1 Arrange folders, sort files, read and merge

1.1 Create folders by conditions, copy files to them

##################### Read file names ##################################################################################
file_names <- dir(wd, pattern = "\\.xls$")
## if above isn't good enough try the following:
# file_names <- list.files(wd)
# file_names <- sop_files[!file.info(sop_files)$isdir]   # exclude directories
# file_names <- sop_files[grep(".xls", sop_files, fixed = TRUE)]


##################### Create folders with Condition names ###############################################################
# this part of script may be re-run if files from wd are updated
dir_names <- c("Unic_CTRL_Instr", "Unic_CTRL_Solo", "Unic_OGL_Instr", "Unic_OGL_Solo")
             
for(dir in dir_names){
  if(!dir.exists(file.path(wd, dir)))
  dir.create(file.path(wd, dir), showWarnings = FALSE)
}


##################### Use file names to sort them to folders ############################################################
sort_files_to_dirs <- function(wd, pattern, dir) {
  check_pattern <- outer(file_names, pattern, stringr::str_detect)               # if all TRUE bye row then it has full pattern
  index <- which(apply(check_pattern, 1, function(x) all(x==TRUE)))              # get index of file_names where all are TRUE
  sorted_files <- file_names[index]                                              # get names of files from indexes
  
  for(files in sorted_files) {                                                   # copy the files to corresponding folder
    file.copy(from = file.path(wd, files), to = file.path(wd, dir))
  }  
}

sort_files_to_dirs(wd = wd, pattern = c("unic", "ecran", "instructor"), dir = "Unic_CTRL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("unic", "ecran", "solo"), dir = "Unic_CTRL_Solo")
sort_files_to_dirs(wd = wd, pattern = c("unic", "oglinda", "instructor"), dir = "Unic_OGL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("unic", "oglinda", "solo"), dir = "Unic_OGL_Solo")

1.2 Reading the data

############ Read in all the .xls from folders and merge them in datasets named after corresponding folder ##############
# this part of script may be re-run if files from wd are updated
# RE-RUN FROM HERE IF FOLDERS AND SORTING WAS ALREADY DONE

wd <- "E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi"
setwd(wd)

The working directory was changed to E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

folders <- list.files(wd)
folders <- folders[file.info(folders)$isdir]   # luam doar folderele
datasetnames <- NULL

for (i in 1:length(folders)) {
  datasetname <- folders[i]
  datasetnames <- c(datasetnames, datasetname)
  current_dir <- file.path(wd, folders[i])
  setwd(current_dir)                                         # small modification from 2019 code
  print(paste0("current_dir: ", current_dir))
  
  paths <- dir(pattern = "\\.xls$")
  names(paths) <- basename(paths)

  assign( paste(datasetname), plyr::ldply(paths, rio::import) )
}

[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi/Unic_CTRL_Instr"
[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi/Unic_CTRL_Solo"
[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi/Unic_OGL_Instr"
[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi/Unic_OGL_Solo"

setwd(wd)
# detach("package:plyr", unload=TRUE)                                                   # detach plyr because of conflicts with dplyr

1.3 Cleaning the data

#################################### Data Cleaning #####################################################################
# Check if ids have > 1 row of data (empty .xls have only 1 row)
# Careful! This function modfies the datasets in the global envinronment
delete_empty_id <- function(df){
  list_empty_id <- 
    df %>%
    dplyr::group_by(.id) %>%
    dplyr::summarise(row_count = n()) %>%
    dplyr::rename("empty_id" = .id) %>%
    mutate(delete_id = if_else(row_count < 3, TRUE, FALSE)) %>%
    dplyr::filter(delete_id == TRUE)
  
  df_modif <- 
    df %>%
    dplyr::filter(!.id %in% list_empty_id$empty_id)
  
  if(!identical(df, df_modif)){
    df <- deparse(substitute(df))
    cat("Deleting from ", print(as.name(df))); print(list_empty_id)                    # print out which ids are deleted from which dataset
    assign(df, df_modif, envir = globalenv())                                          # assign modified df to original dataset from Global
  }else cat("No empty datasets. Nothing to delete")
}

# Apply function to all datasets (tricky to do in for loop because of super assignment)
delete_empty_id(Unic_CTRL_Instr)

No empty datasets. Nothing to delete

delete_empty_id(Unic_CTRL_Solo)

No empty datasets. Nothing to delete

delete_empty_id(Unic_OGL_Instr)

No empty datasets. Nothing to delete

delete_empty_id(Unic_OGL_Solo)

No empty datasets. Nothing to delete

1.4 Exclude SAM_resp based on RT outliers

############################### Exclude Outliers based on RT (by subject and stimulus type) #######################################
## DONT RUN (unless it is needed) ----> eval=FALSE
# Exclude RT outliers (=- 2SD) - instead of simple filter, makeing them NA  is better for paired comparison
remove_outliers <- function(df) {
  df_modif <-
    df %>%
    dplyr::group_by(.id, `Stimulus type`) %>%                  # we could have done before:  dplyr::rename("Stim_type" = `Stimulus type`) 
    mutate(SAM_Resp = if_else(abs(SAM_RT - mean(SAM_RT, na.rm=TRUE)) > 2*sd(SAM_RT, na.rm=TRUE), as.numeric(NA), SAM_Resp))
  
  if(!identical(df, df_modif)){
    df <- deparse(substitute(df))
    cat("Deleting outliers from ", print(as.name(df)));                               # print out datasets which have been modified
    assign(df, df_modif, envir = globalenv())                                          # assign modified df to original dataset from Global
  }else cat("No outlier")
}

remove_outliers(Unic_CTRL_Instr)
remove_outliers(Unic_CTRL_Solo)
remove_outliers(Unic_OGL_Instr)
remove_outliers(Unic_OGL_Solo)

1.5 Test if datasets have same columns

unic_df_obj <- mget(c("Unic_CTRL_Instr", "Unic_CTRL_Solo", "Unic_OGL_Instr", "Unic_OGL_Solo"))
unic_df_obj <-lapply(unic_df_obj, colnames)
outer(unic_df_obj, unic_df_obj, Vectorize(identical))                           # if all are TRUE, all df have same columns

                Unic_CTRL_Instr Unic_CTRL_Solo Unic_OGL_Instr Unic_OGL_Solo
Unic_CTRL_Instr            TRUE           TRUE           TRUE          TRUE
Unic_CTRL_Solo             TRUE           TRUE           TRUE          TRUE
Unic_OGL_Instr             TRUE           TRUE           TRUE          TRUE
Unic_OGL_Solo              TRUE           TRUE           TRUE          TRUE

1.6 Clean IDs

Unic_CTRL_Instr <- 
  Unic_CTRL_Instr %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)

Unic_CTRL_Solo <- 
  Unic_CTRL_Solo %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)

Unic_OGL_Instr <- 
  Unic_OGL_Instr %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)

Unic_OGL_Solo <- 
  Unic_OGL_Solo %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)



# Check IDs matches
checkid_Unic_CTRL_Instr <- 
  Unic_CTRL_Instr %>%
    dplyr::arrange(ID) %>%
    dplyr::count(ID)

checkid_Unic_CTRL_Solo <- 
  Unic_CTRL_Solo %>%
  dplyr::arrange(ID) %>%
  dplyr::count(ID)

checkid_Unic_OGL_Instr <- 
  Unic_OGL_Instr %>%
  dplyr::arrange(ID) %>%
  dplyr::count(ID)

checkid_Unic_OGL_Solo <- 
  Unic_OGL_Solo %>%
  dplyr::arrange(ID) %>%
  dplyr::count(ID)


knitr::asis_output("### Check IDs match")

Check IDs match

checkid_Unic_CTRL_Instr %>% 
  dplyr::full_join(checkid_Unic_CTRL_Solo,  by = "ID", suffix = c("Unic_CTRL_Instr", "Unic_CTRL_Solo")) %>%
  dplyr::full_join(checkid_Unic_OGL_Instr,  by = "ID", suffix = c("Unic_CTRL_Instr", "Unic_OGL_Instr")) %>%
  dplyr::full_join(checkid_Unic_OGL_Solo,  by = "ID", suffix = c("Unic_CTRL_Instr", "Unic_OGL_Solo")) %>%
  print(n = Inf)

2 Analysis - UNICE

2.1 Descriptives

##########################################################################################################################
#################################### Analyses - UNICE ####################################################################
## Descriptives by condition dataset
descriptive_func <- function(df, Stim_type, By_ID = FALSE){
  dfname_stimtype <- paste(deparse(substitute(df)), Stim_type, sep = " - ")
  suppressWarnings({                                                                # if all NAs in SAM_Resp, NaNs and Infs will be produced
    df_modif <- 
      df %>%
      dplyr::select_all(~gsub("\\s+|\\.", "_", .)) %>%                                     # replaces blancks with "_" in colnames 
      dplyr::filter(Stimulus_type == Stim_type)                                            # filter by stimulus type
      
    if(By_ID) {                                                              # if true group by id, if not return descriptives for all ids
      df_modif %>%
      dplyr::group_by(ID) %>%
      tidystats::describe_data(SAM_Resp, na.rm = TRUE) %>%                            # could also add SAM_RT to list of stats, but list is too long
        knitr::kable(caption = dfname_stimtype, digits = 2)
    }else{ 
      df_modif %>%
      tidystats::describe_data(SAM_Resp, na.rm = TRUE) %>%
        knitr::kable(caption = dfname_stimtype, digits = 2)
    }
  })
}  


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "negativ", By_ID = FALSE)           # Negative - General

Unic_CTRL_Instr - negativ
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	5.32	2.65	0.08	1	9	8	5	9	0.05	1.65

descriptive_func(df = Unic_CTRL_Solo, Stim_type = "negativ", By_ID = FALSE)

Unic_CTRL_Solo - negativ
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1190	5.3	2.55	0.07	1	9	8	5	9	0.04	1.76

descriptive_func(df = Unic_OGL_Instr, Stim_type = "negativ", By_ID = FALSE)

Unic_OGL_Instr - negativ
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	5.22	2.64	0.08	1	9	8	5	9	0.02	1.71

descriptive_func(df = Unic_OGL_Solo, Stim_type = "negativ", By_ID = FALSE)

Unic_OGL_Solo - negativ
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	5.17	2.67	0.08	1	9	8	5	9	0.02	1.75


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "negativ", By_ID = TRUE)            # Negative - by id

