1 Arrange folders, sort files, read and merge

1.1 Create folders by conditions, copy files to them

##################### Read file names ##################################################################################
file_names <- dir(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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo", "Repetat_OGL_Instr", "Repetat_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("repetat", "ecran", "instructor"), dir = "Repetat_CTRL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("repetat", "ecran", "solo"), dir = "Repetat_CTRL_Solo")
sort_files_to_dirs(wd = wd, pattern = c("repetat", "oglinda", "instructor"), dir = "Repetat_OGL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("repetat", "oglinda", "solo"), dir = "Repetat_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)/29.10.2019 20sub EEG Repetate/Date"
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 <- setwd(file.path(wd, folders[i]))
  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)/29.10.2019 20sub EEG Repetate/Date"

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table

[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/29.10.2019 20sub EEG Repetate/Date/Repetat_CTRL_Instr"
[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/29.10.2019 20sub EEG Repetate/Date/Repetat_CTRL_Solo"
[1] "current_dir: E:/CINETIC diverse/O.4c (EEG)/29.10.2019 20sub EEG Repetate/Date/Repetat_OGL_Instr"

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) %>%
    dplyr::mutate(delete_id = if_else(row_count < 3, TRUE, FALSE)) %>%
    filter(delete_id == TRUE)
  
  df_modif <- 
    df %>%
    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")
}


delete_empty_id(Repetat_CTRL_Instr)

No empty datasets. Nothing to delete

delete_empty_id(Repetat_CTRL_Solo)

No empty datasets. Nothing to delete

delete_empty_id(Repetat_OGL_Instr)

No empty datasets. Nothing to delete

delete_empty_id(Repetat_OGL_Solo)

No empty datasets. Nothing to delete

1.4 Exclude SAM_resp based on RT outliers - not used here

############################### 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`) 
    dplyr::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(Repetat_CTRL_Instr)
remove_outliers(Repetat_CTRL_Solo)
remove_outliers(Repetat_OGL_Instr)
remove_outliers(Repetat_OGL_Solo)

1.5 Test if datasets have same columns

repetat_df_obj <- mget(c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo", "Repetat_OGL_Instr", "Repetat_OGL_Solo"))
repetat_df_obj <-lapply(repetat_df_obj, colnames)
outer(repetat_df_obj, repetat_df_obj, Vectorize(identical))                           # if all are TRUE, all df have same columns

                   Repetat_CTRL_Instr Repetat_CTRL_Solo Repetat_OGL_Instr Repetat_OGL_Solo
Repetat_CTRL_Instr               TRUE              TRUE              TRUE             TRUE
Repetat_CTRL_Solo                TRUE              TRUE              TRUE             TRUE
Repetat_OGL_Instr                TRUE              TRUE              TRUE             TRUE
Repetat_OGL_Solo                 TRUE              TRUE              TRUE             TRUE

2 Analysis - REPETATE

2.1 Descriptives

##########################################################################################################################
#################################### Analyses - REPETATE #################################################################
## Descriptives by condition dataset
descriptive_func <- function(df, Stim_type, By_ID = FALSE){
  df_name <- deparse(substitute(df))
  suppressWarnings({                                                                # if all NAs in SAM_Resp, NaNs and Infs will be produced
    df_modif <- 
      df %>%
      dplyr::rename("ID" = .id) %>%
      select_all(~gsub("\\s+|\\.", "_", .)) %>%                                     # replaces blancks with "_" in colnames 
      filter(Stimulus_type == Stim_type)                                            # filter by stimulus type
    
    if(isTRUE(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, SAM_RT, na.rm = TRUE) %>%
        knitr::kable(caption = as.name(df_name), format = "pandoc", digits = 2)
    }else{ 
      df_modif %>%
        tidystats::describe_data(SAM_Resp, SAM_RT, na.rm = TRUE) %>%
        knitr::kable(caption = as.name(df_name), format = "pandoc", digits = 2)
    }
  })
}  


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

Repetat_CTRL_Instr
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1400	2.57	1.77	0.05	1	8.00	7.00	2.0	1	0.96	3.13
SAM_RT	0	1400	1.37	0.54	0.01	1	6.41	5.41	1.2	1	3.42	20.23

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

Repetat_CTRL_Solo
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1400	2.68	1.79	0.05	1	9.00	8.00	2.0	1	0.78	2.74
SAM_RT	0	1400	1.47	0.76	0.02	1	6.92	5.92	1.2	1	3.18	15.53

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

Repetat_OGL_Instr
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1380	2.66	1.72	0.05	1	9.00	8.00	2.00	1.00	0.76	2.90
SAM_RT	0	1380	1.48	0.86	0.02	1	10.24	9.24	1.21	2.33	4.42	29.92

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

Repetat_OGL_Solo
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1380	2.58	1.71	0.05	1	8.00	7.00	2.00	1.00	0.72	2.40
SAM_RT	0	1380	1.47	0.86	0.02	1	20.68	19.68	1.24	1.71	10.03	188.31


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

Repetat_CTRL_Instr
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222ecraninstructorrepetat.xls	80	2.79	1.60	0.18	1	6.00	5.00	2.00	2.00	0.63	2.14
SAM_Resp	O4c_Run1_ID11ecraninstructorrepetat.xls	80	1.51	0.84	0.09	1	5.00	4.00	1.00	1.00	1.88	6.60
SAM_Resp	O4c_Run1_ID25ecraninstructorrepetat.xls	60	1.00	0.00	0.00	1	1.00	0.00	1.00	1.00	NaN	NaN
SAM_Resp	O4c_Run1_ID31ecraninstructorrepetat.xls	60	2.30	2.21	0.29	1	7.00	6.00	1.00	1.00	1.13	2.33
SAM_Resp	O4c_Run1_ID34ecraninstructorrepetat.xls	60	2.02	1.35	0.17	1	5.00	4.00	1.00	1.00	1.02	2.71
SAM_Resp	O4c_Run1_ID42ecraninstructorrepetat.xls	60	2.22	1.75	0.23	1	6.00	5.00	1.00	1.00	1.11	2.60
SAM_Resp	O4c_Run1_ID56ecraninstructorrepetatX.xls	60	2.60	1.58	0.20	1	5.00	4.00	2.00	2.00	0.73	1.86
SAM_Resp	O4c_Run1_ID65ecraninstructorrepetat.xls	60	2.88	1.08	0.14	1	5.00	4.00	3.00	3.00	-0.18	2.13
SAM_Resp	O4c_Run1ID21ecraninstructorrepetat.xls	80	3.30	1.15	0.13	1	5.00	4.00	4.00	4.00	-0.80	2.64
SAM_Resp	O4c_Run2_ID14ecraninstructorrepetat.xls	80	3.34	0.90	0.10	2	6.00	4.00	4.00	4.00	-0.19	2.49
SAM_Resp	O4c_Run2_ID18ecraninstructorrepetat.xls	80	4.09	3.16	0.35	1	8.00	7.00	3.00	1.00	0.09	1.11
SAM_Resp	O4c_Run2_ID19ecraninstructorrepetat.xls	80	1.00	0.00	0.00	1	1.00	0.00	1.00	1.00	NaN	NaN
SAM_Resp	O4c_Run2_ID32ecraninstructorrepetat.xls	60	2.85	1.20	0.16	1	4.00	3.00	3.00	4.00	-0.53	1.72
SAM_Resp	O4c_Run2_ID35ecraninstructorrepetat.xls	60	1.43	0.79	0.10	1	3.00	2.00	1.00	1.00	1.37	3.04
SAM_Resp	O4c_Run2_ID38ecraninstructorrepetat.xls	60	1.00	0.00	0.00	1	1.00	0.00	1.00	1.00	NaN	NaN
SAM_Resp	O4c_Run24_ID24ecraninstructorrepetat.xls	80	3.40	1.13	0.13	1	6.00	5.00	4.00	4.00	0.01	2.53
SAM_Resp	O4c_Run30_ID30ecraninstructorrepetat.xls	80	3.65	1.02	0.11	2	5.00	3.00	4.00	4.00	-0.70	2.24
SAM_Resp	O4c_RunN(modifica N!)_ID07ecraninstructorrepetatX.xls	80	3.91	1.44	0.16	1	5.00	4.00	5.00	5.00	-0.99	2.53
SAM_Resp	O4c_RunN(modifica N!)_ID2020ecraninstructorrepetat.xls	60	2.08	1.21	0.16	1	5.00	4.00	2.00	1.00	0.82	2.26
SAM_Resp	O4c_RunN_ID2323ecraninstructorrepetat.xls	80	2.74	2.60	0.29	1	8.00	7.00	1.00	1.00	1.10	2.34
SAM_RT	O4c_ID2222ecraninstructorrepetat.xls	80	1.38	0.36	0.04	1	3.05	2.05	1.29	1.00	1.87	8.42
SAM_RT	O4c_Run1_ID11ecraninstructorrepetat.xls	80	1.29	0.63	0.07	1	4.52	3.52	1.11	1.37	4.11	19.89
SAM_RT	O4c_Run1_ID25ecraninstructorrepetat.xls	60	1.39	0.57	0.07	1	3.36	2.36	1.17	1.40	2.32	7.66
SAM_RT	O4c_Run1_ID31ecraninstructorrepetat.xls	60	1.49	0.72	0.09	1	4.51	3.50	1.24	1.24	2.15	7.62
SAM_RT	O4c_Run1_ID34ecraninstructorrepetat.xls	60	1.40	0.79	0.10	1	6.41	5.41	1.14	2.61	4.56	27.98
SAM_RT	O4c_Run1_ID42ecraninstructorrepetat.xls	60	1.66	1.02	0.13	1	5.96	4.96	1.26	1.67	2.47	8.96
SAM_RT	O4c_Run1_ID56ecraninstructorrepetatX.xls	60	1.27	0.38	0.05	1	2.68	1.68	1.14	1.01	2.33	8.14
SAM_RT	O4c_Run1_ID65ecraninstructorrepetat.xls	60	1.69	0.96	0.12	1	5.04	4.04	1.20	1.19	1.57	4.67
SAM_RT	O4c_Run1ID21ecraninstructorrepetat.xls	80	1.60	0.57	0.06	1	3.89	2.89	1.38	1.26	1.40	5.12
SAM_RT	O4c_Run2_ID14ecraninstructorrepetat.xls	80	1.33	0.40	0.04	1	2.80	1.80	1.22	1.10	1.93	6.65
SAM_RT	O4c_Run2_ID18ecraninstructorrepetat.xls	80	1.30	0.29	0.03	1	2.72	1.72	1.23	1.00	2.05	9.39
SAM_RT	O4c_Run2_ID19ecraninstructorrepetat.xls	80	1.29	0.43	0.05	1	3.22	2.22	1.14	1.00	2.67	11.06
SAM_RT	O4c_Run2_ID32ecraninstructorrepetat.xls	60	1.32	0.33	0.04	1	2.51	1.51	1.27	1.08	1.74	6.22
SAM_RT	O4c_Run2_ID35ecraninstructorrepetat.xls	60	1.17	0.21	0.03	1	1.88	0.88	1.08	1.07	1.89	6.39
SAM_RT	O4c_Run2_ID38ecraninstructorrepetat.xls	60	1.49	0.50	0.07	1	2.81	1.81	1.31	1.00	0.96	2.85
SAM_RT	O4c_Run24_ID24ecraninstructorrepetat.xls	80	1.37	0.39	0.04	1	3.06	2.06	1.23	1.88	1.73	6.82
SAM_RT	O4c_Run30_ID30ecraninstructorrepetat.xls	80	1.41	0.44	0.05	1	2.91	1.91	1.27	1.01	1.53	5.09
SAM_RT	O4c_RunN(modifica N!)_ID07ecraninstructorrepetatX.xls	80	1.28	0.34	0.04	1	2.92	1.92	1.17	2.01	2.04	8.55
SAM_RT	O4c_RunN(modifica N!)_ID2020ecraninstructorrepetat.xls	60	1.15	0.17	0.02	1	1.58	0.58	1.10	1.24	0.87	2.58
SAM_RT	O4c_RunN_ID2323ecraninstructorrepetat.xls	80	1.21	0.30	0.03	1	3.29	2.29	1.14	1.61	4.58	31.68

