median(firstE[[j]], na.rm = T))
    firstE_mean <- c(firstE_mean, mean(firstE[[j]], na.rm = T))
    lastC_median <- c(lastC_median, median(lastC[[j]], na.rm = T))
    lastC_mean <- c(lastC_mean, mean(lastC[[j]], na.rm = T))
    lastE_median <- c(lastE_median, median(lastE[[j]], na.rm = T))
    lastE_mean <- c(lastE_mean, mean(lastE[[j]], na.rm = T))
    durC_median <- c(durC_median, median(durC[[j]], na.rm = T))
    durC_mean <- c(durC_mean, mean(durC[[j]], na.rm = T))
    durE_median <- c(durE_median, median(durE[[j]], na.rm = T))
    durE_mean <- c(durE_mean, mean(durE[[j]], na.rm = T))
    
    nC[j] <- length(firstC[[j]])
    nE[j] <- length(firstE[[j]])
    
    if(nC[j] >= 1 & nE[j] >= 1){
      
      wtF <- wilcox_test(c(firstC[[j]], firstE[[j]]) ~ factor(rep(c("C","E"), c(length(firstC[[j]]), length(firstE[[j]])))), distribution = "exact")
      first_pval[j] <- pvalue(wtF)
      first_Z[j] <- statistic(wtF)
      
      wtL <- wilcox_test(c(lastC[[j]], lastE[[j]]) ~ factor(rep(c("C","E"), c(length(lastC[[j]]), length(lastE[[j]])))), distribution = "exact")
      last_pval[j] <- pvalue(wtL)
      last_Z[j] <- statistic(wtL)
      
      wtD <- wilcox_test(c(durC[[j]], durE[[j]]) ~ factor(rep(c("C","E"), c(length(durC[[j]]), length(durE[[j]])))), distribution = "exact")
      dur_pval[j] <- pvalue(wtD)
      dur_Z[j] <- statistic(wtD)
      
    } else {
      first_pval[j] <- NA
      first_Z[j] <- NA
      last_pval[j] <- NA
      last_Z[j] <- NA
      dur_pval[j] <- NA
      dur_Z[j] <- NA
    }
  }
  names(firstC) <- sp; names(firstE) <- sp;
  names(lastC) <- sp; names(lastE) <- sp;
  names(durC) <- sp; names(durE) <- sp;
  
  firstC_median <- firstC_median[-1]
  firstE_median <- firstE_median[-1]
  lastC_median <- lastC_median[-1]
  lastE_median <- lastE_median[-1]
  durC_median <- durC_median[-1]
  durE_median <- durE_median[-1]
  
  firstC_mean <- firstC_mean[-1]
  firstE_mean <- firstE_mean[-1]
  lastC_mean <- lastC_mean[-1]
  lastE_mean <- lastE_mean[-1]
  durC_mean <- durC_mean[-1]
  durE_mean <- durE_mean[-1]
  
  # Save lists as text files
  capture.output(firstC, file = paste0(growth, "_",   "firstC.txt"))
  capture.output(firstE, file = paste0(growth, "_", "firstE.txt"))
  capture.output(lastC, file = paste0(growth, "_",   "lastC.txt"))
  capture.output(lastE, file = paste0(growth, "_", "lastE.txt"))
  capture.output(durC, file = paste0(growth, "_",   "durC.txt"))
  capture.output(durE, file = paste0(growth, "_", "durE.txt"))
  
  firstC_all <- unlist(firstC)
  firstE_all <- unlist(firstE)
  lastC_all <- unlist(lastC)
  lastE_all <- unlist(lastE)
  durC_all <- unlist(durC)
  durE_all <- unlist(durE)
  
  if((length(firstC_all) > 60) & (length(firstE_all)) > 60){
    dist <- "approximate"
  } else {dist <- "exact"}
  
  wtF <- wilcox_test(c(firstC_all, firstE_all) ~ factor(rep(c("C","E"), c(length(firstC_all), length(firstE_all)))), distribution = dist)
  
