Re arrange eye tracking data

Question

I have an eye tracking data file which I need to transform. Let me explain, my data are formated like this:

Event; Info; Pupil size

Message; Start_trial_0;
Fixation; L; 1020
Fixation; L; 1200
Fixation; L; 980
Fixation; L; 990
Fixation; L; 1003
Message; Trial_0;
Message; ACC_1;
Message; RT_850;
Message; Stop_trial_0;
Message; Start_trial_1;
Fixation; L; 1023
Fixation; L; 1020
Fixation; L; 997
Fixation; L; 1123
Message; Trial_1;
Message; ACC_1;
Message; RT_920;
Message; Stop_trial_1;
Message; Strat_trial_2;
...

Knowing that, I never have the same number of "Fixation" line for each trial.

I want my data to be like that:

Trial_0; ACC_0; RT_850; Fixation; L; 1020
Trial_0; ACC_0; RT_850; Fixation; L; 1200
Trial_0; ACC_0; RT_850; Fixation; L; 980
Trial_0; ACC_0; RT_850; Fixation; L; 990
Trial_0; ACC_0; RT_850; Fixation; L; 1003
Trial_1; ACC_1; RT_920; Fixation; L; 1023
Trial_1; ACC_1; RT_920; Fixation; L; 1020
Trial_1; ACC_1; RT_920; Fixation; L; 997
Trial_1; ACC_1; RT_920; Fixation; L; 1123
...

As I'm not an experimented R user, I absolutely don't know how to do that (if it's possible). And as my data file contain over 1000000 lines, it cannot be done manually ...

Thanks in advance for your precious help ! Jibs.

This is quite a complex data-wrangling process. Do you only have one ACC_* code and one RT_* code per trial, or can they be multiple? — Steve, Jul 31 '19 at 14:39
Yes, it's quite complicated and it go way beyond my abilities in R... And yes I only have one ACC and one RT code per trial as it represent the accuracy and the response time of this trial. I have a 2000 Hz eye tracker that's why I have such a big file. — Jb Melmi, Jul 31 '19 at 19:04

score 1 · Answer 1 · answered Jul 31 '19 at 20:16

The general approach is to split your lines into buckets of all the same trial, then pull out the metadata vs data lines, and make them into a dataframe (assuming that's what you ultimately want).

library(stringr)
library(purrr)

# You may be reading this in with `readLines` or similar,
#   in which case you may not need to split on "\n" below

eye_text <- 
"Event; Info; Pupil size

Message; Start_trial_0;
Fixation; L; 1020
Fixation; L; 1200
Fixation; L; 980
Fixation; L; 990
Fixation; L; 1003
Message; Trial_0;
Message; ACC_1;
Message; RT_850;
Message; Stop_trial_0;
Message; Start_trial_1;
Fixation; L; 1023
Fixation; L; 1020
Fixation; L; 997
Fixation; L; 1123
Message; Trial_1;
Message; ACC_1;
Message; RT_920;
Message; Stop_trial_1;
Message; Start_trial_2;"  # Fixed typo?

# Depending how you read in the data, may already be a vector of lines
eye_lines <- str_split(eye_text, "\n")[[1]]

# Figure out where each trial starts
eye_starts <- cumsum(str_detect(eye_lines, "Start"))

Split the data

str_detect(eye_lines, "Start") gives you a vector of TRUE/FALSE indicating the start of each trial. cumsum coerces that to 1/0 and takes the running total. This way you end up with 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3, or four groups from the sample text (the header, Trial 0, Trial 1, and one line of Trial 2).

eye_parser <- function(strings) {
  message_indices <- str_detect(strings, "Message;") & !str_detect(strings, "Start|Stop")

  messages <- 
    strings[message_indices] %>% 
    str_remove_all("Message; ") %>% 
    str_c(collapse = " ")

  if (length(messages) == 0) return(NULL)

  observations <- strings[!str_detect(strings, "Message")]

  str_c(messages, observations, sep = " ")
}

Here we subset the strings twice: first we get all the Message; lines (but not the Start*/Stop* lines), then we get all the non-Message; lines.

For the messages, we strip out "Message; ", which leaves you with the metadata values (a vector of "Trial_0;", "ACC_1;", ... etc). Then you str_c those all back together into a single metadata line: "Trial_0; ACC_1; RT_850;".
- At this point if the messages are all empty (like the header and partial trial), we just return NULL.
For the observations, we just take them as is. Then we str_c the messages and observations together, repeating messages in front of every observation line.

To use this function, we first split all your lines into the groups from above, then purrr::map the function over each group of strings. unlist takes it from a list of vectors to a single vector, and then str_split(..., "; ", simplify = T) breaks it out into a character matrix with columns. Finally as.data.frame makes it into a dataframe.

split(eye_lines, eye_starts) %>% 
  map(eye_parser) %>% 
  unlist(use.names = F) %>% 
  str_split("; ", simplify = T) %>% 
  as.data.frame()

       V1    V2     V3       V4 V5   V6
1 Trial_0 ACC_1 RT_850 Fixation  L 1020
2 Trial_0 ACC_1 RT_850 Fixation  L 1200
3 Trial_0 ACC_1 RT_850 Fixation  L  980
4 Trial_0 ACC_1 RT_850 Fixation  L  990
5 Trial_0 ACC_1 RT_850 Fixation  L 1003
6 Trial_1 ACC_1 RT_920 Fixation  L 1023
7 Trial_1 ACC_1 RT_920 Fixation  L 1020
8 Trial_1 ACC_1 RT_920 Fixation  L  997
9 Trial_1 ACC_1 RT_920 Fixation  L 1123

Caveats:

If your metadata isn't always exactly "Trial", "ACC", "RT" in that order, you'll probably want to extract those specifically. You can use the same code pattern I used for messages but for each of those individually. Then you can make sure they're present and in the correct order.

Thanks a lot Brian, it looks like a good solution, the thing is that I'm struggling with the first step, str_split and str_detect return me an error : "argument is not an atomic vector: coercing" My first lines of code being : `S1_data <- read.csv(file = "D:/Stim ST & ET/data/S1_data_t.csv", header = TRUE, sep = ";")` `S1_data_filter <- S1_data %>% select(1,2,3,4,5,6,9)` — Jb Melmi, Aug 01 '19 at 14:49
@Jibs I would say don't use `read.csv`. You need to read in the lines as strings in order to split them yourself. Try `readLines` (and then you can skip splitting on `"\n"`). I'm a little confused by how you're selecting multiple columns (1-6,9) from your raw data though, which seems to only have 3. — Brian, Aug 01 '19 at 16:00
Oh ok. I was thinking that it was probably a problem in reading each row as a vector but didn't know how to fix this. For the multiple columns selection, it's coming from an older version where I kept the eye position too. Now I only want the pupil size. — Jb Melmi, Aug 01 '19 at 20:43
Hi Brian, hope you still in there. The readlines works better than read.csv you were right. I now have an issue with the last split function. It returns me an error message: Warning message: In split.default(eye_lines, eye_starts) : data length is not a multiple of split variable — Jb Melmi, Aug 04 '19 at 11:40
Actually I don't know what to do with the ````eye_lines <- str_split(eye_text, "\n")[[1]]```` line. — Jb Melmi, Aug 04 '19 at 11:45
Actually I don't know what to do with the ````eye_lines <- str_split(eye_text, "\n")[[1]]```` line. Edit: I found out how to do, now the whole script work but I don't find where is the final data frame created at the end... — Jb Melmi, Aug 04 '19 at 11:52
Ok an other comment (sorry for the multi-comment by the way). The script works but there's just few things that don't work properly. First, instead of returning the trial number, it returns the row number of the trial, e.g. the first trial has the value 68 in the "Trial" column, the 2nd trial has the value 119, etc. — Jb Melmi, Aug 04 '19 at 19:34
@Jibs, it sounds like you're having some formatting issues since your actual data didn't look quite like your sample data here. Can you post a more representative example of your data? — Brian, Aug 04 '19 at 22:07
Sure, I'll post a new answer with a sample of my dataset. I didn't think that would be this tricky to deal with that's why I posted first a simplify dataset. — Jb Melmi, Aug 04 '19 at 23:47

Steve · Answer 2 · 2019-08-05T11:09:04.230

Brian has offered a perfect approach to your problem. My approach was slightly different, yet with similar results. For the sake of completion and/or variety i am going to post it though.