Unic_CTRL_Instr - negativ
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID06	8.03	1.44	0.24	4	9	5	9	9	-1.59	4.62
SAM_Resp	35	ID10	3.03	1.87	0.32	1	8	7	3	1	0.83	3.03
SAM_Resp	35	ID12	3.14	1.42	0.24	1	5	4	3	5	0.00	1.72
SAM_Resp	35	ID13	8.31	1.81	0.31	1	9	8	9	9	-3.22	12.57
SAM_Resp	35	ID16	5.63	2.29	0.39	3	9	6	6	3	0.20	1.54
SAM_Resp	35	ID17	2.23	0.91	0.15	1	4	3	2	2	0.25	2.26
SAM_Resp	35	ID26	6.51	2.44	0.41	1	9	8	8	8	-0.71	2.27
SAM_Resp	35	ID27	3.26	1.22	0.21	1	6	5	3	3	0.19	2.41
SAM_Resp	35	ID28	4.86	1.57	0.27	2	7	5	5	6	-0.27	1.98
SAM_Resp	35	ID29	5.17	1.69	0.29	1	8	7	5	5	-0.61	3.10
SAM_Resp	35	ID33	2.49	0.70	0.12	2	4	2	2	2	1.09	2.86
SAM_Resp	35	ID36	5.29	2.80	0.47	1	9	8	5	9	0.08	1.66
SAM_Resp	35	ID37	5.26	2.99	0.51	1	9	8	5	9	-0.07	1.50
SAM_Resp	35	ID39	7.97	1.60	0.27	2	9	7	9	9	-2.05	7.36
SAM_Resp	35	ID41	8.77	0.65	0.11	7	9	2	9	9	-2.42	6.88
SAM_Resp	35	ID43	3.69	1.13	0.19	1	6	5	4	3	0.02	2.85
SAM_Resp	35	ID44	4.77	1.24	0.21	2	7	5	5	5	-0.12	3.17
SAM_Resp	35	ID45	7.86	2.28	0.38	1	9	8	9	9	-2.38	7.53
SAM_Resp	35	ID47	6.29	2.16	0.37	2	9	7	6	9	-0.11	1.75
SAM_Resp	35	ID48	5.80	2.29	0.39	2	9	7	6	8	-0.14	1.65
SAM_Resp	35	ID49	6.80	2.18	0.37	2	9	7	7	9	-1.00	3.03
SAM_Resp	35	ID50	3.20	1.28	0.22	2	5	3	3	2	0.39	1.47
SAM_Resp	35	ID51	5.03	2.51	0.43	1	9	8	5	5	0.26	1.91
SAM_Resp	35	ID52	3.23	0.69	0.12	2	5	3	3	3	1.32	4.76
SAM_Resp	35	ID54	7.91	1.54	0.26	4	9	5	9	9	-1.32	3.60
SAM_Resp	35	ID55	7.06	2.17	0.37	2	9	7	8	9	-1.02	2.76
SAM_Resp	35	ID57	4.29	1.60	0.27	2	8	6	4	4	0.70	2.75
SAM_Resp	35	ID58	1.74	0.89	0.15	1	4	3	1	1	0.78	2.41
SAM_Resp	35	ID61	5.57	3.06	0.52	1	9	8	6	9	-0.25	1.54
SAM_Resp	35	ID63	5.97	2.18	0.37	2	9	7	6	6	-0.31	2.08
SAM_Resp	35	ID64	4.43	1.84	0.31	1	8	7	4	6	-0.28	2.26
SAM_Resp	35	ID66	6.17	1.89	0.32	1	8	7	7	7	-1.45	4.19
SAM_Resp	35	ID9	5.86	2.32	0.39	3	9	6	6	3	0.06	1.45

descriptive_func(df = Unic_CTRL_Solo, Stim_type = "negativ", By_ID = TRUE)

Unic_CTRL_Solo - negativ
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	3.17	1.95	0.33	1	8	7	3	2	1.11	3.36
SAM_Resp	35	ID12	3.69	1.59	0.27	1	6	5	4	5	-0.19	1.75
SAM_Resp	35	ID13	7.63	2.13	0.36	1	9	8	9	9	-1.71	5.00
SAM_Resp	35	ID16	5.80	1.94	0.33	2	9	7	6	4	-0.06	1.84
SAM_Resp	35	ID17	3.14	1.68	0.28	1	7	6	3	2	0.75	2.60
SAM_Resp	35	ID26	6.34	2.34	0.40	2	9	7	7	9	-0.48	2.02
SAM_Resp	35	ID27	2.51	1.07	0.18	1	5	4	3	3	0.18	2.35
SAM_Resp	35	ID28	4.83	1.42	0.24	2	7	5	5	4	-0.13	2.22
SAM_Resp	35	ID29	4.66	1.85	0.31	1	8	7	5	5	-0.22	2.37
SAM_Resp	35	ID33	2.63	0.73	0.12	2	4	2	2	2	0.69	2.20
SAM_Resp	35	ID36	4.06	2.87	0.48	1	9	8	3	2	0.55	1.80
SAM_Resp	35	ID37	5.37	3.03	0.51	1	9	8	6	9	-0.28	1.59
SAM_Resp	35	ID39	7.77	1.40	0.24	4	9	5	8	9	-1.10	3.39
SAM_Resp	35	ID41	7.91	1.84	0.31	3	9	6	9	9	-1.26	2.99
SAM_Resp	35	ID43	3.83	1.25	0.21	1	6	5	4	3	0.05	2.53
SAM_Resp	35	ID44	4.26	1.01	0.17	3	6	3	4	4	0.34	2.07
SAM_Resp	35	ID45	8.14	1.82	0.31	1	9	8	9	9	-2.51	8.97
SAM_Resp	35	ID47	6.20	1.92	0.32	3	9	6	7	7	-0.29	1.88
SAM_Resp	35	ID48	5.29	2.52	0.43	2	9	7	5	2	0.14	1.60
SAM_Resp	35	ID49	6.97	1.81	0.31	2	9	7	7	9	-0.72	3.30
SAM_Resp	35	ID50	3.14	1.33	0.23	1	6	5	3	2	0.19	2.05
SAM_Resp	35	ID51	5.14	2.17	0.37	1	9	8	5	5	0.18	2.25
SAM_Resp	35	ID52	3.37	0.81	0.14	3	6	3	3	3	2.30	7.39
SAM_Resp	35	ID54	7.71	1.71	0.29	3	9	6	9	9	-1.13	3.18
SAM_Resp	35	ID55	7.60	1.87	0.32	3	9	6	8	9	-1.25	3.40
SAM_Resp	35	ID57	4.71	1.71	0.29	2	9	7	4	4	0.67	2.87
SAM_Resp	35	ID58	1.89	1.02	0.17	1	4	3	2	1	0.73	2.25
SAM_Resp	35	ID6	8.26	1.22	0.21	4	9	5	9	9	-1.98	6.55
SAM_Resp	35	ID61	5.37	2.82	0.48	1	9	8	5	9	-0.07	1.68
SAM_Resp	35	ID63	6.14	1.97	0.33	2	9	7	6	4	-0.04	2.00
SAM_Resp	35	ID64	5.09	2.09	0.35	2	8	6	6	7	-0.31	1.64
SAM_Resp	35	ID66	6.34	1.64	0.28	2	8	6	7	7	-1.37	4.27
SAM_Resp	35	ID8	5.34	2.21	0.37	1	9	8	5	5	-0.51	2.40
SAM_Resp	35	ID9	5.74	1.82	0.31	3	9	6	6	6	0.36	2.15

descriptive_func(df = Unic_OGL_Instr, Stim_type = "negativ", By_ID = TRUE)

Unic_OGL_Instr - negativ
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	3.86	2.30	0.39	1	8	7	3	2	0.38	1.82
SAM_Resp	35	ID12	2.77	1.46	0.25	1	5	4	3	1	0.23	1.70
SAM_Resp	35	ID13	7.63	2.04	0.35	2	9	7	9	9	-1.29	3.46
SAM_Resp	35	ID15	4.17	1.10	0.19	2	6	4	4	4	-0.07	2.27
SAM_Resp	35	ID16	6.57	1.90	0.32	3	9	6	6	6	-0.18	1.81
SAM_Resp	35	ID17	1.77	0.73	0.12	1	3	2	2	2	0.37	1.98
SAM_Resp	35	ID26	6.89	2.43	0.41	1	9	8	8	9	-0.96	2.75
SAM_Resp	35	ID27	2.74	1.31	0.22	1	5	4	3	3	0.17	1.98
SAM_Resp	35	ID28	5.51	1.44	0.24	3	8	5	6	7	-0.36	1.95
SAM_Resp	35	ID29	4.74	1.90	0.32	1	8	7	5	5	-0.41	2.41
SAM_Resp	35	ID37	6.54	2.68	0.45	1	9	8	7	9	-0.79	2.35
SAM_Resp	35	ID39	7.43	1.65	0.28	2	9	7	8	8	-1.35	4.74
SAM_Resp	35	ID41	8.83	0.75	0.13	5	9	4	9	9	-4.44	21.98
SAM_Resp	35	ID43	2.43	0.98	0.17	1	5	4	2	2	0.58	2.98
SAM_Resp	35	ID44	4.54	1.07	0.18	3	7	4	4	4	0.55	2.83
SAM_Resp	35	ID45	7.57	2.10	0.36	1	9	8	9	9	-1.59	4.69
SAM_Resp	35	ID47	6.09	1.74	0.29	3	9	6	6	6	-0.20	2.20
SAM_Resp	35	ID48	5.71	2.55	0.43	2	9	7	6	8	-0.17	1.58
SAM_Resp	35	ID49	6.06	1.89	0.32	2	9	7	6	8	-0.06	1.96
SAM_Resp	35	ID50	3.63	1.68	0.28	1	8	7	4	2	0.56	2.54
SAM_Resp	35	ID51	5.66	2.46	0.42	2	9	7	5	9	0.02	1.69
SAM_Resp	35	ID52	2.86	0.69	0.12	2	5	3	3	3	0.73	4.17
SAM_Resp	35	ID54	6.89	2.48	0.42	1	9	8	8	9	-0.83	2.47
SAM_Resp	35	ID55	7.43	2.05	0.35	3	9	6	8	9	-1.01	2.54
SAM_Resp	35	ID57	2.49	1.22	0.21	1	6	5	2	2	1.06	3.79
SAM_Resp	35	ID58	2.17	1.29	0.22	1	6	5	2	1	1.08	3.69
SAM_Resp	35	ID6	7.54	1.44	0.24	5	9	4	8	9	-0.48	1.83
SAM_Resp	35	ID61	5.14	2.79	0.47	1	9	8	5	9	-0.03	1.65
SAM_Resp	35	ID63	6.23	2.03	0.34	3	9	6	6	9	-0.19	1.84
SAM_Resp	35	ID64	6.57	2.25	0.38	1	9	8	7	7	-1.14	3.53
SAM_Resp	35	ID66	6.43	1.48	0.25	2	8	6	7	7	-1.04	3.70
SAM_Resp	35	ID8	2.89	1.79	0.30	1	6	5	3	1	0.36	1.79
SAM_Resp	35	ID9	4.63	1.85	0.31	2	8	6	4	3	0.28	1.59

descriptive_func(df = Unic_OGL_Solo, Stim_type = "negativ", By_ID = TRUE)

