install.packages("tidyr")
install.packages("dplyr")
library(tidyr)
library(dplyr)
library(ggplot2)

## Import data RIR
data <- readbulk::read_bulk(extension = "RIR_raw.iqdat", fun = utils::read.delim, verbose = TRUE)
write.csv(data, file = "C:/Users/justine.macgoris/OneDrive/Bureau/RIR_raw.csv")


##Check all the trials for participant
trial.summary <- data %>% 
  dplyr::group_by(subject, blockcode) %>% 
  dplyr::summarise(num_trials = n())
trial.summary
##Remove first and last block for each participant
d2<- filter(data, blockcode != "SETUP")
d3<- filter (d2, trialnum !="20")
#filtering out correct trials
#filtering out responses <100ms 
RIR.correct <- d3 %>%
  filter (correct == 1) %>% 
  filter (latency > 100)

##Rename pain and noPain Block 

RIR.correct$PAIN_bloc <- ifelse(grepl("Pain_1500_start",RIR.correct$blockcode),'PAIN',
                                   ifelse(grepl("Pain_900_start", RIR.correct$blockcode), 'PAIN', 
                                          ifelse(grepl("NoPain_900", RIR.correct$blockcode), 'NO_PAIN',
                                                 ifelse(grepl("NoPain_1500", RIR.correct$blockcode), 'NO_PAIN','error'))))




RIR.correct.no_pain <- filter(RIR.correct, PAIN_bloc== 'NO_PAIN')


#counting number of rows per person
ntrialsperson.pre <- RIR.correct.pain %>% 
  group_by(subject) %>% 
  summarise(num_trials = n())
#counting number of incorrect rows per person
ntrialscorrectperson.pre <-RIR.correct.pain %>%
  group_by(subject) %>% 
  summarise(num_trials = n())
#merge the two (after renaming num_trials which appears in both sets)
names(ntrialscorrectperson.pre)[names(ntrialscorrectperson.pre)=="num_trials"] <- "num_trials_corr"
percent.correct.person.pre <- merge(ntrialsperson.pre, ntrialscorrectperson.pre)
#calculate percent correct as a variable in data "percent.correct.person"
percent.correct.person.pre$per.corr <- ((percent.correct.person.pre$num_trials_corr/percent.correct.person.pre$num_trials)*100)




##Outliers 3SD for pain block

descriptives.pre = group_by(RIR.correct.pain, subject) %>%
summarise( mean = mean(latency),
          sd = sd(latency)) %>%
  mutate(lower_SD3 = mean - 3*sd,
         upper_SD3 = mean + 3*sd)

joined_dat_SD3.pain = left_join(RIR.correct.pain, descriptives.pre, by = c("subject")) %>%
  filter(latency > lower_SD3, latency < upper_SD3)

matrix_sdout3.pre_pain = joined_dat_SD3.pain %>% 
  group_by(subject, correct) %>%
  summarise(mean_SdExcl3 = mean(latency), N_SdExcl3 = n())


nrow(RIR.correct.pain) - nrow(joined_dat_SD3.pain)
100-((nrow(joined_dat_SD3.pain)/nrow(RIR.correct.pain))*100)


##Outliers 3SD for  no pain block 
descriptives.pre = group_by(RIR.correct.no_pain, subject) %>%
  summarise(mean = mean(latency),
            sd = sd(latency)) %>%
  mutate(lower_SD3 = mean - 3*sd,
         upper_SD3 = mean + 3*sd)

joined_dat_SD3.nopain = left_join(RIR.correct.no_pain, descriptives.pre, by = c("subject")) %>%
  filter(latency > lower_SD3, latency < upper_SD3)


matrix_sdout3.pre = joined_dat_SD3.nopain %>% 
  group_by(subject, correct) %>%
  summarise(mean_SdExcl3 = mean(latency), N_SdExcl3 = n())


nrow(RIR.correct.no_pain) - nrow(joined_dat_SD3.nopain)
100-((nrow(joined_dat_SD3.nopain)/nrow(RIR.correct.no_pain))*100)



## Mean latency RT pain block 
Q.RIR_pain <-  aggregate (list(response=(joined_dat_SD3.pain$latency)), list(subject=joined_dat_SD3.pain$subject, blockcode = joined_dat_SD3.pain$PAIN_bloc), mean,na.rm=T)

##Mean latency RT no pain block
Q.RIR_nopain <-  aggregate (list(response=(joined_dat_SD3.nopain$latency)), list(subject=joined_dat_SD3.nopain$subject, blockcode = joined_dat_SD3.nopain$PAIN_bloc), mean,na.rm=T)
##Merging together
mergeblock <- full_join (Q.RIR_nopain, Q.RIR_pain)
Q.RIR_END<- spread(mergeblock, blockcode, response)

Q.RIR_END$Pain_interference<- (Q.RIR_END$NO_PAIN - Q.RIR_END$PAIN)
write.csv(Q.RIR_END, file = "C:/Users/justine.macgoris/OneDrive/Bureau/Q.RIR_LAST23.csv")



##Error RIR 
Error  <- d3 %>%
  filter (latency > 100)
Perc_err <- Error %>%
  group_by(subject) %>%
  summarise(percent_errors = mean(correct == 0) * 100)
Q.RIR_CPT1 <-  aggregate (list(response=(joined_dat_SD3.RIR$latency)), list(subject=joined_dat_SD3.RIR$subject, blockcode = joined_dat_SD3.RIR$blockcode), mean,na.rm=T)
Error1<-spread(Perc_err, key = PAIN_bloc, value = percent_errors)

RIR_phase1 <- full_join(Q.RIR_END, Error1, by= c('subject'))