My way of thinking is as follows:

You first read in your file and pass it into a dataframe df

library(dplyr) # load the libraries we are going to be using first
library(tidyr)
library(zoo)

df <- read.csv('~/Desktop/test', sep = ';', header = T) # I named your .txt file test here and put it on my Desktop
>df
      Event           Info Pupil.size
1   Message  Start_trial_0         NA
2  Fixation              L       1020
3  Fixation              L       1200
4  Fixation              L        980
5  Fixation              L        990
6  Fixation              L       1003
7   Message        Trial_0         NA
8   Message          ACC_0         NA
9   Message         RT_850         NA
10  Message   Stop_trial_0         NA
11  Message  Start_trial_1         NA
12 Fixation              L       1023
13 Fixation              L       1020
14 Fixation              L        997
15 Fixation              L       1123
16  Message        Trial_1         NA
17  Message          ACC_1         NA
18  Message         RT_920         NA
19  Message   Stop_trial_1         NA
20  Message  Strat_trial_2         NA

Then we create a new column, named trial where for every row on Info that has the trial info (the Start and Stop in this case), we pass the corresponding trial, otherwise we fill with NA, as such:

Option 1 (original file data):

df <- df %>% mutate(trial=ifelse(Event=='Message'&grepl('trial', df$Info), gsub('.*_(trial_\\d)$', '\\1', df$Info), NA))
      Event           Info Pupil.size   trial
1   Message  Start_trial_0         NA trial_0
2  Fixation              L       1020    <NA>
3  Fixation              L       1200    <NA>
4  Fixation              L        980    <NA>
5  Fixation              L        990    <NA>
6  Fixation              L       1003    <NA>
7   Message        Trial_0         NA    <NA>
8   Message          ACC_0         NA    <NA>
9   Message         RT_850         NA    <NA>
10  Message   Stop_trial_0         NA trial_0
11  Message  Start_trial_1         NA trial_1
12 Fixation              L       1023    <NA>
13 Fixation              L       1020    <NA>
14 Fixation              L        997    <NA>
15 Fixation              L       1123    <NA>
16  Message        Trial_1         NA    <NA>
17  Message          ACC_1         NA    <NA>
18  Message         RT_920         NA    <NA>
19  Message   Stop_trial_1         NA trial_1
20  Message  Strat_trial_2         NA trial_2

Option 2 (new input file - keep in mind this preserves the in-between trial data that you might want to get rid of):

df <- df %>% mutate(trial=ifelse(Event=='MSG'&grepl('trial', df$Info), gsub('.*_(trial_\\d)$', '\\1', df$Info), 
                                 ifelse(Event=='MSG'&grepl('consigne', df$Info), gsub('.*_(consigne)$', '\\1', df$Info),
                                        NA)))

I am filling with NA since on the next step we want to replace NAs with the earliest previous non NA value (thus assigning the correct trial on every row between the Start-Stop). This can be done with na.locf from the package zoo.

df$trial <- na.locf(df$trial)
> df
      Event           Info Pupil.size   trial
1   Message  Start_trial_0         NA trial_0
2  Fixation              L       1020 trial_0
3  Fixation              L       1200 trial_0
4  Fixation              L        980 trial_0
5  Fixation              L        990 trial_0
6  Fixation              L       1003 trial_0
7   Message        Trial_0         NA trial_0
8   Message          ACC_0         NA trial_0
9   Message         RT_850         NA trial_0
10  Message   Stop_trial_0         NA trial_0
11  Message  Start_trial_1         NA trial_1
12 Fixation              L       1023 trial_1
13 Fixation              L       1020 trial_1
14 Fixation              L        997 trial_1
15 Fixation              L       1123 trial_1
16  Message        Trial_1         NA trial_1
17  Message          ACC_1         NA trial_1
18  Message         RT_920         NA trial_1
19  Message   Stop_trial_1         NA trial_1
20  Message  Strat_trial_2         NA trial_2

We can now get rid of the rows with Trial "metadata" on the Info column.

df <- df %>% filter(!grepl('[T,t]rial', df$Info))

Next, we need the final "metadata" information per trial, namely ACC and RT information. These information are all within the Info column so we have to pull them out somehow. To do that first, we create two new columns named ACC and RT.

df <- df %>% mutate(ACC=ifelse(grepl('ACC', df$Info), as.character(df$Info), NA),
              RT=ifelse(grepl('RT', df$Info), as.character(df$Info), NA))

> df
      Event    Info Pupil.size   trial    ACC      RT
1  Fixation       L       1020 trial_0   <NA>    <NA>
2  Fixation       L       1200 trial_0   <NA>    <NA>
3  Fixation       L        980 trial_0   <NA>    <NA>
4  Fixation       L        990 trial_0   <NA>    <NA>
5  Fixation       L       1003 trial_0   <NA>    <NA>
6   Message   ACC_0         NA trial_0  ACC_0    <NA>
7   Message  RT_850         NA trial_0   <NA>  RT_850
8  Fixation       L       1023 trial_1   <NA>    <NA>
9  Fixation       L       1020 trial_1   <NA>    <NA>
10 Fixation       L        997 trial_1   <NA>    <NA>
11 Fixation       L       1123 trial_1   <NA>    <NA>
12  Message   ACC_1         NA trial_1  ACC_1    <NA>
13  Message  RT_920         NA trial_1   <NA>  RT_920

We also need to make sure which ACC and RT attributes correspond to each trial. For that purpose we create two new small dataframes via dplyr that give us all the ACC and RT info.

infoACC <- df %>% group_by(trial, Info) %>% summarize() %>% filter(grepl('ACC', Info))

> infoACC
# A tibble: 2 x 2
# Groups:   trial [2]
  trial   Info    
  <chr>   <fct>   
1 trial_0 " ACC_0"
2 trial_1 " ACC_1"

infoRT <- df %>% group_by(trial, Info) %>% summarize() %>% filter(grepl('RT', Info))

> infoRT
# A tibble: 2 x 2
# Groups:   trial [2]
  trial   Info     
  <chr>   <fct>    
1 trial_0 " RT_850"
2 trial_1 " RT_920"

Then it's just a matter of joining our df and the two new dataframes to get the ACC and RT info in, dropping the additional columns and left-over rows (Message rows)

df <- left_join(left_join(df, infoACC, by='trial'), infoRT, by='trial') %>% select(-ACC, -RT) %>% filter(!Event=='Message')

And wrap this up with fixing up the column names.

colnames(df) <- c('Event', 'Info', 'Pupil.size', 'Trial', 'ACC', 'RT')
> df
     Event Info Pupil.size   Trial    ACC      RT
1 Fixation    L       1020 trial_0  ACC_0  RT_850
2 Fixation    L       1200 trial_0  ACC_0  RT_850
3 Fixation    L        980 trial_0  ACC_0  RT_850
4 Fixation    L        990 trial_0  ACC_0  RT_850
5 Fixation    L       1003 trial_0  ACC_0  RT_850
6 Fixation    L       1023 trial_1  ACC_1  RT_920
7 Fixation    L       1020 trial_1  ACC_1  RT_920
8 Fixation    L        997 trial_1  ACC_1  RT_920
9 Fixation    L       1123 trial_1  ACC_1  RT_920

You can now save this as a new .csv or keep it as a dataframe for further operations in R.