Unic_OGL_Solo - negativ
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	3.34	1.94	0.33	1	8	7	3	3	0.66	2.62
SAM_Resp	35	ID12	2.91	1.46	0.25	1	5	4	3	1	-0.02	1.65
SAM_Resp	35	ID13	8.40	1.58	0.27	1	9	8	9	9	-3.48	15.59
SAM_Resp	35	ID16	6.54	1.96	0.33	3	9	6	6	9	-0.22	1.88
SAM_Resp	35	ID17	1.71	0.67	0.11	1	3	2	2	2	0.38	2.24
SAM_Resp	35	ID26	6.46	2.70	0.46	1	9	8	7	9	-0.68	2.05
SAM_Resp	35	ID27	2.11	1.35	0.23	1	5	4	1	1	0.75	2.16
SAM_Resp	35	ID28	5.49	1.56	0.26	1	8	7	6	6	-0.70	3.38
SAM_Resp	35	ID29	5.20	2.04	0.34	1	8	7	5	5	-0.67	2.57
SAM_Resp	35	ID33	2.94	0.80	0.14	1	4	3	3	3	-0.59	3.16
SAM_Resp	35	ID37	6.03	2.63	0.44	1	9	8	6	9	-0.62	2.32
SAM_Resp	35	ID39	7.23	2.00	0.34	1	9	8	8	8	-1.57	4.94
SAM_Resp	35	ID41	9.00	0.00	0.00	9	9	0	9	9	NaN	NaN
SAM_Resp	35	ID43	3.60	1.61	0.27	1	7	6	4	5	-0.14	1.99
SAM_Resp	35	ID44	4.40	1.19	0.20	3	7	4	4	4	0.45	2.00
SAM_Resp	35	ID45	7.86	2.18	0.37	1	9	8	9	9	-2.08	6.05
SAM_Resp	35	ID47	6.09	1.63	0.28	3	9	6	6	6	0.19	2.47
SAM_Resp	35	ID48	5.60	2.70	0.46	1	9	8	6	8	-0.28	1.76
SAM_Resp	35	ID49	6.83	1.90	0.32	2	9	7	7	9	-0.56	2.67
SAM_Resp	35	ID50	3.83	1.79	0.30	1	7	6	4	2	0.26	1.93
SAM_Resp	35	ID51	5.34	2.54	0.43	1	9	8	5	9	0.34	1.85
SAM_Resp	35	ID52	2.63	1.19	0.20	1	6	5	3	3	0.43	3.09
SAM_Resp	35	ID54	7.46	2.03	0.34	3	9	6	9	9	-0.87	2.17
SAM_Resp	35	ID55	7.54	1.92	0.32	2	9	7	8	9	-1.37	3.80
SAM_Resp	35	ID57	3.57	1.48	0.25	1	8	7	4	4	0.60	3.79
SAM_Resp	35	ID58	2.91	1.82	0.31	1	7	6	2	2	0.81	2.60
SAM_Resp	35	ID6	7.26	1.79	0.30	1	9	8	7	9	-1.21	5.24
SAM_Resp	35	ID61	2.77	2.00	0.34	1	9	8	2	1	1.50	4.66
SAM_Resp	35	ID63	5.77	2.16	0.36	2	9	7	5	4	0.30	1.83
SAM_Resp	35	ID64	4.66	2.15	0.36	1	8	7	5	6	-0.46	1.95
SAM_Resp	35	ID66	6.51	1.34	0.23	2	8	6	7	7	-1.61	6.10
SAM_Resp	35	ID8	3.46	1.99	0.34	1	7	6	4	5	-0.11	1.66
SAM_Resp	35	ID9	5.23	2.03	0.34	2	9	7	5	3	0.07	1.77


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "pozitiv", By_ID = FALSE)           # Positive - General

Unic_CTRL_Instr - pozitiv
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	3.78	2.31	0.07	1	9	8	3	1	0.57	2.29

descriptive_func(df = Unic_CTRL_Solo, Stim_type = "pozitiv", By_ID = FALSE)

Unic_CTRL_Solo - pozitiv
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1190	3.82	2.25	0.07	1	9	8	3	2	0.57	2.4

descriptive_func(df = Unic_OGL_Instr, Stim_type = "pozitiv", By_ID = FALSE)

Unic_OGL_Instr - pozitiv
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	3.75	2.26	0.07	1	9	8	3	1	0.66	2.64

descriptive_func(df = Unic_OGL_Solo, Stim_type = "pozitiv", By_ID = FALSE)

Unic_OGL_Solo - pozitiv
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	3.87	2.35	0.07	1	9	8	3	1	0.54	2.26


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "pozitiv", By_ID = TRUE)            # Positive - by id

Unic_CTRL_Instr - pozitiv
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID06	6.89	2.31	0.39	1	9	8	8	8	-1.41	3.88
SAM_Resp	35	ID10	2.51	1.04	0.18	1	5	4	2	2	0.44	2.47
SAM_Resp	35	ID12	1.00	0.00	0.00	1	1	0	1	1	NaN	NaN
SAM_Resp	35	ID13	4.94	2.52	0.43	1	9	8	5	7	-0.23	1.67
SAM_Resp	35	ID16	3.20	1.13	0.19	1	6	5	3	3	0.46	2.73
SAM_Resp	35	ID17	1.69	0.76	0.13	1	3	2	2	1	0.58	2.00
SAM_Resp	35	ID26	3.49	2.54	0.43	1	9	8	3	1	0.67	2.21
SAM_Resp	35	ID27	1.71	0.71	0.12	1	3	2	2	2	0.46	2.10
SAM_Resp	35	ID28	4.11	1.02	0.17	2	6	4	4	4	0.11	2.20
SAM_Resp	35	ID29	4.86	1.46	0.25	2	7	5	5	6	-0.73	2.15
SAM_Resp	35	ID33	2.60	0.77	0.13	1	5	4	3	2	0.82	4.19
SAM_Resp	35	ID36	1.69	0.87	0.15	1	3	2	1	1	0.65	1.68
SAM_Resp	35	ID37	3.06	2.58	0.44	1	9	8	2	1	1.11	2.77
SAM_Resp	35	ID39	2.03	1.42	0.24	1	5	4	1	1	1.25	3.14
SAM_Resp	35	ID41	6.43	2.40	0.41	1	9	8	7	5	-0.46	2.39
SAM_Resp	35	ID43	2.69	0.99	0.17	1	5	4	3	2	0.30	2.39
SAM_Resp	35	ID44	5.26	1.04	0.18	3	7	4	5	6	-0.69	2.88
SAM_Resp	35	ID45	3.37	2.40	0.41	1	9	8	3	1	1.03	3.28
SAM_Resp	35	ID47	6.09	1.74	0.29	3	9	6	6	7	-0.51	2.22
SAM_Resp	35	ID48	3.66	1.71	0.29	1	7	6	3	2	0.40	2.04
SAM_Resp	35	ID49	3.06	1.24	0.21	1	6	5	3	2	0.27	2.48
SAM_Resp	35	ID50	2.14	1.17	0.20	1	5	4	2	2	0.96	3.14
SAM_Resp	35	ID51	2.14	1.00	0.17	1	5	4	2	2	0.77	3.33
SAM_Resp	35	ID52	4.51	1.17	0.20	2	7	5	5	5	0.24	3.08
SAM_Resp	35	ID54	6.83	2.20	0.37	1	9	8	7	9	-0.85	2.78
SAM_Resp	35	ID55	5.40	2.58	0.44	1	9	8	6	7	-0.28	1.84
SAM_Resp	35	ID57	3.89	1.02	0.17	2	6	4	4	4	-0.11	2.20
SAM_Resp	35	ID58	2.11	1.08	0.18	1	4	3	2	2	0.63	2.17
SAM_Resp	35	ID61	2.60	2.48	0.42	1	8	7	1	1	1.21	2.85
SAM_Resp	35	ID63	4.26	1.88	0.32	1	7	6	4	5	-0.27	2.12
SAM_Resp	35	ID64	4.83	1.67	0.28	2	7	5	5	3	0.08	1.51
SAM_Resp	35	ID66	6.74	1.04	0.18	2	8	6	7	7	-2.67	13.56
SAM_Resp	35	ID9	4.91	1.50	0.25	3	8	5	5	5	0.52	2.48

descriptive_func(df = Unic_CTRL_Solo, Stim_type = "pozitiv", By_ID = TRUE)

Unic_CTRL_Solo - pozitiv
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	3.09	1.40	0.24	1	6	5	3	2	0.30	2.22
SAM_Resp	35	ID12	1.00	0.00	0.00	1	1	0	1	1	NaN	NaN
SAM_Resp	35	ID13	4.57	2.50	0.42	1	9	8	4	2	0.29	2.03
SAM_Resp	35	ID16	2.94	0.97	0.16	1	5	4	3	3	0.51	2.85
SAM_Resp	35	ID17	1.94	0.84	0.14	1	4	3	2	2	0.41	2.29
SAM_Resp	35	ID26	3.26	1.74	0.29	1	7	6	3	2	0.65	2.41
SAM_Resp	35	ID27	1.51	0.56	0.10	1	3	2	1	1	0.45	2.13
SAM_Resp	35	ID28	4.03	1.20	0.20	2	6	4	4	4	-0.06	2.12
SAM_Resp	35	ID29	4.26	1.88	0.32	1	8	7	4	3	0.05	1.94
SAM_Resp	35	ID33	2.91	0.98	0.17	2	6	4	3	2	1.12	4.17
SAM_Resp	35	ID36	2.14	1.06	0.18	1	4	3	2	1	0.46	2.00
SAM_Resp	35	ID37	3.43	2.39	0.40	1	9	8	3	1	0.89	2.69
SAM_Resp	35	ID39	2.37	1.70	0.29	1	8	7	2	1	1.48	4.91
SAM_Resp	35	ID41	5.34	2.04	0.35	1	9	8	5	5	0.49	2.83
SAM_Resp	35	ID43	2.34	1.00	0.17	1	5	4	2	2	0.71	3.08
SAM_Resp	35	ID44	5.20	0.90	0.15	3	7	4	5	6	-0.40	2.49
SAM_Resp	35	ID45	2.86	1.68	0.28	1	9	8	3	2	1.47	6.13
SAM_Resp	35	ID47	5.60	1.82	0.31	2	9	7	6	6	-0.37	2.65
SAM_Resp	35	ID48	3.29	1.93	0.33	1	7	6	3	2	0.63	2.16
SAM_Resp	35	ID49	3.34	1.91	0.32	1	9	8	3	2	0.86	3.38
SAM_Resp	35	ID50	2.46	1.27	0.21	1	5	4	2	2	0.58	2.29
SAM_Resp	35	ID51	2.80	1.08	0.18	1	5	4	3	3	-0.17	2.18
SAM_Resp	35	ID52	4.94	1.35	0.23	3	8	5	5	5	0.18	2.45
SAM_Resp	35	ID54	6.83	1.87	0.32	1	9	8	7	7	-0.92	3.91
SAM_Resp	35	ID55	6.03	2.62	0.44	2	9	7	7	9	-0.30	1.59
SAM_Resp	35	ID57	4.11	1.23	0.21	2	7	5	4	4	0.55	2.95
SAM_Resp	35	ID58	1.71	0.86	0.15	1	4	3	1	1	0.86	2.68
SAM_Resp	35	ID6	7.14	1.88	0.32	2	9	7	8	8	-1.20	3.73
SAM_Resp	35	ID61	3.09	2.97	0.50	1	9	8	1	1	1.08	2.50
SAM_Resp	35	ID63	4.89	1.57	0.26	2	9	7	5	6	0.10	3.07
SAM_Resp	35	ID64	5.20	2.08	0.35	1	8	7	6	7	-0.41	1.90
SAM_Resp	35	ID66	6.66	1.26	0.21	2	9	7	7	7	-1.12	6.80
SAM_Resp	35	ID8	3.51	1.82	0.31	1	7	6	4	5	0.04	2.04
SAM_Resp	35	ID9	5.03	1.20	0.20	3	7	4	5	4	0.36	2.08

descriptive_func(df = Unic_OGL_Instr, Stim_type = "pozitiv", By_ID = TRUE)