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

Repetat_CTRL_Solo
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222ecransolo repetat.xls	80	2.88	1.72	0.19	1	6.00	5.00	2.00	2.00	0.69	2.17
SAM_Resp	O4c_Run1_ID14ecransolo repetat.xls	80	3.55	0.86	0.10	2	6.00	4.00	4.00	4.00	-0.46	3.06
SAM_Resp	O4c_Run1_ID18ecransolo repetat.xls	80	4.08	3.11	0.35	1	9.00	8.00	3.50	1.00	0.10	1.15
SAM_Resp	O4c_Run1_ID19ecransolo repetat.xls	80	1.19	0.87	0.10	1	8.00	7.00	1.00	1.00	6.41	48.30
SAM_Resp	O4c_Run1_ID32ecransolo repetat.xls	60	2.82	1.28	0.17	1	7.00	6.00	3.00	3.00	0.54	3.73
SAM_Resp	O4c_Run1_ID35ecransolo repetat.xls	60	1.40	0.74	0.10	1	3.00	2.00	1.00	1.00	1.48	3.48
SAM_Resp	O4c_Run1_ID38ecransolo repetat.xls	60	1.08	0.53	0.07	1	5.00	4.00	1.00	1.00	6.98	51.50
SAM_Resp	O4c_Run1ID21ecransolo repetat.xls	80	3.50	1.27	0.14	1	5.00	4.00	4.00	4.00	-0.74	2.51
SAM_Resp	O4c_Run2_ID11ecransolo repetat.xls	80	2.17	1.73	0.19	1	8.00	7.00	1.00	1.00	1.61	4.83
SAM_Resp	O4c_Run2_ID25ecransolo repetat.xls	60	1.07	0.52	0.07	1	5.00	4.00	1.00	1.00	7.55	58.02
SAM_Resp	O4c_Run2_ID31ecransolo repetat.xls	60	2.00	1.69	0.22	1	5.00	4.00	1.00	1.00	1.11	2.30
SAM_Resp	O4c_Run2_ID34ecransolo repetat.xls	60	2.52	1.62	0.21	1	5.00	4.00	2.00	1.00	0.36	1.47
SAM_Resp	O4c_Run2_ID42ecransolo repetat.xls	60	2.25	2.10	0.27	1	6.00	5.00	1.00	1.00	1.10	2.26
SAM_Resp	O4c_Run2_ID56ecransolo repetat.xls	60	2.58	1.53	0.20	1	5.00	4.00	2.00	2.00	0.78	1.99
SAM_Resp	O4c_Run2_ID65ecransolo repetat.xls	60	2.50	1.14	0.15	1	4.00	3.00	2.00	2.00	0.03	1.61
SAM_Resp	O4c_Run24_ID24ecransolo repetat.xls	80	3.55	1.04	0.12	2	6.00	4.00	4.00	4.00	-0.23	2.40
SAM_Resp	O4c_Run30_ID30ecransolo repetatX.xls	80	3.77	0.99	0.11	2	5.00	3.00	4.00	4.00	-0.55	2.32
SAM_Resp	O4c_RunN(modifica N!)_ID07ecransolo repetat.xls	80	4.34	1.08	0.12	1	5.00	4.00	5.00	5.00	-1.80	5.57
SAM_Resp	O4c_RunN(modifica N!)_ID2020ecransolo repetat.xls	60	2.78	1.35	0.17	1	5.00	4.00	3.00	2.00	0.27	1.88
SAM_Resp	O4c_RunN_ID2323ecransolo repetat.xls	80	2.21	2.10	0.23	1	8.00	7.00	1.00	1.00	1.71	4.41
SAM_RT	O4c_ID2222ecransolo repetat.xls	80	1.32	0.55	0.06	1	4.68	3.68	1.22	1.32	4.32	23.39
SAM_RT	O4c_Run1_ID14ecransolo repetat.xls	80	1.61	0.71	0.08	1	4.97	3.97	1.40	1.28	2.18	9.09
SAM_RT	O4c_Run1_ID18ecransolo repetat.xls	80	1.47	0.68	0.08	1	5.94	4.94	1.26	1.57	4.07	25.01
SAM_RT	O4c_Run1_ID19ecransolo repetat.xls	80	1.48	0.98	0.11	1	5.93	4.93	1.06	1.63	2.69	10.13
SAM_RT	O4c_Run1_ID32ecransolo repetat.xls	60	1.28	0.29	0.04	1	2.16	1.16	1.20	1.00	1.21	3.76
SAM_RT	O4c_Run1_ID35ecransolo repetat.xls	60	1.16	0.25	0.03	1	2.77	1.77	1.09	1.35	4.57	29.10
SAM_RT	O4c_Run1_ID38ecransolo repetat.xls	60	1.86	0.95	0.12	1	4.85	3.85	1.47	1.87	1.10	3.29
SAM_RT	O4c_Run1ID21ecransolo repetat.xls	80	1.51	0.62	0.07	1	3.26	2.25	1.28	2.57	1.38	3.85
SAM_RT	O4c_Run2_ID11ecransolo repetat.xls	80	1.30	0.53	0.06	1	4.46	3.46	1.13	1.00	3.56	18.88
SAM_RT	O4c_Run2_ID25ecransolo repetat.xls	60	1.54	0.66	0.09	1	4.06	3.06	1.33	1.35	2.02	6.64
SAM_RT	O4c_Run2_ID31ecransolo repetat.xls	60	2.02	1.43	0.18	1	6.48	5.48	1.36	1.40	1.78	5.15
SAM_RT	O4c_Run2_ID34ecransolo repetat.xls	60	1.62	0.95	0.12	1	4.68	3.68	1.19	3.74	1.89	5.56
SAM_RT	O4c_Run2_ID42ecransolo repetat.xls	60	1.26	0.25	0.03	1	2.32	1.32	1.21	1.24	1.70	7.10
SAM_RT	O4c_Run2_ID56ecransolo repetat.xls	60	1.27	0.48	0.06	1	4.06	3.06	1.10	1.14	3.73	20.08
SAM_RT	O4c_Run2_ID65ecransolo repetat.xls	60	1.61	0.95	0.12	1	5.01	4.01	1.18	2.97	1.87	5.75
SAM_RT	O4c_Run24_ID24ecransolo repetat.xls	80	1.23	0.29	0.03	1	2.89	1.89	1.18	1.69	2.88	15.56
SAM_RT	O4c_Run30_ID30ecransolo repetatX.xls	80	1.84	1.09	0.12	1	6.92	5.92	1.50	3.61	2.82	12.29
SAM_RT	O4c_RunN(modifica N!)_ID07ecransolo repetat.xls	80	1.18	0.25	0.03	1	2.09	1.09	1.08	1.11	1.85	5.87
SAM_RT	O4c_RunN(modifica N!)_ID2020ecransolo repetat.xls	60	1.14	0.20	0.03	1	1.76	0.76	1.01	1.02	1.30	3.69
SAM_RT	O4c_RunN_ID2323ecransolo repetat.xls	80	1.62	0.85	0.09	1	4.97	3.97	1.35	1.12	2.27	8.23

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

Repetat_OGL_Instr
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222oglindainstructorrepetat.xls	80	2.56	1.63	0.18	1.00	6.00	5.00	2.00	2.00	0.99	2.46
SAM_Resp	O4c_Run1_ID19oglindainstructorrepetat.xls	80	1.00	0.00	0.00	1.00	1.00	0.00	1.00	1.00	NaN	NaN
SAM_Resp	O4c_Run1_ID21oglindainstructorrepetat.xls	60	3.37	1.12	0.14	1.00	5.00	4.00	4.00	4.00	-0.32	2.24
SAM_Resp	O4c_Run1_ID23oglindainstructorrepetat.xls	80	4.26	2.75	0.31	1.00	9.00	8.00	5.00	1.00	0.23	1.69
SAM_Resp	O4c_Run1_ID30oglindainstructorrepetat.xls	80	3.88	0.64	0.07	2.00	5.00	3.00	4.00	4.00	-0.17	3.07
SAM_Resp	O4c_Run1_ID32oglindainstructorrepetat.xls	60	3.07	1.15	0.15	1.00	4.00	3.00	3.50	4.00	-0.88	2.27
SAM_Resp	O4c_Run1_ID38oglindainstructorrepetat.xls	60	1.08	0.28	0.04	1.00	2.00	1.00	1.00	1.00	3.02	10.09
SAM_Resp	O4c_Run14_IDz214oglindainstructorrepetat.xls	80	3.08	1.05	0.12	2.00	5.00	3.00	3.00	2.00	0.11	1.36
SAM_Resp	O4c_Run2_ID20oglindainstructorrepetat.xls	60	1.87	1.32	0.17	1.00	5.00	4.00	1.00	1.00	1.18	2.84
SAM_Resp	O4c_Run2_ID25oglindainstructorrepetat.xls	60	1.00	0.00	0.00	1.00	1.00	0.00	1.00	1.00	NaN	NaN
SAM_Resp	O4c_Run2_ID31oglindainstructorrepetat.xls	60	3.13	2.01	0.26	1.00	5.00	4.00	5.00	5.00	-0.13	1.02
SAM_Resp	O4c_Run2_ID34oglindainstructorrepetat.xls	60	2.33	1.36	0.18	1.00	5.00	4.00	2.00	1.00	0.31	1.53
SAM_Resp	O4c_Run2_ID42oglindainstructorrepetat.xls	60	1.72	1.26	0.16	1.00	4.00	3.00	1.00	1.00	1.21	2.51
SAM_Resp	O4c_Run2_ID56oglindainstructorrepetat.xls	60	2.72	1.34	0.17	1.00	5.00	4.00	2.00	2.00	1.08	2.32
SAM_Resp	O4c_Run2_ID65oglindainstructorrepetat.xls	60	2.50	1.13	0.15	1.00	4.00	3.00	2.50	3.00	0.00	1.64
SAM_Resp	O4c_Run35_ID35oglindainstructorrepetat.xls	60	1.72	0.88	0.11	1.00	3.00	2.00	1.00	1.00	0.58	1.55
SAM_Resp	O4c_RunN(modifica N!)_ID0707oglindainstructorrepetat.xls	80	4.01	1.19	0.13	1.00	5.00	4.00	4.00	5.00	-0.94	2.79
SAM_Resp	O4c_RunN(modifica N!)_ID11oglindainstructorrepetat.xls	80	1.85	1.48	0.17	1.00	7.00	6.00	1.00	1.00	1.95	6.09
SAM_Resp	O4c_RunN_ID1818oglindainstructorrepetat.xls	80	3.27	2.33	0.26	1.00	7.00	6.00	3.50	1.00	0.22	1.34
SAM_Resp	O4c_RunN_ID2424oglindainstructorrepetat.xls	80	3.56	1.27	0.14	1.00	5.00	4.00	4.00	4.00	-0.88	2.57
SAM_RT	O4c_ID2222oglindainstructorrepetat.xls	80	1.45	0.52	0.06	1.00	3.94	2.94	1.29	2.33	2.12	8.81
SAM_RT	O4c_Run1_ID19oglindainstructorrepetat.xls	80	1.32	0.52	0.06	1.00	3.91	2.91	1.09	3.91	2.79	11.60
SAM_RT	O4c_Run1_ID21oglindainstructorrepetat.xls	60	1.58	0.65	0.08	1.00	3.95	2.95	1.41	1.61	1.96	6.90
SAM_RT	O4c_Run1_ID23oglindainstructorrepetat.xls	80	1.60	0.58	0.06	1.00	4.29	3.29	1.44	1.00	1.54	7.35
SAM_RT	O4c_Run1_ID30oglindainstructorrepetat.xls	80	1.25	0.46	0.05	1.00	3.10	2.10	1.07	2.41	2.65	9.55
SAM_RT	O4c_Run1_ID32oglindainstructorrepetat.xls	60	1.37	0.55	0.07	1.00	3.88	2.88	1.19	1.03	2.85	12.30
SAM_RT	O4c_Run1_ID38oglindainstructorrepetat.xls	60	1.38	0.52	0.07	1.00	3.19	2.19	1.14	1.29	1.63	4.90
SAM_RT	O4c_Run14_IDz214oglindainstructorrepetat.xls	80	1.33	0.34	0.04	1.00	2.55	1.55	1.24	1.29	1.80	6.31
SAM_RT	O4c_Run2_ID20oglindainstructorrepetat.xls	60	1.10	0.15	0.02	1.00	1.83	0.83	1.01	1.27	2.42	11.27
SAM_RT	O4c_Run2_ID25oglindainstructorrepetat.xls	60	1.44	0.49	0.06	1.00	3.57	2.56	1.32	1.31	2.45	9.91
SAM_RT	O4c_Run2_ID31oglindainstructorrepetat.xls	60	1.30	0.44	0.06	1.00	3.46	2.46	1.15	1.00	2.92	13.41
SAM_RT	O4c_Run2_ID34oglindainstructorrepetat.xls	60	1.46	0.76	0.10	1.00	5.86	4.86	1.24	1.25	3.76	20.34
SAM_RT	O4c_Run2_ID42oglindainstructorrepetat.xls	60	1.22	0.32	0.04	1.00	2.54	1.54	1.08	1.48	2.19	7.84
SAM_RT	O4c_Run2_ID56oglindainstructorrepetat.xls	60	1.27	0.32	0.04	1.00	2.72	1.72	1.21	1.02	2.58	10.97
SAM_RT	O4c_Run2_ID65oglindainstructorrepetat.xls	60	1.18	0.27	0.04	1.00	2.15	1.15	1.04	2.15	1.88	5.89
SAM_RT	O4c_Run35_ID35oglindainstructorrepetat.xls	60	1.49	0.82	0.11	1.00	5.30	4.30	1.18	1.32	2.75	11.10
SAM_RT	O4c_RunN(modifica N!)_ID0707oglindainstructorrepetat.xls	80	1.31	0.31	0.03	1.00	2.56	1.56	1.24	1.01	1.45	5.48
SAM_RT	O4c_RunN(modifica N!)_ID11oglindainstructorrepetat.xls	80	1.41	0.90	0.10	1.00	6.67	5.66	1.12	1.13	3.87	19.83
SAM_RT	O4c_RunN_ID1818oglindainstructorrepetat.xls	80	3.48	1.97	0.22	1.12	10.24	9.12	2.72	5.94	1.35	4.39
SAM_RT	O4c_RunN_ID2424oglindainstructorrepetat.xls	80	1.27	0.27	0.03	1.00	2.27	1.27	1.18	1.01	1.34	4.68