I admit its a bit of a more complicated solution, but i wanted to offer my thinking process hoping to show you that there are many ways to approach your problems in R and you can tackle your questions in a stepwise manner.

Hope this helps

Hi Steve, Thanks a lot for your reply. It is a good solution too. It starts good but when replacing NAs with na.locf, it throw me an error; replacement has 939 rows, data has 997. I think I have found the problem. In my data, I have some inter-trial pupil size measures. So the NAs bteween each trial may be the problem. I not sure how to fix this. — Jb Melmi, Aug 01 '19 at 22:11
I see your point. Are there any distinguishing characteristics for those rows perhaps? Like Message;Mid-trial? or something that we can use to first fix these rows and then run na.locf? — Steve, Aug 02 '19 at 07:35
Unfortunately there no difference between inter trial lines and the trial ones. They just look the same... Do you think it will be possible to exclude them? Like if they are after a stop and before a start? — Jb Melmi, Aug 02 '19 at 13:49
Yes, that we could do. Can you add a dput() output containing that part? — Steve, Aug 02 '19 at 13:51
Is dput() is for you to be able to read or have an overview of it data frame? Is it returning me a file or something I can return to you? I'll do that! — Jb Melmi, Aug 02 '19 at 15:23
No worries at all! dput() output is something i can copy and paste in my R environment and immediately have the same data structure you have. See more information [here](https://stackoverflow.com/a/5963610/7856717) — Steve, Aug 02 '19 at 20:17
Sorry for the time it took, I was out with the kids ^^ Here is the result of the dput() function on my data set (see in the next answer): — Jb Melmi, Aug 03 '19 at 17:19
Hey Jibs! I am taking a look at your `dput` output. Are you sure this is all? It should start with `structure(....` , but i don't see it here and cant load it in. — Steve, Aug 05 '19 at 07:21
Hi Steve, I should have done it wrong the first time, I added the ````dput()```` output in a new comment and now it starts by ````structure(...```` — Jb Melmi, Aug 05 '19 at 09:45
Alright, so i've detected why you get the error. At the beginning of your dput() -this is different than your original table though - you have some parameters not part of a trial but part of "consigne". I've classified these as well now with Option 2 on my original post. However this preserves the inter-trial data observations which you'd like (?) to get rid off. — Steve, Aug 05 '19 at 11:05
Ok Steve, you're now officially my new hero ! It works just fine now. The inter-trial observations are not a problem as they might be informative for us. And if not, I can modify my experiment script to exclude them. Thanks a lot for your time ! You werre very helpfull ! — Jb Melmi, Aug 05 '19 at 14:00
Glad i could help:) I really liked Brian's solution, especially on the part of getting your meta.data out, so a combination of the two methods/answers is probably your best bet. Good luck and keep up the good work! — Steve, Aug 05 '19 at 14:07
Yes his solution is really nice too. I would like to make it work too ! The program kind of works but it returns me really weird data-frame in the end. I'm sure it's a little thing to fix so I'm still trying. But otherwise, your solution works just fine ^^ Thank you again and sorry for my english if it's not perfect because I'm french. — Jb Melmi, Aug 05 '19 at 14:51