  wtL <- wilcox_test(c(lastC_all, lastE_all) ~ factor(rep(c("C","E"), c(length(lastC_all), length(lastE_all)))), distribution = dist)
  
  wtD <- wilcox_test(c(durC_all, durE_all) ~ factor(rep(c("C","E"), c(length(durC_all), length(durE_all)))), distribution = dist)
  
  summaries <- data.frame(Species = sp, firstC_median = firstC_median, firstC_mean = firstC_mean, firstE_median = firstE_median, firstE_mean = firstE_mean, diff_first_median = firstC_median - firstE_median, diff_first_mean = firstC_mean - firstE_mean, first_Z = first_Z, first_pval = first_pval, lastC_median = lastC_median, lastC_mean = lastC_mean, lastE_median = lastE_median, lastE_mean = lastE_mean, diff_last_median = lastC_median - lastE_median, diff_last_mean = lastC_mean - lastE_mean, last_Z = last_Z, last_pval = last_pval, durC_median = durC_median, durC_mean = durC_mean, durE_median = durE_median, durE_mean = durE_mean, diff_dur_median = durC_median - durE_median, diff_dur_mean = durC_mean - durE_mean, dur_Z = dur_Z, dur_pval = dur_pval, nC = nC, nE = nE)
  aux <- c(median(firstC_all, na.rm = T),
           mean(firstC_all, na.rm = T),
           median(firstE_all, na.rm = T),
           mean(firstE_all, na.rm = T),
           median(firstC_all, na.rm = T) - median(firstE_all, na.rm = T),
           mean(firstC_all, na.rm = T) - mean(firstE_all, na.rm = T),
           statistic(wtF),
           pvalue(wtF),
           median(lastC_all, na.rm = T),
           mean(lastC_all, na.rm = T),
           median(lastE_all, na.rm = T),
           mean(lastE_all, na.rm = T),
           median(lastC_all, na.rm = T) - median(lastE_all, na.rm = T),
           mean(lastC_all, na.rm = T) - mean(lastE_all, na.rm = T),
           statistic(wtL),
           pvalue(wtL),
           median(durC_all, na.rm = T),
           mean(durC_all, na.rm = T),
           median(durE_all, na.rm = T),
           mean(durE_all, na.rm = T),
           median(durC_all, na.rm = T) - median(durE_all, na.rm = T),
           mean(durC_all, na.rm = T) - mean(durE_all, na.rm = T),
           statistic(wtD),
           pvalue(wtD),
           length(firstC_all),
           length(firstE_all)
  )
  summaries <- rbind(summaries, c(NA, aux))
  summaries$Species[length(summaries$Species)] <- "All"
  write_csv(summaries, paste0(getwd(),"/summaries_", growth, ".csv"))
  
  table <- knitr::kable(summaries, format = "html", 
                        booktabs = TRUE,
                        caption = paste("Summaries for", growth),
                        digits = 4
)
  print(kableExtra::kable_styling(kableExtra::scroll_box(table, width = "100%", height = "100%")))
  cat("\n")
  
  # considering each species as an observation
  
  #wilcoxsign_test(firstC_median ~ firstE_median, distribution = dist)
  #wilcoxsign_test(firstC_mean ~ firstE_mean, distribution = dist)
  
  #wilcoxsign_test(lastC_median ~ lastE_median, distribution = dist)
  #wilcoxsign_test(lastC_mean ~ lastE_mean, distribution = dist)
  
  #wilcoxsign_test(durC_median ~ durE_median, distribution = dist)
  #wilcoxsign_test(durC_mean ~ durE_mean, distribution = dist)
}

