##### Developmental Step 1 #####


###### Group one Gate conditioning run 2 ######


####### R script #######

###### R version 4.0.3 ######

##### Analysis #####

##### Group one Gate conditioning run 1 #####

#### Plot ####

setwd("XXX")
PC2=read.delim2("Data Group one Gate conditioning run 2.txt")
names(PC2)
str(PC2)

PC2$day<-as.factor(PC2$day)
PC2$animal<-as.factor(PC2$animal)
PC2$Tone<-as.factor(PC2$Tone)
PC2$NP<-as.numeric(PC2$NP)  

boxplot(NP~Tone, data=PC2)
 
library("ggplot2")
library("ggsci")
library("ggpubr")

C2 <- ggplot(PC2, aes(x = day, y = Percent, fill=Tone )) +  
        geom_boxplot()+
        scale_x_discrete(name = "Day") +
        scale_fill_manual(values=c("#FF3333","#3399FF"))+
        scale_y_continuous(name="Number of nosepokes [%]")
C2

ggpar(C2, 
      font.main = c(18, "bold"),
      font.x = c(18, "bold"),
      font.y = c(18, "bold"),
      font.caption = c(18, "bold"), 
      font.legend = c(18, "bold"), 
      font.tickslab = c(18, "bold"))
      
      
      
#### Analysis ####

contrasts (Conditionin2 [, 'Tone']) <- contr.sum (2)

contrasts (Conditionin2 [, 'day']) <- contr.sum (7)

summary (Conditionin2)

library (nlme)
Co2 <- lme (Percent ~ day * Tone, random= ~ 1 | animal/day, data= PC2, method= 'ML')
summary (Co2)
anova (Co2, type= 'marginal')

qqnorm(residuals(Co2)) 
plot(fitted(Co2),residuals(Co2))