log_tab$fwd_asv <- rep(NA, nrow(log_tab))
log_tab$rev_asv <- rep(NA, nrow(log_tab))
log_tab$merged_reads <- rep(NA, nrow(log_tab))
log_tab$merged_asv <- rep(NA, nrow(log_tab))
samples <- NULL
## select samples to process
if(is.null(samples)) {
sel <- 1:length(fwd_reads)
} else {
sel <- samples
}
length(fwd_reads)
## Individual sample processing in loop
for(i in names(fwd_reads)[sel]) {
cat("Processing:", i, "\n")
# record time for analysis end
log_tab[i, "timestamp_start"] <- Sys.time()
tryCatch({
# remove primers
filterAndTrim(fwd_reads[i], fwd_reads_filtN[i], rev_reads[i],
rev_reads_filtN[i], maxN = 0, multithread = F)
log_tab[i, "fwd_primer_match"] <- sum(sapply(fwd_orients, primerHits,
fn = fwd_reads_filtN[[i]]), sapply(fwd_orients, primerHits,
fn = rev_reads_filtN[[i]]))
log_tab[i, "rev_primer_match"] <- sum(sapply(rev_orients, primerHits,
fn = fwd_reads_filtN[[i]]), sapply(rev_orients, primerHits,
fn = rev_reads_filtN[[i]]))
system2(cutadapt, args = c(fwd_flags, "-n", 1, "-o",
fwd_reads_cut[i], fwd_reads_filtN[i]))
system2(cutadapt, args = c(rev_flags, "-n", 1, "-o",
rev_reads_cut[i], rev_reads_filtN[i]))
log_tab[i, "fwd_primer_match_cut"] <- sum(sapply(fwd_orients, primerHits,
fn = fwd_reads_cut[[i]]), sapply(fwd_orients, primerHits,
fn = rev_reads_cut[[i]]))
log_tab[i, "rev_primer_match_cut"] <- sum(sapply(rev_orients, primerHits,
fn = fwd_reads_cut[[i]]), sapply(rev_orients, primerHits,
fn = rev_reads_cut[[i]]))
# quality-filter sequences
fwd_filt <- tempfile(fileext = ".fastq.gz")
rev_filt <- tempfile(fileext = ".fastq.gz")
temp <- filterAndTrim(fwd = fwd_reads_cut[i], filt = fwd_filt,
rev = rev_reads_cut[i], filt.rev = rev_filt, maxN = 0, maxEE = c(2, 2),
truncQ = 2, minLen = 50, compress = T, verbose = T)
log_tab[i , "filename"] <- rownames(temp)
log_tab[i , "reads_filt_in"] <- temp[1]
log_tab[i , "reads_filt_out"] <- temp[2]
# plot QC plots
pdf(paste0(output_QC, "/", i, "_fwd.pdf"), w = 4, h = 3)
x <- tryCatch(plotQualityProfile(fwd_reads[i]),
error = function(e) plot(1, 1, type = "n", axes = F, ylab = NA,
xlab = NA))
print(x)
dev.off()
pdf(paste0(output_QC, "/", i, "_rev.pdf"), w = 4, h = 3)
x <- tryCatch(plotQualityProfile(rev_reads[i]),
error = function(e) plot(1, 1, type = "n", axes = F, ylab = NA,
citation()
knitr::opts_chunk$set(echo = TRUE)
data1 <- as.numeric(c(19, 30, 29, 31, 21, 25, 21, 28))
trt1 <- c(rep(c('trt'), 4), rep(c('untrt'),4))
frame1 <- data.frame(trt1,data1)
frame1$block <- as.factor(rep(c(1:4),2))
frame1$non_data1 <- 50-frame1$data1
library(ggplot2)
p<- ggplot(frame1, aes(x=trt1, y=data1)) +
geom_boxplot()
g <- glm(cbind(data1, non_data1) ~ trt1 + block, data=frame1,
family = "binomial") #may need to use family='quasibinomial'
summary(g)
#mean trt
g
library(DHARMa)
# DHARMa package explanation  -------------------------------------------------------------
# default DHARMa dispersion test - simulation-based
simulationOutput <- simulateResiduals(fittedModel = myregression)
myregression <- g
# DHARMa package explanation  -------------------------------------------------------------
# default DHARMa dispersion test - simulation-based
simulationOutput <- simulateResiduals(fittedModel = myregression)