Summaries for F

Species	firstC_median	firstC_mean	firstE_median	firstE_mean	diff_first_median	diff_first_mean	first_Z	first_pval	lastC_median	lastC_mean	lastE_median	lastE_mean	diff_last_median	diff_last_mean	last_Z	last_pval	durC_median	durC_mean	durE_median	durE_mean	diff_dur_median	diff_dur_mean	dur_Z	dur_pval	nC	nE
AA	158	157.6000	157.5	157.5000	0.5	0.1000	0.2828	1.0000	164.0	165.0000	169.5	171.7500	-5.5	-6.750	-1.9846	0.0556	7.0	7.4000	12	14.2500	-5.0	-6.8500	-2.3368	0.0238	5	4
DO	161	161.3333	165.0	164.3333	-4.0	-3.0000	-1.7979	0.2000	173.0	171.6667	173.0	174.6667	0.0	-3.000	-0.6956	0.7000	11.0	10.3333	10	10.3333	1.0	0.0000	0.0000	1.0000	3	3
PL	179	179.2500	174.0	173.0000	5.0	6.2500	2.0837	0.0857	185.5	187.2500	185.5	185.0000	0.0	2.250	0.5808	0.6286	7.0	8.0000	12	12.0000	-5.0	-4.0000	-1.7425	0.0857	4	4
RC	168	168.0000	167.5	171.0000	0.5	-3.0000	0.0000	1.0000	182.0	182.0000	185.0	185.5000	-3.0	-3.500	-0.9258	0.5333	14.0	14.0000	14	14.5000	0.0	-0.5000	0.0000	1.0000	2	4
All	162	166.0714	165.0	166.6000	-3.0	-0.5286	-0.2852	0.7871	173.5	175.2143	180.0	179.5333	-6.5	-4.319	-1.2024	0.2375	8.5	9.1429	11	12.9333	-2.5	-3.7905	-2.3456	0.0178	14	15

Summaries for ES

Species	firstC_median	firstC_mean	firstE_median	firstE_mean	diff_first_median	diff_first_mean	first_Z	first_pval	lastC_median	lastC_mean	lastE_median	lastE_mean	diff_last_median	diff_last_mean	last_Z	last_pval	durC_median	durC_mean	durE_median	durE_mean	diff_dur_median	diff_dur_mean	dur_Z	dur_pval	nC	nE
AP	168.0	168.6667	192	189.6667	-24.0	-21.0000	-1.3284	0.3000	187.0	183.3333	218.0	208.0000	-31.0	-24.6667	-1.0911	0.4000	13.0	14.6667	15	18.3333	-2.0	-3.6667	-1.0911	0.4000	3	3
CT	162.5	162.5000	164	164.5000	-1.5	-2.0000	-1.6686	0.2667	176.0	176.0000	180.5	179.0000	-4.5	-3.0000	-0.9393	0.4667	13.5	13.5000	15	14.5000	-1.5	-1.0000	-0.9393	0.4667	2	4
KP	161.0	161.0000	165	165.0000	-4.0	-4.0000	-1.6330	0.3333	174.5	174.5000	182.0	182.0000	-7.5	-7.5000	-1.5492	0.3333	13.5	13.5000	17	17.0000	-3.5	-3.5000	-1.6330	0.3333	2	2
LP	166.0	167.3333	169	169.5000	-3.0	-2.1667	-0.9001	0.4286	179.0	175.6667	179.5	179.5000	-0.5	-3.8333	0.0000	1.0000	12.0	8.3333	9	10.0000	3.0	-1.6667	0.0000	1.0000	3	4
VU	166.0	166.6667	164	162.6667	2.0	4.0000	1.3284	0.3000	176.0	177.0000	178.0	176.0000	-2.0	1.0000	-0.2214	1.0000	12.0	10.3333	13	13.3333	-1.0	-3.0000	-0.6547	0.7000	3	3
VVI	169.0	170.5556	168	170.2727	1.0	0.2828	0.2669	0.8117	196.0	193.1111	196.0	194.1818	0.0	-1.0707	-0.2297	0.8376	19.0	22.5556	24	23.9091	-5.0	-1.3535	-0.4951	0.6414	9	11
All	166.0	167.7273	167	170.2222	-1.0	-2.4949	-0.5444	0.5928	184.5	183.9545	184.0	188.3704	0.5	-4.4158	-0.9259	0.3604	13.5	16.2273	17	18.1481	-3.5	-1.9209	-0.8556	0.3984	22	27

