---
title: "Macronutrient Activation Screen Data"
output: html_notebook
---

get the data in
```{r}
require(car)
require(lsmeans)
require(lme4)
require(nlme)
require(dplyr)
require(tidyverse)
require(gridExtra)

macro.activation.data <- read_csv("./MacronutrientActivationScreen_RepresentativeData_JoVE_Pocas.csv", col_names = TRUE, na = "NA",
                        col_types = list(Genotype = col_factor(c("R12E06", "R14B11","R19G11","R21B06", "R22H01", "R29C02", "R40D06", "R45C03", "R45D11", "R48F09", "attP2")),
                                         Food = col_double(),
                                         Date = col_character(),
                                         Abs600nm = col_double(),
                                         Concentration = col_double(),
                                         Number = col_integer(),
                                         ConcPerL3 = col_double()))

macro.activation.data
```

first plot the data

Gonçalo, use the scale_fill_manual and the scale_colour_manual to set the colours of the different genotypes
```{r}

Fig1 <- ggplot(data = macro.activation.data, aes(x= Food, y= ConcPerL3))+ 
xlab("P:C Ratio")+
ylab("Food eaten per larva (mL)")+
theme_bw()+
theme(panel.grid=element_blank(),axis.title.x=element_text(size=20), axis.title.y=element_text(size=20), 
      axis.text.y=element_text(size=16), axis.text.x=element_text(size=16), panel.background = element_rect(colour = "black"), legend.background = element_rect(), legend.key = element_rect(colour = "white"), legend.text=element_text(face="italic", size=14))+
theme(axis.title.x = element_text(vjust=-0.5), axis.title.y = element_text(vjust=1.5))+
 theme(legend.title=element_blank())+
 theme(legend.title=element_blank())+
  guides(colour = guide_legend(nrow = 15))+
#scale_y_continuous(limits=c(-0.01,0.03))+
scale_color_manual(values = c("#f21818", "#f7439d", "#f7a643", "#f7df43", "#269142", "#27e86e", "#2ce5f2", "#2ca3f2", "#3046f0", "#aa45f7", "#d1cfcf"))+
geom_point(aes(colour = as.factor(Genotype))) +
geom_smooth(aes(colour = as.factor(Genotype)), se = FALSE, method = "lm") + 
geom_point(data = subset(macro.activation.data, Genotype == 'attP2'), colour = "#d1cfcf")+
geom_smooth(data = subset(macro.activation.data, Genotype == 'attP2'), colour = "#d1cfcf", se = FALSE, method = "lm", size = 2)+
#geom_point() +
#geom_smooth(se = FALSE, method = "lm", colour = "black") + 
geom_blank()

Fig1

pdf("./FigFoodIntake.pdf", width=10, height=8, useDingbats=FALSE) 
grid.arrange(Fig1, ncol=1)
dev.off()

```


```{r}

macro.activation.data$Genotype <- relevel(macro.activation.data$Genotype, ref='attP2')

intake.activation.lm <- lm(ConcPerL3 ~ Food * Genotype, data = macro.activation.data)
Anova(intake.activation.lm)
summary(intake.activation.lm)

intake.activation<-lmer(ConcPerL3 ~ Food * Genotype + (1 | Date), data = macro.activation.data)
Anova(intake.activation)
summary(intake.activation)

```