testDispersion(simulationOutput)
testDispersion(simulationOutput, alternative = "less", plot = FALSE) # only under-dispersion
testDispersion(simulationOutput, alternative = "greater", plot = FALSE) # only over-dispersion
#
plot(simulationOutput)
a <- testDispersion(simulationOutput)
a$data.name
a$statistic
a$method
a$alternative
a$p.value
testDispersion(simulationOutput)
knitr::opts_chunk$set(echo = TRUE)
data1 <- as.numeric(c(19, 30, 29, 31, 21, 25, 21, 28))
trt1 <- c(rep(c('trt'), 4), rep(c('untrt'),4))
frame1 <- data.frame(trt1,data1)
library(ggplot2)
p<- ggplot(frame1, aes(x=trt1, y=data1)) +
geom_boxplot()
frame1$non_data1 <- 50-frame1$data1
g <- glm(cbind(data1, non_data1) ~ trt1, data=frame1,
family = "binomial")
summary(g)
mean(c(19, 30, 29, 31))
mean(c(21, 25, 21, 28))
knitr::opts_chunk$set(echo = TRUE)
#set working directory
setwd ("~/Desktop")
#set working directory
setwd ("~/Desktop/")
getwd()
#set working directory
setwd ("M:/Desktop/")
source("02 data analysis.R") #the output of that script is full_summary2
setwd('C:/Users/diaka001/OneDrive - Wageningen University & Research/Makrina_PhD/Data/Data for R/R outputs/001 Bioassays/Summary_bioassays')
source("02 data analysis.R") #the output of that script is full_summary2
#need to set working directory
setwd('C:/Users/diaka001/OneDrive - Wageningen University & Research/Makrina_PhD/Writing/My manuscripts/Manuscript 1/Review/Data and scripts')
source("02 data analysis.R") #the output of that script is full_summary2
all_data <- full_summary2
head(all_data)
# FDR ---------------------------------------------------------------------
#test FDR per bioassay for the p vals of those seed lots that were not affected by the MRT
data <- all_data
data1 <- data[data$Ger_energy != "affected", ]
data2 <- data1[data1$Ger_capacity != "affected", ]
total <- unique(all_data$BioassayType)
total1 <- total[total!= "Onion_Phoma"]
total <- total1[total1!= "Spinach_Fusarium"]
FDRs_bio1 <- c()
FDRs_bio2 <- c()
for(i in 1:length(total)){
FDRs_bio1[i] <- sum((p.adjust(data2[data2$BioassayType==total[[i]],]$P_value_bio1, method = "BH") <0.05) * 0.10)
FDRs_bio2[i] <- sum((p.adjust(data2[data2$BioassayType==total[[i]],]$P_value_bio2, method = "BH") <0.05) * 0.10)
}
#table of FDRs; not including "Onion_Phoma" and "Spinach_Fusarium"
FDRs <- data.frame(SeedLot = total, FDRs_bio1 = FDRs_bio1, FDRs_bio2 = FDRs_bio2)
all_data$Bio_var1[which(
all_data$SeedLot %in% c(325, 337)&
all_data$BioassayType %in%
c('Spinach_Pythium'))] <- 'Non-responsive seed lots'
# visualise non-affected and adjust for FDR -----------------------------------------
# packages also loaded in sourced script '01 data analysis.R'
library(ggplot2)
library(tidyverse)
library(ggpubr)
library(ggrepel)
#switch var1 and var2 for onion-Phoma bioassay to give correct order
all_data$new <- all_data$Bio_var1
#for some reason if the next few lines of code are ran quickly, they don't work
#disease variable 1 for Onion - Phoma needs to show 7 negatively resp. lots
#disease variable 1 for Onion - Phoma needs to show 6 neg. resp. and 1 pos. resp.