Summaries for DS

Species	firstC_median	firstC_mean	firstE_median	firstE_mean	diff_first_median	diff_first_mean	first_Z	first_pval	lastC_median	lastC_mean	lastE_median	lastE_mean	diff_last_median	diff_last_mean	last_Z	last_pval	durC_median	durC_mean	durE_median	durE_mean	diff_dur_median	diff_dur_mean	dur_Z	dur_pval	nC	nE
BN	170	170.0000	157.5	163.8333	12.5	6.1667	1.0377	0.7143	207.0	207.0000	196.0	196.000	11.0	11.0000	1.0000	0.5714	37.0	37.0000	34	32.1667	3.0	4.8333	0.0000	1.0000	1	6
SP	158	165.3333	171.5	171.5000	-13.5	-6.1667	-0.5774	0.8000	168.0	180.6667	184.5	184.500	-16.5	-3.8333	-0.5774	0.8000	11.0	15.3333	13	13.0000	-2.0	2.3333	0.0000	1.0000	3	2
All	164	166.5000	160.5	165.7500	3.5	0.7500	0.3464	0.7899	187.5	187.2500	190.5	193.125	-3.0	-5.8750	-0.3397	0.8081	21.5	20.7500	25	27.3750	-3.5	-6.6250	-0.8492	0.4606	4	8

Summaries for GS

Species	firstC_median	firstC_mean	firstE_median	firstE_mean	diff_first_median	diff_first_mean	first_Z	first_pval	lastC_median	lastC_mean	lastE_median	lastE_mean	diff_last_median	diff_last_mean	last_Z	last_pval	durC_median	durC_mean	durE_median	durE_mean	diff_dur_median	diff_dur_mean	dur_Z	dur_pval	nC	nE
CB	163	163	161.5	161.5	1.5	1.5	1.6330	0.3333	174.5	174.50	201.0	201.00	-26.5	-26.5	-0.7746	0.6667	11.5	11.50	39.5	39.50	-28.0	-28.0	-0.7746	0.6667	2	2
HA	163	163	168.5	168.5	-5.5	-5.5	-1.5492	0.3333	192.0	192.00	200.5	200.50	-8.5	-8.5	-0.7746	0.6667	29.0	29.00	32.0	32.00	-3.0	-3.0	0.0000	1.0000	2	2
All	163	163	163.5	165.0	-0.5	-2.0	-0.1461	0.9714	181.5	183.25	200.5	200.75	-19.0	-17.5	-1.0164	0.4000	18.0	20.25	33.5	35.75	-15.5	-15.5	-0.5774	0.6857	4	4

