General project set-up

# Get all libraries and sources required to run the script

library(dplyr)
library(plyr)
library(reshape2)
library(ggplot2)
library(ggthemes)

theme_set (theme_classic() + theme(panel.grid.major = element_blank(),
                              panel.grid.minor = element_blank(), 
                              axis.line = element_line(colour = "black"),
                              legend.position="none",
                              axis.text.x = element_text(angle = 90, vjust = 0.5),
                              plot.title = element_text(size=12, face="bold"),
                              #panel.border = element_rect(colour = "black", fill=NA, size=1)
                              panel.border = element_blank()
                              ))

Data and data clean-up

# 1. Import data: 

  # Long format Ssid YII
    YII.Tall<-read.csv("Data/YII_tall.csv")

  # Wide format 
    # YII.wide<- YII.Tall<-readRDS("Data/YII.Wide.rds")
    # YII.Wide<-read.csv("Data/YII_Wide.csv")
  
  #summary(YII.Tall)


# 2. Data clean-up an types: 
  
  # Variable types 
    #YII.nutrients$Time <- as.factor(YII.nutrients$Time)
    YII.Tall$Time<-as.numeric(YII.Tall$Time)
    YII.Tall$Date<-as.Date(YII.Tall$Date, "%Y-%m-%d")
    YII.Tall$Days<-(as.numeric(YII.Tall$Date)-18486)
  
  # Remove-unused data    
    Extras <- YII.Tall[which (YII.Tall$Nutrients=="Extra"), ]
    YII.Tall <- droplevels(YII.Tall[!rownames(YII.Tall) %in%
                                        rownames(Extras), ])
    
    Error <- YII.Tall[which (YII.Tall$YII==0), ]
    YII.Tall <- droplevels(YII.Tall[!rownames(YII.Tall) %in%
                                        rownames(Error), ])
  # Treatments
    YII.Tall$Nutrients<-factor(YII.Tall$Nutrients, 
                             levels= c("Ambient", "NH4"), ordered=TRUE)
    YII.Tall$Disease<-factor(YII.Tall$Disease, 
                             levels= c("Placebo", "Pathogen"), ordered=TRUE)
  # Replicates
    YII.Tall$Tank<-factor(YII.Tall$Tank, ordered=FALSE)
    YII.Tall$Genotype<-factor(YII.Tall$Genotype, ordered=FALSE)
  
  summary(YII.Tall)
##       Time             Date               Genotype      Fragment  
##  Min.   :0.0000   Min.   :2020-08-12   FM14   : 48   201    :  2  
##  1st Qu.:0.0000   1st Qu.:2020-08-12   FM19   : 48   202    :  2  
##  Median :0.0000   Median :2020-08-12   FM6    : 48   204    :  2  
##  Mean   :0.9688   Mean   :2020-08-22   FM9    : 48   205    :  2  
##  3rd Qu.:2.0000   3rd Qu.:2020-09-02   U44    : 47   207    :  2  
##  Max.   :2.0000   Max.   :2020-09-02   Elkhorn: 46   208    :  2  
##                                        (Other):196   (Other):469  
##       Tank         A_Tank      Nutrients       Disease         YII        
##  1      : 55   1      : 62   Ambient:245   Placebo :229   Min.   :0.4900  
##  2      : 55   2      : 62   NH4    :236   Pathogen:252   1st Qu.:0.6230  
##  3      : 55   6      : 62                                Median :0.6650  
##  6      : 54   7      : 62                                Mean   :0.6519  
##  8      : 54   8      : 61                                3rd Qu.:0.6880  
##  5      : 53   5      : 60                                Max.   :0.7150  
##  (Other):155   (Other):112                                                
##             Sample         Days      
##  2020-08-12_201:  1   Min.   : 0.00  
##  2020-08-12_202:  1   1st Qu.: 0.00  
##  2020-08-12_204:  1   Median : 0.00  
##  2020-08-12_205:  1   Mean   :10.17  
##  2020-08-12_207:  1   3rd Qu.:21.00  
##  2020-08-12_208:  1   Max.   :21.00  
##  (Other)       :475

Data exploration

Genotype

# Genotype

YII_Genet<- ggplot(YII.Tall, aes (Genotype, YII, colour=factor(Time))) +
  stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
  stat_summary(fun.y=mean, geom="line") + 
  geom_jitter(alpha=0.5)+
  scale_y_continuous(limits = c(0, .73),
                         breaks = seq(0, 0.7,0.2),  
                         expand = c(0.01, 0.01),
                         name=("YII (Fv/Fm)"))
  
YII_Genet

YII_Genet+ facet_wrap(~Nutrients)

YII_Genet+ facet_wrap(~Disease)

YII_Genet+ facet_wrap(Nutrients~Disease)

Tank

YII_Tank<- ggplot(YII.Tall, aes (Tank, YII)) +
  stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
  stat_summary(fun.y=mean, geom="line") + 
  scale_y_continuous(limits = c(0.4, .73),
                         breaks = seq(0, 0.7,0.2),  
                         expand = c(0, 0),
                         name=("YII (Fv/Fm)"))+
  geom_jitter(aes(colour=Genotype), alpha=0.3)+
  facet_wrap(~Time) +
  theme(legend.position = "bottom")
YII_Tank

YII_Tank+ facet_wrap(Time~Nutrients)

YII_Tank+ facet_wrap(Time~Disease)

Treatments

YII_Disease<- ggplot(YII.Tall, aes (Disease, YII)) +
  stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
  stat_summary(fun.y=mean, geom="line") + 
  scale_y_continuous(limits = c(0.4, .73),
                         breaks = seq(0, 0.7,0.2),  
                         expand = c(0, 0),
                         name=("YII (Fv/Fm)"))+
  geom_jitter(aes(colour=Genotype), alpha=0.3)+
  facet_wrap(~Time) +
  theme(legend.position = "bottom")
YII_Disease

YII_Tank+ facet_wrap(Time~Nutrients)

YII_Tank+ facet_wrap(Time~Disease)

Packages used

# Creates bibliography 
#knitr::write_bib(c(.packages()), "packages.bib")

R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.