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

Repetat_OGL_Solo
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222oglindasolo repetat.xls	80	2.61	1.61	0.18	1.00	6.00	5.00	2.00	2.00	0.83	2.14
SAM_Resp	O4c_Run1_ID20oglindasolo repetat.xls	60	1.58	1.06	0.14	1.00	5.00	4.00	1.00	1.00	1.75	4.79
SAM_Resp	O4c_Run1_ID25oglindasolo repetat.xls	60	1.07	0.52	0.07	1.00	5.00	4.00	1.00	1.00	7.55	58.02
SAM_Resp	O4c_Run1_ID31oglindasolo repetat.xls	60	2.65	1.93	0.25	1.00	5.00	4.00	1.00	1.00	0.36	1.18
SAM_Resp	O4c_Run1_ID34oglindasolo repetat.xls	60	2.57	1.42	0.18	1.00	5.00	4.00	2.00	1.00	0.22	1.58
SAM_Resp	O4c_Run1_ID42oglindasolo repetat.xls	60	1.82	1.51	0.20	1.00	6.00	5.00	1.00	1.00	1.47	3.49
SAM_Resp	O4c_Run1_ID56oglindasolo repetat.xls	60	2.73	1.30	0.17	1.00	5.00	4.00	2.00	2.00	1.06	2.41
SAM_Resp	O4c_Run1_ID65oglindasolo repetat.xls	60	2.50	1.13	0.15	1.00	4.00	3.00	2.50	3.00	0.00	1.64
SAM_Resp	O4c_Run14z2_ID14oglindasolo repetatX.xls	80	2.59	1.22	0.14	1.00	5.00	4.00	2.50	4.00	0.07	1.68
SAM_Resp	O4c_Run2_ID19oglindasolo repetat.xls	80	1.00	0.00	0.00	1.00	1.00	0.00	1.00	1.00	NaN	NaN
SAM_Resp	O4c_Run2_ID21oglindasolo repetat.xls	60	3.17	1.21	0.16	1.00	5.00	4.00	3.00	4.00	-0.32	2.12
SAM_Resp	O4c_Run2_ID23oglindasolo repetat.xls	80	2.81	2.39	0.27	1.00	8.00	7.00	2.00	1.00	0.90	2.05
SAM_Resp	O4c_Run2_ID30oglindasolo repetat.xls	80	4.54	0.84	0.09	2.00	5.00	3.00	5.00	5.00	-1.53	3.93
SAM_Resp	O4c_Run2_ID32oglindasolo repetat.xls	60	3.13	1.27	0.16	1.00	5.00	4.00	4.00	4.00	-0.91	2.15
SAM_Resp	O4c_Run2_ID38oglindasolo repetat.xls	60	1.22	0.42	0.05	1.00	2.00	1.00	1.00	1.00	1.38	2.89
SAM_Resp	O4c_Run35_ID35oglindasolo repetat.xls	60	1.58	0.98	0.13	1.00	5.00	4.00	1.00	1.00	1.46	4.19
SAM_Resp	O4c_RunN(modifica N!)_ID07067oglindasolo repetat.xls	80	3.92	1.02	0.11	1.00	5.00	4.00	4.00	5.00	-0.72	3.05
SAM_Resp	O4c_RunN(modifica N!)_ID11oglindasolo repetat.xls	80	2.05	1.59	0.18	1.00	8.00	7.00	1.00	1.00	1.59	4.96
SAM_Resp	O4c_RunN_ID18oglindasolo repetat.xls	80	3.44	2.67	0.30	1.00	8.00	7.00	1.50	1.00	0.35	1.37
SAM_Resp	O4c_RunN_ID2424oglindasolo repetat.xls	80	3.49	1.31	0.15	1.00	5.00	4.00	4.00	4.00	-0.72	2.29
SAM_RT	O4c_ID2222oglindasolo repetat.xls	80	1.81	0.84	0.09	1.00	5.94	4.94	1.61	1.71	2.18	9.71
SAM_RT	O4c_Run1_ID20oglindasolo repetat.xls	60	1.36	0.37	0.05	1.00	2.92	1.92	1.29	1.08	1.84	7.39
SAM_RT	O4c_Run1_ID25oglindasolo repetat.xls	60	1.71	0.74	0.10	1.01	3.92	2.92	1.41	2.62	1.31	4.09
SAM_RT	O4c_Run1_ID31oglindasolo repetat.xls	60	1.54	0.99	0.13	1.00	5.59	4.59	1.17	2.79	2.77	10.44
SAM_RT	O4c_Run1_ID34oglindasolo repetat.xls	60	1.76	1.09	0.14	1.00	5.47	4.47	1.31	1.04	1.82	5.38
SAM_RT	O4c_Run1_ID42oglindasolo repetat.xls	60	1.23	0.36	0.05	1.00	2.75	1.75	1.11	1.34	2.82	11.41
SAM_RT	O4c_Run1_ID56oglindasolo repetat.xls	60	1.35	0.38	0.05	1.00	2.73	1.73	1.21	2.73	1.63	5.85
SAM_RT	O4c_Run1_ID65oglindasolo repetat.xls	60	1.29	0.39	0.05	1.00	2.71	1.71	1.14	1.00	1.67	5.44
SAM_RT	O4c_Run14z2_ID14oglindasolo repetatX.xls	80	1.35	0.41	0.05	1.00	3.24	2.24	1.28	3.24	2.22	8.95
SAM_RT	O4c_Run2_ID19oglindasolo repetat.xls	80	1.15	0.17	0.02	1.00	1.66	0.66	1.11	1.01	1.22	3.96
SAM_RT	O4c_Run2_ID21oglindasolo repetat.xls	60	1.72	0.67	0.09	1.00	3.58	2.58	1.50	2.27	1.03	3.23
SAM_RT	O4c_Run2_ID23oglindasolo repetat.xls	80	1.24	0.32	0.04	1.00	2.68	1.68	1.15	1.75	2.36	9.19
SAM_RT	O4c_Run2_ID30oglindasolo repetat.xls	80	1.41	0.60	0.07	1.00	4.89	3.89	1.20	1.41	3.17	16.22
SAM_RT	O4c_Run2_ID32oglindasolo repetat.xls	60	1.34	0.29	0.04	1.00	2.13	1.13	1.24	2.12	0.84	3.01
SAM_RT	O4c_Run2_ID38oglindasolo repetat.xls	60	1.20	0.27	0.03	1.00	2.15	1.15	1.10	1.00	1.75	6.07
SAM_RT	O4c_Run35_ID35oglindasolo repetat.xls	60	1.18	0.25	0.03	1.00	2.14	1.14	1.07	2.14	1.85	6.31
SAM_RT	O4c_RunN(modifica N!)_ID07067oglindasolo repetat.xls	80	1.31	0.36	0.04	1.00	2.92	1.92	1.22	1.39	1.89	7.35
SAM_RT	O4c_RunN(modifica N!)_ID11oglindasolo repetat.xls	80	2.23	2.63	0.29	1.00	20.68	19.68	1.30	2.24	4.86	32.07
SAM_RT	O4c_RunN_ID18oglindasolo repetat.xls	80	1.48	0.47	0.05	1.00	3.12	2.12	1.37	2.44	1.34	4.42
SAM_RT	O4c_RunN_ID2424oglindasolo repetat.xls	80	1.53	0.45	0.05	1.00	3.49	2.49	1.44	1.00	1.65	7.12


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

Repetat_CTRL_Instr
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1400	6.84	1.64	0.04	1	9.00	8.00	7.00	8.00	-0.61	3.27
SAM_RT	0	1400	1.40	0.55	0.01	1	5.11	4.11	1.22	1.45	2.56	10.75

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

Repetat_CTRL_Solo
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1400	6.73	1.61	0.04	1	9.00	8.00	7.00	5.00	-0.25	2.43
SAM_RT	0	1400	1.49	0.78	0.02	1	8.38	7.38	1.21	1.26	3.22	17.15

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

Repetat_OGL_Instr
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1380	6.76	1.62	0.04	1	9.00	8.00	7.00	7.00	-0.43	2.51
SAM_RT	0	1380	1.53	0.95	0.03	1	10.11	9.11	1.22	1.01	4.02	24.44

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

Repetat_OGL_Solo
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1380	6.80	1.63	0.04	1	9.00	8.00	7.00	8.0	-0.42	2.50
SAM_RT	0	1380	1.48	0.80	0.02	1	9.33	8.33	1.21	1.4	3.91	25.21


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

Repetat_CTRL_Instr
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222ecraninstructorrepetat.xls	80	6.69	1.14	0.13	5.00	9.00	4.00	7.00	8.00	-0.03	1.87
SAM_Resp	O4c_Run1_ID11ecraninstructorrepetat.xls	80	6.50	1.68	0.19	3.00	9.00	6.00	7.00	6.00	-0.30	2.23
SAM_Resp	O4c_Run1_ID25ecraninstructorrepetat.xls	60	7.98	0.13	0.02	7.00	8.00	1.00	8.00	8.00	-7.55	58.02
SAM_Resp	O4c_Run1_ID31ecraninstructorrepetat.xls	60	8.35	1.22	0.16	6.00	9.00	3.00	9.00	9.00	-1.37	2.95
SAM_Resp	O4c_Run1_ID34ecraninstructorrepetat.xls	60	7.58	1.34	0.17	5.00	9.00	4.00	8.00	9.00	-0.64	2.39
SAM_Resp	O4c_Run1_ID42ecraninstructorrepetat.xls	60	6.80	1.80	0.23	1.00	9.00	8.00	6.50	5.00	-0.38	2.80
SAM_Resp	O4c_Run1_ID56ecraninstructorrepetatX.xls	60	6.20	0.44	0.06	5.00	7.00	2.00	6.00	6.00	0.89	3.33
SAM_Resp	O4c_Run1_ID65ecraninstructorrepetat.xls	60	7.02	1.52	0.20	5.00	9.00	4.00	7.00	5.00	-0.03	1.59
SAM_Resp	O4c_Run1ID21ecraninstructorrepetat.xls	80	5.71	0.86	0.10	5.00	8.00	3.00	5.00	5.00	0.83	2.51
SAM_Resp	O4c_Run2_ID14ecraninstructorrepetat.xls	80	6.88	1.31	0.15	5.00	9.00	4.00	7.00	6.00	0.47	2.05
SAM_Resp	O4c_Run2_ID18ecraninstructorrepetat.xls	80	6.78	1.52	0.17	4.00	9.00	5.00	7.00	5.00	-0.01	1.64
SAM_Resp	O4c_Run2_ID19ecraninstructorrepetat.xls	80	8.40	0.95	0.11	6.00	9.00	3.00	9.00	9.00	-1.14	2.72
SAM_Resp	O4c_Run2_ID32ecraninstructorrepetat.xls	60	7.38	0.69	0.09	5.00	9.00	4.00	7.00	7.00	-0.66	4.11
SAM_Resp	O4c_Run2_ID35ecraninstructorrepetat.xls	60	8.38	0.85	0.11	6.00	9.00	3.00	9.00	9.00	-0.99	2.63
SAM_Resp	O4c_Run2_ID38ecraninstructorrepetat.xls	60	5.77	1.89	0.24	3.00	8.00	5.00	5.50	8.00	-0.25	1.61
SAM_Resp	O4c_Run24_ID24ecraninstructorrepetat.xls	80	7.45	0.95	0.11	4.00	8.00	4.00	8.00	8.00	-1.97	6.50
SAM_Resp	O4c_Run30_ID30ecraninstructorrepetat.xls	80	5.85	1.23	0.14	5.00	8.00	3.00	5.00	5.00	1.02	2.28
SAM_Resp	O4c_RunN(modifica N!)_ID07ecraninstructorrepetatX.xls	80	5.36	2.29	0.26	1.00	9.00	8.00	5.00	5.00	-0.18	2.35
SAM_Resp	O4c_RunN(modifica N!)_ID2020ecraninstructorrepetat.xls	60	5.70	2.09	0.27	1.00	9.00	8.00	6.00	7.00	-0.92	2.53
SAM_Resp	O4c_RunN_ID2323ecraninstructorrepetat.xls	80	6.74	1.36	0.15	4.00	9.00	5.00	6.00	6.00	0.79	2.32
SAM_RT	O4c_ID2222ecraninstructorrepetat.xls	80	1.47	0.50	0.06	1.01	4.30	3.29	1.39	1.45	3.47	18.40
SAM_RT	O4c_Run1_ID11ecraninstructorrepetat.xls	80	1.46	0.77	0.09	1.00	4.12	3.12	1.19	2.75	2.38	7.84
SAM_RT	O4c_Run1_ID25ecraninstructorrepetat.xls	60	1.51	0.70	0.09	1.00	4.38	3.38	1.28	1.25	2.27	8.35
SAM_RT	O4c_Run1_ID31ecraninstructorrepetat.xls	60	1.57	0.89	0.12	1.00	5.11	4.11	1.12	3.02	1.93	6.44
SAM_RT	O4c_Run1_ID34ecraninstructorrepetat.xls	60	1.41	0.58	0.07	1.00	3.81	2.81	1.25	1.35	2.51	9.67
SAM_RT	O4c_Run1_ID42ecraninstructorrepetat.xls	60	1.41	0.54	0.07	1.00	3.28	2.28	1.23	1.49	2.07	6.85
SAM_RT	O4c_Run1_ID56ecraninstructorrepetatX.xls	60	1.45	0.50	0.06	1.00	3.18	2.18	1.28	1.47	1.62	5.12
SAM_RT	O4c_Run1_ID65ecraninstructorrepetat.xls	60	1.94	0.94	0.12	1.00	3.96	2.96	1.71	1.81	0.39	1.60
SAM_RT	O4c_Run1ID21ecraninstructorrepetat.xls	80	1.48	0.55	0.06	1.00	3.61	2.61	1.30	1.58	1.68	5.97
SAM_RT	O4c_Run2_ID14ecraninstructorrepetat.xls	80	1.29	0.32	0.04	1.00	2.72	1.72	1.19	2.09	2.16	8.54
SAM_RT	O4c_Run2_ID18ecraninstructorrepetat.xls	80	1.50	0.45	0.05	1.00	3.20	2.20	1.40	1.15	1.71	6.36
SAM_RT	O4c_Run2_ID19ecraninstructorrepetat.xls	80	1.33	0.54	0.06	1.00	4.22	3.22	1.19	3.59	3.36	15.93
SAM_RT	O4c_Run2_ID32ecraninstructorrepetat.xls	60	1.31	0.37	0.05	1.00	3.03	2.03	1.18	1.01	2.26	9.73
SAM_RT	O4c_Run2_ID35ecraninstructorrepetat.xls	60	1.13	0.16	0.02	1.00	1.62	0.62	1.05	1.20	1.34	4.22
SAM_RT	O4c_Run2_ID38ecraninstructorrepetat.xls	60	1.57	0.65	0.08	1.00	4.06	3.06	1.33	1.26	1.83	6.67
SAM_RT	O4c_Run24_ID24ecraninstructorrepetat.xls	80	1.36	0.34	0.04	1.00	2.34	1.33	1.26	1.65	1.07	3.31
SAM_RT	O4c_Run30_ID30ecraninstructorrepetat.xls	80	1.44	0.47	0.05	1.00	3.22	2.22	1.30	1.76	1.76	6.39
SAM_RT	O4c_RunN(modifica N!)_ID07ecraninstructorrepetatX.xls	80	1.20	0.26	0.03	1.00	2.45	1.45	1.09	1.00	1.92	8.07
SAM_RT	O4c_RunN(modifica N!)_ID2020ecraninstructorrepetat.xls	60	1.12	0.15	0.02	1.00	1.53	0.53	1.05	1.23	1.30	3.96
SAM_RT	O4c_RunN_ID2323ecraninstructorrepetat.xls	80	1.19	0.17	0.02	1.00	1.90	0.90	1.14	1.36	1.44	6.02