Summaries for All

Species	firstC_median	firstC_mean	firstE_median	firstE_mean	diff_first_median	diff_first_mean	first_Z	first_pval	lastC_median	lastC_mean	lastE_median	lastE_mean	diff_last_median	diff_last_mean	last_Z	last_pval	durC_median	durC_mean	durE_median	durE_mean	diff_dur_median	diff_dur_mean	dur_Z	dur_pval	nC	nE
AA	158.0	157.6000	157.5	157.5000	0.5	0.1000	0.2828	1.0000	164.0	165.0000	169.5	171.7500	-5.5	-6.7500	-1.9846	0.0556	7.0	7.4000	12.0	14.2500	-5.0	-6.8500	-2.3368	0.0238	5	4
AP	168.0	168.6667	192.0	189.6667	-24.0	-21.0000	-1.3284	0.3000	187.0	183.3333	218.0	208.0000	-31.0	-24.6667	-1.0911	0.4000	13.0	14.6667	15.0	18.3333	-2.0	-3.6667	-1.0911	0.4000	3	3
BN	170.0	170.0000	157.5	163.8333	12.5	6.1667	1.0377	0.7143	207.0	207.0000	196.0	196.0000	11.0	11.0000	1.0000	0.5714	37.0	37.0000	34.0	32.1667	3.0	4.8333	0.0000	1.0000	1	6
CB	163.0	163.0000	161.5	161.5000	1.5	1.5000	1.6330	0.3333	174.5	174.5000	201.0	201.0000	-26.5	-26.5000	-0.7746	0.6667	11.5	11.5000	39.5	39.5000	-28.0	-28.0000	-0.7746	0.6667	2	2
CT	162.5	162.5000	164.0	164.5000	-1.5	-2.0000	-1.6686	0.2667	176.0	176.0000	180.5	179.0000	-4.5	-3.0000	-0.9393	0.4667	13.5	13.5000	15.0	14.5000	-1.5	-1.0000	-0.9393	0.4667	2	4
DO	161.0	161.3333	165.0	164.3333	-4.0	-3.0000	-1.7979	0.2000	173.0	171.6667	173.0	174.6667	0.0	-3.0000	-0.6956	0.7000	11.0	10.3333	10.0	10.3333	1.0	0.0000	0.0000	1.0000	3	3
HA	163.0	163.0000	168.5	168.5000	-5.5	-5.5000	-1.5492	0.3333	192.0	192.0000	200.5	200.5000	-8.5	-8.5000	-0.7746	0.6667	29.0	29.0000	32.0	32.0000	-3.0	-3.0000	0.0000	1.0000	2	2
KP	161.0	161.0000	165.0	165.0000	-4.0	-4.0000	-1.6330	0.3333	174.5	174.5000	182.0	182.0000	-7.5	-7.5000	-1.5492	0.3333	13.5	13.5000	17.0	17.0000	-3.5	-3.5000	-1.6330	0.3333	2	2
LP	166.0	167.3333	169.0	169.5000	-3.0	-2.1667	-0.9001	0.4286	179.0	175.6667	179.5	179.5000	-0.5	-3.8333	0.0000	1.0000	12.0	8.3333	9.0	10.0000	3.0	-1.6667	0.0000	1.0000	3	4
PL	179.0	179.2500	174.0	173.0000	5.0	6.2500	2.0837	0.0857	185.5	187.2500	185.5	185.0000	0.0	2.2500	0.5808	0.6286	7.0	8.0000	12.0	12.0000	-5.0	-4.0000	-1.7425	0.0857	4	4
RC	168.0	168.0000	167.5	171.0000	0.5	-3.0000	0.0000	1.0000	182.0	182.0000	185.0	185.5000	-3.0	-3.5000	-0.9258	0.5333	14.0	14.0000	14.0	14.5000	0.0	-0.5000	0.0000	1.0000	2	4
SP	158.0	165.3333	171.5	171.5000	-13.5	-6.1667	-0.5774	0.8000	168.0	180.6667	184.5	184.5000	-16.5	-3.8333	-0.5774	0.8000	11.0	15.3333	13.0	13.0000	-2.0	2.3333	0.0000	1.0000	3	2
VU	166.0	166.6667	164.0	162.6667	2.0	4.0000	1.3284	0.3000	176.0	177.0000	178.0	176.0000	-2.0	1.0000	-0.2214	1.0000	12.0	10.3333	13.0	13.3333	-1.0	-3.0000	-0.6547	0.7000	3	3
VVI	169.0	170.5556	168.0	170.2727	1.0	0.2828	0.2669	0.8117	196.0	193.1111	196.0	194.1818	0.0	-1.0707	-0.2297	0.8376	19.0	22.5556	24.0	23.9091	-5.0	-1.3535	-0.4951	0.6414	9	11
All	165.5	166.6591	166.0	168.1667	-0.5	-1.5076	-0.5690	0.5722	181.0	181.4091	183.5	187.5370	-2.5	-6.1279	-1.7581	0.0790	12.5	14.7500	15.0	19.3704	-2.5	-4.6204	-2.0096	0.0443	44	54