setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
remove(list=ls()) # Remove all variables from memory
graphics.off()    # Closes all figures
cat("\014")       # Clears the console
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
setwd("[WRITE THE PATH LOCATION OF THE CURRENT R-SCRIPT HERE]")
workdir = getwd()
### 1.1 Sort FMV17 data. OK!
FMV <- read.csv("FMV17_SAMPLING_rawdata_duplicateT0.csv", header=T) # Read raw data set
head(FMV) # Show header of data set
levels(FMV$Time)      # Show the different Time treatments
levels(FMV$Vaucheria) # Show the different Vaucheria treatments
levels(FMV$Treatment) # Show all combinations of Time and Vaucheria treatments
### 2.1 WC: overview plot and summary table. OK!
png('output/WC_boxplot.png')
par(cex=0.8) # Prepare plot
plot(WC_perc ~ Treatment, data=FMV) # Simple boxplot
dev.off()
sem <- function(x) { sd(x)/sqrt(length(x)) } # Define standard error of the mean
library(doBy)
options(digits=7)
## Transformed data
# WCsum <- summaryBy(WC_perc ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (one-way anova)
WCsum <- summaryBy(WC_perc ~ Time*Vaucheria, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary
WCsum
write.csv(WCsum,"output/WC_summary.csv")
## Original (untransformed) data
# WCsumORIG <- summaryBy(WC_perc_ORIGINAL ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (one-way anova)
WCsumORIG <- summaryBy(WC_perc_ORIGINAL ~ Time*Vaucheria, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary
WCsumORIG
write.csv(WCsumORIG,"output/WC_ORIGINAL_summary.csv")
# lm.FMV <- lm(WC_perc ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(WC_perc ~ Time*Vaucheria, data=FMV) # (two-way anova)
WCanova <- anova(lm.FMV)
WCanova
write.csv(WCanova,"output/WC_anova.csv")
attach(FMV)
# (WCthsd <- TukeyHSD(aov(WC_perc ~ Treatment, data=FMV))) # (one-way anova)
(WCthsd <- TukeyHSD(aov(WC_perc ~ Time*Vaucheria, data=FMV))) # (two-way anova)
write.csv(WCthsd$Treatment,"output/WC_tHSD.csv")
attach(FMV)
# (WCthsd <- TukeyHSD(aov(WC_perc ~ Treatment, data=FMV))) # (one-way anova)
# write.csv(WCthsd$Treatment,"output/WC_tHSD.csv") # (one-way anova)
(WCthsd <- TukeyHSD(aov(WC_perc ~ Time*Vaucheria, data=FMV))) # (two-way anova)
write.csv(WCthsd$Time,"output/WC_tHSD_Time.csv") # (two-way anova)
write.csv(WCthsd$Vaucheria,"output/WC_tHSD_Vaucheria.csv") # (two-way anova)
write.csv(WCthsd$`Time:Vaucheria`,"output/WC_tHSD_Time+Vaucheria.csv") # (two-way anova)
png('output/WC_tHSDplot.png')
par(mfrow=c(1,1),mar=c(3.5, 9, 2.5, 1) + 0.1, mgp=c(2.5,1.5,0),las=1,cex=0.8)
plot(WCthsd)
dev.off()
library(car)
# WClevene <- leveneTest(WC_perc ~ Treatment, data=FMV) # (one-way anova)
WClevene <- leveneTest(WC_perc ~ Time*Vaucheria, data=FMV) # (two-way anova)
WClevene
write.csv(WClevene,"output/WC_levene.csv")
### ==> H0 (equal variances) not rejected ==> Assumption not invalid. OK!
png('output/WC_qq.png')
# lm.FMV <- lm(WC_perc ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(WC_perc ~ Time*Vaucheria, data=FMV) # (two-way anova)
res <- resid(lm.FMV)
qqPlot(res)
dev.off()
### ==> Assumption of normality doesn't seem valid!
WCshapiro <- print(shapiro.test(res))
write.csv(WCshapiro$p.value,"output/WC_shapiro_pvalue.csv")
### ==> H0 (normal distribution) not rejected ==> Assumption not invalid
png('output/WC_hist.png')
hist(res)
dev.off()