all_data$Bio_var1[which(all_data$BioassayType=='Onion_Phoma')] <- all_data$Bio_var2[which(all_data$BioassayType=='Onion_Phoma')]
all_data$Bio_var2[which(all_data$BioassayType=='Onion_Phoma')] <- all_data$new[which(all_data$BioassayType=='Onion_Phoma')]
keep <- all_data
all_data <- all_data[ -c(14) ]
#have the right name appear for bioassay pepper B
all_data$BioassayType[which(all_data$BioassayType=='Pepper seeds_Phytophthora capsici')] <- 'Pepper seedlings_Phytophthora capsici'
data <- all_data
data1 <- data[data$Ger_energy != "affected", ]
data2 <- data1[data1$Ger_capacity != "affected", ]
#calculate frequency for Bio_var1 and Bio_var2
try1 <- data2
try1$Variable <- 'Disease Variable 1'
try1$Bio_var<- try1$Bio_var1
try1$BioassayCODE<- paste(try1$BioassayType, '_', try1$Variable)
try2 <- data2
try2$Variable <- 'Disease Variable 2'
try2$Bio_var<- try2$Bio_var2
try2$BioassayCODE<- paste(try2$BioassayType, '_', try2$Variable)
try12 <- try1[,c(1, 4, 5, 14, 15, 16)]
try22 <- try2[,c(1, 4, 5, 14, 15, 16)]
dataa <- rbind(try12, try22)
#important! uninstall plyr for the code below to work
detach("package:plyr", unload = TRUE)
dataa$Row1 <- 1
x1<- dataa%>%
group_by(BioassayCODE, Bio_var)%>%
summarise(Frequency = sum(Row1))
df5 <- x1
head(df5)
split_data <- data.frame(str_split_fixed(df5$BioassayCODE, '_', 3))
colnames(split_data) <- c('Crop', 'BioassayType', 'Variable')
split_data$BioassayType<- paste(split_data$Crop, '-', split_data$BioassayType)
df5 <- data.frame(df5, split_data)
#adjust for var2 grass bioassays; otherwise ggballoonplot won't work
df5$Frequency <- as.numeric(df5$Frequency)
df5[c(27, 29, 31), 3] <- 'NA'
df5[c(27, 29, 31),2] <- "Non-responsive seed lots"
df5$Frequency <- as.numeric(df5$Frequency)
df5$Crop[which(df5$Crop == 'Pepper plants')] <- 'Pepper'
df5$Crop[which(df5$Crop == 'Pepper seedlings')] <- 'Pepper'
c <- ggballoonplot(df5, x = "Bio_var", y = "BioassayType" , fill = "Crop",
ggtheme = theme_bw()) +
geom_label_repel(aes(label = Frequency), size = 3.5,
box.padding   = 0.01,
point.padding = 0.01,
segment.color = "black",
nudge_x = 0.2, nudge_y = 0.2,
min.segment.length = Inf)
c + facet_grid(~ Variable, scale = 'free')  +
scale_y_discrete(limits = c("Perennial ryegrass - Puccinia sp. ",
"Perennial ryegrass - Laetisaria fuciformis ",
"Red fescue - Laetisaria fuciformis ",
"Pepper plants - Phytophthora capsici ",
"Pepper seedlings - Phytophthora capsici ",
"Coriander - Pythium ",
"Spinach - Fusarium ",
"Spinach - Pythium ",
"Onion - Fusarium ",
"Onion - Phoma ",
"Beetroot - Pythium "),
labels=c("Perennial ryegrass -"~italic(Puccinia)~"sp. ",
"Perennial ryegrass -"~italic(Laetisaria)~~italic(fuciformis)~" ",
"Red fescue -"~italic(Laetisaria)~~italic(fuciformis)~" ",
"Pepper (plants) -"~italic(Phytophthora)~~italic(capsici)~" ",
"Pepper (seedlings) -"~italic(Phytophthora)~~italic(capsici)~" ",
"Coriander -"~italic(Pythium)~"sp.",
"Spinach -"~italic(Fusarium)~~italic(oxysporum)~" ",
"Spinach -"~italic(Pythium)~~italic(ultimum)~" ",
"Onion -"~italic(Fusarium)~~italic(oxysporum)~" ",
"Onion -"~italic(Setophoma)~~italic(terrestris)~" ",
"Beetroot -"~italic(Pythium)~~italic(ultimum)~" ")
