group_av_acf_step2.Rmd

```{r}
packages <- c("tidyr", "stringr","data.table", "dplyr","stringi")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())))  
}
lapply(packages, library, character.only = TRUE)
options(digits=5)
```

#import individual ACF estimates from FX models
```{r}

dirs = list.files('/proj/telzerlab/NT_SPM12',  pattern="NT*", full.names = TRUE)
dirs_2 = paste0(dirs, '/Shapes_Zero')
file_list=lapply(dirs_2, list.files, pattern="*ACF.txt", full.names = TRUE)

logs = list.files(dirs_2, pattern="*ACF.txt", full.names = TRUE)

df.logs=lapply(logs, read.table) # read in the list of files
df.logs_df=lapply(df.logs, data.frame) # combine the list into one dataframe

file_list = list.files(fileDir, pattern="acf.txt",recursive=T)


files <- purrr::map_df(df.logs_df, function(i) {
  dplyr::summarise_all(i, mean)
})


```

#import final subject list and limit ACF estimates to these subjects 
```{r}
included_subs <- read.csv('~/Desktop/included_subs.csv',header=T) #this is just a csv of two columns SID =subject ID, final_sample = 1,0 (1=yes, 0=no) 
included_subs <- included_subs %>% filter(final_sample==1) #filter to just include subs you want to include
files <- files %>% filter(sid %in% included_subs$SID) #limit FX models to final sub list (i.e. excluding those with bad motion)
```

# calculate group-level ACF summary
```{r}
summaryACF <- files %>% dplyr::group_by() %>%
    dplyr::summarise(Xmean = mean(V6,na.rm=T),
              Ymean = mean(V7,na.rm=T),
              Zmean = mean(V8,na.rm=T),
              Xmedian = median(V6,na.rm=T),
              Ymedian = median(V7,na.rm=T),
              Zmedian = median(V8,na.rm=T))
write.csv(summaryACF, "summaryACF.csv")
```