score 0 · Answer 3 · answered Aug 03 '19 at 17:20

1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("EFI", 
"MSG"), class = "factor"), Info = structure(c(127L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 172L, 51L, 128L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
11L, 1L, 220L, 3L, 95L, 7L, 218L, 173L, 129L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 35L, 61L, 219L, 3L, 86L, 7L, 218L, 174L, 140L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 41L, 66L, 219L, 
3L, 107L, 7L, 216L, 185L, 51L, 151L, 51L, 51L, 51L, 51L, 27L, 
83L, 220L, 3L, 98L, 7L, 216L, 196L, 162L, 51L, 51L, 51L, 51L, 
51L, 30L, 57L, 219L, 3L, 88L, 7L, 217L, 207L, 167L, 51L, 51L, 
51L, 51L, 51L, 51L, 36L, 62L, 220L, 3L, 93L, 7L, 217L, 211L, 
168L, 51L, 51L, 51L, 51L, 48L, 71L, 219L, 3L, 85L, 7L, 216L, 
212L, 169L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 26L, 83L, 
220L, 3L, 102L, 7L, 216L, 213L, 170L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 29L, 56L, 220L, 3L, 101L, 4L, 218L, 214L, 51L, 171L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 49L, 72L, 220L, 2L, 103L, 
4L, 216L, 215L, 130L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 20L, 80L, 219L, 3L, 116L, 4L, 218L, 175L, 131L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 25L, 83L, 219L, 3L, 125L, 4L, 216L, 176L, 132L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 32L, 52L, 219L, 3L, 126L, 4L, 218L, 
177L, 133L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 8L, 
53L, 220L, 3L, 97L, 4L, 218L, 178L, 134L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 40L, 65L, 219L, 
3L, 117L, 4L, 216L, 179L, 135L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 9L, 74L, 220L, 
3L, 121L, 4L, 216L, 180L, 136L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 28L, 55L, 220L, 3L, 84L, 6L, 218L, 181L, 137L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 31L, 58L, 219L, 3L, 112L, 6L, 218L, 
182L, 138L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 45L, 69L, 219L, 3L, 120L, 6L, 216L, 
183L, 139L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 46L, 70L, 220L, 2L, 90L, 6L, 216L, 184L, 141L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 37L, 63L, 
219L, 3L, 114L, 6L, 216L, 186L, 142L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 39L, 65L, 220L, 3L, 100L, 6L, 216L, 187L, 143L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 21L, 81L, 220L, 
2L, 89L, 6L, 217L, 188L, 144L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 22L, 82L, 220L, 3L, 106L, 6L, 217L, 189L, 145L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 33L, 59L, 219L, 
3L, 110L, 5L, 216L, 190L, 146L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 44L, 68L, 220L, 3L, 99L, 5L, 216L, 191L, 147L, 51L, 
51L, 51L, 51L, 51L, 50L, 73L, 220L, 3L, 91L, 5L, 218L, 192L, 
148L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 10L, 75L, 219L, 2L, 115L, 5L, 218L, 193L, 149L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 38L, 64L, 220L, 3L, 124L, 5L, 218L, 194L, 150L, 51L, 
51L, 51L, 51L, 51L, 51L, 14L, 76L, 220L, 3L, 94L, 5L, 216L, 195L, 
152L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 15L, 77L, 219L, 3L, 118L, 5L, 218L, 197L, 153L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 18L, 79L, 219L, 3L, 122L, 5L, 216L, 198L, 154L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 34L, 60L, 220L, 3L, 119L, 7L, 216L, 199L, 155L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 42L, 67L, 220L, 
3L, 108L, 7L, 218L, 200L, 51L, 156L, 51L, 51L, 51L, 51L, 51L, 
43L, 68L, 219L, 3L, 96L, 7L, 216L, 201L, 157L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 19L, 80L, 219L, 
3L, 123L, 7L, 218L, 202L, 158L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 12L, 76L, 219L, 3L, 111L, 7L, 217L, 203L, 159L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 13L, 76L, 220L, 
3L, 113L, 7L, 217L, 204L, 160L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 16L, 78L, 220L, 2L, 104L, 7L, 216L, 205L, 161L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 17L, 79L, 219L, 
3L, 109L, 7L, 216L, 206L, 163L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 23L, 54L, 219L, 3L, 105L, 7L, 216L, 208L, 51L, 164L, 
51L, 51L, 51L, 51L, 51L, 24L, 54L, 220L, 3L, 92L, 7L, 217L, 209L, 
165L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 47L, 70L, 
220L, 3L, 87L, 7L, 217L, 210L, 166L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L), .Label = c("36=32mod4", "correct_0", 
"correct_1", "difficulty_Easy", "difficulty_Hard", "difficulty_Intermediate", 
"difficulty_Very", "id_1058", "id_10975", "id_11207", "id_1129", 
"id_12052", "id_12069", "id_12131", "id_12453", "id_13285", "id_13741", 
"id_13817", "id_14467", "id_14596", "id_14907", "id_15262", "id_1544", 
"id_1555", "id_15661", "id_15684", "id_15693", "id_1685", "id_2295", 
"id_2479", "id_2820", "id_313", "id_3645", "id_3985", "id_4333", 
"id_4541", "id_5249", "id_5426", "id_5684", "id_5756", "id_6016", 
"id_6326", "id_7019", "id_7064", "id_7885", "id_8660", "id_8728", 
"id_9028", "id_9263", "id_9419", "L", "modulo_26", "modulo_36", 
"modulo_40", "modulo_42", "modulo_46", "modulo_47", "modulo_50", 
"modulo_55", "modulo_57", "modulo_58", "modulo_59", "modulo_63", 
"modulo_64", "modulo_65", "modulo_66", "modulo_68", "modulo_71", 
"modulo_74", "modulo_77", "modulo_78", "modulo_79", "modulo_80", 
"modulo_85", "modulo_86", "modulo_89", "modulo_90", "modulo_93", 
"modulo_94", "modulo_96", "modulo_97", "modulo_98", "modulo_99", 
"RT_10590", "RT_1367", "RT_14182", "RT_15412", "RT_1550", "RT_17151", 
"RT_17302", "RT_1736", "RT_1891", "RT_2002", "RT_2227", "RT_2241", 
"RT_2432", "RT_2510", "RT_2624", "RT_2660", "RT_2840", "RT_2956", 
"RT_2984", "RT_3029", "RT_3154", "RT_3273", "RT_3283", "RT_3727", 
"RT_3900", "RT_4493", "RT_4544", "RT_4840", "RT_5095", "RT_5368", 
"RT_5583", "RT_5618", "RT_6009", "RT_6385", "RT_6423", "RT_6489", 
"RT_6689", "RT_7471", "RT_7669", "RT_7697", "RT_8156", "RT_8752", 
"RT_8784", "start_consigne", "start_trial_0", "start_trial_1", 
"start_trial_10", "start_trial_11", "start_trial_12", "start_trial_13", 
"start_trial_14", "start_trial_15", "start_trial_16", "start_trial_17", 
"start_trial_18", "start_trial_19", "start_trial_2", "start_trial_20", 
"start_trial_21", "start_trial_22", "start_trial_23", "start_trial_24", 
"start_trial_25", "start_trial_26", "start_trial_27", "start_trial_28", 
"start_trial_29", "start_trial_3", "start_trial_30", "start_trial_31", 
"start_trial_32", "start_trial_33", "start_trial_34", "start_trial_35", 
"start_trial_36", "start_trial_37", "start_trial_38", "start_trial_39", 
"start_trial_4", "start_trial_40", "start_trial_41", "start_trial_42", 
"start_trial_43", "start_trial_5", "start_trial_6", "start_trial_7", 
"start_trial_8", "start_trial_9", "stop_consigne", "stop_trial_0", 
"stop_trial_1", "stop_trial_10", "stop_trial_11", "stop_trial_12", 
"stop_trial_13", "stop_trial_14", "stop_trial_15", "stop_trial_16", 
"stop_trial_17", "stop_trial_18", "stop_trial_19", "stop_trial_2", 
"stop_trial_20", "stop_trial_21", "stop_trial_22", "stop_trial_23", 
"stop_trial_24", "stop_trial_25", "stop_trial_26", "stop_trial_27", 
"stop_trial_28", "stop_trial_29", "stop_trial_3", "stop_trial_30", 
"stop_trial_31", "stop_trial_32", "stop_trial_33", "stop_trial_34", 
"stop_trial_35", "stop_trial_36", "stop_trial_37", "stop_trial_38", 
"stop_trial_39", "stop_trial_4", "stop_trial_40", "stop_trial_41", 
"stop_trial_42", "stop_trial_5", "stop_trial_6", "stop_trial_7", 
"stop_trial_8", "stop_trial_9", "strat_1", "strat_2", "strat_4", 
"val_0", "val_1"), class = "factor"), PS = c(NA, 904L, 906L, 
838L, 805L, 789L, 797L, 876L, 924L, 928L, 964L, 957L, 935L, 861L, 
834L, 856L, 846L, 811L, 825L, 869L, 904L, 936L, 969L, 965L, 1016L, 
1018L, 1030L, 1015L, 999L, 987L, 1017L, 1064L, 1080L, 1061L, 
1075L, 1046L, 1005L, 1014L, 1023L, 1040L, 1051L, 1046L, 1010L, 
971L, 994L, 1071L, 1082L, 1120L, 1119L, 1044L, 1023L, 978L, 947L, 
925L, 900L, 940L, NA, 963L, NA, 995L, 1013L, 1046L, 1005L, 1013L, 
1043L, 1146L, 1205L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1306L, 
1334L, 1285L, 1297L, 1257L, 1206L, 1206L, 1256L, 1252L, 1189L, 
1254L, 1214L, 1203L, 1207L, 1263L, 1224L, 1235L, 1258L, 1210L, 
1186L, 1201L, 1271L, 1246L, 1274L, 1337L, 1325L, 1551L, 1733L, 