) +
scale_x_discrete(labels=c('Negatively responsive seed lots',
'Non-responsive seed lots',
'Positively responsive seed lots')) +
labs(size="Seed lots") +
guides(fill='none') +
theme(axis.text.x=element_text(size=rel(1.3), angle = 35, colour="black"),
axis.text.y=element_text(size=rel(1.3), colour="black"),
strip.text.x = element_text(size=rel(1.5), colour="black"),
plot.margin=unit(c(1,1,1,1),"cm"),
legend.position=c(-0.8,0.82),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_text(size=rel(.9)),
legend.background = element_rect(linetype = 1, size = 0.1, colour = 1))
all_data <- full_summary2
all_data$Bio_var1[which(
all_data$SeedLot %in% c(325, 337)&
all_data$BioassayType %in%
c('Spinach_Pythium'))] <- 'Non-responsive seed lots'
#have the right name appear for bioassay pepper B
all_data$BioassayType[which(all_data$BioassayType=='Pepper seeds_Phytophthora capsici')] <- 'Pepper seedlings_Phytophthora capsici'
data <- all_data
data1 <- data[data$Ger_energy != "affected", ]
data2 <- data1[data1$Ger_capacity != "affected", ]
#calculate frequency for Bio_var1 and Bio_var2
try1 <- data2
try1$Variable <- 'Disease Variable 1'
try1$Bio_var<- try1$Bio_var1
try1$BioassayCODE<- paste(try1$BioassayType, '_', try1$Variable)
try2 <- data2
try2$Variable <- 'Disease Variable 2'
try2$Bio_var<- try2$Bio_var2
try2$BioassayCODE<- paste(try2$BioassayType, '_', try2$Variable)
try12 <- try1[,c(1, 4, 5, 14, 15, 16)]
try22 <- try2[,c(1, 4, 5, 14, 15, 16)]
dataa <- rbind(try12, try22)
dataa$Row1 <- 1
x1<- dataa%>%
group_by(BioassayCODE, Bio_var)%>%
summarise(Frequency = sum(Row1))
df5 <- x1
head(df5)
split_data <- data.frame(str_split_fixed(df5$BioassayCODE, '_', 3))
colnames(split_data) <- c('Crop', 'BioassayType', 'Variable')
split_data$BioassayType<- paste(split_data$Crop, '-', split_data$BioassayType)
df5 <- data.frame(df5, split_data)
#adjust for var2 grass bioassays; otherwise ggballoonplot won't work
df5$Frequency <- as.numeric(df5$Frequency)
df5[c(27, 29, 31), 3] <- 'NA'
df5[c(27, 29, 31),2] <- "Non-responsive seed lots"
df5$Frequency <- as.numeric(df5$Frequency)
df5$Crop[which(df5$Crop == 'Pepper plants')] <- 'Pepper'
df5$Crop[which(df5$Crop == 'Pepper seedlings')] <- 'Pepper'
c <- ggballoonplot(df5, x = "Bio_var", y = "BioassayType" , fill = "Crop",
ggtheme = theme_bw()) +
geom_label_repel(aes(label = Frequency), size = 3.5,
box.padding   = 0.01,
point.padding = 0.01,
segment.color = "black",
nudge_x = 0.2, nudge_y = 0.2,
min.segment.length = Inf)
c + facet_grid(~ Variable, scale = 'free')  +
scale_y_discrete(limits = c("Perennial ryegrass - Puccinia sp. ",
"Perennial ryegrass - Laetisaria fuciformis ",
"Red fescue - Laetisaria fuciformis ",
"Pepper plants - Phytophthora capsici ",
"Pepper seedlings - Phytophthora capsici ",
"Coriander - Pythium ",
"Spinach - Fusarium ",
"Spinach - Pythium ",
"Onion - Fusarium ",
"Onion - Phoma ",
"Beetroot - Pythium "),
labels=c("Perennial ryegrass -"~italic(Puccinia)~"sp. ",
"Perennial ryegrass -"~italic(Laetisaria)~~italic(fuciformis)~" ",
"Red fescue -"~italic(Laetisaria)~~italic(fuciformis)~" ",
"Pepper (plants) -"~italic(Phytophthora)~~italic(capsici)~" ",