Unic_OGL_Instr - pozitiv
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	3.86	1.59	0.27	1	8	7	4	4	0.33	2.85
SAM_Resp	35	ID12	1.29	0.52	0.09	1	3	2	1	1	1.57	4.56
SAM_Resp	35	ID13	4.20	2.35	0.40	1	9	8	4	2	0.30	2.18
SAM_Resp	35	ID15	2.40	0.77	0.13	1	4	3	2	2	-0.05	2.58
SAM_Resp	35	ID16	3.89	1.18	0.20	2	6	4	4	4	0.22	2.26
SAM_Resp	35	ID17	1.31	0.47	0.08	1	2	1	1	1	0.80	1.64
SAM_Resp	35	ID26	4.77	1.66	0.28	1	8	7	5	5	-0.25	3.15
SAM_Resp	35	ID27	1.63	0.73	0.12	1	3	2	1	1	0.69	2.20
SAM_Resp	35	ID28	4.40	1.09	0.18	3	7	4	4	4	0.40	2.37
SAM_Resp	35	ID29	3.51	1.62	0.27	1	8	7	3	5	0.44	2.91
SAM_Resp	35	ID37	4.69	2.73	0.46	1	9	8	4	3	0.19	1.75
SAM_Resp	35	ID39	1.80	1.11	0.19	1	5	4	1	1	1.20	3.49
SAM_Resp	35	ID41	7.80	2.18	0.37	1	9	8	9	9	-1.54	4.23
SAM_Resp	35	ID43	2.34	1.35	0.23	1	6	5	2	1	0.89	3.12
SAM_Resp	35	ID44	4.77	1.03	0.17	3	7	4	5	4	0.14	2.12
SAM_Resp	35	ID45	1.94	1.06	0.18	1	4	3	2	1	0.72	2.24
SAM_Resp	35	ID47	5.63	1.55	0.26	2	9	7	6	6	-0.17	2.73
SAM_Resp	35	ID48	3.63	1.73	0.29	1	7	6	3	3	0.24	2.14
SAM_Resp	35	ID49	2.97	1.34	0.23	1	6	5	3	2	0.43	2.30
SAM_Resp	35	ID50	3.86	1.50	0.25	1	7	6	4	4	0.09	2.97
SAM_Resp	35	ID51	1.91	0.92	0.16	1	5	4	2	2	1.32	5.23
SAM_Resp	35	ID52	4.03	1.46	0.25	2	7	5	3	3	0.69	2.08
SAM_Resp	35	ID54	5.37	2.49	0.42	1	9	8	5	3	0.10	1.62
SAM_Resp	35	ID55	6.17	3.07	0.52	1	9	8	8	9	-0.59	1.69
SAM_Resp	35	ID57	2.37	0.94	0.16	1	4	3	3	3	-0.16	2.00
SAM_Resp	35	ID58	2.71	1.18	0.20	1	4	3	3	3	-0.53	1.79
SAM_Resp	35	ID6	5.14	1.38	0.23	2	7	5	5	4	-0.12	2.16
SAM_Resp	35	ID61	2.71	2.09	0.35	1	9	8	2	1	1.34	4.02
SAM_Resp	35	ID63	4.49	1.80	0.31	1	8	7	4	4	0.56	2.74
SAM_Resp	35	ID64	6.09	2.37	0.40	1	9	8	6	6	-0.74	2.67
SAM_Resp	35	ID66	5.63	1.78	0.30	2	8	6	6	7	-0.59	2.33
SAM_Resp	35	ID8	2.00	1.48	0.25	1	5	4	1	1	1.06	2.54
SAM_Resp	35	ID9	4.34	1.37	0.23	1	7	6	4	5	-0.22	2.89

descriptive_func(df = Unic_OGL_Solo, Stim_type = "pozitiv", By_ID = TRUE)

Unic_OGL_Solo - pozitiv
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	3.23	1.44	0.24	1	7	6	3	4	0.50	3.19
SAM_Resp	35	ID12	1.34	0.48	0.08	1	2	1	1	1	0.66	1.44
SAM_Resp	35	ID13	5.60	2.52	0.43	1	9	8	6	8	-0.51	2.04
SAM_Resp	35	ID16	3.69	1.13	0.19	2	6	4	3	3	0.64	2.44
SAM_Resp	35	ID17	1.49	0.51	0.09	1	2	1	1	1	0.06	1.00
SAM_Resp	35	ID26	4.11	2.26	0.38	1	8	7	4	6	0.06	1.72
SAM_Resp	35	ID27	1.34	0.59	0.10	1	3	2	1	1	1.50	4.19
SAM_Resp	35	ID28	4.60	1.46	0.25	2	8	6	5	5	0.20	2.44
SAM_Resp	35	ID29	4.23	1.46	0.25	2	7	5	5	5	-0.40	2.05
SAM_Resp	35	ID33	2.97	1.40	0.24	1	7	6	3	3	0.63	3.29
SAM_Resp	35	ID37	4.40	2.79	0.47	1	9	8	3	2	0.53	1.71
SAM_Resp	35	ID39	1.77	0.97	0.16	1	4	3	1	1	0.86	2.44
SAM_Resp	35	ID41	8.03	1.96	0.33	1	9	8	9	9	-2.12	6.86
SAM_Resp	35	ID43	2.54	1.56	0.26	1	7	6	2	2	1.26	4.01
SAM_Resp	35	ID44	4.83	0.89	0.15	3	7	4	5	5	-0.17	3.30
SAM_Resp	35	ID45	1.83	0.92	0.16	1	4	3	2	1	0.57	1.97
SAM_Resp	35	ID47	4.94	1.91	0.32	1	8	7	5	6	-0.17	2.06
SAM_Resp	35	ID48	4.40	2.43	0.41	1	8	7	4	6	0.01	1.77
SAM_Resp	35	ID49	3.97	1.71	0.29	2	8	6	4	3	0.95	3.03
SAM_Resp	35	ID50	2.91	1.09	0.19	1	5	4	3	3	0.17	2.40
SAM_Resp	35	ID51	1.83	0.92	0.16	1	4	3	2	1	0.80	2.64
SAM_Resp	35	ID52	3.34	1.39	0.24	1	7	6	3	3	0.89	3.26
SAM_Resp	35	ID54	5.77	2.39	0.40	1	9	8	6	3	-0.36	1.95
SAM_Resp	35	ID55	6.37	2.54	0.43	1	9	8	7	8	-0.75	2.14
SAM_Resp	35	ID57	3.54	1.22	0.21	2	6	4	3	5	0.15	1.76
SAM_Resp	35	ID58	2.83	1.34	0.23	1	6	5	3	2	0.39	2.38
SAM_Resp	35	ID6	5.49	1.76	0.30	1	8	7	6	7	-0.74	2.74
SAM_Resp	35	ID61	1.49	1.44	0.24	1	7	6	1	1	2.86	9.94
SAM_Resp	35	ID63	5.31	1.66	0.28	2	8	6	5	4	0.08	1.98
SAM_Resp	35	ID64	5.77	2.35	0.40	1	9	8	6	7	-0.66	2.50
SAM_Resp	35	ID66	6.31	1.49	0.25	2	9	7	7	7	-0.98	3.99
SAM_Resp	35	ID8	2.49	1.58	0.27	1	5	4	2	1	0.39	1.54
SAM_Resp	35	ID9	4.91	1.79	0.30	1	9	8	5	4	0.35	2.76


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "neutru", By_ID = FALSE)           # Neutral - General

Unic_CTRL_Instr - neutru
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	2.08	1.59	0.05	1	9	8	1	1	1.7	5.38

descriptive_func(df = Unic_CTRL_Solo, Stim_type = "neutru", By_ID = FALSE)

Unic_CTRL_Solo - neutru
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1190	2.14	1.58	0.05	1	9	8	1	1	1.5	4.8

descriptive_func(df = Unic_OGL_Instr, Stim_type = "neutru", By_ID = FALSE)

Unic_OGL_Instr - neutru
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	2.07	1.51	0.04	1	9	8	1	1	1.65	5.52

descriptive_func(df = Unic_OGL_Solo, Stim_type = "neutru", By_ID = FALSE)

Unic_OGL_Solo - neutru
variable	missing	N	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1155	2.07	1.55	0.05	1	9	8	1	1	1.77	6.07


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "neutru", By_ID = TRUE)            # Neutral - by id

Unic_CTRL_Instr - neutru
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID06	2.03	1.72	0.29	1	8	7	1	1	1.98	6.31
SAM_Resp	35	ID10	2.03	1.34	0.23	1	5	4	2	1	1.21	3.29
SAM_Resp	35	ID12	1.03	0.17	0.03	1	2	1	1	1	5.66	33.03
SAM_Resp	35	ID13	2.26	1.99	0.34	1	8	7	1	1	1.68	4.93
SAM_Resp	35	ID16	2.11	0.96	0.16	1	4	3	2	2	0.57	2.45
SAM_Resp	35	ID17	1.23	0.60	0.10	1	3	2	1	1	2.40	7.17
SAM_Resp	35	ID26	1.37	0.94	0.16	1	5	4	1	1	2.63	9.07
SAM_Resp	35	ID27	1.29	0.62	0.11	1	4	3	1	1	2.72	11.40
SAM_Resp	35	ID28	2.94	1.30	0.22	1	6	5	2	2	0.75	2.73
SAM_Resp	35	ID29	1.37	0.60	0.10	1	3	2	1	1	1.34	3.76
SAM_Resp	35	ID33	1.31	0.63	0.11	1	3	2	1	1	1.80	4.88
SAM_Resp	35	ID36	1.17	0.86	0.14	1	6	5	1	1	5.36	30.54
SAM_Resp	35	ID37	1.11	0.40	0.07	1	3	2	1	1	3.65	15.81
SAM_Resp	35	ID39	2.69	1.60	0.27	1	7	6	2	2	1.22	3.89
SAM_Resp	35	ID41	1.91	1.84	0.31	1	7	6	1	1	1.74	4.58
SAM_Resp	35	ID43	1.51	0.78	0.13	1	4	3	1	1	1.46	4.46
SAM_Resp	35	ID44	4.29	1.25	0.21	2	7	5	4	5	0.27	2.65
SAM_Resp	35	ID45	1.86	1.73	0.29	1	8	7	1	1	2.32	7.62
SAM_Resp	35	ID47	4.40	2.26	0.38	1	9	8	4	2	0.16	1.74
SAM_Resp	35	ID48	1.89	1.51	0.26	1	7	6	1	1	2.02	6.42
SAM_Resp	35	ID49	2.97	1.85	0.31	1	7	6	3	1	0.69	2.46
SAM_Resp	35	ID50	1.09	0.28	0.05	1	2	1	1	1	2.96	9.76
SAM_Resp	35	ID51	1.23	0.65	0.11	1	4	3	1	1	3.09	12.20
SAM_Resp	35	ID52	3.00	1.28	0.22	1	5	4	3	2	0.34	1.96
SAM_Resp	35	ID54	2.37	1.85	0.31	1	7	6	2	1	1.31	3.60
SAM_Resp	35	ID55	3.74	2.36	0.40	1	8	7	3	1	0.41	1.93
SAM_Resp	35	ID57	3.06	1.43	0.24	1	7	6	3	2	1.05	3.50
SAM_Resp	35	ID58	1.26	0.61	0.10	1	3	2	1	1	2.18	6.25
SAM_Resp	35	ID61	1.74	1.67	0.28	1	8	7	1	1	2.38	7.95
SAM_Resp	35	ID63	2.14	1.31	0.22	1	5	4	2	1	0.85	2.63
SAM_Resp	35	ID64	1.49	0.85	0.14	1	5	4	1	1	2.35	9.43
SAM_Resp	35	ID66	1.83	1.04	0.18	1	6	5	2	2	2.08	8.65
SAM_Resp	35	ID9	2.83	1.32	0.22	1	6	5	2	2	1.18	3.66

descriptive_func(df = Unic_CTRL_Solo, Stim_type = "neutru", By_ID = TRUE)