1812L, 1568L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1272L, 1218L, 
1227L, 1165L, 1145L, 1192L, 1199L, 1208L, 1248L, 1280L, 1224L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1220L, NA, 1229L, 1250L, 1372L, 
1102L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1141L, 1163L, 1146L, 
1129L, 1190L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1182L, 1152L, 
1134L, 1179L, 1178L, 1267L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1272L, 1186L, 1164L, 1173L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1265L, 1191L, 1109L, 1150L, 1125L, 1090L, 1139L, 1205L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1277L, 1164L, 1122L, 1113L, 1115L, 
1121L, 1168L, NA, NA, NA, NA, NA, NA, NA, NA, 1235L, NA, 1207L, 
1164L, 1145L, 1177L, 1242L, 1224L, 1281L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1234L, 1232L, 1204L, 1198L, 1108L, 1131L, 1220L, 
1228L, 1227L, 1231L, 1299L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1211L, 1266L, 1294L, 1292L, 1129L, 1182L, 1175L, 1211L, 1233L, 
1206L, 1185L, 1307L, 1209L, 1206L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1245L, 1264L, 1283L, 1246L, 1290L, 1344L, 1311L, 1267L, 
1201L, 1188L, 1164L, 1218L, 1188L, 1156L, 1144L, 1121L, 1145L, 
1176L, 1155L, 1103L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1173L, 
1223L, 1218L, 1170L, 1120L, 1084L, 1096L, 1092L, 985L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1043L, 1092L, 1090L, 1126L, 1099L, 
1125L, 1175L, 1099L, 1102L, 1188L, 1215L, 1225L, 1197L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1292L, 1338L, 1322L, 1284L, 
1296L, 1273L, 1251L, 1216L, 1205L, 1200L, 1165L, 1097L, 1132L, 
1209L, 1243L, 1295L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1288L, 
1286L, 1243L, 1245L, 1215L, 1213L, 1215L, 1283L, 1280L, 1275L, 
1334L, 1301L, 1205L, 1215L, 1267L, 1245L, 1203L, 1071L, 1113L, 
1160L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1243L, 1249L, 
1268L, 1266L, 1299L, 1363L, 1215L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 938L, 831L, 929L, 999L, 1033L, 1090L, 1092L, 1094L, 1139L, 
1144L, 1225L, 1203L, 1199L, 1261L, 1221L, 1230L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1291L, 1308L, 1270L, 1250L, 1276L, 1226L, 
1197L, 1201L, 1213L, 1195L, 1202L, 1201L, 1194L, 1192L, 1190L, 
1206L, 1244L, 1203L, 1228L, 1239L, 1218L, 1218L, 1217L, 1218L, 
1202L, 1224L, 1177L, 1134L, 1134L, 1152L, 1159L, 1162L, 1168L, 
1107L, 1175L, 1200L, 1173L, 1203L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1266L, 1278L, 1227L, 1188L, 1184L, 1178L, 1167L, 1194L, 
1131L, 1166L, 1203L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1223L, 1226L, 1218L, 1208L, 1142L, 1105L, 1122L, 1156L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1118L, 1133L, 1150L, 1115L, 1070L, 
1078L, 1145L, 1156L, 1175L, 1172L, 1129L, 1134L, 1089L, 1144L, 
1171L, 1179L, 1195L, 1194L, 1231L, 1275L, 1250L, 1273L, 1268L, 
1221L, 1245L, 1211L, 1195L, 1197L, 1194L, 1140L, 1168L, 1220L, 
1197L, 1191L, 1240L, 1288L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1339L, 1327L, 1324L, 1320L, 1242L, 1231L, 1253L, 1255L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1297L, 1303L, 1282L, 1252L, 
1200L, 1202L, 1191L, 1177L, 1220L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1221L, 1224L, 1203L, 1162L, 1175L, 1187L, 1184L, 1165L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1200L, 1225L, 1200L, 1205L, 
1219L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1269L, 1209L, 1161L, 
1171L, 1165L, 1140L, 1120L, 1127L, 1076L, 1081L, 1081L, 1114L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1181L, 1186L, 1189L, 1200L, 
1179L, 1186L, 1171L, 1134L, 1012L, 1004L, 1134L, 1090L, 1146L, 
1222L, 1309L, 1334L, 1354L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1240L, 1121L, 1101L, 1104L, 1142L, 1157L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1197L, 1264L, 1217L, 1181L, 1173L, 1160L, 1147L, 
1174L, 1188L, 1183L, 1162L, 1188L, 1273L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1303L, 1335L, 1346L, 1284L, 1227L, 1245L, 1295L, 
1291L, 1284L, 1125L, 1176L, 1214L, 1206L, 1216L, 1232L, 1234L, 
1268L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1326L, 1284L, 1265L, 
1237L, 1206L, 1212L, 1197L, 1181L, 1216L, 1222L, 1205L, 1148L, 
1163L, 1154L, 1138L, 1146L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1250L, 1229L, 1209L, 1199L, 1165L, 1191L, 1145L, 1130L, 1116L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1101L, NA, 1113L, 1126L, 1138L, 
1160L, 1128L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1155L, 1132L, 
1122L, 1146L, 1145L, 1146L, 1171L, 1103L, 1170L, 1136L, 1177L, 
1108L, 1106L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1175L, 1192L, 
1129L, 1163L, 1187L, 1177L, 1162L, 1184L, 1129L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1221L, 1113L, 1089L, 1099L, 1022L, 995L, 
947L, 1012L, 1065L, 1114L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1163L, 1094L, 1098L, 1139L, 1130L, 1117L, 1087L, 1084L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1137L, 1145L, 1130L, 1105L, 1123L, 
1112L, 1048L, 1055L, 1078L, 1147L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1207L, 1164L, 1169L, 1188L, 1189L, 1140L, 1099L, 1178L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1208L, NA, 1258L, 1207L, 1158L, 
1140L, 1099L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1123L, 1083L, 
1043L, 1066L, 1082L, 1049L, 1040L, 1090L, 1112L, 1069L, 1079L, 
1061L, 1029L, 1032L, 1046L, 1170L, 1197L, 956L, 941L, 1076L, 
1136L, 1208L, 1213L, 1207L, 1186L, 1225L, 1222L, 1232L, 1169L, 
1102L, 1144L, 1178L, 1218L, 1211L, 1229L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1255L, 1260L, 1236L, 1271L, 1312L, 1346L, 1272L, 
1171L, 1192L, 1235L, 1296L), Modulo = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 19L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 44L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 14L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 26L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 43L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 27L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 39L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 42L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
18L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 29L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 23L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 24L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 15L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 17L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 40L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 41L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 11L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
22L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 30L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 16L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 33L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 34L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
37L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 20L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 21L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 38L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 31L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 32L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 35L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 36L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 25L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = c(" ", "26=12mod6", "36=16mod4", "36=32mod4", 
"40=33mod8", "40=36mod2", "42=12mod6", "46=34mod6", "47=44mod2", 
"50=20mod4", "55=20mod4", "57=15mod6", "58=52mod4", "59=57mod2", 
"63=33mod4", "64=24mod8", "65=35mod6", "65=51mod6", "66=61mod6", 
"68=26mod6", "71=21mod6", "71=31mod4", "74=53mod8", "77=53mod4", 
"77=69mod2", "78=75mod4", "79=67mod6", "80=40mod4", "85=65mod4", 
"86=50mod8", "89=32mod2", "89=35mod2", "89=49mod4", "90=50mod6", 
"93=13mod8", "94=43mod4", "94=61mod4", "96=54mod4", "96=82mod6", 
"97=75mod2", "98=76mod2", "99=85mod8", "99=91mod8", "99=93mod6"
), class = "factor")), class = "data.frame", row.names = c(NA, 
-997L))