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

Repetat_CTRL_Solo
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222ecransolo repetat.xls	80	6.81	1.15	0.13	5	9.00	4.00	7.00	7.00	-0.08	2.07
SAM_Resp	O4c_Run1_ID14ecransolo repetat.xls	80	6.44	1.36	0.15	5	9.00	4.00	6.00	6.00	0.73	2.32
SAM_Resp	O4c_Run1_ID18ecransolo repetat.xls	80	6.55	1.83	0.21	2	9.00	7.00	6.50	5.00	-0.26	2.22
SAM_Resp	O4c_Run1_ID19ecransolo repetat.xls	80	8.20	0.85	0.09	6	9.00	3.00	8.00	9.00	-0.52	1.93
SAM_Resp	O4c_Run1_ID32ecransolo repetat.xls	60	7.38	0.92	0.12	5	9.00	4.00	7.00	7.00	0.22	2.82
SAM_Resp	O4c_Run1_ID35ecransolo repetat.xls	60	8.47	0.77	0.10	6	9.00	3.00	9.00	9.00	-1.24	3.59
SAM_Resp	O4c_Run1_ID38ecransolo repetat.xls	60	5.85	1.73	0.22	3	9.00	6.00	5.50	8.00	0.01	1.73
SAM_Resp	O4c_Run1ID21ecransolo repetat.xls	80	5.58	0.76	0.08	4	7.00	3.00	5.00	5.00	0.36	2.50
SAM_Resp	O4c_Run2_ID11ecransolo repetat.xls	80	6.79	1.76	0.20	2	9.00	7.00	7.00	6.00	-0.63	3.11
SAM_Resp	O4c_Run2_ID25ecransolo repetat.xls	60	8.00	0.00	0.00	8	8.00	0.00	8.00	8.00	NaN	NaN
SAM_Resp	O4c_Run2_ID31ecransolo repetat.xls	60	7.88	1.88	0.24	4	9.00	5.00	9.00	9.00	-1.10	2.28
SAM_Resp	O4c_Run2_ID34ecransolo repetat.xls	60	6.77	1.24	0.16	5	9.00	4.00	6.00	6.00	0.50	2.16
SAM_Resp	O4c_Run2_ID42ecransolo repetat.xls	60	6.83	2.08	0.27	1	9.00	8.00	6.00	9.00	-0.25	1.93
SAM_Resp	O4c_Run2_ID56ecransolo repetat.xls	60	6.22	0.42	0.05	6	7.00	1.00	6.00	6.00	1.38	2.89
SAM_Resp	O4c_Run2_ID65ecransolo repetat.xls	60	7.20	1.50	0.19	5	9.00	4.00	8.00	8.00	-0.37	1.72
SAM_Resp	O4c_Run24_ID24ecransolo repetat.xls	80	7.10	1.06	0.12	4	9.00	5.00	7.00	8.00	-0.65	2.66
SAM_Resp	O4c_Run30_ID30ecransolo repetatX.xls	80	5.84	1.27	0.14	5	8.00	3.00	5.00	5.00	1.02	2.20
SAM_Resp	O4c_RunN(modifica N!)_ID07ecransolo repetat.xls	80	5.29	1.32	0.15	2	9.00	7.00	5.00	5.00	0.38	3.69
SAM_Resp	O4c_RunN(modifica N!)_ID2020ecransolo repetat.xls	60	4.73	1.36	0.18	2	7.00	5.00	5.00	6.00	-0.68	2.25
SAM_Resp	O4c_RunN_ID2323ecransolo repetat.xls	80	7.11	1.17	0.13	5	9.00	4.00	7.00	7.00	0.64	2.09
SAM_RT	O4c_ID2222ecransolo repetat.xls	80	1.46	0.92	0.10	1	6.46	5.46	1.27	1.26	4.26	21.22
SAM_RT	O4c_Run1_ID14ecransolo repetat.xls	80	1.81	1.05	0.12	1	8.38	7.38	1.47	1.00	3.42	20.62
SAM_RT	O4c_Run1_ID18ecransolo repetat.xls	80	1.80	0.81	0.09	1	5.08	4.08	1.51	1.13	1.61	5.83
SAM_RT	O4c_Run1_ID19ecransolo repetat.xls	80	1.65	0.96	0.11	1	6.06	5.05	1.25	2.94	2.25	8.56
SAM_RT	O4c_Run1_ID32ecransolo repetat.xls	60	1.31	0.43	0.06	1	2.82	1.82	1.15	1.40	1.99	6.38
SAM_RT	O4c_Run1_ID35ecransolo repetat.xls	60	1.18	0.22	0.03	1	2.20	1.20	1.10	1.22	2.34	9.76
SAM_RT	O4c_Run1_ID38ecransolo repetat.xls	60	1.75	0.88	0.11	1	4.51	3.51	1.38	1.31	1.53	4.62
SAM_RT	O4c_Run1ID21ecransolo repetat.xls	80	1.52	0.78	0.09	1	6.78	5.78	1.30	1.41	4.30	27.55
SAM_RT	O4c_Run2_ID11ecransolo repetat.xls	80	1.28	0.70	0.08	1	6.22	5.22	1.07	1.00	5.10	33.32
SAM_RT	O4c_Run2_ID25ecransolo repetat.xls	60	1.48	0.75	0.10	1	4.71	3.71	1.17	1.63	2.57	9.83
SAM_RT	O4c_Run2_ID31ecransolo repetat.xls	60	1.95	1.31	0.17	1	6.58	5.58	1.35	1.15	1.60	4.80
SAM_RT	O4c_Run2_ID34ecransolo repetat.xls	60	1.44	0.85	0.11	1	6.68	5.68	1.20	1.24	4.35	25.36
SAM_RT	O4c_Run2_ID42ecransolo repetat.xls	60	1.20	0.24	0.03	1	2.29	1.29	1.11	1.01	2.03	8.65
SAM_RT	O4c_Run2_ID56ecransolo repetat.xls	60	1.34	0.45	0.06	1	3.33	2.33	1.19	1.18	2.29	9.24
SAM_RT	O4c_Run2_ID65ecransolo repetat.xls	60	1.65	0.90	0.12	1	3.76	2.76	1.12	1.01	1.21	2.95
SAM_RT	O4c_Run24_ID24ecransolo repetat.xls	80	1.35	0.47	0.05	1	3.97	2.97	1.20	1.18	2.90	14.05
SAM_RT	O4c_Run30_ID30ecransolo repetatX.xls	80	1.56	0.70	0.08	1	4.64	3.64	1.35	2.27	2.18	8.51
SAM_RT	O4c_RunN(modifica N!)_ID07ecransolo repetat.xls	80	1.22	0.24	0.03	1	2.12	1.12	1.14	2.12	1.55	5.27
SAM_RT	O4c_RunN(modifica N!)_ID2020ecransolo repetat.xls	60	1.19	0.29	0.04	1	2.50	1.50	1.06	1.00	2.29	9.02
SAM_RT	O4c_RunN_ID2323ecransolo repetat.xls	80	1.59	0.86	0.10	1	4.11	3.11	1.25	2.90	1.82	5.14

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

Repetat_OGL_Instr
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222oglindainstructorrepetat.xls	80	6.96	1.05	0.12	5.00	8.00	3.00	7.00	8.00	-0.26	1.51
SAM_Resp	O4c_Run1_ID19oglindainstructorrepetat.xls	80	7.21	1.12	0.13	6.00	9.00	3.00	7.00	7.00	0.66	2.06
SAM_Resp	O4c_Run1_ID21oglindainstructorrepetat.xls	60	5.62	0.80	0.10	4.00	8.00	4.00	5.00	5.00	0.80	3.02
SAM_Resp	O4c_Run1_ID23oglindainstructorrepetat.xls	80	6.40	1.68	0.19	4.00	9.00	5.00	6.00	6.00	0.34	1.93
SAM_Resp	O4c_Run1_ID30oglindainstructorrepetat.xls	80	5.66	0.79	0.09	4.00	7.00	3.00	5.00	5.00	0.52	2.05
SAM_Resp	O4c_Run1_ID32oglindainstructorrepetat.xls	60	6.60	0.99	0.13	4.00	8.00	4.00	7.00	7.00	-1.22	4.54
SAM_Resp	O4c_Run1_ID38oglindainstructorrepetat.xls	60	6.32	2.23	0.29	3.00	9.00	6.00	6.00	8.00	-0.15	1.46
SAM_Resp	O4c_Run14_IDz214oglindainstructorrepetat.xls	80	6.97	0.76	0.09	5.00	8.00	3.00	7.00	7.00	-0.48	3.04
SAM_Resp	O4c_Run2_ID20oglindainstructorrepetat.xls	60	7.32	2.59	0.33	2.00	9.00	7.00	9.00	9.00	-1.10	2.35
SAM_Resp	O4c_Run2_ID25oglindainstructorrepetat.xls	60	8.05	0.29	0.04	7.00	9.00	2.00	8.00	8.00	1.64	11.41
SAM_Resp	O4c_Run2_ID31oglindainstructorrepetat.xls	60	7.82	1.77	0.23	5.00	9.00	4.00	9.00	9.00	-0.90	1.91
SAM_Resp	O4c_Run2_ID34oglindainstructorrepetat.xls	60	7.67	1.24	0.16	5.00	9.00	4.00	8.00	9.00	-0.31	1.83
SAM_Resp	O4c_Run2_ID42oglindainstructorrepetat.xls	60	6.10	2.17	0.28	4.00	9.00	5.00	5.50	4.00	0.24	1.31
SAM_Resp	O4c_Run2_ID56oglindainstructorrepetat.xls	60	5.97	0.71	0.09	5.00	7.00	2.00	6.00	6.00	0.05	2.00
SAM_Resp	O4c_Run2_ID65oglindainstructorrepetat.xls	60	7.25	1.49	0.19	5.00	9.00	4.00	7.50	7.00	-0.43	1.85
SAM_Resp	O4c_Run35_ID35oglindainstructorrepetat.xls	60	7.32	0.93	0.12	4.00	9.00	5.00	7.00	7.00	-0.67	4.83
SAM_Resp	O4c_RunN(modifica N!)_ID0707oglindainstructorrepetat.xls	80	6.05	1.68	0.19	3.00	9.00	6.00	5.50	5.00	0.21	1.67
SAM_Resp	O4c_RunN(modifica N!)_ID11oglindainstructorrepetat.xls	80	5.76	2.21	0.25	1.00	9.00	8.00	6.00	4.00	-0.03	2.04
SAM_Resp	O4c_RunN_ID1818oglindainstructorrepetat.xls	80	6.70	1.41	0.16	3.00	9.00	6.00	7.00	7.00	-0.28	2.67
SAM_Resp	O4c_RunN_ID2424oglindainstructorrepetat.xls	80	7.94	0.54	0.06	6.00	9.00	3.00	8.00	8.00	-0.56	5.10
SAM_RT	O4c_ID2222oglindainstructorrepetat.xls	80	1.55	0.70	0.08	1.00	5.38	4.38	1.33	2.08	2.86	13.65
SAM_RT	O4c_Run1_ID19oglindainstructorrepetat.xls	80	1.30	0.40	0.04	1.00	3.41	2.41	1.19	1.83	2.82	13.37
SAM_RT	O4c_Run1_ID21oglindainstructorrepetat.xls	60	1.38	0.46	0.06	1.00	3.48	2.47	1.25	1.85	2.66	10.74
SAM_RT	O4c_Run1_ID23oglindainstructorrepetat.xls	80	1.46	0.91	0.10	1.00	7.93	6.93	1.21	4.89	5.30	35.57
SAM_RT	O4c_Run1_ID30oglindainstructorrepetat.xls	80	1.28	0.43	0.05	1.00	3.60	2.60	1.10	1.42	2.88	13.44
SAM_RT	O4c_Run1_ID32oglindainstructorrepetat.xls	60	1.37	0.45	0.06	1.00	2.87	1.87	1.19	1.26	1.50	4.54
SAM_RT	O4c_Run1_ID38oglindainstructorrepetat.xls	60	1.50	0.68	0.09	1.00	3.87	2.87	1.22	1.17	1.63	4.94
SAM_RT	O4c_Run14_IDz214oglindainstructorrepetat.xls	80	1.43	0.51	0.06	1.00	3.12	2.12	1.28	1.06	1.62	5.25
SAM_RT	O4c_Run2_ID20oglindainstructorrepetat.xls	60	1.16	0.21	0.03	1.00	1.84	0.84	1.05	1.40	1.66	5.49
SAM_RT	O4c_Run2_ID25oglindainstructorrepetat.xls	60	1.77	0.97	0.12	1.00	4.37	3.36	1.32	1.33	1.36	3.55
SAM_RT	O4c_Run2_ID31oglindainstructorrepetat.xls	60	1.48	0.71	0.09	1.00	4.75	3.75	1.23	3.12	2.69	10.86
SAM_RT	O4c_Run2_ID34oglindainstructorrepetat.xls	60	1.40	0.53	0.07	1.00	3.32	2.32	1.21	1.07	1.97	6.54
SAM_RT	O4c_Run2_ID42oglindainstructorrepetat.xls	60	1.23	0.25	0.03	1.00	2.09	1.09	1.14	1.63	1.38	4.41
SAM_RT	O4c_Run2_ID56oglindainstructorrepetat.xls	60	1.43	0.54	0.07	1.00	3.71	2.70	1.24	2.84	2.02	7.62
SAM_RT	O4c_Run2_ID65oglindainstructorrepetat.xls	60	1.18	0.21	0.03	1.00	1.91	0.91	1.10	1.14	1.34	4.36
SAM_RT	O4c_Run35_ID35oglindainstructorrepetat.xls	60	1.45	0.81	0.11	1.00	5.12	4.12	1.17	1.49	3.27	14.10
SAM_RT	O4c_RunN(modifica N!)_ID0707oglindainstructorrepetat.xls	80	1.19	0.24	0.03	1.00	2.15	1.15	1.13	1.67	1.71	6.13
SAM_RT	O4c_RunN(modifica N!)_ID11oglindainstructorrepetat.xls	80	1.35	0.69	0.08	1.00	5.77	4.77	1.10	1.27	4.06	23.74
SAM_RT	O4c_RunN_ID1818oglindainstructorrepetat.xls	80	3.99	1.88	0.21	1.66	10.11	8.45	3.30	2.46	1.30	4.05
SAM_RT	O4c_RunN_ID2424oglindainstructorrepetat.xls	80	1.34	0.31	0.03	1.00	2.68	1.68	1.27	1.86	1.57	6.49