"Pepper (seedlings) -"~italic(Phytophthora)~~italic(capsici)~" ",
"Coriander -"~italic(Pythium)~"sp.",
"Spinach -"~italic(Fusarium)~~italic(oxysporum)~" ",
"Spinach -"~italic(Pythium)~~italic(ultimum)~" ",
"Onion -"~italic(Fusarium)~~italic(oxysporum)~" ",
"Onion -"~italic(Setophoma)~~italic(terrestris)~" ",
"Beetroot -"~italic(Pythium)~~italic(ultimum)~" ")
) +
scale_x_discrete(labels=c('Negatively responsive seed lots',
'Non-responsive seed lots',
'Positively responsive seed lots')) +
labs(size="Seed lots") +
guides(fill='none') +
theme(axis.text.x=element_text(size=rel(1.3), angle = 35, colour="black"),
axis.text.y=element_text(size=rel(1.3), colour="black"),
strip.text.x = element_text(size=rel(1.5), colour="black"),
plot.margin=unit(c(1,1,1,1),"cm"),
legend.position=c(-0.8,0.82),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_text(size=rel(.9)),
legend.background = element_rect(linetype = 1, size = 0.1, colour = 1))
#switch var1 and var2 for onion-Phoma bioassay to give correct order
all_data$new <- all_data$Bio_var1
#for some reason if the next few lines of code are ran quickly, they don't work
#disease variable 1 for Onion - Phoma needs to show 7 negatively resp. lots
#disease variable 1 for Onion - Phoma needs to show 6 neg. resp. and 1 pos. resp.
all_data$Bio_var1[which(all_data$BioassayType=='Onion_Phoma')] <- all_data$Bio_var2[which(all_data$BioassayType=='Onion_Phoma')]
all_data$Bio_var2[which(all_data$BioassayType=='Onion_Phoma')] <- all_data$new[which(all_data$BioassayType=='Onion_Phoma')]
keep <- all_data
all_data <- all_data[ -c(14) ]
#have the right name appear for bioassay pepper B
all_data$BioassayType[which(all_data$BioassayType=='Pepper seeds_Phytophthora capsici')] <- 'Pepper seedlings_Phytophthora capsici'
data <- all_data
data1 <- data[data$Ger_energy != "affected", ]
data2 <- data1[data1$Ger_capacity != "affected", ]
#calculate frequency for Bio_var1 and Bio_var2
try1 <- data2
try1$Variable <- 'Disease Variable 1'
try1$Bio_var<- try1$Bio_var1
try1$BioassayCODE<- paste(try1$BioassayType, '_', try1$Variable)
try2 <- data2
try2$Variable <- 'Disease Variable 2'
try2$Bio_var<- try2$Bio_var2
try2$BioassayCODE<- paste(try2$BioassayType, '_', try2$Variable)
try12 <- try1[,c(1, 4, 5, 14, 15, 16)]
try22 <- try2[,c(1, 4, 5, 14, 15, 16)]
dataa <- rbind(try12, try22)
dataa$Row1 <- 1
x1<- dataa%>%
group_by(BioassayCODE, Bio_var)%>%
summarise(Frequency = sum(Row1))
df5 <- x1
head(df5)
split_data <- data.frame(str_split_fixed(df5$BioassayCODE, '_', 3))
colnames(split_data) <- c('Crop', 'BioassayType', 'Variable')
split_data$BioassayType<- paste(split_data$Crop, '-', split_data$BioassayType)
df5 <- data.frame(df5, split_data)
#adjust for var2 grass bioassays; otherwise ggballoonplot won't work
df5$Frequency <- as.numeric(df5$Frequency)
df5[c(27, 29, 31), 3] <- 'NA'
df5[c(27, 29, 31),2] <- "Non-responsive seed lots"
df5$Frequency <- as.numeric(df5$Frequency)
df5$Crop[which(df5$Crop == 'Pepper plants')] <- 'Pepper'
df5$Crop[which(df5$Crop == 'Pepper seedlings')] <- 'Pepper'
c <- ggballoonplot(df5, x = "Bio_var", y = "BioassayType" , fill = "Crop",
ggtheme = theme_bw()) +