### Histogram appears quite normal.
WClevene
WCshapiro
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_SAMPLING_statistics.r', echo=TRUE)
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_SAMPLING_statistics.r', echo=TRUE)
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
remove(list=ls()) # Remove all variables from memory
graphics.off()    # Closes all figures
cat("\014")       # Clears the console
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
#setwd("[WRITE HERE THE PATH LOCATION OF THE CURRENT R-SCRIPT]")
workdir = getwd()
FMV <- read.csv("FMV17_ElevChange_outliers.csv", header=T) # Read raw data set
head(FMV) # Show header of data set
levels(FMV$Time)      # Show the different Time treatments
levels(FMV$Vaucheria) # Show the different Vaucheria treatments
levels(FMV$Treatment) # Show all combinations of Time and Vaucheria treatments
png('output/ElevChange_boxplot.png')
par(cex=0.8) # Prepare plot
plot(ElevChange_mL ~ Treatment, data=FMV) # Simple boxplot
dev.off()
png('output/ElevChange_ORIGINAL_boxplot.png')
par(cex=0.8) # Prepare plot
plot(ElevChange_mL_ORIGINAL ~ Treatment, data=FMV) # Simple boxplot
dev.off()
sem <- function(x) { sd(x)/sqrt(length(x)) } # Define standard error of the mean
library(doBy)
options(digits=7)
## Transformed data
# ElevChangesum <- summaryBy(ElevChange_mL ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (one-way anova)
ElevChangesum <- summaryBy(ElevChange_mL ~ Time*Vaucheria, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (two-way anova)
ElevChangesum
write.csv(ElevChangesum,"output/ElevChange_summary.csv")
# ElevChangesumORIG <- summaryBy(ElevChange_mL_ORIGINAL ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (one-way anova)
ElevChangesumORIG <- summaryBy(ElevChange_mL_ORIGINAL ~ Time*Vaucheria, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (two-way anova)
ElevChangesumORIG
write.csv(ElevChangesumORIG,"output/ElevChange_ORIGINAL_summary.csv")
# lm.FMV <- lm(ElevChange_mL ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(ElevChange_mL ~ Time*Vaucheria, data=FMV) # (two-way anova)
ElevChangeanova <- anova(lm.FMV)
ElevChangeanova
write.csv(ElevChangeanova,"output/ElevChange_anova.csv")
attach(FMV)
# (ElevChangethsd <- TukeyHSD(aov(ElevChange_mL ~ Treatment, data=FMV))) # (one-way anova)
# write.csv(ElevChangethsd$Treatment,"output/ElevChange_tHSD.csv") # (one-way anova)
(ElevChangethsd <- TukeyHSD(aov(ElevChange_mL ~ Time*Vaucheria, data=FMV))) # (one-way anova)
attach(FMV)
# (ElevChangethsd <- TukeyHSD(aov(ElevChange_mL ~ Treatment, data=FMV))) # (one-way anova)
# write.csv(ElevChangethsd$Treatment,"output/ElevChange_tHSD.csv") # (one-way anova)
(ElevChangethsd <- TukeyHSD(aov(ElevChange_mL ~ Time*Vaucheria, data=FMV))) # (two-way anova)
write.csv(ElevChangethsd$Time,"output/ElevChange_tHSD_Time.csv")
write.csv(ElevChangethsd$Vaucheria,"output/ElevChange_tHSD_Vaucheria.csv")
write.csv(ElevChangethsd$`Time:Vaucheria`,"output/ElevChange_tHSD_Time+Vaucheria.csv")
png('output/ElevChange_tHSDplot.png')
par(mfrow=c(1,1),mar=c(3.5, 9, 2.5, 1) + 0.1, mgp=c(2.5,1.5,0),las=1,cex=0.8)
plot(ElevChangethsd)
dev.off()
library(car)
# ElevChangelevene <- leveneTest(ElevChange_mL ~ Treatment, data=FMV) # (one-way anova)
ElevChangelevene <- leveneTest(ElevChange_mL ~ Time*Vaucheria, data=FMV) # (two-way anova)
ElevChangelevene
write.csv(ElevChangelevene,"output/ElevChange_levene.csv")
### ==> H0 (equal variances) not rejected ==> Assumption not invalid. OK!
png('output/ElevChange_qq.png')
# lm.FMV <- lm(ElevChange_mL ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(ElevChange_mL ~ Time*Vaucheria, data=FMV) # (two-way anova)
res <- resid(lm.FMV)
qqPlot(res)
dev.off()