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

Repetat_OGL_Solo
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222oglindasolo repetat.xls	80	6.90	1.09	0.12	5	8.00	3.00	7.00	8.00	-0.16	1.42
SAM_Resp	O4c_Run1_ID20oglindasolo repetat.xls	60	7.17	2.23	0.29	3	9.00	6.00	8.00	8.00	-1.05	2.41
SAM_Resp	O4c_Run1_ID25oglindasolo repetat.xls	60	8.00	0.18	0.02	7	9.00	2.00	8.00	8.00	0.00	30.00
SAM_Resp	O4c_Run1_ID31oglindasolo repetat.xls	60	8.00	1.75	0.23	5	9.00	4.00	9.00	9.00	-1.15	2.33
SAM_Resp	O4c_Run1_ID34oglindasolo repetat.xls	60	8.12	0.99	0.13	5	9.00	4.00	8.00	9.00	-0.76	2.81
SAM_Resp	O4c_Run1_ID42oglindasolo repetat.xls	60	5.93	2.01	0.26	3	9.00	6.00	5.00	4.00	0.35	1.74
SAM_Resp	O4c_Run1_ID56oglindasolo repetat.xls	60	5.98	0.89	0.12	3	8.00	5.00	6.00	6.00	-0.26	3.87
SAM_Resp	O4c_Run1_ID65oglindasolo repetat.xls	60	7.27	1.49	0.19	5	9.00	4.00	8.00	8.00	-0.46	1.85
SAM_Resp	O4c_Run14z2_ID14oglindasolo repetatX.xls	80	7.17	0.57	0.06	5	8.00	3.00	7.00	7.00	-0.82	6.80
SAM_Resp	O4c_Run2_ID19oglindasolo repetat.xls	80	7.24	1.12	0.12	6	9.00	3.00	7.00	7.00	0.62	2.02
SAM_Resp	O4c_Run2_ID21oglindasolo repetat.xls	60	5.55	0.81	0.10	4	7.00	3.00	5.00	5.00	0.41	2.41
SAM_Resp	O4c_Run2_ID23oglindasolo repetat.xls	80	6.84	1.34	0.15	5	9.00	4.00	6.00	6.00	0.59	2.08
SAM_Resp	O4c_Run2_ID30oglindasolo repetat.xls	80	5.33	0.73	0.08	4	7.00	3.00	5.00	5.00	1.43	4.04
SAM_Resp	O4c_Run2_ID32oglindasolo repetat.xls	60	6.38	1.57	0.20	4	9.00	5.00	7.00	7.00	-0.68	1.91
SAM_Resp	O4c_Run2_ID38oglindasolo repetat.xls	60	6.50	2.31	0.30	3	9.00	6.00	6.50	9.00	-0.12	1.33
SAM_Resp	O4c_Run35_ID35oglindasolo repetat.xls	60	7.98	0.93	0.12	5	9.00	4.00	8.00	9.00	-0.61	3.07
SAM_Resp	O4c_RunN(modifica N!)_ID07067oglindasolo repetat.xls	80	5.79	0.91	0.10	4	9.00	5.00	6.00	5.00	1.04	4.18
SAM_Resp	O4c_RunN(modifica N!)_ID11oglindasolo repetat.xls	80	5.45	2.13	0.24	1	9.00	8.00	5.00	4.00	-0.02	2.32
SAM_Resp	O4c_RunN_ID18oglindasolo repetat.xls	80	6.75	1.51	0.17	3	9.00	6.00	7.00	8.00	-0.31	2.39
SAM_Resp	O4c_RunN_ID2424oglindasolo repetat.xls	80	8.14	0.50	0.06	7	9.00	2.00	8.00	8.00	0.29	3.63
SAM_RT	O4c_ID2222oglindasolo repetat.xls	80	1.64	0.75	0.08	1	5.15	4.15	1.40	1.40	2.50	10.77
SAM_RT	O4c_Run1_ID20oglindasolo repetat.xls	60	1.39	0.43	0.06	1	2.68	1.67	1.23	2.68	1.42	4.15
SAM_RT	O4c_Run1_ID25oglindasolo repetat.xls	60	1.61	1.08	0.14	1	8.89	7.88	1.33	2.20	5.34	35.72
SAM_RT	O4c_Run1_ID31oglindasolo repetat.xls	60	1.71	1.05	0.14	1	5.22	4.22	1.21	1.17	1.76	5.29
SAM_RT	O4c_Run1_ID34oglindasolo repetat.xls	60	1.68	0.96	0.12	1	5.28	4.28	1.31	1.04	1.99	6.53
SAM_RT	O4c_Run1_ID42oglindasolo repetat.xls	60	1.34	0.38	0.05	1	2.79	1.79	1.22	1.84	1.83	6.61
SAM_RT	O4c_Run1_ID56oglindasolo repetat.xls	60	1.46	0.59	0.08	1	4.16	3.16	1.25	1.19	2.18	8.92
SAM_RT	O4c_Run1_ID65oglindasolo repetat.xls	60	1.27	0.49	0.06	1	4.18	3.18	1.07	1.39	3.96	22.79
SAM_RT	O4c_Run14z2_ID14oglindasolo repetatX.xls	80	1.55	0.86	0.10	1	6.76	5.76	1.26	1.13	3.49	19.15
SAM_RT	O4c_Run2_ID19oglindasolo repetat.xls	80	1.13	0.14	0.02	1	1.84	0.84	1.10	1.19	1.93	9.73
SAM_RT	O4c_Run2_ID21oglindasolo repetat.xls	60	1.54	0.68	0.09	1	3.78	2.78	1.26	1.80	1.64	4.91
SAM_RT	O4c_Run2_ID23oglindasolo repetat.xls	80	1.23	0.41	0.05	1	3.81	2.81	1.11	1.03	4.07	22.81
SAM_RT	O4c_Run2_ID30oglindasolo repetat.xls	80	1.26	0.34	0.04	1	2.76	1.76	1.15	1.58	2.20	8.71
SAM_RT	O4c_Run2_ID32oglindasolo repetat.xls	60	1.23	0.28	0.04	1	2.46	1.46	1.15	1.27	2.08	8.39
SAM_RT	O4c_Run2_ID38oglindasolo repetat.xls	60	1.30	0.57	0.07	1	3.73	2.73	1.12	1.35	3.35	13.63
SAM_RT	O4c_Run35_ID35oglindasolo repetat.xls	60	1.15	0.22	0.03	1	2.17	1.17	1.07	1.51	2.75	11.73
SAM_RT	O4c_RunN(modifica N!)_ID07067oglindasolo repetat.xls	80	1.32	0.36	0.04	1	3.15	2.15	1.21	1.00	2.30	10.94
SAM_RT	O4c_RunN(modifica N!)_ID11oglindasolo repetat.xls	80	2.20	1.71	0.19	1	9.33	8.33	1.48	1.00	1.98	7.01
SAM_RT	O4c_RunN_ID18oglindasolo repetat.xls	80	2.01	1.08	0.12	1	5.61	4.61	1.61	2.25	1.57	5.19
SAM_RT	O4c_RunN_ID2424oglindasolo repetat.xls	80	1.44	0.43	0.05	1	2.73	1.73	1.30	1.17	1.16	3.38


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

Repetat_CTRL_Instr
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1400	5.31	1.07	0.03	1	9.00	8.00	5.00	5	0.85	5.47
SAM_RT	0	1400	1.40	0.58	0.02	1	6.19	5.19	1.23	1	3.34	17.56

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

Repetat_CTRL_Solo
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1400	5.26	1.15	0.03	1	9	8	5.00	5.00	0.76	5.54
SAM_RT	0	1400	1.48	0.78	0.02	1	7	6	1.21	1.56	3.10	14.60

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

Repetat_OGL_Instr
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1380	5.20	1.05	0.03	1	9.00	8.00	5.00	5	-0.02	6.31
SAM_RT	0	1380	1.56	1.07	0.03	1	12.75	11.75	1.21	1	4.86	36.77

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

Repetat_OGL_Solo
var	missing	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	0	1380	5.31	1.10	0.03	1	9.00	8.00	5.00	5.00	0.45	6.53
SAM_RT	0	1380	1.46	0.74	0.02	1	10.22	9.22	1.22	1.94	4.22	31.34


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

Repetat_CTRL_Instr
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222ecraninstructorrepetat.xls	80	5.26	0.44	0.05	5	6.00	1.00	5.00	5.00	1.08	2.17
SAM_Resp	O4c_Run1_ID11ecraninstructorrepetat.xls	80	5.15	1.20	0.13	3	8.00	5.00	5.00	5.00	0.54	2.77
SAM_Resp	O4c_Run1_ID25ecraninstructorrepetat.xls	60	4.75	0.44	0.06	4	5.00	1.00	5.00	5.00	-1.15	2.33
SAM_Resp	O4c_Run1_ID31ecraninstructorrepetat.xls	60	5.18	0.60	0.08	2	6.00	4.00	5.00	5.00	-2.01	14.85
SAM_Resp	O4c_Run1_ID34ecraninstructorrepetat.xls	60	4.97	1.18	0.15	1	7.00	6.00	5.00	6.00	-0.81	3.85
SAM_Resp	O4c_Run1_ID42ecraninstructorrepetat.xls	60	5.60	0.89	0.11	3	8.00	5.00	6.00	6.00	-0.31	4.40
SAM_Resp	O4c_Run1_ID56ecraninstructorrepetatX.xls	60	5.52	0.91	0.12	5	8.00	3.00	5.00	5.00	1.24	2.73
SAM_Resp	O4c_Run1_ID65ecraninstructorrepetat.xls	60	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_Run1ID21ecraninstructorrepetat.xls	80	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_Run2_ID14ecraninstructorrepetat.xls	80	6.19	1.14	0.13	5	8.00	3.00	6.00	6.00	0.61	1.98
SAM_Resp	O4c_Run2_ID18ecraninstructorrepetat.xls	80	5.65	1.89	0.21	2	9.00	7.00	6.00	6.00	-0.28	2.24
SAM_Resp	O4c_Run2_ID19ecraninstructorrepetat.xls	80	6.05	2.15	0.24	3	9.00	6.00	6.00	6.00	-0.07	1.96
SAM_Resp	O4c_Run2_ID32ecraninstructorrepetat.xls	60	5.15	0.99	0.13	4	8.00	4.00	5.00	5.00	0.86	3.21
SAM_Resp	O4c_Run2_ID35ecraninstructorrepetat.xls	60	5.35	0.66	0.09	5	7.00	2.00	5.00	5.00	1.64	4.26
SAM_Resp	O4c_Run2_ID38ecraninstructorrepetat.xls	60	5.52	1.14	0.15	4	7.00	3.00	5.50	7.00	-0.01	1.61
SAM_Resp	O4c_Run24_ID24ecraninstructorrepetat.xls	80	5.00	0.48	0.05	4	7.00	3.00	5.00	5.00	0.70	7.41
SAM_Resp	O4c_Run30_ID30ecraninstructorrepetat.xls	80	4.85	0.39	0.04	4	6.00	2.00	5.00	5.00	-1.31	4.61
SAM_Resp	O4c_RunN(modifica N!)_ID07ecraninstructorrepetatX.xls	80	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_RunN(modifica N!)_ID2020ecraninstructorrepetat.xls	60	5.33	0.82	0.11	4	8.00	4.00	5.00	5.00	1.21	4.33
SAM_Resp	O4c_RunN_ID2323ecraninstructorrepetat.xls	80	5.55	0.99	0.11	4	8.00	4.00	5.00	5.00	1.23	3.30
SAM_RT	O4c_ID2222ecraninstructorrepetat.xls	80	1.40	0.38	0.04	1	3.34	2.34	1.31	1.17	2.34	11.08
SAM_RT	O4c_Run1_ID11ecraninstructorrepetat.xls	80	1.39	0.60	0.07	1	5.42	4.42	1.22	1.19	4.38	27.95
SAM_RT	O4c_Run1_ID25ecraninstructorrepetat.xls	60	1.57	0.87	0.11	1	6.19	5.19	1.26	1.61	3.28	16.13
SAM_RT	O4c_Run1_ID31ecraninstructorrepetat.xls	60	1.84	1.20	0.15	1	5.03	4.03	1.21	1.01	1.42	3.61
SAM_RT	O4c_Run1_ID34ecraninstructorrepetat.xls	60	1.55	0.72	0.09	1	5.39	4.39	1.34	1.17	3.12	15.53
SAM_RT	O4c_Run1_ID42ecraninstructorrepetat.xls	60	1.49	0.62	0.08	1	3.96	2.96	1.32	1.45	2.38	9.22
SAM_RT	O4c_Run1_ID56ecraninstructorrepetatX.xls	60	1.33	0.52	0.07	1	3.68	2.68	1.15	1.03	2.53	9.91
SAM_RT	O4c_Run1_ID65ecraninstructorrepetat.xls	60	1.67	1.01	0.13	1	4.66	3.66	1.15	1.00	1.55	4.16
SAM_RT	O4c_Run1ID21ecraninstructorrepetat.xls	80	1.28	0.33	0.04	1	3.20	2.20	1.21	1.03	2.93	15.85
SAM_RT	O4c_Run2_ID14ecraninstructorrepetat.xls	80	1.33	0.40	0.05	1	2.63	1.63	1.19	2.45	1.73	5.38
SAM_RT	O4c_Run2_ID18ecraninstructorrepetat.xls	80	1.55	0.55	0.06	1	4.48	3.48	1.43	1.06	2.71	12.87
SAM_RT	O4c_Run2_ID19ecraninstructorrepetat.xls	80	1.33	0.45	0.05	1	3.68	2.68	1.15	3.68	2.47	11.35
SAM_RT	O4c_Run2_ID32ecraninstructorrepetat.xls	60	1.23	0.26	0.03	1	2.29	1.29	1.13	1.01	1.63	6.29
SAM_RT	O4c_Run2_ID35ecraninstructorrepetat.xls	60	1.19	0.24	0.03	1	2.39	1.39	1.12	1.20	2.52	12.05
SAM_RT	O4c_Run2_ID38ecraninstructorrepetat.xls	60	1.56	0.62	0.08	1	3.76	2.75	1.33	2.10	1.88	6.38
SAM_RT	O4c_Run24_ID24ecraninstructorrepetat.xls	80	1.30	0.40	0.04	1	3.91	2.91	1.20	1.71	4.10	25.37
SAM_RT	O4c_Run30_ID30ecraninstructorrepetat.xls	80	1.49	0.57	0.06	1	3.69	2.69	1.35	1.00	1.85	6.17
SAM_RT	O4c_RunN(modifica N!)_ID07ecraninstructorrepetatX.xls	80	1.19	0.20	0.02	1	1.73	0.73	1.11	1.00	0.94	2.73
SAM_RT	O4c_RunN(modifica N!)_ID2020ecraninstructorrepetat.xls	60	1.19	0.24	0.03	1	2.38	1.38	1.12	1.39	2.61	12.39
SAM_RT	O4c_RunN_ID2323ecraninstructorrepetat.xls	80	1.23	0.22	0.03	1	2.06	1.06	1.18	1.08	1.76	6.47