score 0 · Answer 4 · answered Aug 04 '19 at 23:49

@Brian here is a sample of my dataset:

1     MSG          start_consigne   NA          
2     EFI                       L  904          
3     EFI                       L  906          
4     EFI                       L  838          
5     EFI                       L  805          
6     EFI                       L  789          
7     EFI                       L  797          
8     EFI                       L  876          
9     EFI                       L  924          
10    EFI                       L  928          
11    EFI                       L  964          
12    EFI                       L  957          
13    EFI                       L  935          
14    EFI                       L  861          
15    EFI                       L  834          
16    EFI                       L  856          
17    EFI                       L  846          
18    EFI                       L  811          
19    EFI                       L  825          
20    EFI                       L  869          
21    EFI                       L  904          
22    EFI                       L  936          
23    EFI                       L  969          
24    EFI                       L  965          
25    EFI                       L 1016          
26    EFI                       L 1018          
27    EFI                       L 1030          
28    EFI                       L 1015          
29    EFI                       L  999          
30    EFI                       L  987          
31    EFI                       L 1017          
32    EFI                       L 1064          
33    EFI                       L 1080          
34    EFI                       L 1061          
35    EFI                       L 1075          
36    EFI                       L 1046          
37    EFI                       L 1005          
38    EFI                       L 1014          
39    EFI                       L 1023          
40    EFI                       L 1040          
41    EFI                       L 1051          
42    EFI                       L 1046          
43    EFI                       L 1010          
44    EFI                       L  971          
45    EFI                       L  994          
46    EFI                       L 1071          
47    EFI                       L 1082          
48    EFI                       L 1120          
49    EFI                       L 1119          
50    EFI                       L 1044          
51    EFI                       L 1023          
52    EFI                       L  978          
53    EFI                       L  947          
54    EFI                       L  925          
55    EFI                       L  900          
56    EFI                       L  940          
57    MSG           stop_consigne   NA          
58    EFI                       L  963          
59    MSG           start_trial_0   NA          
60    EFI                       L  995          
61    EFI                       L 1013          
62    EFI                       L 1046          
63    EFI                       L 1005          
64    EFI                       L 1013          
65    EFI                       L 1043          
66    EFI                       L 1146          
67    EFI                       L 1205          
68    MSG                 id_1129   NA          
69    MSG               36=32mod4   NA 36=32mod4
70    MSG                   val_1   NA          
71    MSG               correct_1   NA          
72    MSG                 RT_2241   NA          
73    MSG         difficulty_Very   NA          
74    MSG                 strat_4   NA          
75    MSG            stop_trial_0   NA          
76    MSG           start_trial_1   NA          
77    EFI                       L 1306          
78    EFI                       L 1334          
79    EFI                       L 1285          
80    EFI                       L 1297          
81    EFI                       L 1257          
82    EFI                       L 1206          
83    EFI                       L 1206          
84    EFI                       L 1256          
85    EFI                       L 1252          
86    EFI                       L 1189          
87    EFI                       L 1254          
88    EFI                       L 1214          
89    EFI                       L 1203          
90    EFI                       L 1207          
91    EFI                       L 1263          
92    EFI                       L 1224          
93    EFI                       L 1235          
94    EFI                       L 1258          
95    EFI                       L 1210          
96    EFI                       L 1186          
97    EFI                       L 1201          
98    EFI                       L 1271          
99    EFI                       L 1246          
100   EFI                       L 1274          
101   EFI                       L 1337          
102   EFI                       L 1325          
103   EFI                       L 1551          
104   EFI                       L 1733          
105   EFI                       L 1812          
106   EFI                       L 1568          
107   MSG                 id_4333   NA          
108   MSG               modulo_58   NA 58=52mod4
109   MSG                   val_0   NA          
110   MSG               correct_1   NA          
111   MSG                RT_14182   NA          
112   MSG         difficulty_Very   NA          
113   MSG                 strat_4   NA          
114   MSG            stop_trial_1   NA          
115   MSG           start_trial_2   NA          
116   EFI                       L 1272          
117   EFI                       L 1218          
118   EFI                       L 1227          
119   EFI                       L 1165          
120   EFI                       L 1145          
121   EFI                       L 1192          
122   EFI                       L 1199          
123   EFI                       L 1208          
124   EFI                       L 1248          
125   EFI                       L 1280          
126   EFI                       L 1224          
127   MSG                 id_6016   NA          
128   MSG               modulo_66   NA 66=61mod6
129   MSG                   val_0   NA          
130   MSG               correct_1   NA          
131   MSG                 RT_3727   NA          
132   MSG         difficulty_Very   NA          
133   MSG                 strat_1   NA          
134   MSG            stop_trial_2   NA          
135   EFI                       L 1220          
136   MSG           start_trial_3   NA          
137   EFI                       L 1229          
138   EFI                       L 1250          
139   EFI                       L 1372          
140   EFI                       L 1102          
141   MSG                id_15693   NA          
142   MSG               modulo_99   NA 99=93mod6
143   MSG                   val_1   NA          
144   MSG               correct_1   NA          
145   MSG                 RT_2624   NA          
146   MSG         difficulty_Very   NA          
147   MSG                 strat_1   NA          
148   MSG            stop_trial_3   NA          
149   MSG           start_trial_4   NA````

This (and your other answer) look very different from the sample you posted in the original question. Am I correct in assuming this is what printed to your console after you called `read.csv(somefile, sep = ";")`? — Brian, Aug 05 '19 at 00:42
Yes you right, that's what the read.csv returned with my data file. With the ````sep = ";" ```` — Jb Melmi, Aug 05 '19 at 09:50