Unic_CTRL_Solo - neutru
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	2.06	1.28	0.22	1	5	4	2	1	1.08	3.11
SAM_Resp	35	ID12	1.09	0.51	0.09	1	4	3	1	1	5.66	33.03
SAM_Resp	35	ID13	3.29	2.27	0.38	1	9	8	3	1	0.64	2.37
SAM_Resp	35	ID16	1.80	0.76	0.13	1	4	3	2	2	0.75	3.38
SAM_Resp	35	ID17	1.49	0.98	0.17	1	5	4	1	1	2.12	6.86
SAM_Resp	35	ID26	1.11	0.32	0.05	1	2	1	1	1	2.42	6.88
SAM_Resp	35	ID27	1.37	0.73	0.12	1	4	3	1	1	2.06	6.70
SAM_Resp	35	ID28	2.43	0.85	0.14	1	5	4	2	2	1.39	4.27
SAM_Resp	35	ID29	1.54	0.98	0.17	1	6	5	1	1	2.92	13.30
SAM_Resp	35	ID33	1.43	0.78	0.13	1	4	3	1	1	1.76	5.25
SAM_Resp	35	ID36	1.14	0.49	0.08	1	3	2	1	1	3.31	12.41
SAM_Resp	35	ID37	1.26	0.56	0.09	1	3	2	1	1	2.06	6.16
SAM_Resp	35	ID39	3.34	1.68	0.28	1	8	7	3	2	1.26	3.83
SAM_Resp	35	ID41	1.97	1.77	0.30	1	9	8	1	1	2.23	8.33
SAM_Resp	35	ID43	1.03	0.17	0.03	1	2	1	1	1	5.66	33.03
SAM_Resp	35	ID44	4.20	0.83	0.14	2	5	3	4	5	-0.69	2.64
SAM_Resp	35	ID45	1.69	1.59	0.27	1	8	7	1	1	2.73	9.89
SAM_Resp	35	ID47	4.11	1.89	0.32	1	7	6	4	6	-0.03	1.61
SAM_Resp	35	ID48	1.66	1.08	0.18	1	6	5	1	1	2.12	8.35
SAM_Resp	35	ID49	2.74	2.08	0.35	1	8	7	2	1	1.11	3.09
SAM_Resp	35	ID50	1.37	0.69	0.12	1	3	2	1	1	1.56	3.94
SAM_Resp	35	ID51	1.26	0.85	0.14	1	5	4	1	1	3.54	14.57
SAM_Resp	35	ID52	3.46	1.38	0.23	1	6	5	3	3	0.64	2.47
SAM_Resp	35	ID54	2.46	1.52	0.26	1	7	6	2	1	1.02	3.83
SAM_Resp	35	ID55	3.20	2.59	0.44	1	9	8	2	1	0.79	2.19
SAM_Resp	35	ID57	3.26	0.95	0.16	1	5	4	3	4	-0.32	2.46
SAM_Resp	35	ID58	1.11	0.40	0.07	1	3	2	1	1	3.65	15.81
SAM_Resp	35	ID6	2.09	1.54	0.26	1	6	5	1	1	1.03	2.63
SAM_Resp	35	ID61	1.23	0.77	0.13	1	5	4	1	1	3.90	18.32
SAM_Resp	35	ID63	2.86	1.61	0.27	1	7	6	2	2	0.66	2.64
SAM_Resp	35	ID64	1.43	0.74	0.12	1	4	3	1	1	1.80	5.84
SAM_Resp	35	ID66	2.40	1.61	0.27	1	6	5	2	2	1.51	3.95
SAM_Resp	35	ID8	2.37	1.66	0.28	1	6	5	1	1	0.71	1.99
SAM_Resp	35	ID9	3.63	1.31	0.22	1	7	6	3	3	0.47	3.05

descriptive_func(df = Unic_OGL_Instr, Stim_type = "neutru", By_ID = TRUE)

Unic_OGL_Instr - neutru
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	1.80	1.32	0.22	1	6	5	1	1	1.84	5.49
SAM_Resp	35	ID12	1.17	0.57	0.10	1	4	3	1	1	3.94	19.03
SAM_Resp	35	ID13	2.09	2.06	0.35	1	9	8	1	1	2.31	7.88
SAM_Resp	35	ID15	2.00	0.91	0.15	1	4	3	2	2	0.72	2.86
SAM_Resp	35	ID16	2.94	1.21	0.20	1	6	5	3	2	0.41	2.62
SAM_Resp	35	ID17	1.14	0.36	0.06	1	2	1	1	1	2.04	5.17
SAM_Resp	35	ID26	1.34	0.84	0.14	1	4	3	1	1	2.33	7.09
SAM_Resp	35	ID27	1.11	0.40	0.07	1	3	2	1	1	3.65	15.81
SAM_Resp	35	ID28	3.11	1.55	0.26	1	7	6	3	2	1.16	3.34
SAM_Resp	35	ID29	1.74	0.78	0.13	1	4	3	2	2	0.85	3.32
SAM_Resp	35	ID37	1.49	0.89	0.15	1	4	3	1	1	1.46	3.58
SAM_Resp	35	ID39	2.60	1.22	0.21	1	5	4	2	2	0.81	2.49
SAM_Resp	35	ID41	2.49	2.24	0.38	1	9	8	1	1	1.41	3.93
SAM_Resp	35	ID43	1.14	0.36	0.06	1	2	1	1	1	2.04	5.17
SAM_Resp	35	ID44	3.11	1.02	0.17	2	6	4	3	3	0.94	3.48
SAM_Resp	35	ID45	1.60	1.40	0.24	1	6	5	1	1	2.19	6.22
SAM_Resp	35	ID47	3.43	1.56	0.26	1	7	6	4	4	0.02	2.31
SAM_Resp	35	ID48	1.63	0.97	0.16	1	5	4	1	1	1.77	5.91
SAM_Resp	35	ID49	3.77	1.78	0.30	1	7	6	4	2	0.22	1.92
SAM_Resp	35	ID50	2.43	1.72	0.29	1	7	6	2	1	1.00	2.95
SAM_Resp	35	ID51	1.20	0.63	0.11	1	4	3	1	1	3.37	13.78
SAM_Resp	35	ID52	2.51	1.60	0.27	1	6	5	2	1	1.01	2.89
SAM_Resp	35	ID54	2.20	1.98	0.34	1	7	6	1	1	1.44	3.54
SAM_Resp	35	ID55	3.03	2.35	0.40	1	8	7	3	1	0.92	2.65
SAM_Resp	35	ID57	2.00	0.97	0.16	1	4	3	2	2	0.78	2.73
SAM_Resp	35	ID58	1.37	1.06	0.18	1	5	4	1	1	2.83	9.50
SAM_Resp	35	ID6	1.60	1.24	0.21	1	7	6	1	1	2.76	11.50
SAM_Resp	35	ID61	1.23	0.73	0.12	1	4	3	1	1	3.29	12.59
SAM_Resp	35	ID63	1.91	1.27	0.21	1	6	5	1	1	1.56	4.97
SAM_Resp	35	ID64	2.17	2.02	0.34	1	8	7	1	1	1.58	4.19
SAM_Resp	35	ID66	3.20	1.37	0.23	2	7	5	3	2	0.96	3.08
SAM_Resp	35	ID8	1.17	0.57	0.10	1	3	2	1	1	2.96	9.76
SAM_Resp	35	ID9	2.46	1.27	0.21	1	6	5	2	2	0.76	3.15

descriptive_func(df = Unic_OGL_Solo, Stim_type = "neutru", By_ID = TRUE)

Unic_OGL_Solo - neutru
variable	N	ID	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	35	ID10	1.94	1.33	0.22	1	5	4	1	1	1.18	3.12
SAM_Resp	35	ID12	1.20	0.41	0.07	1	2	1	1	1	1.50	3.25
SAM_Resp	35	ID13	3.17	2.43	0.41	1	8	7	2	1	0.84	2.32
SAM_Resp	35	ID16	2.63	1.11	0.19	1	6	5	2	2	0.90	3.57
SAM_Resp	35	ID17	1.14	0.43	0.07	1	3	2	1	1	3.08	11.98
SAM_Resp	35	ID26	1.57	0.85	0.14	1	4	3	1	1	1.23	3.37
SAM_Resp	35	ID27	1.06	0.24	0.04	1	2	1	1	1	3.82	15.56
SAM_Resp	35	ID28	3.14	1.54	0.26	1	6	5	2	2	0.84	2.28
SAM_Resp	35	ID29	1.69	1.02	0.17	1	5	4	1	1	1.50	4.68
SAM_Resp	35	ID33	1.57	1.14	0.19	1	5	4	1	1	1.97	5.75
SAM_Resp	35	ID37	1.40	0.98	0.17	1	6	5	1	1	3.36	15.41
SAM_Resp	35	ID39	2.91	1.40	0.24	1	7	6	2	2	1.20	3.63
SAM_Resp	35	ID41	2.89	2.87	0.48	1	9	8	1	1	1.38	3.38
SAM_Resp	35	ID43	1.37	0.81	0.14	1	5	4	1	1	2.98	12.96
SAM_Resp	35	ID44	3.71	1.05	0.18	2	6	4	4	3	0.12	2.20
SAM_Resp	35	ID45	1.74	1.69	0.29	1	7	6	1	1	2.32	7.10
SAM_Resp	35	ID47	3.49	1.38	0.23	1	6	5	4	4	-0.11	2.18
SAM_Resp	35	ID48	2.09	1.54	0.26	1	6	5	1	1	1.22	3.36
SAM_Resp	35	ID49	3.37	1.33	0.22	2	7	5	3	3	1.04	3.48
SAM_Resp	35	ID50	1.23	0.49	0.08	1	3	2	1	1	2.02	6.32
SAM_Resp	35	ID51	1.09	0.37	0.06	1	3	2	1	1	4.44	21.98
SAM_Resp	35	ID52	1.77	1.00	0.17	1	5	4	2	1	1.54	5.05
SAM_Resp	35	ID54	1.54	1.42	0.24	1	8	7	1	1	3.28	14.08
SAM_Resp	35	ID55	3.37	2.47	0.42	1	8	7	2	1	0.51	1.72
SAM_Resp	35	ID57	2.74	1.40	0.24	1	6	5	3	2	0.60	2.48
SAM_Resp	35	ID58	1.57	1.20	0.20	1	6	5	1	1	2.45	8.38
SAM_Resp	35	ID6	1.60	0.98	0.17	1	5	4	1	1	1.83	6.05
SAM_Resp	35	ID61	1.11	0.53	0.09	1	4	3	1	1	4.95	26.89
SAM_Resp	35	ID63	2.14	1.31	0.22	1	6	5	2	1	1.09	3.64
SAM_Resp	35	ID64	1.46	1.22	0.21	1	6	5	1	1	3.10	11.70
SAM_Resp	35	ID66	2.29	1.47	0.25	1	8	7	2	2	2.73	10.43
SAM_Resp	35	ID8	1.51	1.22	0.21	1	6	5	1	1	2.47	8.19
SAM_Resp	35	ID9	2.74	1.24	0.21	1	6	5	3	2	0.59	2.89