### ==> Assumption of normality doesn't seem valid!
ElevChangeshapiro <- print(shapiro.test(res))
write.csv(ElevChangeshapiro$p.value,"output/ElevChange_shapiro_pvalue.csv")
### ==> H0 (normal distribution) not rejected ==> Assumption not invalid
ElevChangeshapiro <- print(shapiro.test(res))
write.csv(ElevChangeshapiro$p.value,"output/ElevChange_shapiro_pvalue.csv")
### ==> H0 (normal distribution) not rejected ==> Assumption not invalid
png('output/ElevChange_hist.png')
hist(res)
dev.off()
### Histogram appears quite normal.
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_ElevChange_statistics.R', echo=TRUE)
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
remove(list=ls()) # Remove all variables from memory
graphics.off()    # Closes all figures
cat("\014")       # Clears the console
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
# setwd("[WRITE THE PATH LOCATION OF THE CURRENT R-SCRIPT HERE]")
workdir = getwd()
FMV <- read.csv("FMV17_CHLA.csv", header=T) # Read raw data set
head(FMV) # Show header of data set
levels(FMV$Time)      # Show the different Time treatments
levels(FMV$Vaucheria) # Show the different Vaucheria treatments
levels(FMV$Treatment) # Show all combinations of Time and Vaucheria treatments
png('output/CHLA_boxplot.png')
par(cex=0.8) # Prepare plot
plot(CHLA_mug_per_g ~ Treatment, data=FMV) # Simple boxplot
dev.off()
sem <- function(x) { sd(x)/sqrt(length(x)) } # Define standard error of the mean
library(doBy)
options(digits=7)
## Transformed data
# CHLAsum <- summaryBy(CHLA_mug_per_g ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (one-way anova)
CHLAsum <- summaryBy(CHLA_mug_per_g ~ Time*Vaucheria, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (two-way anova)
CHLAsum
write.csv(CHLAsum,"output/CHLA_summary.csv")
# lm.FMV <- lm(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (two-way anova)
CHLAanova <- anova(lm.FMV)
CHLAanova
write.csv(CHLAanova,"output/CHLA_anova.csv")
attach(FMV)
# (CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Treatment, data=FMV))) # (one-way anova)
(CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV))) # (two-way anova)
write.csv(CHLAthsd$Treatment,"output/CHLA_tHSD.csv")
# write.csv(CHLAthsd$Treatment,"output/CHLA_tHSD.csv") # (one-way anova)
(CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV))) # (two-way anova)
attach(FMV)
# (CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Treatment, data=FMV))) # (one-way anova)
# write.csv(CHLAthsd$Treatment,"output/CHLA_tHSD.csv") # (one-way anova)
(CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV))) # (two-way anova)
write.csv(CHLAthsd$Time,"output/CHLA_tHSD_Time.csv") # (two-way anova)
write.csv(CHLAthsd$Vaucheria,"output/CHLA_tHSD_Vaucheria.csv") # (two-way anova)
write.csv(CHLAthsd$`Time:Vaucheria`,"output/CHLA_tHSD_Time+Vaucheria.csv") # (two-way anova)
png('output/CHLA_tHSDplot.png')
par(mfrow=c(1,1),mar=c(3.5, 9, 2.5, 1) + 0.1, mgp=c(2.5,1.5,0),las=1,cex=0.8)
plot(CHLAthsd)
dev.off()
library(car)
# CHLAlevene <- leveneTest(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
CHLAlevene <- leveneTest(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (two-way anova)
CHLAlevene
write.csv(CHLAlevene,"output/CHLA_levene.csv")
png('output/CHLA_qq.png')
# lm.FMV <- lm(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (one-way anova)
res <- resid(lm.FMV)
qqPlot(res)
dev.off()