score 0 · Answer 5 · answered Aug 05 '19 at 09:48

structure(list(Event = structure(c(2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("EFI", 
"MSG"), class = "factor"), Info = structure(c(127L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 172L, 51L, 128L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
11L, 1L, 220L, 3L, 95L, 7L, 218L, 173L, 129L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 35L, 61L, 219L, 3L, 86L, 7L, 218L, 174L, 140L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 41L, 66L, 219L, 
3L, 107L, 7L, 216L, 185L, 51L, 151L, 51L, 51L, 51L, 51L, 27L, 
83L, 220L, 3L, 98L, 7L, 216L, 196L, 162L, 51L, 51L, 51L, 51L, 
51L, 30L, 57L, 219L, 3L, 88L, 7L, 217L, 207L, 167L, 51L, 51L, 
51L, 51L, 51L, 51L, 36L, 62L, 220L, 3L, 93L, 7L, 217L, 211L, 
168L, 51L, 51L, 51L, 51L, 48L, 71L, 219L, 3L, 85L, 7L, 216L, 
212L, 169L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 26L, 83L, 
220L, 3L, 102L, 7L, 216L, 213L, 170L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 29L, 56L, 220L, 3L, 101L, 4L, 218L, 214L, 51L, 171L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 49L, 72L, 220L, 2L, 103L, 
4L, 216L, 215L, 130L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 20L, 80L, 219L, 3L, 116L, 4L, 218L, 175L, 131L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 25L, 83L, 219L, 3L, 125L, 4L, 216L, 176L, 132L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 32L, 52L, 219L, 3L, 126L, 4L, 218L, 
177L, 133L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 8L, 
53L, 220L, 3L, 97L, 4L, 218L, 178L, 134L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 40L, 65L, 219L, 
3L, 117L, 4L, 216L, 179L, 135L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 9L, 74L, 220L, 
3L, 121L, 4L, 216L, 180L, 136L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 28L, 55L, 220L, 3L, 84L, 6L, 218L, 181L, 137L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 31L, 58L, 219L, 3L, 112L, 6L, 218L, 
182L, 138L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 45L, 69L, 219L, 3L, 120L, 6L, 216L, 
183L, 139L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 46L, 70L, 220L, 2L, 90L, 6L, 216L, 184L, 141L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 37L, 63L, 
219L, 3L, 114L, 6L, 216L, 186L, 142L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 39L, 65L, 220L, 3L, 100L, 6L, 216L, 187L, 143L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 21L, 81L, 220L, 
2L, 89L, 6L, 217L, 188L, 144L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 22L, 82L, 220L, 3L, 106L, 6L, 217L, 189L, 145L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 33L, 59L, 219L, 
3L, 110L, 5L, 216L, 190L, 146L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 44L, 68L, 220L, 3L, 99L, 5L, 216L, 191L, 147L, 51L, 
51L, 51L, 51L, 51L, 50L, 73L, 220L, 3L, 91L, 5L, 218L, 192L, 
148L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 10L, 75L, 219L, 2L, 115L, 5L, 218L, 193L, 149L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 38L, 64L, 220L, 3L, 124L, 5L, 218L, 194L, 150L, 51L, 
51L, 51L, 51L, 51L, 51L, 14L, 76L, 220L, 3L, 94L, 5L, 216L, 195L, 
152L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 15L, 77L, 219L, 3L, 118L, 5L, 218L, 197L, 153L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 18L, 79L, 219L, 3L, 122L, 5L, 216L, 198L, 154L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 34L, 60L, 220L, 3L, 119L, 7L, 216L, 199L, 155L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 42L, 67L, 220L, 
3L, 108L, 7L, 218L, 200L, 51L, 156L, 51L, 51L, 51L, 51L, 51L, 
43L, 68L, 219L, 3L, 96L, 7L, 216L, 201L, 157L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 19L, 80L, 219L, 
3L, 123L, 7L, 218L, 202L, 158L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 12L, 76L, 219L, 3L, 111L, 7L, 217L, 203L, 159L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 13L, 76L, 220L, 
3L, 113L, 7L, 217L, 204L, 160L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 16L, 78L, 220L, 2L, 104L, 7L, 216L, 205L, 161L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 17L, 79L, 219L, 
3L, 109L, 7L, 216L, 206L, 163L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 23L, 54L, 219L, 3L, 105L, 7L, 216L, 208L, 51L, 164L, 
51L, 51L, 51L, 51L, 51L, 24L, 54L, 220L, 3L, 92L, 7L, 217L, 209L, 
165L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 47L, 70L, 
220L, 3L, 87L, 7L, 217L, 210L, 166L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L), .Label = c("36=32mod4", "correct_0", 
"correct_1", "difficulty_Easy", "difficulty_Hard", "difficulty_Intermediate", 
"difficulty_Very", "id_1058", "id_10975", "id_11207", "id_1129", 
"id_12052", "id_12069", "id_12131", "id_12453", "id_13285", "id_13741", 
"id_13817", "id_14467", "id_14596", "id_14907", "id_15262", "id_1544", 
"id_1555", "id_15661", "id_15684", "id_15693", "id_1685", "id_2295", 
"id_2479", "id_2820", "id_313", "id_3645", "id_3985", "id_4333", 
"id_4541", "id_5249", "id_5426", "id_5684", "id_5756", "id_6016", 
"id_6326", "id_7019", "id_7064", "id_7885", "id_8660", "id_8728", 
"id_9028", "id_9263", "id_9419", "L", "modulo_26", "modulo_36", 
"modulo_40", "modulo_42", "modulo_46", "modulo_47", "modulo_50", 
"modulo_55", "modulo_57", "modulo_58", "modulo_59", "modulo_63", 
"modulo_64", "modulo_65", "modulo_66", "modulo_68", "modulo_71", 
"modulo_74", "modulo_77", "modulo_78", "modulo_79", "modulo_80", 
"modulo_85", "modulo_86", "modulo_89", "modulo_90", "modulo_93", 
"modulo_94", "modulo_96", "modulo_97", "modulo_98", "modulo_99", 
"RT_10590", "RT_1367", "RT_14182", "RT_15412", "RT_1550", "RT_17151", 
"RT_17302", "RT_1736", "RT_1891", "RT_2002", "RT_2227", "RT_2241", 
"RT_2432", "RT_2510", "RT_2624", "RT_2660", "RT_2840", "RT_2956", 
"RT_2984", "RT_3029", "RT_3154", "RT_3273", "RT_3283", "RT_3727", 
"RT_3900", "RT_4493", "RT_4544", "RT_4840", "RT_5095", "RT_5368", 
"RT_5583", "RT_5618", "RT_6009", "RT_6385", "RT_6423", "RT_6489", 
"RT_6689", "RT_7471", "RT_7669", "RT_7697", "RT_8156", "RT_8752", 
"RT_8784", "start_consigne", "start_trial_0", "start_trial_1", 
"start_trial_10", "start_trial_11", "start_trial_12", "start_trial_13", 
"start_trial_14", "start_trial_15", "start_trial_16", "start_trial_17", 
"start_trial_18", "start_trial_19", "start_trial_2", "start_trial_20", 
"start_trial_21", "start_trial_22", "start_trial_23", "start_trial_24", 
"start_trial_25", "start_trial_26", "start_trial_27", "start_trial_28", 
"start_trial_29", "start_trial_3", "start_trial_30", "start_trial_31", 
"start_trial_32", "start_trial_33", "start_trial_34", "start_trial_35", 
"start_trial_36", "start_trial_37", "start_trial_38", "start_trial_39", 
"start_trial_4", "start_trial_40", "start_trial_41", "start_trial_42", 
"start_trial_43", "start_trial_5", "start_trial_6", "start_trial_7", 
"start_trial_8", "start_trial_9", "stop_consigne", "stop_trial_0", 
"stop_trial_1", "stop_trial_10", "stop_trial_11", "stop_trial_12", 
"stop_trial_13", "stop_trial_14", "stop_trial_15", "stop_trial_16", 
"stop_trial_17", "stop_trial_18", "stop_trial_19", "stop_trial_2", 
"stop_trial_20", "stop_trial_21", "stop_trial_22", "stop_trial_23", 
"stop_trial_24", "stop_trial_25", "stop_trial_26", "stop_trial_27", 
"stop_trial_28", "stop_trial_29", "stop_trial_3", "stop_trial_30", 
"stop_trial_31", "stop_trial_32", "stop_trial_33", "stop_trial_34", 
"stop_trial_35", "stop_trial_36", "stop_trial_37", "stop_trial_38", 
"stop_trial_39", "stop_trial_4", "stop_trial_40", "stop_trial_41", 
"stop_trial_42", "stop_trial_5", "stop_trial_6", "stop_trial_7", 
"stop_trial_8", "stop_trial_9", "strat_1", "strat_2", "strat_4", 
"val_0", "val_1"), class = "factor"), PS = c(NA, 904L, 906L, 
838L, 805L, 789L, 797L, 876L, 924L, 928L, 964L, 957L, 935L, 861L, 
834L, 856L, 846L, 811L, 825L, 869L, 904L, 936L, 969L, 965L, 1016L, 
1018L, 1030L, 1015L, 999L, 987L, 1017L, 1064L, 1080L, 1061L, 
1075L, 1046L, 1005L, 1014L, 1023L, 1040L, 1051L, 1046L, 1010L, 
971L, 994L, 1071L, 1082L, 1120L, 1119L, 1044L, 1023L, 978L, 947L, 
925L, 900L, 940L, NA, 963L, NA, 995L, 1013L, 1046L, 1005L, 1013L, 
1043L, 1146L, 1205L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1306L, 
1334L, 1285L, 1297L, 1257L, 1206L, 1206L, 1256L, 1252L, 1189L, 
1254L, 1214L, 1203L, 1207L, 1263L, 1224L, 1235L, 1258L, 1210L, 
1186L, 1201L, 1271L, 1246L, 1274L, 1337L, 1325L, 1551L, 1733L, 