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

Repetat_CTRL_Solo
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222ecransolo repetat.xls	80	5.26	0.44	0.05	5	6.00	1.00	5.00	5.00	1.08	2.17
SAM_Resp	O4c_Run1_ID14ecransolo repetat.xls	80	5.90	1.12	0.13	5	8.00	3.00	5.00	5.00	0.85	2.24
SAM_Resp	O4c_Run1_ID18ecransolo repetat.xls	80	5.61	2.14	0.24	1	9.00	8.00	6.00	6.00	-0.41	2.20
SAM_Resp	O4c_Run1_ID19ecransolo repetat.xls	80	6.58	2.38	0.27	2	9.00	7.00	7.00	9.00	-0.65	1.98
SAM_Resp	O4c_Run1_ID32ecransolo repetat.xls	60	5.20	1.22	0.16	3	7.00	4.00	5.00	5.00	-0.05	2.26
SAM_Resp	O4c_Run1_ID35ecransolo repetat.xls	60	5.37	0.69	0.09	4	7.00	3.00	5.00	5.00	1.28	3.75
SAM_Resp	O4c_Run1_ID38ecransolo repetat.xls	60	5.30	0.98	0.13	3	7.00	4.00	5.00	6.00	-0.08	2.26
SAM_Resp	O4c_Run1ID21ecransolo repetat.xls	80	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_Run2_ID11ecransolo repetat.xls	80	5.10	1.57	0.18	1	8.00	7.00	5.00	5.00	-0.17	2.95
SAM_Resp	O4c_Run2_ID25ecransolo repetat.xls	60	4.75	0.44	0.06	4	5.00	1.00	5.00	5.00	-1.15	2.33
SAM_Resp	O4c_Run2_ID31ecransolo repetat.xls	60	4.92	0.33	0.04	3	5.00	2.00	5.00	5.00	-4.27	21.62
SAM_Resp	O4c_Run2_ID34ecransolo repetat.xls	60	5.12	0.74	0.10	4	6.00	2.00	5.00	5.00	-0.18	1.88
SAM_Resp	O4c_Run2_ID42ecransolo repetat.xls	60	5.53	0.95	0.12	4	9.00	5.00	5.00	5.00	1.65	5.47
SAM_Resp	O4c_Run2_ID56ecransolo repetat.xls	60	5.52	0.87	0.11	5	7.00	2.00	5.00	5.00	1.10	2.25
SAM_Resp	O4c_Run2_ID65ecransolo repetat.xls	60	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_Run24_ID24ecransolo repetat.xls	80	4.76	0.43	0.05	4	5.00	1.00	5.00	5.00	-1.23	2.52
SAM_Resp	O4c_Run30_ID30ecransolo repetatX.xls	80	4.80	0.40	0.05	4	5.00	1.00	5.00	5.00	-1.50	3.25
SAM_Resp	O4c_RunN(modifica N!)_ID07ecransolo repetat.xls	80	4.99	0.11	0.01	4	5.00	1.00	5.00	5.00	-8.78	78.01
SAM_Resp	O4c_RunN(modifica N!)_ID2020ecransolo repetat.xls	60	4.77	0.56	0.07	3	6.00	3.00	5.00	5.00	-0.59	3.76
SAM_Resp	O4c_RunN_ID2323ecransolo repetat.xls	80	5.42	0.91	0.10	3	8.00	5.00	5.00	5.00	0.58	3.73
SAM_RT	O4c_ID2222ecransolo repetat.xls	80	1.37	0.77	0.09	1	5.93	4.93	1.21	1.56	5.10	29.57
SAM_RT	O4c_Run1_ID14ecransolo repetat.xls	80	1.67	0.87	0.10	1	5.23	4.23	1.35	1.12	2.05	7.54
SAM_RT	O4c_Run1_ID18ecransolo repetat.xls	80	1.67	0.79	0.09	1	4.95	3.95	1.39	1.45	2.21	8.24
SAM_RT	O4c_Run1_ID19ecransolo repetat.xls	80	1.77	0.99	0.11	1	5.73	4.73	1.37	1.09	1.85	6.72
SAM_RT	O4c_Run1_ID32ecransolo repetat.xls	60	1.28	0.37	0.05	1	3.26	2.26	1.16	1.12	3.07	15.34
SAM_RT	O4c_Run1_ID35ecransolo repetat.xls	60	1.22	0.30	0.04	1	2.90	1.90	1.13	1.54	3.21	16.92
SAM_RT	O4c_Run1_ID38ecransolo repetat.xls	60	1.58	0.73	0.09	1	4.09	3.08	1.31	1.62	1.90	5.98
SAM_RT	O4c_Run1ID21ecransolo repetat.xls	80	1.41	0.59	0.07	1	3.82	2.82	1.14	1.00	2.17	7.65
SAM_RT	O4c_Run2_ID11ecransolo repetat.xls	80	1.24	0.54	0.06	1	4.25	3.25	1.08	1.25	4.42	23.79
SAM_RT	O4c_Run2_ID25ecransolo repetat.xls	60	1.71	0.95	0.12	1	4.82	3.82	1.36	2.29	1.80	5.47
SAM_RT	O4c_Run2_ID31ecransolo repetat.xls	60	1.94	1.36	0.18	1	6.38	5.38	1.16	1.04	1.53	4.36
SAM_RT	O4c_Run2_ID34ecransolo repetat.xls	60	1.63	0.93	0.12	1	6.51	5.51	1.28	1.13	3.08	14.64
SAM_RT	O4c_Run2_ID42ecransolo repetat.xls	60	1.28	0.32	0.04	1	3.01	2.01	1.21	1.22	2.98	16.24
SAM_RT	O4c_Run2_ID56ecransolo repetat.xls	60	1.48	0.93	0.12	1	5.48	4.48	1.14	1.82	2.76	10.14
SAM_RT	O4c_Run2_ID65ecransolo repetat.xls	60	1.45	0.72	0.09	1	3.78	2.78	1.14	1.00	1.96	5.73
SAM_RT	O4c_Run24_ID24ecransolo repetat.xls	80	1.23	0.24	0.03	1	2.33	1.33	1.18	1.26	1.84	7.50
SAM_RT	O4c_Run30_ID30ecransolo repetatX.xls	80	1.76	1.15	0.13	1	7.00	6.00	1.39	1.53	2.86	11.58
SAM_RT	O4c_RunN(modifica N!)_ID07ecransolo repetat.xls	80	1.15	0.16	0.02	1	1.79	0.79	1.11	1.00	1.34	5.09
SAM_RT	O4c_RunN(modifica N!)_ID2020ecransolo repetat.xls	60	1.11	0.14	0.02	1	1.52	0.52	1.02	1.14	1.22	3.19
SAM_RT	O4c_RunN_ID2323ecransolo repetat.xls	80	1.68	0.74	0.08	1	4.99	3.99	1.43	1.82	1.74	6.96

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

Repetat_OGL_Instr
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222oglindainstructorrepetat.xls	80	5.44	0.52	0.06	4.00	6.00	2.00	5.00	5.00	-0.01	1.61
SAM_Resp	O4c_Run1_ID19oglindainstructorrepetat.xls	80	4.64	1.68	0.19	2.00	7.00	5.00	5.00	5.00	-0.53	2.17
SAM_Resp	O4c_Run1_ID21oglindainstructorrepetat.xls	60	5.07	0.25	0.03	5.00	6.00	1.00	5.00	5.00	3.47	13.07
SAM_Resp	O4c_Run1_ID23oglindainstructorrepetat.xls	80	4.31	1.34	0.15	1.00	7.00	6.00	5.00	5.00	-0.71	2.86
SAM_Resp	O4c_Run1_ID30oglindainstructorrepetat.xls	80	4.81	0.39	0.04	4.00	5.00	1.00	5.00	5.00	-1.60	3.56
SAM_Resp	O4c_Run1_ID32oglindainstructorrepetat.xls	60	4.77	0.53	0.07	3.00	6.00	3.00	5.00	5.00	-0.85	4.14
SAM_Resp	O4c_Run1_ID38oglindainstructorrepetat.xls	60	5.42	1.05	0.14	4.00	7.00	3.00	5.50	6.00	0.00	1.81
SAM_Resp	O4c_Run14_IDz214oglindainstructorrepetat.xls	80	6.01	1.30	0.15	4.00	9.00	5.00	5.50	5.00	0.85	2.25
SAM_Resp	O4c_Run2_ID20oglindainstructorrepetat.xls	60	5.72	1.26	0.16	5.00	8.00	3.00	5.00	5.00	1.21	2.51
SAM_Resp	O4c_Run2_ID25oglindainstructorrepetat.xls	60	4.70	0.65	0.08	1.00	5.00	4.00	5.00	5.00	-3.44	18.95
SAM_Resp	O4c_Run2_ID31oglindainstructorrepetat.xls	60	4.98	0.13	0.02	4.00	5.00	1.00	5.00	5.00	-7.55	58.02
SAM_Resp	O4c_Run2_ID34oglindainstructorrepetat.xls	60	5.50	0.77	0.10	5.00	9.00	4.00	5.00	5.00	2.02	8.51
SAM_Resp	O4c_Run2_ID42oglindainstructorrepetat.xls	60	5.52	0.93	0.12	5.00	8.00	3.00	5.00	5.00	1.48	3.72
SAM_Resp	O4c_Run2_ID56oglindainstructorrepetat.xls	60	5.67	1.19	0.15	5.00	8.00	3.00	5.00	5.00	1.28	2.77
SAM_Resp	O4c_Run2_ID65oglindainstructorrepetat.xls	60	4.95	0.39	0.05	2.00	5.00	3.00	5.00	5.00	-7.55	58.02
SAM_Resp	O4c_Run35_ID35oglindainstructorrepetat.xls	60	5.23	0.50	0.06	4.00	7.00	3.00	5.00	5.00	1.22	4.61
SAM_Resp	O4c_RunN(modifica N!)_ID0707oglindainstructorrepetat.xls	80	5.05	0.22	0.02	5.00	6.00	1.00	5.00	5.00	4.13	18.05
SAM_Resp	O4c_RunN(modifica N!)_ID11oglindainstructorrepetat.xls	80	4.99	1.02	0.11	1.00	7.00	6.00	5.00	5.00	-1.54	6.74
SAM_Resp	O4c_RunN_ID1818oglindainstructorrepetat.xls	80	5.75	1.61	0.18	2.00	8.00	6.00	6.00	7.00	-0.69	2.79
SAM_Resp	O4c_RunN_ID2424oglindainstructorrepetat.xls	80	5.51	0.53	0.06	5.00	7.00	2.00	5.50	5.00	0.21	1.63
SAM_RT	O4c_ID2222oglindainstructorrepetat.xls	80	1.52	0.56	0.06	1.00	4.59	3.59	1.42	1.61	2.61	13.22
SAM_RT	O4c_Run1_ID19oglindainstructorrepetat.xls	80	1.26	0.32	0.04	1.00	2.51	1.51	1.16	1.56	1.81	6.34
SAM_RT	O4c_Run1_ID21oglindainstructorrepetat.xls	60	1.69	0.90	0.12	1.00	4.48	3.48	1.24	1.42	1.56	4.44
SAM_RT	O4c_Run1_ID23oglindainstructorrepetat.xls	80	1.43	0.47	0.05	1.00	3.46	2.46	1.26	1.27	1.66	6.26
SAM_RT	O4c_Run1_ID30oglindainstructorrepetat.xls	80	1.21	0.38	0.04	1.00	3.59	2.59	1.07	1.12	3.84	21.59
SAM_RT	O4c_Run1_ID32oglindainstructorrepetat.xls	60	1.44	0.59	0.08	1.00	4.17	3.17	1.19	4.17	2.35	9.83
SAM_RT	O4c_Run1_ID38oglindainstructorrepetat.xls	60	1.50	0.69	0.09	1.00	3.94	2.94	1.24	1.28	2.10	7.16
SAM_RT	O4c_Run14_IDz214oglindainstructorrepetat.xls	80	1.37	0.42	0.05	1.00	3.65	2.65	1.26	2.32	2.43	12.15
SAM_RT	O4c_Run2_ID20oglindainstructorrepetat.xls	60	1.14	0.15	0.02	1.00	1.61	0.61	1.08	1.09	1.19	3.68
SAM_RT	O4c_Run2_ID25oglindainstructorrepetat.xls	60	1.69	0.96	0.12	1.00	5.59	4.59	1.21	1.20	2.17	8.02
SAM_RT	O4c_Run2_ID31oglindainstructorrepetat.xls	60	1.38	0.63	0.08	1.00	4.71	3.71	1.20	1.60	3.23	15.44
SAM_RT	O4c_Run2_ID34oglindainstructorrepetat.xls	60	1.50	0.76	0.10	1.00	4.51	3.51	1.21	1.00	2.49	9.42
SAM_RT	O4c_Run2_ID42oglindainstructorrepetat.xls	60	1.33	0.33	0.04	1.00	2.59	1.59	1.23	1.62	1.49	5.49
SAM_RT	O4c_Run2_ID56oglindainstructorrepetat.xls	60	1.45	0.53	0.07	1.00	3.31	2.31	1.35	1.00	1.87	6.59
SAM_RT	O4c_Run2_ID65oglindainstructorrepetat.xls	60	1.23	0.46	0.06	1.00	3.12	2.12	1.01	1.23	2.47	8.67
SAM_RT	O4c_Run35_ID35oglindainstructorrepetat.xls	60	1.81	1.16	0.15	1.00	5.98	4.98	1.28	3.34	1.67	4.93
SAM_RT	O4c_RunN(modifica N!)_ID0707oglindainstructorrepetat.xls	80	1.19	0.18	0.02	1.00	1.78	0.78	1.14	1.78	1.05	3.51
SAM_RT	O4c_RunN(modifica N!)_ID11oglindainstructorrepetat.xls	80	1.34	0.76	0.09	1.00	6.47	5.47	1.12	1.37	4.59	28.22
SAM_RT	O4c_RunN_ID1818oglindainstructorrepetat.xls	80	4.19	2.49	0.28	1.63	12.75	11.12	3.25	2.31	1.72	5.70
SAM_RT	O4c_RunN_ID2424oglindainstructorrepetat.xls	80	1.26	0.30	0.03	1.00	2.78	1.78	1.21	1.00	2.36	10.87