2.2 Merge

############################## Merge condition dataset ############################################################
# Must first rename .id to ID in oder to have .id for df names
ID_rename <- function(df){
  if(".id" %in% colnames(df)) {
    df_modif <- 
      df %>%
      dplyr::rename("ID_file" = .id)
    df <- deparse(substitute(df))
    cat("Changed .id to ID_file for: ", as.name(df))
    assign(df, df_modif, envir = globalenv())
  }  
}

ID_rename(Unic_CTRL_Instr)

Changed .id to ID_file for:  Unic_CTRL_Instr

ID_rename(Unic_CTRL_Solo)

Changed .id to ID_file for:  Unic_CTRL_Solo

ID_rename(Unic_OGL_Instr)

Changed .id to ID_file for:  Unic_OGL_Instr

ID_rename(Unic_OGL_Solo)

Changed .id to ID_file for:  Unic_OGL_Solo

# Merge into one df
list_df_merge <- list(Unic_CTRL_Instr, Unic_CTRL_Solo, Unic_OGL_Instr, Unic_OGL_Solo)
names(list_df_merge) <- c("Unic_CTRL_Instr", "Unic_CTRL_Solo", "Unic_OGL_Instr", "Unic_OGL_Solo")
Unic_merged <- plyr::ldply(list_df_merge, data.frame)                    # also works for this job bind_rows(list_df_merge, .id = "column_label")

2.3 Analyses on merged (Anova & post-hoc)

############################## Analyses on Merged ################################################################
## Just a Test 
  # Unic_merged_spread_Neg <- 
  #   Unic_merged %>%
  #   filter(!is.na(SAM_Resp)) %>%                                           # some files had only NA on SAM_Resp and SAM_RT
  #   select(.id, ID, Subj_id, 
  #          Stimuli.order, MarkerStimuli, Stimulus.type, 
  #          SAM_Resp, SAM_RT) %>%
  #   filter(Stimulus.type == "negativ") %>%                                 # dont forget to pick stymulus type
  #   spread(.id, SAM_Resp)
  # 
  # t.test(Unic_merged_spread_Neg$Unic_CTRL_Instr, Unic_merged_spread_Neg$Unic_CTRL_Solo, na.rm = TRUE)
  # t.test(Unic_merged_spread_Neg$Unic_OGL_Instr, Unic_merged_spread_Neg$Unic_OGL_Solo, na.rm = TRUE)
  # t.test(Unic_merged_spread_Neg$Unic_OGL_Instr, Unic_merged_spread_Neg$Unic_CTRL_Instr, na.rm = TRUE)
  # t.test(Unic_merged_spread_Neg$Unic_OGL_Solo, Unic_merged_spread_Neg$Unic_CTRL_Solo, na.rm = TRUE)

## Function prepair data for analyses
prepaire_merged_func <- function(Stim_type){
  Unic_merged %>%
    filter(!is.na(SAM_Resp)) %>%                                           # some files had only NA on SAM_Resp and SAM_RT
    select(.id, ID, Subj_id, 
           Stimuli.order, MarkerStimuli, Stimulus.type, 
           SAM_Resp, SAM_RT) %>%
    dplyr::rename(Cond = .id) %>% 
    filter(Stimulus.type == Stim_type) %>%                                 # dont forget to pick stymulus type
    mutate(Cond = as.factor(Cond))                                         # tunr to factor for aov family functions
}

Unic_merged_Neg <- prepaire_merged_func("negativ")
Unic_merged_Neu <- prepaire_merged_func("neutru")
Unic_merged_Poz <- prepaire_merged_func("pozitiv")

## Anova and Post-Hoc
# Normality 
Unic_merged_Neg %>%
  select(SAM_Resp) %>%                                                     # must select variables outside function 
  tadaatoolbox::tadaa_normtest(method = "shapiro")                         # , print = "markdown"  for Notebook

# Levene Test (p>.05 = homogeneity of variances)
Unic_merged_Neg %>%
  tadaatoolbox::tadaa_levene(data = ., SAM_Resp ~ Cond)                    # , print = "markdown"  for Notebook

# Anova
Unic_merged_Neg %>%
  #do(broom::glance(aov(.$SAM_Resp ~ .$Cond)))                             # regular anova do(broom::tidy(aov(.$SAM_Resp ~ .$Cond)))
  tadaatoolbox::tadaa_aov(data = ., SAM_Resp ~ Cond, type = 1)             # , print = "markdown"  for Notebook

# Post-Hoc 
Unic_merged_Neg %>%
  # Tukey for equal variance 
  tadaatoolbox::tadaa_pairwise_tukey(data = ., SAM_Resp, Cond)             # , print = "markdown"  for Notebook
  # Games Howell does not assume equal variances
  #tadaatoolbox::tadaa_pairwise_gh(data = ., SAM_Resp, Cond)                # , print = "markdown"  for Notebook

2.4 Plots with p values

# by dataset
ggplot(Unic_merged, aes(x = Stimulus.type, y = SAM_Resp)) +
  geom_boxplot() +
  stat_summary(fun.data = mean_se,  colour = "darkred") +
  xlab("") +
  facet_wrap(~.id) +
  ggpubr::stat_compare_means(method = "t.test", 
                             label = "p.signif",                                         # to avoid scientific notation of very small p-values
                             #paired = TRUE, 
                             comparisons = list(c("negativ", "neutru"),
                                                c("neutru", "pozitiv"),
                                                c("negativ", "pozitiv")))


# by Stimulus type
ggplot(Unic_merged, aes(x = .id, y = SAM_Resp)) +
  geom_boxplot() +
  stat_summary(fun.data = mean_se,  colour = "darkred") +
  xlab("") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  facet_wrap(~Stimulus.type) +
  ggpubr::stat_compare_means(method = "t.test",
                             label = "p.format",                                         # formated p-values
                             #paired = TRUE, 
                             comparisons = list(c("Unic_CTRL_Instr", "Unic_CTRL_Solo"),
                                                c("Unic_CTRL_Instr", "Unic_OGL_Instr"),
                                                c("Unic_CTRL_Solo", "Unic_OGL_Instr"),
                                                c("Unic_CTRL_Solo", "Unic_OGL_Solo"),
                                                c("Unic_OGL_Instr", "Unic_OGL_Solo"),
                                                c("Unic_CTRL_Instr", "Unic_OGL_Solo")))



# drop to CTRL vs OGL - by Stimulus type
Unic_merged %>%
  mutate(.id = case_when(.id %in% c("Unic_CTRL_Instr", "Unic_CTRL_Solo") ~ "Unic_CTRL",
                         .id %in% c("Unic_OGL_Instr", "Unic_OGL_Solo") ~ "Unic_OGL",
                         TRUE ~ as.character(.id))) %>%
    ggplot(aes(x = .id, y = SAM_Resp)) +
    geom_boxplot() +
    stat_summary(fun.data = mean_se,  colour = "darkred") +
    xlab("") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    facet_wrap(~Stimulus.type) +
    ggpubr::stat_compare_means(method = "t.test",
                               label = "p.format",                                         # formated p-values
                               #paired = TRUE, 
                               comparisons = list(c("Unic_CTRL", "Unic_OGL")))


# drop to Instr vs Solo - by Stimulus type
Unic_merged %>%
  mutate(.id = case_when(.id %in% c("Unic_CTRL_Instr", "Unic_OGL_Instr") ~ "Unic_Instr",
                         .id %in% c("Unic_CTRL_Solo", "Unic_OGL_Solo") ~ "Unic_Solo",
                         TRUE ~ as.character(.id))) %>%
    ggplot(aes(x = .id, y = SAM_Resp)) +
    geom_boxplot() +
    stat_summary(fun.data = mean_se,  colour = "darkred") +
    xlab("") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    facet_wrap(~Stimulus.type) +
    ggpubr::stat_compare_means(method = "t.test",
                               label = "p.format",                                         # formated p-values
                               #paired = TRUE, 
                               comparisons = list(c("Unic_Instr", "Unic_Solo")))

3 Session Info

R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)

Matrix products: default

locale:
[1] LC_COLLATE=Romanian_Romania.1250  LC_CTYPE=Romanian_Romania.1250    LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C                     
[5] LC_TIME=Romanian_Romania.1250    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] plyr_1.8.6         summarytools_0.8.8 DT_0.5             ggpubr_0.4.0       broom_0.7.5        papaja_0.1.0.9997  psych_2.0.12       forcats_0.5.1     
 [9] stringr_1.4.0      dplyr_1.0.5        purrr_0.3.4        readr_1.4.0        tidyr_1.1.3        tibble_3.1.0       ggplot2_3.3.3      tidyverse_1.3.0   
[17] pacman_0.5.1      

loaded via a namespace (and not attached):
 [1] httr_1.4.2         jsonlite_1.7.2     carData_3.0-2      tmvnsim_1.0-2      modelr_0.1.8       assertthat_0.2.1   highr_0.8          pander_0.6.3      
 [9] cellranger_1.1.0   yaml_2.2.1         pillar_1.5.1       backports_1.2.1    lattice_0.20-38    glue_1.4.2         digest_0.6.27      ggsignif_0.6.1    
[17] pryr_0.1.4         rvest_1.0.0        colorspace_2.0-0   htmltools_0.5.1.1  pkgconfig_2.0.3    haven_2.3.1        scales_1.1.1       openxlsx_4.1.0    
[25] rio_0.5.26         farver_2.1.0       generics_0.1.0     car_3.0-10         ellipsis_0.3.1     withr_2.4.1        cli_2.3.1          mnormt_2.0.2      
[33] magrittr_2.0.1     crayon_1.4.1       readxl_1.3.1       evaluate_0.14      fs_1.5.0           fansi_0.4.2        nlme_3.1-140       rstatix_0.7.0     
[41] xml2_1.3.2         foreign_0.8-71     rapportools_1.0    tools_3.6.1        data.table_1.14.0  hms_1.0.0          lifecycle_1.0.0    matrixStats_0.54.0
[49] munsell_0.5.0      reprex_1.0.0       zip_1.0.0          compiler_3.6.1     rlang_0.4.10       RCurl_1.95-4.11    grid_3.6.1         rstudioapi_0.13   
[57] htmlwidgets_1.5.3  labeling_0.4.2     rmarkdown_2.7      bitops_1.0-6       codetools_0.2-16   gtable_0.3.0       abind_1.4-5        DBI_1.0.0         
[65] curl_4.3           R6_2.5.0           tidystats_0.5      lubridate_1.7.4    knitr_1.31         utf8_1.2.1         stringi_1.5.3      parallel_3.6.1    
[73] Rcpp_1.0.6         vctrs_0.3.6        dbplyr_2.1.0       tidyselect_1.1.0   xfun_0.22