### ==> Assumption of normality doesn't seem valid!
CHLAshapiro <- print(shapiro.test(res))
write.csv(CHLAshapiro$p.value,"output/CHLA_shapiro_pvalue.csv")
### ==> H0 (normal distribution) rejected ==> Assumption not valid
png('output/CHLA_hist.png')
hist(res)
dev.off()
### Histogram appears quite normal.
?kruskal.test
?leveneTest
?kruskal.test
FMV17_CHLA_Kruskal <- kruskal.test(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (two-way anova)
Time*Vaucheria
FMV17_CHLA_Kruskal <- kruskal.test(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
FMV17_CHLA_Kruskal
remove(list=ls()) # Remove all variables from memory
graphics.off()    # Closes all figures
cat("\014")       # Clears the console
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
# setwd("[WRITE THE PATH LOCATION OF THE CURRENT R-SCRIPT HERE]")
workdir = getwd()
### 1.1 Sort FMV17 data. OK!
FMV <- read.csv("FMV17_CHLA.csv", header=T) # Read raw data set
head(FMV) # Show header of data set
levels(FMV$Time)      # Show the different Time treatments
levels(FMV$Vaucheria) # Show the different Vaucheria treatments
levels(FMV$Treatment) # Show all combinations of Time and Vaucheria treatments
##########################################################################
# Chlorophyll-a content (CHLA)
##########################################################################
### 2.1 CHLA: plot. OK!
png('output/CHLA_boxplot.png')
par(cex=0.8) # Prepare plot
plot(CHLA_mug_per_g ~ Treatment, data=FMV) # Simple boxplot
dev.off()
# png('output/CHLA_ORIGINAL_boxplot.png')
# par(cex=0.8) # Prepare plot
# plot(CHLA_mug_per_g_ORIGINAL ~ Treatment, data=FMV) # Simple boxplot
# dev.off()
sem <- function(x) { sd(x)/sqrt(length(x)) } # Define standard error of the mean
library(doBy)
options(digits=7)
## Transformed data
# CHLAsum <- summaryBy(CHLA_mug_per_g ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (one-way anova)
CHLAsum <- summaryBy(CHLA_mug_per_g ~ Time*Vaucheria, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary (two-way anova)
CHLAsum
write.csv(CHLAsum,"output/CHLA_summary.csv")
## Original (untransformed) data
# CHLAsumORIG <- summaryBy(CHLA_mug_per_g_ORIGINAL ~ Treatment, data = FMV, FUN=c(length, mean, sd, sem, min, max)) # Give data summary
# CHLAsumORIG
# write.csv(CHLAsumORIG,"output/CHLA_ORIGINAL_summary.csv")
### 2.2 CHLA: ANOVA. OK!
# lm.FMV <- lm(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (two-way anova)
CHLAanova <- anova(lm.FMV)
CHLAanova
write.csv(CHLAanova,"output/CHLA_anova.csv")
### 2.3: CHLA Post-hoc tests. OK!
attach(FMV)
# (CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Treatment, data=FMV))) # (one-way anova)
# write.csv(CHLAthsd$Treatment,"output/CHLA_tHSD.csv") # (one-way anova)
(CHLAthsd <- TukeyHSD(aov(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV))) # (two-way anova)
write.csv(CHLAthsd$Time,"output/CHLA_tHSD_Time.csv") # (two-way anova)
write.csv(CHLAthsd$Vaucheria,"output/CHLA_tHSD_Vaucheria.csv") # (two-way anova)
write.csv(CHLAthsd$`Time:Vaucheria`,"output/CHLA_tHSD_Time+Vaucheria.csv") # (two-way anova)
png('output/CHLA_tHSDplot.png')
par(mfrow=c(1,1),mar=c(3.5, 9, 2.5, 1) + 0.1, mgp=c(2.5,1.5,0),las=1,cex=0.8)
plot(CHLAthsd)
dev.off()
### 2.4: CHLA Test assumption of equal variances.
library(car)
# CHLAlevene <- leveneTest(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
CHLAlevene <- leveneTest(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (two-way anova)
CHLAlevene
write.csv(CHLAlevene,"output/CHLA_levene.csv")
