damrau-buridan-PER-tbetah/04_data_analysis/buridanrescue/createfigure.R

## All GAL4-rescues into one figure


## data for each group were calculated by runCetran.r code and saved in one table
## groups were added to the table manually

library(dplyr)
library("plyr")
library("ggplot2")
library(EnvStats)

allori  <- read.csv("03_data/990_processed_data/rescues_buridan_uasonIII_output/output _table_corr.csv",
                    dec=",")


grouping = data.frame(group =unique(allori$group))
grouping$genorder = c(1:20)
grouping$order_for_figure = rep(c("a","b","c","d"),5)
grouping$females = rep(c("w+;;UAS-t-beta-h","t-beta-h",rep("t-beta-h;;UAS-t-beta-h",2)),5)
grouping$gal4= c(rep("actin-",4),rep("nsyb-",4),rep("tdc1-",4),rep("tdc2-",4),rep("np7088-",4))
grouping$males2 =   rep(rep(c("CS", "Gal4"),2),5)                   
grouping=grouping %>% mutate (males =ifelse(males2 !="Gal4",males2 ,paste0(gal4,"Gal4")))
grouping$males
all = left_join(allori,grouping)
#all <- read.csv2("E:/DiffDom manuscript data/TA-rec_homo.csv")
#attach(all)

#install.packages("ddply")

## speed ####

  call<- ddply(all, .(genorder,group,order_for_figure), summarise,                          # .()includes all the variables
               N      = length(median_speed),
               speeds = mean(median_speed),
               sd     = sd(median_speed),
               se     = sd(median_speed) / sqrt(length(median_speed)) )
 
p1=call %>%
  arrange(genorder) %>%    # First sort by val. This sort the dataframe but NOT the factor levels
  mutate(group=factor(group, levels=group)) %>% 
  ggplot( aes(x=group, y=speeds, fill=order_for_figure)) + 
  geom_bar(stat="identity",position=position_dodge(),col="black") +
  geom_errorbar(aes(ymin=speeds-se, ymax=speeds+se),
                width=.2,position=position_dodge(.9)) +
  ylab("Walking Speed [mm/s]") +
  xlab("") + # column fill and legend title/ -lables
  scale_y_continuous(breaks=0:16*4, limits = c(0,30)) +
  scale_fill_manual(values=c("white","grey","grey","lightblue")) +   #fill_manual muss nach  fill_discrete stehen
    theme(axis.title.x = element_text(size=20),                               #(face="bold", colour="black")
          axis.title.y = element_text(angle=90,size=20),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          #axis.line = element_line(colour = "black"),
          panel.grid.major = element_blank(),
          legend.position ="none",
          panel.grid.minor = element_blank(),
          axis.line.x = element_line(color="black", size = 0.5), 
          axis.line.y = element_line(color="black", size = 0.5),
          panel.background = element_blank(),
          panel.border = element_blank())
  



## fixation ####
p2=all %>%
  arrange(genorder) %>%    # First sort by val. This sort the dataframe but NOT the factor levels
  mutate(group=factor(group,levels = unique(group))) %>%
  ggplot( aes(x=group, y=stripe_deviation, fill=order_for_figure)) + 
  geom_boxplot() +
  stat_n_text(size = 2)+
  ylab("Stripe deviation [?]") +
  xlab("") +
  scale_fill_manual(values=c("white","grey","grey","lightblue"),name="") + # column fill and legend title/ -lables
  scale_y_continuous(breaks=0:16*5, limits = c(-10,75)) +
  theme_bw() +                                                           # to make background white, has to be before "opts"
  theme(axis.title.x = element_text(size=20),                               #(face="bold", colour="black")
       axis.title.y = element_text(angle=90,size=20),
       #axis.text.x  = element_text(hjust=0.5, size=16),
       axis.line = element_line(colour = "black"),
       panel.grid.major = element_blank(),
       panel.grid.minor = element_blank(),
       legend.position ="none",
       axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
       #panel.grid.major = theme_blank(),
       #panel.grid.minor = theme_blank(),
       #panel.border = theme_blank(),
       #panel.background = theme_blank()
       )  
pdf ("05_figures/fig2/outputfigurerescueIII_2.pdf")
gridExtra::grid.arrange(p1, p2, nrow = 2)
dev.off()

#aSTATISTICS

sink ("05_figures/fig2/outputfigurerescueIII_stats.txt")
for (i in c(0:4)){
  #i=0
  data = all %>% filter (group %in% unique(all$group)[(4*i+1):(4*i+4)])
  data =droplevels(data)
  x=aov(data$median_speed~data$group)
  print ("speed")
  print(summary(x))
  
  print(TukeyHSD(x))
  print ("angledev")
  print(pairwise.wilcox.test(data$stripe_deviation,data$group,p.adjust="bonferroni"))
}
sink()