geom_label_repel(aes(label = Frequency), size = 3.5,
box.padding   = 0.01,
point.padding = 0.01,
segment.color = "black",
nudge_x = 0.2, nudge_y = 0.2,
min.segment.length = Inf)
c + facet_grid(~ Variable, scale = 'free')  +
scale_y_discrete(limits = c("Perennial ryegrass - Puccinia sp. ",
"Perennial ryegrass - Laetisaria fuciformis ",
"Red fescue - Laetisaria fuciformis ",
"Pepper plants - Phytophthora capsici ",
"Pepper seedlings - Phytophthora capsici ",
"Coriander - Pythium ",
"Spinach - Fusarium ",
"Spinach - Pythium ",
"Onion - Fusarium ",
"Onion - Phoma ",
"Beetroot - Pythium "),
labels=c("Perennial ryegrass -"~italic(Puccinia)~"sp. ",
"Perennial ryegrass -"~italic(Laetisaria)~~italic(fuciformis)~" ",
"Red fescue -"~italic(Laetisaria)~~italic(fuciformis)~" ",
"Pepper (plants) -"~italic(Phytophthora)~~italic(capsici)~" ",
"Pepper (seedlings) -"~italic(Phytophthora)~~italic(capsici)~" ",
"Coriander -"~italic(Pythium)~"sp.",
"Spinach -"~italic(Fusarium)~~italic(oxysporum)~" ",
"Spinach -"~italic(Pythium)~~italic(ultimum)~" ",
"Onion -"~italic(Fusarium)~~italic(oxysporum)~" ",
"Onion -"~italic(Setophoma)~~italic(terrestris)~" ",
"Beetroot -"~italic(Pythium)~~italic(ultimum)~" ")
) +
scale_x_discrete(labels=c('Negatively responsive seed lots',
'Non-responsive seed lots',
'Positively responsive seed lots')) +
labs(size="Seed lots") +
guides(fill='none') +
theme(axis.text.x=element_text(size=rel(1.3), angle = 35, colour="black"),
axis.text.y=element_text(size=rel(1.3), colour="black"),
strip.text.x = element_text(size=rel(1.5), colour="black"),
plot.margin=unit(c(1,1,1,1),"cm"),
legend.position=c(-0.8,0.82),
legend.key.size = unit(0.2, 'cm'),
legend.title = element_text(size=rel(.9)),
legend.background = element_rect(linetype = 1, size = 0.1, colour = 1))
View(all_data)
View(all_data[which(BioassayType=='Spinach_Pythium')])
View(all_data[which(all_data$BioassayType=='Spinach_Pythium')])
View(all_data[which(all_data$BioassayType=='Spinach_Pythium')],)
View(all_data[which(all_data$BioassayType=='Spinach_Pythium'),])
full_summary_keep <- full_summary
head(full_summary_keep)
View(full_summary_keep[which(all_data$BioassayType=='Spinach_Pythium'),])
# Spinach - Pythium                           # spi1
difference_var1 <- c()
for(i in 1:length(unique(spinach$SeedLot))){
difference_var1[i] <- mean(spinach[spinach$MRTrt=='untrt' &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Emerge_fin -
spinach[spinach$MRTrt=='trt'   &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Emerge_fin)
}
difference_var2 <- c()
for(i in 1:length(unique(spinach$SeedLot))){
difference_var2[i] <- mean(spinach[spinach$MRTrt=='untrt' &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Inf_post -
spinach[spinach$MRTrt=='trt'   &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Inf_post)
}
#keep the info
difference_var1_spi1 <- data.frame(difference_var1=difference_var1)
difference_var2_spi1 <- data.frame(difference_var2=difference_var2)
difference_var1_spi1
# import data -------------------------------------------------------------
#need to set working directory