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

Repetat_OGL_Solo
var	ID	n	M	SD	SE	min	max	range	median	mode	skew	kurtosis
SAM_Resp	O4c_ID2222oglindasolo repetat.xls	80	5.53	0.53	0.06	5	7.00	2.00	6.00	6.00	0.16	1.62
SAM_Resp	O4c_Run1_ID20oglindasolo repetat.xls	60	5.58	1.25	0.16	4	8.00	4.00	5.00	5.00	1.04	2.77
SAM_Resp	O4c_Run1_ID25oglindasolo repetat.xls	60	4.68	0.65	0.08	1	5.00	4.00	5.00	5.00	-3.31	18.05
SAM_Resp	O4c_Run1_ID31oglindasolo repetat.xls	60	4.93	0.78	0.10	1	9.00	8.00	5.00	5.00	0.11	24.24
SAM_Resp	O4c_Run1_ID34oglindasolo repetat.xls	60	5.30	0.56	0.07	4	7.00	3.00	5.00	5.00	0.54	3.24
SAM_Resp	O4c_Run1_ID42oglindasolo repetat.xls	60	5.23	0.70	0.09	3	7.00	4.00	5.00	5.00	1.16	6.17
SAM_Resp	O4c_Run1_ID56oglindasolo repetat.xls	60	5.48	0.85	0.11	5	7.00	2.00	5.00	5.00	1.21	2.49
SAM_Resp	O4c_Run1_ID65oglindasolo repetat.xls	60	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_Run14z2_ID14oglindasolo repetatX.xls	80	6.28	1.65	0.18	5	9.00	4.00	6.00	5.00	0.93	2.11
SAM_Resp	O4c_Run2_ID19oglindasolo repetat.xls	80	4.79	1.86	0.21	2	9.00	7.00	5.00	5.00	-0.26	2.21
SAM_Resp	O4c_Run2_ID21oglindasolo repetat.xls	60	5.13	0.39	0.05	4	6.00	2.00	5.00	5.00	1.24	5.07
SAM_Resp	O4c_Run2_ID23oglindasolo repetat.xls	80	5.28	0.89	0.10	4	8.00	4.00	5.00	5.00	0.98	3.60
SAM_Resp	O4c_Run2_ID30oglindasolo repetat.xls	80	4.99	0.25	0.03	4	6.00	2.00	5.00	5.00	-0.65	15.93
SAM_Resp	O4c_Run2_ID32oglindasolo repetat.xls	60	4.73	0.58	0.07	3	6.00	3.00	5.00	5.00	-1.51	5.09
SAM_Resp	O4c_Run2_ID38oglindasolo repetat.xls	60	5.62	1.22	0.16	4	7.00	3.00	5.50	7.00	-0.08	1.43
SAM_Resp	O4c_Run35_ID35oglindasolo repetat.xls	60	5.25	0.51	0.07	4	7.00	3.00	5.00	5.00	1.12	4.24
SAM_Resp	O4c_RunN(modifica N!)_ID07067oglindasolo repetat.xls	80	5.00	0.00	0.00	5	5.00	0.00	5.00	5.00	NaN	NaN
SAM_Resp	O4c_RunN(modifica N!)_ID11oglindasolo repetat.xls	80	5.01	1.47	0.16	1	9.00	8.00	5.00	5.00	-0.65	4.69
SAM_Resp	O4c_RunN_ID18oglindasolo repetat.xls	80	6.33	1.73	0.19	2	9.00	7.00	7.00	7.00	-0.90	3.41
SAM_Resp	O4c_RunN_ID2424oglindasolo repetat.xls	80	5.64	0.64	0.07	4	8.00	4.00	6.00	6.00	0.49	4.15
SAM_RT	O4c_ID2222oglindasolo repetat.xls	80	1.84	1.00	0.11	1	7.70	6.70	1.52	1.94	2.94	16.21
SAM_RT	O4c_Run1_ID20oglindasolo repetat.xls	60	1.33	0.34	0.04	1	2.45	1.45	1.23	2.01	1.33	4.32
SAM_RT	O4c_Run1_ID25oglindasolo repetat.xls	60	1.52	0.73	0.09	1	4.78	3.78	1.25	1.44	2.33	9.11
SAM_RT	O4c_Run1_ID31oglindasolo repetat.xls	60	1.73	1.12	0.14	1	5.65	4.65	1.30	5.20	2.09	6.54
SAM_RT	O4c_Run1_ID34oglindasolo repetat.xls	60	1.34	0.36	0.05	1	2.40	1.40	1.25	1.93	1.04	3.19
SAM_RT	O4c_Run1_ID42oglindasolo repetat.xls	60	1.38	0.53	0.07	1	4.03	3.03	1.20	1.32	2.63	11.94
SAM_RT	O4c_Run1_ID56oglindasolo repetat.xls	60	1.57	0.62	0.08	1	4.36	3.36	1.37	2.90	2.14	8.72
SAM_RT	O4c_Run1_ID65oglindasolo repetat.xls	60	1.40	0.69	0.09	1	4.35	3.35	1.09	2.80	2.73	11.05
SAM_RT	O4c_Run14z2_ID14oglindasolo repetatX.xls	80	1.46	0.68	0.08	1	5.38	4.38	1.23	1.30	3.32	17.09
SAM_RT	O4c_Run2_ID19oglindasolo repetat.xls	80	1.15	0.20	0.02	1	2.15	1.15	1.05	1.00	2.29	10.25
SAM_RT	O4c_Run2_ID21oglindasolo repetat.xls	60	1.56	0.63	0.08	1	3.41	2.41	1.39	1.48	1.41	3.93
SAM_RT	O4c_Run2_ID23oglindasolo repetat.xls	80	1.21	0.25	0.03	1	1.98	0.98	1.13	1.04	1.53	4.71
SAM_RT	O4c_Run2_ID30oglindasolo repetat.xls	80	1.25	0.31	0.03	1	2.50	1.50	1.16	1.19	1.83	6.31
SAM_RT	O4c_Run2_ID32oglindasolo repetat.xls	60	1.33	0.37	0.05	1	2.96	1.96	1.23	1.39	1.82	7.57
SAM_RT	O4c_Run2_ID38oglindasolo repetat.xls	60	1.29	0.36	0.05	1	2.97	1.97	1.21	1.93	2.33	9.82
SAM_RT	O4c_Run35_ID35oglindasolo repetat.xls	60	1.19	0.20	0.03	1	1.70	0.70	1.14	1.58	0.84	2.71
SAM_RT	O4c_RunN(modifica N!)_ID07067oglindasolo repetat.xls	80	1.28	0.35	0.04	1	2.64	1.64	1.18	1.08	1.71	5.95
SAM_RT	O4c_RunN(modifica N!)_ID11oglindasolo repetat.xls	80	2.04	1.70	0.19	1	10.22	9.22	1.29	2.05	2.52	10.16
SAM_RT	O4c_RunN_ID18oglindasolo repetat.xls	80	1.78	0.86	0.10	1	5.26	4.25	1.44	2.12	1.93	6.77
SAM_RT	O4c_RunN_ID2424oglindasolo repetat.xls	80	1.46	0.53	0.06	1	3.15	2.14	1.27	1.61	1.56	4.67

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" = .id)
    df <- deparse(substitute(df))
    cat("Changed .id to ID for: ", as.name(df))
    assign(df, df_modif, envir = globalenv())
  }  
}

ID_rename(Repetat_CTRL_Instr)

Changed .id to ID for:  Repetat_CTRL_Instr

ID_rename(Repetat_CTRL_Solo)

Changed .id to ID for:  Repetat_CTRL_Solo

ID_rename(Repetat_OGL_Instr)

Changed .id to ID for:  Repetat_OGL_Instr

ID_rename(Repetat_OGL_Solo)

Changed .id to ID for:  Repetat_OGL_Solo

# Merge into one df
list_df_merge <- list(Repetat_CTRL_Instr, Repetat_CTRL_Solo, Repetat_OGL_Instr, Repetat_OGL_Solo)
names(list_df_merge) <- c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo", "Repetat_OGL_Instr", "Repetat_OGL_Solo")
Repetat_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 
# Repetat_merged_spread_Neg <- 
#   Repetat_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(Repetat_merged_spread_Neg$Repetat_CTRL_Instr, Repetat_merged_spread_Neg$Repetat_CTRL_Solo, na.rm = TRUE)
# t.test(Repetat_merged_spread_Neg$Repetat_OGL_Instr, Repetat_merged_spread_Neg$Repetat_OGL_Solo, na.rm = TRUE)
# t.test(Repetat_merged_spread_Neg$Repetat_OGL_Instr, Repetat_merged_spread_Neg$Repetat_CTRL_Instr, na.rm = TRUE)
# t.test(Repetat_merged_spread_Neg$Repetat_OGL_Solo, Repetat_merged_spread_Neg$Repetat_CTRL_Solo, na.rm = TRUE)

## Function prepair data for analyses
prepaire_merged_func <- function(Stim_type){
  Repetat_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
    dplyr::mutate(Cond = as.factor(Cond))                                         # tunr to factor for aov family functions
}

Repetat_merged_Neg <- prepaire_merged_func("negativ")
Repetat_merged_Neu <- prepaire_merged_func("neutru")
Repetat_merged_Poz <- prepaire_merged_func("pozitiv")

## Anova and Post-Hoc
# Normality 
Repetat_merged_Neg %>%
  dplyr::sample_n(5000) %>%                                                # randomly sample 5000 rows because shapiro.test with with max 5000
  select(SAM_Resp) %>%                                                     # must select variables outside function 
  tadaatoolbox::tadaa_normtest(method = "shapiro")                         # , print = "markdown"  for Notebook

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

# Anova
Repetat_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 
Repetat_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

## Plots
options(scipen = 999)                                 # positive values bias towards fixed and negative towards scientific notation
theme_set(papaja::theme_apa())                        # theme for plots          
# by dataset
ggplot(Repetat_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(Repetat_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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Solo"),
                                                c("Repetat_OGL_Instr", "Repetat_OGL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Solo")))



# drop to CTRL vs OGL - by Stimulus type
Repetat_merged %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo") ~ "Repetat_CTRL",
                         .id %in% c("Repetat_OGL_Instr", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_CTRL", "Repetat_OGL")))


# drop to Instr vs Solo - by Stimulus type
Repetat_merged %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_OGL_Instr") ~ "Repetat_Instr",
                         .id %in% c("Repetat_CTRL_Solo", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_Instr", "Repetat_Solo")))

3 Analyses by Gender

3.1 Data frames by Gender

id_names_fem <- 
  Repetat_merged %>%
  filter(Gender == "F") %>%
  select(ID) %>%
  purrr::flatten_chr()       # 40 unique ids
  
id_names_masc <- 
  Repetat_merged %>%
  filter(Gender == "M") %>%
  select(ID) %>%
  purrr::flatten_chr()       # 40 unique ids

Repetat_merged_fem <-
  Repetat_merged %>%
  filter(ID %in% id_names_fem)

Repetat_merged_masc <-
  Repetat_merged %>%
  filter(ID %in% id_names_masc)

# Some IDs have 180 and some 240 repetitions
# Repetat_merged_fem %>%
#   dplyr::count(ID)

Repetat_merged_gender <-
  Repetat_merged %>%
  group_by(ID) %>%
  dplyr::mutate(Gender = dplyr::first(Gender)) %>%
  ungroup() %>%
  dplyr::mutate(Gender = as.factor(Gender)) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  tidyr::unite("Cond_Gender", c(".id", "Gender"), remove = FALSE)

3.2 Plots with p values - Females

## Plots
options(scipen = 999)                                 # positive values bias towards fixed and negative towards scientific notation
theme_set(papaja::theme_apa())                        # theme for plots          
# by dataset
ggplot(Repetat_merged_fem, 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(Repetat_merged_fem, 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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Solo"),
                                                c("Repetat_OGL_Instr", "Repetat_OGL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Solo")))



# drop to CTRL vs OGL - by Stimulus type
Repetat_merged_fem %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo") ~ "Repetat_CTRL",
                         .id %in% c("Repetat_OGL_Instr", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_CTRL", "Repetat_OGL")))


# drop to Instr vs Solo - by Stimulus type
Repetat_merged_fem %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_OGL_Instr") ~ "Repetat_Instr",
                         .id %in% c("Repetat_CTRL_Solo", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_Instr", "Repetat_Solo")))

3.3 Plots with p values - Males

## Plots
options(scipen = 999)                                 # positive values bias towards fixed and negative towards scientific notation
theme_set(papaja::theme_apa())                        # theme for plots          
# by dataset
ggplot(Repetat_merged_masc, 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(Repetat_merged_masc, 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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Solo"),
                                                c("Repetat_OGL_Instr", "Repetat_OGL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Solo")))



# drop to CTRL vs OGL - by Stimulus type
Repetat_merged_masc %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo") ~ "Repetat_CTRL",
                         .id %in% c("Repetat_OGL_Instr", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_CTRL", "Repetat_OGL")))


# drop to Instr vs Solo - by Stimulus type
Repetat_merged_masc %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_OGL_Instr") ~ "Repetat_Instr",
                         .id %in% c("Repetat_CTRL_Solo", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_Instr", "Repetat_Solo")))

3.4 Comparisons between Genders

3.4.1 unadjusted p-values


comparisons_gender <- gtools::combinations(v = unique(Repetat_merged_gender$Cond_Gender), r=2, n = length(unique(Repetat_merged_gender$Cond_Gender)))
comparisons_gender <- as.list(as.data.frame(t(comparisons_gender), stringsAsFactors = FALSE))

# by Stimulus type
ggplot(Repetat_merged_gender, aes(x = Cond_Gender, 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.signif",                                        
                             hide.ns = TRUE,
                             #paired = TRUE, 
                             comparisons = comparisons_gender)

3.4.2 adjusted p-values

stat.test <-
  Repetat_merged_gender %>%
  group_by(Stimulus.type) %>%
  rstatix::t_test(SAM_Resp ~ Cond_Gender, p.adjust.method = "holm") %>%              
  rstatix::add_significance("p") %>%                                                       
  filter(p.adj.signif != "ns") 

p<-
  ggplot(Repetat_merged_gender, aes(x = Cond_Gender, 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)

if(nrow(stat.test) > 0){
  p <- 
    p + stat_pvalue_manual(stat.test, label = "p.adj.signif", 
                          vjust = 0.9, tip.length = 0.01,
                           y.position = seq(max(Repetat_merged_gender$SAM_Resp, na.rm = TRUE)*1.01, max(Repetat_merged_gender$SAM_Resp, na.rm = TRUE)*7, 
                                        length.out = nrow(stat.test)))                             # very hacky
}

print(p)

return(stat.test) %>% print(n = Inf)

4 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] summarytools_0.8.8 DT_0.5             ggpubr_0.2.5       magrittr_1.5       broom_0.5.2        papaja_0.1.0.9842  psych_1.8.12       forcats_0.4.0     
 [9] stringr_1.4.0      dplyr_0.8.3        purrr_0.3.2        readr_1.3.1        tidyr_1.0.0        tibble_2.1.3       ggplot2_3.2.1      tidyverse_1.2.1   
[17] pacman_0.5.1      

loaded via a namespace (and not attached):
 [1] nlme_3.1-140        bitops_1.0-6        matrixStats_0.54.0  lubridate_1.7.4     httr_1.4.0          tools_3.6.1         backports_1.1.4     tidystats_0.3      
 [9] R6_2.4.0            nortest_1.0-4       lazyeval_0.2.2      colorspace_1.4-1    withr_2.1.2         tidyselect_0.2.5    gridExtra_2.3       mnormt_1.5-5       
[17] pixiedust_0.8.6     curl_3.2            compiler_3.6.1      tadaatoolbox_0.16.1 cli_1.1.0           rvest_0.3.2         expm_0.999-3        xml2_1.2.0         
[25] labeling_0.3        scales_1.0.0        mvtnorm_1.0-11      digest_0.6.21       foreign_0.8-71      rmarkdown_1.17      rio_0.5.16          pkgconfig_2.0.3    
[33] htmltools_0.3.6     highr_0.8           pwr_1.2-2           htmlwidgets_1.3     rlang_0.4.0         readxl_1.1.0        rstudioapi_0.8      pryr_0.1.4         
[41] shiny_1.2.0         generics_0.0.2      jsonlite_1.6        gtools_3.8.1        car_3.0-2           zip_1.0.0           RCurl_1.95-4.11     rapportools_1.0    
[49] Matrix_1.2-17       Rcpp_1.0.2          DescTools_0.99.29   munsell_0.5.0       abind_1.4-5         viridis_0.5.1       lifecycle_0.1.0     stringi_1.4.3      
[57] carData_3.0-2       MASS_7.3-51.4       plyr_1.8.4          grid_3.6.1          parallel_3.6.1      promises_1.0.1      crayon_1.3.4        lattice_0.20-38    
[65] haven_2.1.1         pander_0.6.3        hms_0.5.1           zeallot_0.1.0       knitr_1.25          pillar_1.4.2        boot_1.3-22         ggsignif_0.4.0     
[73] codetools_0.2-16    glue_1.3.1          evaluate_0.14       data.table_1.11.8   modelr_0.1.5        vctrs_0.2.0         httpuv_1.4.5        cellranger_1.1.0   
[81] gtable_0.3.0        assertthat_0.2.1    xfun_0.9            openxlsx_4.1.0      mime_0.7            xtable_1.8-4        rstatix_0.2.0       later_0.7.5        
[89] viridisLite_0.3.0   ellipsis_0.3.0