A work by Claudiu Papasteri

claudiu.papasteri@gmail.com

---
title: "<br> O.4c - Unice" 
subtitle: "Preliminary Report"
author: "<br> Claudiu Papasteri"
date: "`r format(Sys.time(), '%d %m %Y')`"
output: 
    html_notebook:
            code_folding: hide
            toc: true
            toc_depth: 2
            number_sections: true
            theme: spacelab
            highlight: tango
            font-family: Arial
            fig_width: 10
            fig_height: 9
    pdf_document: 
            toc: true
            toc_depth: 2
            number_sections: true
            # fontsize: 11pt
            # geometry: margin=1in
            # fig_width: 7
            # fig_height: 6
            # fig_caption: true
    # github_document: 
            # toc: true
            # toc_depth: 2
            # html_preview: false
            # fig_width: 5
            # fig_height: 5
            # dev: jpeg
---


<!-- Setup -->


```{r setup, include=FALSE}
# kintr options
knitr::opts_chunk$set(
  comment = "#",
  collapse = TRUE,
  echo = TRUE, warning = TRUE, message = TRUE, cache = TRUE       # echo = False for github_document, but will be folded in html_notebook
)

# General R options and info
set.seed(111)               # in case we use randomized procedures       
options(scipen = 999)       # positive values bias towards fixed and negative towards scientific notation

# Load packages
if (!require("pacman")) install.packages("pacman")
packages <- c(
  "tidyverse",      # best thing that happend to me
  "psych",          # general purpose toolbox for personality, psychometric theory and experimental psychology
  "papaja",         # for APA style
  "broom",          # for tidy modelling
  "ggplot2",        # best plots
  "ggpubr",         # ggplot2 to publication quality
  "DT",             # nice searchable and downloadable tables
  "summarytools",
  "plyr", 
  "stringr"
  # , ...
)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(char = packages)

# Themes for ggplot2 ploting (here used APA style)
theme_set(theme_apa())
```

```{r working_directory, include = FALSE}
### O.4 R code - Unice and Retatate
### R code for sorting, integrating and analyses 
# Task output files: 
# ID, experimental condition ("oglinda" / "ecran"), condition ("instructor", "solo"), type of task ("unic", "repetat")
wd <- "E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice"
setwd(wd)
```


<!-- Report -->


# Arrange folders, sort files, read and merge

## Create folders by conditions, copy files to them

```{r makedir_sortfiles, eval=FALSE}
##################### Read file names ##################################################################################
file_names <- dir(wd, pattern = "\\.xls$")
## if above isn't good enough try the following:
# file_names <- list.files(wd)
# file_names <- sop_files[!file.info(sop_files)$isdir]   # exclude directories
# file_names <- sop_files[grep(".xls", sop_files, fixed = TRUE)]


##################### Create folders with Condition names ###############################################################
# this part of script may be re-run if files from wd are updated
dir_names <- c("Unic_CTRL_Instr", "Unic_CTRL_Solo", "Unic_OGL_Instr", "Unic_OGL_Solo")
             
for(dir in dir_names){
  if(!dir.exists(file.path(wd, dir)))
  dir.create(file.path(wd, dir), showWarnings = FALSE)
}


##################### Use file names to sort them to folders ############################################################
sort_files_to_dirs <- function(wd, pattern, dir) {
  check_pattern <- outer(file_names, pattern, stringr::str_detect)               # if all TRUE bye row then it has full pattern
  index <- which(apply(check_pattern, 1, function(x) all(x==TRUE)))              # get index of file_names where all are TRUE
  sorted_files <- file_names[index]                                              # get names of files from indexes
  
  for(files in sorted_files) {                                                   # copy the files to corresponding folder
    file.copy(from = file.path(wd, files), to = file.path(wd, dir))
  }  
}

sort_files_to_dirs(wd = wd, pattern = c("unic", "ecran", "instructor"), dir = "Unic_CTRL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("unic", "ecran", "solo"), dir = "Unic_CTRL_Solo")
sort_files_to_dirs(wd = wd, pattern = c("unic", "oglinda", "instructor"), dir = "Unic_OGL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("unic", "oglinda", "solo"), dir = "Unic_OGL_Solo")
```


## Reading the data

```{r raw_read, hide=TRUE}
############ Read in all the .xls from folders and merge them in datasets named after corresponding folder ##############
# this part of script may be re-run if files from wd are updated
# RE-RUN FROM HERE IF FOLDERS AND SORTING WAS ALREADY DONE

wd <- "E:/CINETIC diverse/O.4c (EEG)/18.03.2021 Unice/Procesate&Exclusi"
setwd(wd)
folders <- list.files(wd)
folders <- folders[file.info(folders)$isdir]   # luam doar folderele
datasetnames <- NULL

for (i in 1:length(folders)) {
  datasetname <- folders[i]
  datasetnames <- c(datasetnames, datasetname)
  current_dir <- file.path(wd, folders[i])
  setwd(current_dir)                                         # small modification from 2019 code
  print(paste0("current_dir: ", current_dir))
  
  paths <- dir(pattern = "\\.xls$")
  names(paths) <- basename(paths)

  assign( paste(datasetname), plyr::ldply(paths, rio::import) )
}

setwd(wd)
# detach("package:plyr", unload=TRUE)                                                   # detach plyr because of conflicts with dplyr
```


## Cleaning the data

```{r clean_data, hide=TRUE}
#################################### Data Cleaning #####################################################################
# Check if ids have > 1 row of data (empty .xls have only 1 row)
# Careful! This function modfies the datasets in the global envinronment
delete_empty_id <- function(df){
  list_empty_id <- 
    df %>%
    dplyr::group_by(.id) %>%
    dplyr::summarise(row_count = n()) %>%
    dplyr::rename("empty_id" = .id) %>%
    mutate(delete_id = if_else(row_count < 3, TRUE, FALSE)) %>%
    dplyr::filter(delete_id == TRUE)
  
  df_modif <- 
    df %>%
    dplyr::filter(!.id %in% list_empty_id$empty_id)
  
  if(!identical(df, df_modif)){
    df <- deparse(substitute(df))
    cat("Deleting from ", print(as.name(df))); print(list_empty_id)                    # print out which ids are deleted from which dataset
    assign(df, df_modif, envir = globalenv())                                          # assign modified df to original dataset from Global
  }else cat("No empty datasets. Nothing to delete")
}

# Apply function to all datasets (tricky to do in for loop because of super assignment)
delete_empty_id(Unic_CTRL_Instr)
delete_empty_id(Unic_CTRL_Solo)
delete_empty_id(Unic_OGL_Instr)
delete_empty_id(Unic_OGL_Solo)
```


## Exclude SAM_resp based on RT outliers

```{r nooutlier_data}
############################### Exclude Outliers based on RT (by subject and stimulus type) #######################################
## DONT RUN (unless it is needed) ----> eval=FALSE
# Exclude RT outliers (=- 2SD) - instead of simple filter, makeing them NA  is better for paired comparison
remove_outliers <- function(df) {
  df_modif <-
    df %>%
    dplyr::group_by(.id, `Stimulus type`) %>%                  # we could have done before:  dplyr::rename("Stim_type" = `Stimulus type`) 
    mutate(SAM_Resp = if_else(abs(SAM_RT - mean(SAM_RT, na.rm=TRUE)) > 2*sd(SAM_RT, na.rm=TRUE), as.numeric(NA), SAM_Resp))
  
  if(!identical(df, df_modif)){
    df <- deparse(substitute(df))
    cat("Deleting outliers from ", print(as.name(df)));                               # print out datasets which have been modified
    assign(df, df_modif, envir = globalenv())                                          # assign modified df to original dataset from Global
  }else cat("No outlier")
}

remove_outliers(Unic_CTRL_Instr)
remove_outliers(Unic_CTRL_Solo)
remove_outliers(Unic_OGL_Instr)
remove_outliers(Unic_OGL_Solo)
```


## Test if datasets have same columns

```{r test_cols}
unic_df_obj <- mget(c("Unic_CTRL_Instr", "Unic_CTRL_Solo", "Unic_OGL_Instr", "Unic_OGL_Solo"))
unic_df_obj <-lapply(unic_df_obj, colnames)
outer(unic_df_obj, unic_df_obj, Vectorize(identical))                           # if all are TRUE, all df have same columns
```


## Clean IDs

```{r clean_ids}
Unic_CTRL_Instr <- 
  Unic_CTRL_Instr %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)

Unic_CTRL_Solo <- 
  Unic_CTRL_Solo %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)

Unic_OGL_Instr <- 
  Unic_OGL_Instr %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)

Unic_OGL_Solo <- 
  Unic_OGL_Solo %>%
  dplyr::mutate(ID = stringr::str_match(.id, "ID[0-9]+")) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  dplyr::relocate(ID)



# Check IDs matches
checkid_Unic_CTRL_Instr <- 
  Unic_CTRL_Instr %>%
    dplyr::arrange(ID) %>%
    dplyr::count(ID)

checkid_Unic_CTRL_Solo <- 
  Unic_CTRL_Solo %>%
  dplyr::arrange(ID) %>%
  dplyr::count(ID)

checkid_Unic_OGL_Instr <- 
  Unic_OGL_Instr %>%
  dplyr::arrange(ID) %>%
  dplyr::count(ID)

checkid_Unic_OGL_Solo <- 
  Unic_OGL_Solo %>%
  dplyr::arrange(ID) %>%
  dplyr::count(ID)


knitr::asis_output("### Check IDs match")
checkid_Unic_CTRL_Instr %>% 
  dplyr::full_join(checkid_Unic_CTRL_Solo,  by = "ID", suffix = c("Unic_CTRL_Instr", "Unic_CTRL_Solo")) %>%
  dplyr::full_join(checkid_Unic_OGL_Instr,  by = "ID", suffix = c("Unic_CTRL_Instr", "Unic_OGL_Instr")) %>%
  dplyr::full_join(checkid_Unic_OGL_Solo,  by = "ID", suffix = c("Unic_CTRL_Instr", "Unic_OGL_Solo")) %>%
  print(n = Inf)
```