setwd('C:/Users/diaka001/OneDrive - Wageningen University & Research/Makrina_PhD/Writing/My manuscripts/Manuscript 1/Review/Data and scripts')
germination_bee <- read.csv("Beetroot_4Rgermination.csv")
germination_oni <- read.csv("Onion_4Rgermination.csv")
germination_spi <- read.csv("Spinach_4Rgermination.csv")
germination_cor <- read.csv("Coriander_4Rgermination.csv")
germination_pep <- read.csv("Pepper_4Rgermination.csv")
beetroot <- read.csv("Beetroot4R_data.csv")
onion <- read.csv("Onion4R_data.csv")
spinach <- read.csv("Spinach4R_data.csv")
coriander <- read.csv("Coriander4R_data.csv")
pepper <- read.csv("Pepper_plant_4Rdata.csv")
pepperSeeds <- read.csv('Pepper_seeds_4Rdata.csv')
perennial_puccinia <- read.csv("Perennial_puccinia_4Rdata.csv")
perennial_thread <- read.csv('Perennial_thread_4Rdata.csv')
fescue_thread <- read.csv('Red_fescue_4Rdata.csv')
# Spinach - Pythium-------------------------------------------------------------
#
# (note: replicates were not paired and thus not considered a covariate of the model)
spinach$nonEmerge_count_fin <- as.numeric(63-spinach$Emerge_count_fin)
total <- unique(spinach$SeedLot)
head(spinach)
#for emergence at the end of the experiment
regression_list_Emerge_fin <- list()
summary_list_Emerge_fin <- list()
for(i in 1:length(total)){
regression_list_Emerge_fin[[i]] <- glm(cbind(nonEmerge_count_fin, Emerge_count_fin)
~ MRTrt, data=spinach[spinach$SeedLot==total[[i]],],
family = "quasibinomial")
names(regression_list_Emerge_fin)[i] <- total[i]
summary_list_Emerge_fin[[i]]<-summary(regression_list_Emerge_fin[[i]])
names(summary_list_Emerge_fin)[i] <- total[i]
}
#for post-damping off infection (%) at the end of the experiment
regression_list_post <- list()
summary_list_post <- list()
for(i in 1:length(total)){
regression_list_post[[i]] <- glm(cbind(Uninf_count_post, Inf_count_post)
~ MRTrt, data=spinach[spinach$SeedLot==total[[i]],],
family = "quasibinomial")
names(regression_list_post)[i] <- total[i]
summary_list_post[[i]]<-summary(regression_list_post[[i]])
names(summary_list_post)[i] <- total[i]
}
#p values
pval_Emerge_fin <-c()
pval_post <-c()
for(i in 1:length(total)){
pval_Emerge_fin[i]<-summary_list_Emerge_fin[[i]]$coefficients[2,4]
pval_post[i]<-summary_list_post[[i]]$coefficients[2,4]
names(pval_Emerge_fin)[i] <- total[i]
names(pval_post)[i] <- total[i]
}
SeedLot <- total
pval_bio_spi1 <- data.frame(SeedLot,P_value_bio1=c(round(pval_Emerge_fin,digits=6)),
P_value_bio2=c(round(pval_post,digits=6)))
head(pval_bio_spi1)
summary(full_summary$SeedLot == full_summary_bio1$SeedLot)
# Spinach - Pythium                           # spi1
difference_var1 <- c()
for(i in 1:length(unique(spinach$SeedLot))){
difference_var1[i] <- mean(spinach[spinach$MRTrt=='untrt' &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Emerge_fin -
spinach[spinach$MRTrt=='trt'   &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Emerge_fin)
}
difference_var1
unique(spinach$SeedLot
0
unique(spinach$SeedLot))
unique(spinach$SeedLot)
spinach$MRTrt=='untrt' &
spinach$SeedLot==unique(spinach$SeedLot)[1],]$Emerge_fin
spinach[spinach$MRTrt=='untrt' &
spinach$SeedLot==unique(spinach$SeedLot)[i],]$Emerge_fin)
colnames(spinach)