### ==> H0 (equal variances) rejected ==> Assumption invalid!
### 2.5: CHLA Test assumption of normality.
png('output/CHLA_qq.png')
# lm.FMV <- lm(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
lm.FMV <- lm(CHLA_mug_per_g ~ Time*Vaucheria, data=FMV) # (one-way anova)
res <- resid(lm.FMV)
qqPlot(res)
dev.off()
### ==> Assumption of normality doesn't seem valid!
CHLAshapiro <- print(shapiro.test(res))
write.csv(CHLAshapiro$p.value,"output/CHLA_shapiro_pvalue.csv")
### ==> H0 (normal distribution) rejected ==> Assumption not valid
png('output/CHLA_hist.png')
hist(res)
dev.off()
### Histogr
## Try non-parametric test instead: can only be applied with one factor, so use Treatment instead of Time*Vaucheria
FMV17_CHLA_Kruskal <- kruskal.test(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
FMV17_CHLA_Kruskal
FMV17_CHLA_Kruskal <- kruskal.test(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
FMV17_CHLA_Kruskal
write.csv(FMV17_CHLA_Kruskal,"output/FMV17_CHLA_kruskal.csv")
FMV17_CHLA_Kruskal$statistic
FMV17_CHLA_Kruskal$statistic
FMV17_CHLA_Kruskal$parameter
FMV17_CHLA_Kruskal$method
FMV17_CHLA_Kruskal$data.name
FMV17_CHLA_Kruskal$p.value
## Try non-parametric test instead: can only be applied with one factor, so use Treatment instead of Time*Vaucheria
FMV17_CHLA_Kruskal <- kruskal.test(CHLA_mug_per_g ~ Treatment, data=FMV) # (one-way anova)
FMV17_CHLA_Kruskal
write.csv(FMV17_CHLA_Kruskal$statistic,"output/FMV17_CHLA_kruskal_chi2.csv")
write.csv(FMV17_CHLA_Kruskal$parameter,"output/FMV17_CHLA_kruskal_df.csv")
write.csv(FMV17_CHLA_Kruskal$p.value,"output/FMV17_CHLA_kruskal_pvalue.csv")
## Pairwise comparison
library("dunn.test")
DATA <- FMV$CHLA_mug_per_g
GROUPS <- FMV$Treatment
dunn <- dunn.test(DATA, g=GROUPS, method="bonferroni",kw = TRUE, label = TRUE, wrap = TRUE, alpha = 0.05 )
dunn
wise comparison
library("dunn.test")
DATA <- FMV$CHLA_mug_per_g
GROUPS <- FMV$Treatment
dunn <- dunn.test(DATA, g=GROUPS, method="bonferroni",kw = TRUE, label = TRUE, wrap = TRUE, alpha = 0.05 )
dunn
write.csv(dunn,"output/FMV17_CHLA_dunn.csv")
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_CHLA_statistics.r', echo=TRUE)
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_CHLA_statistics.r', echo=TRUE)
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_CHLA_statistics.r', echo=TRUE)
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
remove(list=ls()) # Remove all variables from memory
graphics.off()    # Closes all figures
cat("\014")       # Clears the console
setwd("~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata")
# setwd("[WRITE THE PAT
workdir = getwd()
FMV <- read.csv("FMV17_CHLA.csv", header=T) # Read raw data set
head(FMV) # Show header of data set
levels(FMV$Time)      # Show the different Time treatments
levels(FMV$Vaucheria) # Show the different Vaucheria treatments
levels(FMV$Treatment) # Show all combinations of Time and Vaucheria treatments
png('output/CHLA_boxplot.png')
par(cex=0.8) # Prepare plot
plot(CHLA_mug_per_g ~ Treatment, data=FMV) # Simple boxplot
dev.off()
sem <- function(x) { sd(x)/sqrt(length(x)) } # Define standard error of the mean
library(doBy)
options(digits=7)
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_CHLA_statistics.r', echo=TRUE)
source('~/Documents/20181029_Documents_OLD/Articles/Writing/Article3_Experiments_BGM_feedbacks/v11_Revision1/REVISION1/Fig5+6_two-way_ANOVA/Figure6_R1/rawdata/FMV17_CHLA_statistics.r', echo=TRUE)