<br>
<br>


# Analysis - UNICE

## Descriptives

```{r desc_unice}
##########################################################################################################################
#################################### Analyses - UNICE ####################################################################
## Descriptives by condition dataset
descriptive_func <- function(df, Stim_type, By_ID = FALSE){
  dfname_stimtype <- paste(deparse(substitute(df)), Stim_type, sep = " - ")
  suppressWarnings({                                                                # if all NAs in SAM_Resp, NaNs and Infs will be produced
    df_modif <- 
      df %>%
      dplyr::select_all(~gsub("\\s+|\\.", "_", .)) %>%                                     # replaces blancks with "_" in colnames 
      dplyr::filter(Stimulus_type == Stim_type)                                            # filter by stimulus type
      
    if(By_ID) {                                                              # if true group by id, if not return descriptives for all ids
      df_modif %>%
      dplyr::group_by(ID) %>%
      tidystats::describe_data(SAM_Resp, na.rm = TRUE) %>%                            # could also add SAM_RT to list of stats, but list is too long
        knitr::kable(caption = dfname_stimtype, digits = 2)
    }else{ 
      df_modif %>%
      tidystats::describe_data(SAM_Resp, na.rm = TRUE) %>%
        knitr::kable(caption = dfname_stimtype, digits = 2)
    }
  })
}  


descriptive_func(df = Unic_CTRL_Instr, Stim_type = "negativ", By_ID = FALSE)           # Negative - General
descriptive_func(df = Unic_CTRL_Solo, Stim_type = "negativ", By_ID = FALSE)
descriptive_func(df = Unic_OGL_Instr, Stim_type = "negativ", By_ID = FALSE)
descriptive_func(df = Unic_OGL_Solo, Stim_type = "negativ", By_ID = FALSE)

descriptive_func(df = Unic_CTRL_Instr, Stim_type = "negativ", By_ID = TRUE)            # Negative - by id
descriptive_func(df = Unic_CTRL_Solo, Stim_type = "negativ", By_ID = TRUE)
descriptive_func(df = Unic_OGL_Instr, Stim_type = "negativ", By_ID = TRUE)
descriptive_func(df = Unic_OGL_Solo, Stim_type = "negativ", By_ID = TRUE)

descriptive_func(df = Unic_CTRL_Instr, Stim_type = "pozitiv", By_ID = FALSE)           # Positive - General
descriptive_func(df = Unic_CTRL_Solo, Stim_type = "pozitiv", By_ID = FALSE)
descriptive_func(df = Unic_OGL_Instr, Stim_type = "pozitiv", By_ID = FALSE)
descriptive_func(df = Unic_OGL_Solo, Stim_type = "pozitiv", By_ID = FALSE)

descriptive_func(df = Unic_CTRL_Instr, Stim_type = "pozitiv", By_ID = TRUE)            # Positive - by id
descriptive_func(df = Unic_CTRL_Solo, Stim_type = "pozitiv", By_ID = TRUE)
descriptive_func(df = Unic_OGL_Instr, Stim_type = "pozitiv", By_ID = TRUE)
descriptive_func(df = Unic_OGL_Solo, Stim_type = "pozitiv", By_ID = TRUE)

descriptive_func(df = Unic_CTRL_Instr, Stim_type = "neutru", By_ID = FALSE)           # Neutral - General
descriptive_func(df = Unic_CTRL_Solo, Stim_type = "neutru", By_ID = FALSE)
descriptive_func(df = Unic_OGL_Instr, Stim_type = "neutru", By_ID = FALSE)
descriptive_func(df = Unic_OGL_Solo, Stim_type = "neutru", By_ID = FALSE)

descriptive_func(df = Unic_CTRL_Instr, Stim_type = "neutru", By_ID = TRUE)            # Neutral - by id
descriptive_func(df = Unic_CTRL_Solo, Stim_type = "neutru", By_ID = TRUE)
descriptive_func(df = Unic_OGL_Instr, Stim_type = "neutru", By_ID = TRUE)
descriptive_func(df = Unic_OGL_Solo, Stim_type = "neutru", By_ID = TRUE)
```


## Merge

```{r merged_unice_data, hide=TRUE}
############################## Merge condition dataset ############################################################
# Must first rename .id to ID in oder to have .id for df names
ID_rename <- function(df){
  if(".id" %in% colnames(df)) {
    df_modif <- 
      df %>%
      dplyr::rename("ID_file" = .id)
    df <- deparse(substitute(df))
    cat("Changed .id to ID_file for: ", as.name(df))
    assign(df, df_modif, envir = globalenv())
  }  
}

ID_rename(Unic_CTRL_Instr)
ID_rename(Unic_CTRL_Solo)
ID_rename(Unic_OGL_Instr)
ID_rename(Unic_OGL_Solo)

# Merge into one df
list_df_merge <- list(Unic_CTRL_Instr, Unic_CTRL_Solo, Unic_OGL_Instr, Unic_OGL_Solo)
names(list_df_merge) <- c("Unic_CTRL_Instr", "Unic_CTRL_Solo", "Unic_OGL_Instr", "Unic_OGL_Solo")
Unic_merged <- plyr::ldply(list_df_merge, data.frame)                    # also works for this job bind_rows(list_df_merge, .id = "column_label")
```


## Analyses on merged (Anova & post-hoc)

```{r anova_unice, hide=TRUE, eval=FALSE}
############################## Analyses on Merged ################################################################
## Just a Test 
  # Unic_merged_spread_Neg <- 
  #   Unic_merged %>%
  #   filter(!is.na(SAM_Resp)) %>%                                           # some files had only NA on SAM_Resp and SAM_RT
  #   select(.id, ID, Subj_id, 
  #          Stimuli.order, MarkerStimuli, Stimulus.type, 
  #          SAM_Resp, SAM_RT) %>%
  #   filter(Stimulus.type == "negativ") %>%                                 # dont forget to pick stymulus type
  #   spread(.id, SAM_Resp)
  # 
  # t.test(Unic_merged_spread_Neg$Unic_CTRL_Instr, Unic_merged_spread_Neg$Unic_CTRL_Solo, na.rm = TRUE)
  # t.test(Unic_merged_spread_Neg$Unic_OGL_Instr, Unic_merged_spread_Neg$Unic_OGL_Solo, na.rm = TRUE)
  # t.test(Unic_merged_spread_Neg$Unic_OGL_Instr, Unic_merged_spread_Neg$Unic_CTRL_Instr, na.rm = TRUE)
  # t.test(Unic_merged_spread_Neg$Unic_OGL_Solo, Unic_merged_spread_Neg$Unic_CTRL_Solo, na.rm = TRUE)

## Function prepair data for analyses
prepaire_merged_func <- function(Stim_type){
  Unic_merged %>%
    filter(!is.na(SAM_Resp)) %>%                                           # some files had only NA on SAM_Resp and SAM_RT
    select(.id, ID, Subj_id, 
           Stimuli.order, MarkerStimuli, Stimulus.type, 
           SAM_Resp, SAM_RT) %>%
    dplyr::rename(Cond = .id) %>% 
    filter(Stimulus.type == Stim_type) %>%                                 # dont forget to pick stymulus type
    mutate(Cond = as.factor(Cond))                                         # tunr to factor for aov family functions
}

Unic_merged_Neg <- prepaire_merged_func("negativ")
Unic_merged_Neu <- prepaire_merged_func("neutru")
Unic_merged_Poz <- prepaire_merged_func("pozitiv")

## Anova and Post-Hoc
# Normality 
Unic_merged_Neg %>%
  select(SAM_Resp) %>%                                                     # must select variables outside function 
  tadaatoolbox::tadaa_normtest(method = "shapiro")                         # , print = "markdown"  for Notebook

# Levene Test (p>.05 = homogeneity of variances)
Unic_merged_Neg %>%
  tadaatoolbox::tadaa_levene(data = ., SAM_Resp ~ Cond)                    # , print = "markdown"  for Notebook

# Anova
Unic_merged_Neg %>%
  #do(broom::glance(aov(.$SAM_Resp ~ .$Cond)))                             # regular anova do(broom::tidy(aov(.$SAM_Resp ~ .$Cond)))
  tadaatoolbox::tadaa_aov(data = ., SAM_Resp ~ Cond, type = 1)             # , print = "markdown"  for Notebook

# Post-Hoc 
Unic_merged_Neg %>%
  # Tukey for equal variance 
  tadaatoolbox::tadaa_pairwise_tukey(data = ., SAM_Resp, Cond)             # , print = "markdown"  for Notebook
  # Games Howell does not assume equal variances
  #tadaatoolbox::tadaa_pairwise_gh(data = ., SAM_Resp, Cond)                # , print = "markdown"  for Notebook
```


## Plots with p values
```{r plot_unice, fig.height=7}
# by dataset
ggplot(Unic_merged, aes(x = Stimulus.type, y = SAM_Resp)) +
  geom_boxplot() +
  stat_summary(fun.data = mean_se,  colour = "darkred") +
  xlab("") +
  facet_wrap(~.id) +
  ggpubr::stat_compare_means(method = "t.test", 
                             label = "p.signif",                                         # to avoid scientific notation of very small p-values
                             #paired = TRUE, 
                             comparisons = list(c("negativ", "neutru"),
                                                c("neutru", "pozitiv"),
                                                c("negativ", "pozitiv")))  

# by Stimulus type
ggplot(Unic_merged, aes(x = .id, y = SAM_Resp)) +
  geom_boxplot() +
  stat_summary(fun.data = mean_se,  colour = "darkred") +
  xlab("") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  facet_wrap(~Stimulus.type) +
  ggpubr::stat_compare_means(method = "t.test",
                             label = "p.format",                                         # formated p-values
                             #paired = TRUE, 
                             comparisons = list(c("Unic_CTRL_Instr", "Unic_CTRL_Solo"),
                                                c("Unic_CTRL_Instr", "Unic_OGL_Instr"),
                                                c("Unic_CTRL_Solo", "Unic_OGL_Instr"),
                                                c("Unic_CTRL_Solo", "Unic_OGL_Solo"),
                                                c("Unic_OGL_Instr", "Unic_OGL_Solo"),
                                                c("Unic_CTRL_Instr", "Unic_OGL_Solo"))) 


# drop to CTRL vs OGL - by Stimulus type
Unic_merged %>%
  mutate(.id = case_when(.id %in% c("Unic_CTRL_Instr", "Unic_CTRL_Solo") ~ "Unic_CTRL",
                         .id %in% c("Unic_OGL_Instr", "Unic_OGL_Solo") ~ "Unic_OGL",
                         TRUE ~ as.character(.id))) %>%
    ggplot(aes(x = .id, y = SAM_Resp)) +
    geom_boxplot() +
    stat_summary(fun.data = mean_se,  colour = "darkred") +
    xlab("") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    facet_wrap(~Stimulus.type) +
    ggpubr::stat_compare_means(method = "t.test",
                               label = "p.format",                                         # formated p-values
                               #paired = TRUE, 
                               comparisons = list(c("Unic_CTRL", "Unic_OGL")))          

# drop to Instr vs Solo - by Stimulus type
Unic_merged %>%
  mutate(.id = case_when(.id %in% c("Unic_CTRL_Instr", "Unic_OGL_Instr") ~ "Unic_Instr",
                         .id %in% c("Unic_CTRL_Solo", "Unic_OGL_Solo") ~ "Unic_Solo",
                         TRUE ~ as.character(.id))) %>%
    ggplot(aes(x = .id, y = SAM_Resp)) +
    geom_boxplot() +
    stat_summary(fun.data = mean_se,  colour = "darkred") +
    xlab("") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    facet_wrap(~Stimulus.type) +
    ggpubr::stat_compare_means(method = "t.test",
                               label = "p.format",                                         # formated p-values
                               #paired = TRUE, 
                               comparisons = list(c("Unic_Instr", "Unic_Solo"))) 
```







<!-- Session Info and License -->

<br>

# Session Info
```{r session_info, echo = FALSE, results = 'markup'}
sessionInfo()    
```

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&nbsp;
<hr />
<p style="text-align: center;">A work by <a href="https://github.com/ClaudiuPapasteri/">Claudiu Papasteri</a></p>
<p style="text-align: center;"><span style="color: #808080;"><em>claudiu.papasteri@gmail.com</em></span></p>
&nbsp;


O.4c - Unice

Preliminary Report

Claudiu Papasteri

15 04 2021