A work by Claudiu Papasteri

claudiu.papasteri@gmail.com

---
title: "<br> O.4 - 20 subjects Repetate" 
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, warning=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)/29.10.2019 20sub EEG Repetate/Date"
setwd(wd)

# knitr::opts_knit$set(root.dir = wd)   # new way of setwd for mardown
```


<!-- 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(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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo", "Repetat_OGL_Instr", "Repetat_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("repetat", "ecran", "instructor"), dir = "Repetat_CTRL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("repetat", "ecran", "solo"), dir = "Repetat_CTRL_Solo")
sort_files_to_dirs(wd = wd, pattern = c("repetat", "oglinda", "instructor"), dir = "Repetat_OGL_Instr")
sort_files_to_dirs(wd = wd, pattern = c("repetat", "oglinda", "solo"), dir = "Repetat_OGL_Solo")
```


## Reading the data

```{r raw_read, hide=TRUE, warning=FALSE}
############ 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)/29.10.2019 20sub EEG Repetate/Date"
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 <- setwd(file.path(wd, folders[i]))
  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) %>%
    dplyr::mutate(delete_id = if_else(row_count < 3, TRUE, FALSE)) %>%
    filter(delete_id == TRUE)
  
  df_modif <- 
    df %>%
    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")
}


delete_empty_id(Repetat_CTRL_Instr)
delete_empty_id(Repetat_CTRL_Solo)
delete_empty_id(Repetat_OGL_Instr)
delete_empty_id(Repetat_OGL_Solo)
```


## Exclude SAM_resp based on RT outliers - not used here

```{r nooutlier_data, eval=FALSE}
############################### 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`) 
    dplyr::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(Repetat_CTRL_Instr)
remove_outliers(Repetat_CTRL_Solo)
remove_outliers(Repetat_OGL_Instr)
remove_outliers(Repetat_OGL_Solo)
```


## Test if datasets have same columns

```{r test_cols}
repetat_df_obj <- mget(c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo", "Repetat_OGL_Instr", "Repetat_OGL_Solo"))
repetat_df_obj <-lapply(repetat_df_obj, colnames)
outer(repetat_df_obj, repetat_df_obj, Vectorize(identical))                           # if all are TRUE, all df have same columns
```

<br>
<br>


# Analysis - REPETATE

## Descriptives

```{r desc_repetat, results="asis"}
##########################################################################################################################
#################################### Analyses - REPETATE #################################################################
## Descriptives by condition dataset
descriptive_func <- function(df, Stim_type, By_ID = FALSE){
  df_name <- deparse(substitute(df))
  suppressWarnings({                                                                # if all NAs in SAM_Resp, NaNs and Infs will be produced
    df_modif <- 
      df %>%
      dplyr::rename("ID" = .id) %>%
      select_all(~gsub("\\s+|\\.", "_", .)) %>%                                     # replaces blancks with "_" in colnames 
      filter(Stimulus_type == Stim_type)                                            # filter by stimulus type
    
    if(isTRUE(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, SAM_RT, na.rm = TRUE) %>%
        knitr::kable(caption = as.name(df_name), format = "pandoc", digits = 2)
    }else{ 
      df_modif %>%
        tidystats::describe_data(SAM_Resp, SAM_RT, na.rm = TRUE) %>%
        knitr::kable(caption = as.name(df_name), format = "pandoc", digits = 2)
    }
  })
}  


descriptive_func(df = Repetat_CTRL_Instr, Stim_type = "negativ", By_ID = FALSE)           # Negative - General
descriptive_func(df = Repetat_CTRL_Solo, Stim_type = "negativ", By_ID = FALSE)
descriptive_func(df = Repetat_OGL_Instr, Stim_type = "negativ", By_ID = FALSE)
descriptive_func(df = Repetat_OGL_Solo, Stim_type = "negativ", By_ID = FALSE)

descriptive_func(df = Repetat_CTRL_Instr, Stim_type = "negativ", By_ID = TRUE)            # Negative - by id
descriptive_func(df = Repetat_CTRL_Solo, Stim_type = "negativ", By_ID = TRUE)
descriptive_func(df = Repetat_OGL_Instr, Stim_type = "negativ", By_ID = TRUE)
descriptive_func(df = Repetat_OGL_Solo, Stim_type = "negativ", By_ID = TRUE)

descriptive_func(df = Repetat_CTRL_Instr, Stim_type = "pozitiv", By_ID = FALSE)           # Positive - General
descriptive_func(df = Repetat_CTRL_Solo, Stim_type = "pozitiv", By_ID = FALSE)
descriptive_func(df = Repetat_OGL_Instr, Stim_type = "pozitiv", By_ID = FALSE)
descriptive_func(df = Repetat_OGL_Solo, Stim_type = "pozitiv", By_ID = FALSE)

descriptive_func(df = Repetat_CTRL_Instr, Stim_type = "pozitiv", By_ID = TRUE)            # Positive - by id
descriptive_func(df = Repetat_CTRL_Solo, Stim_type = "pozitiv", By_ID = TRUE)
descriptive_func(df = Repetat_OGL_Instr, Stim_type = "pozitiv", By_ID = TRUE)
descriptive_func(df = Repetat_OGL_Solo, Stim_type = "pozitiv", By_ID = TRUE)

descriptive_func(df = Repetat_CTRL_Instr, Stim_type = "neutru", By_ID = FALSE)           # Neutral - General
descriptive_func(df = Repetat_CTRL_Solo, Stim_type = "neutru", By_ID = FALSE)
descriptive_func(df = Repetat_OGL_Instr, Stim_type = "neutru", By_ID = FALSE)
descriptive_func(df = Repetat_OGL_Solo, Stim_type = "neutru", By_ID = FALSE)

descriptive_func(df = Repetat_CTRL_Instr, Stim_type = "neutru", By_ID = TRUE)            # Neutral - by id
descriptive_func(df = Repetat_CTRL_Solo, Stim_type = "neutru", By_ID = TRUE)
descriptive_func(df = Repetat_OGL_Instr, Stim_type = "neutru", By_ID = TRUE)
descriptive_func(df = Repetat_OGL_Solo, Stim_type = "neutru", By_ID = TRUE)
```


## Merge

```{r merged_repetat_data}
############################## 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" = .id)
    df <- deparse(substitute(df))
    cat("Changed .id to ID for: ", as.name(df))
    assign(df, df_modif, envir = globalenv())
  }  
}

ID_rename(Repetat_CTRL_Instr)
ID_rename(Repetat_CTRL_Solo)
ID_rename(Repetat_OGL_Instr)
ID_rename(Repetat_OGL_Solo)

# Merge into one df
list_df_merge <- list(Repetat_CTRL_Instr, Repetat_CTRL_Solo, Repetat_OGL_Instr, Repetat_OGL_Solo)
names(list_df_merge) <- c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo", "Repetat_OGL_Instr", "Repetat_OGL_Solo")
Repetat_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_repetat, hide=TRUE, eval=FALSE}
############################## Analyses on Merged ################################################################
## Just a Test 
# Repetat_merged_spread_Neg <- 
#   Repetat_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(Repetat_merged_spread_Neg$Repetat_CTRL_Instr, Repetat_merged_spread_Neg$Repetat_CTRL_Solo, na.rm = TRUE)
# t.test(Repetat_merged_spread_Neg$Repetat_OGL_Instr, Repetat_merged_spread_Neg$Repetat_OGL_Solo, na.rm = TRUE)
# t.test(Repetat_merged_spread_Neg$Repetat_OGL_Instr, Repetat_merged_spread_Neg$Repetat_CTRL_Instr, na.rm = TRUE)
# t.test(Repetat_merged_spread_Neg$Repetat_OGL_Solo, Repetat_merged_spread_Neg$Repetat_CTRL_Solo, na.rm = TRUE)

## Function prepair data for analyses
prepaire_merged_func <- function(Stim_type){
  Repetat_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
    dplyr::mutate(Cond = as.factor(Cond))                                         # tunr to factor for aov family functions
}

Repetat_merged_Neg <- prepaire_merged_func("negativ")
Repetat_merged_Neu <- prepaire_merged_func("neutru")
Repetat_merged_Poz <- prepaire_merged_func("pozitiv")

## Anova and Post-Hoc
# Normality 
Repetat_merged_Neg %>%
  dplyr::sample_n(5000) %>%                                                # randomly sample 5000 rows because shapiro.test with with max 5000
  select(SAM_Resp) %>%                                                     # must select variables outside function 
  tadaatoolbox::tadaa_normtest(method = "shapiro")                         # , print = "markdown"  for Notebook

# Levene Test (p>.05 = homogeneity of variances)
Repetat_merged_Neg %>%
  tadaatoolbox::tadaa_levene(data = ., SAM_Resp ~ Cond)                    # , print = "markdown"  for Notebook

# Anova
Repetat_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 
Repetat_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_repetate, fig.height=7}
## Plots
options(scipen = 999)                                 # positive values bias towards fixed and negative towards scientific notation
theme_set(papaja::theme_apa())                        # theme for plots          
# by dataset
ggplot(Repetat_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(Repetat_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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Solo"),
                                                c("Repetat_OGL_Instr", "Repetat_OGL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Solo"))) 


# drop to CTRL vs OGL - by Stimulus type
Repetat_merged %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo") ~ "Repetat_CTRL",
                         .id %in% c("Repetat_OGL_Instr", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_CTRL", "Repetat_OGL")))          

# drop to Instr vs Solo - by Stimulus type
Repetat_merged %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_OGL_Instr") ~ "Repetat_Instr",
                         .id %in% c("Repetat_CTRL_Solo", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_Instr", "Repetat_Solo"))) 
```


# Analyses by Gender

## Data frames by Gender

```{r gender_repetat_data}
id_names_fem <- 
  Repetat_merged %>%
  filter(Gender == "F") %>%
  select(ID) %>%
  purrr::flatten_chr()       # 40 unique ids
  
id_names_masc <- 
  Repetat_merged %>%
  filter(Gender == "M") %>%
  select(ID) %>%
  purrr::flatten_chr()       # 40 unique ids

Repetat_merged_fem <-
  Repetat_merged %>%
  filter(ID %in% id_names_fem)

Repetat_merged_masc <-
  Repetat_merged %>%
  filter(ID %in% id_names_masc)

# Some IDs have 180 and some 240 repetitions
# Repetat_merged_fem %>%
#   dplyr::count(ID)

Repetat_merged_gender <-
  Repetat_merged %>%
  group_by(ID) %>%
  dplyr::mutate(Gender = dplyr::first(Gender)) %>%
  ungroup() %>%
  dplyr::mutate(Gender = as.factor(Gender)) %>%
  dplyr::mutate(ID = as.factor(ID)) %>%
  tidyr::unite("Cond_Gender", c(".id", "Gender"), remove = FALSE)
```


## Plots with p values - Females

```{r plot_repetate_fem, fig.height=7}
## Plots
options(scipen = 999)                                 # positive values bias towards fixed and negative towards scientific notation
theme_set(papaja::theme_apa())                        # theme for plots          
# by dataset
ggplot(Repetat_merged_fem, 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(Repetat_merged_fem, 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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Solo"),
                                                c("Repetat_OGL_Instr", "Repetat_OGL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Solo"))) 


# drop to CTRL vs OGL - by Stimulus type
Repetat_merged_fem %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo") ~ "Repetat_CTRL",
                         .id %in% c("Repetat_OGL_Instr", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_CTRL", "Repetat_OGL")))          

# drop to Instr vs Solo - by Stimulus type
Repetat_merged_fem %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_OGL_Instr") ~ "Repetat_Instr",
                         .id %in% c("Repetat_CTRL_Solo", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_Instr", "Repetat_Solo"))) 
```


## Plots with p values - Males

```{r plot_repetate_masc, fig.height=7}
## Plots
options(scipen = 999)                                 # positive values bias towards fixed and negative towards scientific notation
theme_set(papaja::theme_apa())                        # theme for plots          
# by dataset
ggplot(Repetat_merged_masc, 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(Repetat_merged_masc, 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("Repetat_CTRL_Instr", "Repetat_CTRL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Instr"),
                                                c("Repetat_CTRL_Solo", "Repetat_OGL_Solo"),
                                                c("Repetat_OGL_Instr", "Repetat_OGL_Solo"),
                                                c("Repetat_CTRL_Instr", "Repetat_OGL_Solo"))) 


# drop to CTRL vs OGL - by Stimulus type
Repetat_merged_masc %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_CTRL_Solo") ~ "Repetat_CTRL",
                         .id %in% c("Repetat_OGL_Instr", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_CTRL", "Repetat_OGL")))          

# drop to Instr vs Solo - by Stimulus type
Repetat_merged_masc %>%
  dplyr::mutate(.id = case_when(.id %in% c("Repetat_CTRL_Instr", "Repetat_OGL_Instr") ~ "Repetat_Instr",
                         .id %in% c("Repetat_CTRL_Solo", "Repetat_OGL_Solo") ~ "Repetat_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("Repetat_Instr", "Repetat_Solo"))) 
```


## Comparisons between Genders
### unadjusted p-values

```{r comp_gender, fig.height=10}

comparisons_gender <- gtools::combinations(v = unique(Repetat_merged_gender$Cond_Gender), r=2, n = length(unique(Repetat_merged_gender$Cond_Gender)))
comparisons_gender <- as.list(as.data.frame(t(comparisons_gender), stringsAsFactors = FALSE))

# by Stimulus type
ggplot(Repetat_merged_gender, aes(x = Cond_Gender, 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.signif",                                        
                             hide.ns = TRUE,
                             #paired = TRUE, 
                             comparisons = comparisons_gender) 
```

### adjusted p-values

```{r comp_gender2, fig.height=14}
stat.test <-
  Repetat_merged_gender %>%
  group_by(Stimulus.type) %>%
  rstatix::t_test(SAM_Resp ~ Cond_Gender, p.adjust.method = "holm") %>%              
  rstatix::add_significance("p") %>%                                                       
  filter(p.adj.signif != "ns") 

p<-
  ggplot(Repetat_merged_gender, aes(x = Cond_Gender, 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)

if(nrow(stat.test) > 0){
  p <- 
    p + stat_pvalue_manual(stat.test, label = "p.adj.signif", 
                          vjust = 0.9, tip.length = 0.01,
                           y.position = seq(max(Repetat_merged_gender$SAM_Resp, na.rm = TRUE)*1.01, max(Repetat_merged_gender$SAM_Resp, na.rm = TRUE)*7, 
                                        length.out = nrow(stat.test)))                             # very hacky
}

print(p)
return(stat.test) %>% print(n = Inf)
```



<!-- Session Info and License -->

<br>

# Session Info
```{r session_info, echo = FALSE, results = 'markup'}
sessionInfo()    
```

<!-- Footer -->
&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.4 - 20 subjects Repetate

Preliminary Report

Claudiu Papasteri

03 03 2020