1812L, 1568L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1272L, 1218L, 
1227L, 1165L, 1145L, 1192L, 1199L, 1208L, 1248L, 1280L, 1224L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1220L, NA, 1229L, 1250L, 1372L, 
1102L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1141L, 1163L, 1146L, 
1129L, 1190L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1182L, 1152L, 
1134L, 1179L, 1178L, 1267L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1272L, 1186L, 1164L, 1173L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1265L, 1191L, 1109L, 1150L, 1125L, 1090L, 1139L, 1205L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1277L, 1164L, 1122L, 1113L, 1115L, 
1121L, 1168L, NA, NA, NA, NA, NA, NA, NA, NA, 1235L, NA, 1207L, 
1164L, 1145L, 1177L, 1242L, 1224L, 1281L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1234L, 1232L, 1204L, 1198L, 1108L, 1131L, 1220L, 
1228L, 1227L, 1231L, 1299L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1211L, 1266L, 1294L, 1292L, 1129L, 1182L, 1175L, 1211L, 1233L, 
1206L, 1185L, 1307L, 1209L, 1206L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1245L, 1264L, 1283L, 1246L, 1290L, 1344L, 1311L, 1267L, 
1201L, 1188L, 1164L, 1218L, 1188L, 1156L, 1144L, 1121L, 1145L, 
1176L, 1155L, 1103L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1173L, 
1223L, 1218L, 1170L, 1120L, 1084L, 1096L, 1092L, 985L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1043L, 1092L, 1090L, 1126L, 1099L, 
1125L, 1175L, 1099L, 1102L, 1188L, 1215L, 1225L, 1197L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1292L, 1338L, 1322L, 1284L, 
1296L, 1273L, 1251L, 1216L, 1205L, 1200L, 1165L, 1097L, 1132L, 
1209L, 1243L, 1295L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1288L, 
1286L, 1243L, 1245L, 1215L, 1213L, 1215L, 1283L, 1280L, 1275L, 
1334L, 1301L, 1205L, 1215L, 1267L, 1245L, 1203L, 1071L, 1113L, 
1160L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1243L, 1249L, 
1268L, 1266L, 1299L, 1363L, 1215L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 938L, 831L, 929L, 999L, 1033L, 1090L, 1092L, 1094L, 1139L, 
1144L, 1225L, 1203L, 1199L, 1261L, 1221L, 1230L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1291L, 1308L, 1270L, 1250L, 1276L, 1226L, 
1197L, 1201L, 1213L, 1195L, 1202L, 1201L, 1194L, 1192L, 1190L, 
1206L, 1244L, 1203L, 1228L, 1239L, 1218L, 1218L, 1217L, 1218L, 
1202L, 1224L, 1177L, 1134L, 1134L, 1152L, 1159L, 1162L, 1168L, 
1107L, 1175L, 1200L, 1173L, 1203L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1266L, 1278L, 1227L, 1188L, 1184L, 1178L, 1167L, 1194L, 
1131L, 1166L, 1203L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1223L, 1226L, 1218L, 1208L, 1142L, 1105L, 1122L, 1156L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1118L, 1133L, 1150L, 1115L, 1070L, 
1078L, 1145L, 1156L, 1175L, 1172L, 1129L, 1134L, 1089L, 1144L, 
1171L, 1179L, 1195L, 1194L, 1231L, 1275L, 1250L, 1273L, 1268L, 
1221L, 1245L, 1211L, 1195L, 1197L, 1194L, 1140L, 1168L, 1220L, 
1197L, 1191L, 1240L, 1288L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1339L, 1327L, 1324L, 1320L, 1242L, 1231L, 1253L, 1255L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1297L, 1303L, 1282L, 1252L, 
1200L, 1202L, 1191L, 1177L, 1220L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1221L, 1224L, 1203L, 1162L, 1175L, 1187L, 1184L, 1165L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1200L, 1225L, 1200L, 1205L, 
1219L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1269L, 1209L, 1161L, 
1171L, 1165L, 1140L, 1120L, 1127L, 1076L, 1081L, 1081L, 1114L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1181L, 1186L, 1189L, 1200L, 
1179L, 1186L, 1171L, 1134L, 1012L, 1004L, 1134L, 1090L, 1146L, 
1222L, 1309L, 1334L, 1354L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1240L, 1121L, 1101L, 1104L, 1142L, 1157L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1197L, 1264L, 1217L, 1181L, 1173L, 1160L, 1147L, 
1174L, 1188L, 1183L, 1162L, 1188L, 1273L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1303L, 1335L, 1346L, 1284L, 1227L, 1245L, 1295L, 
1291L, 1284L, 1125L, 1176L, 1214L, 1206L, 1216L, 1232L, 1234L, 
1268L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1326L, 1284L, 1265L, 
1237L, 1206L, 1212L, 1197L, 1181L, 1216L, 1222L, 1205L, 1148L, 
1163L, 1154L, 1138L, 1146L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1250L, 1229L, 1209L, 1199L, 1165L, 1191L, 1145L, 1130L, 1116L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1101L, NA, 1113L, 1126L, 1138L, 
1160L, 1128L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1155L, 1132L, 
1122L, 1146L, 1145L, 1146L, 1171L, 1103L, 1170L, 1136L, 1177L, 
1108L, 1106L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1175L, 1192L, 
1129L, 1163L, 1187L, 1177L, 1162L, 1184L, 1129L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1221L, 1113L, 1089L, 1099L, 1022L, 995L, 
947L, 1012L, 1065L, 1114L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1163L, 1094L, 1098L, 1139L, 1130L, 1117L, 1087L, 1084L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1137L, 1145L, 1130L, 1105L, 1123L, 
1112L, 1048L, 1055L, 1078L, 1147L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1207L, 1164L, 1169L, 1188L, 1189L, 1140L, 1099L, 1178L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1208L, NA, 1258L, 1207L, 1158L, 
1140L, 1099L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1123L, 1083L, 
1043L, 1066L, 1082L, 1049L, 1040L, 1090L, 1112L, 1069L, 1079L, 
1061L, 1029L, 1032L, 1046L, 1170L, 1197L, 956L, 941L, 1076L, 
1136L, 1208L, 1213L, 1207L, 1186L, 1225L, 1222L, 1232L, 1169L, 
1102L, 1144L, 1178L, 1218L, 1211L, 1229L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1255L, 1260L, 1236L, 1271L, 1312L, 1346L, 1272L, 
1171L, 1192L, 1235L, 1296L), Modulo = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 19L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 44L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 14L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 26L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 43L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 27L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 39L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 42L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
18L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 29L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 23L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 24L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 15L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 17L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 40L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 41L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 11L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
22L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 30L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 16L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 33L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 34L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
37L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 20L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 21L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 38L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 31L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 32L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 35L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 36L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 25L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = c(" ", "26=12mod6", "36=16mod4", "36=32mod4", 
"40=33mod8", "40=36mod2", "42=12mod6", "46=34mod6", "47=44mod2", 
"50=20mod4", "55=20mod4", "57=15mod6", "58=52mod4", "59=57mod2", 
"63=33mod4", "64=24mod8", "65=35mod6", "65=51mod6", "66=61mod6", 
"68=26mod6", "71=21mod6", "71=31mod4", "74=53mod8", "77=53mod4", 
"77=69mod2", "78=75mod4", "79=67mod6", "80=40mod4", "85=65mod4", 
"86=50mod8", "89=32mod2", "89=35mod2", "89=49mod4", "90=50mod6", 
"93=13mod8", "94=43mod4", "94=61mod4", "96=54mod4", "96=82mod6", 
"97=75mod2", "98=76mod2", "99=85mod8", "99=91mod8", "99=93mod6"
), class = "factor")), class = "data.frame", row.names = c(NA, 
-997L))````

Re arrange eye tracking data

5 Answers5

Split the data

Caveats: