mt_import_long imports the wrong trajectories when mt_id_label contains mixed case data

[LiKao]

Description

If the function mt_import_long is called with a mt_id_label value that references columns that contain mixed case, then the data is imported in the wrong order, i.e. the wrong mousetracks are assigned to the incorrect mt_ids.

Reproducible example

dt <- expand.grid(sbj=c(1,2,3),condition=c(3,2,1),timestamp=seq(1:10)) %>%
    mutate(condition=c("a","b","C")[condition]) %>%
    mutate(xpos=c(a=10,b=20,C=30)[condition]*timestamp) %>%
    mutate(ypos=c(a=1,b=2,C=3)[condition]+timestamp)

 mt_import_long(dt,mt_id_label = c("sbj","condition"),timestamps_label = "timestamp")

Expected:

, , xpos

    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
1_C   30   60   90  120  150  180  210  240  270   300
2_C   30   60   90  120  150  180  210  240  270   300
3_C   30   60   90  120  150  180  210  240  270   300
1_b   20   40   60   80  100  120  140  160  180   200
2_b   20   40   60   80  100  120  140  160  180   200
3_b   20   40   60   80  100  120  140  160  180   200
1_a   10   20   30   40   50   60   70   80   90   100
2_a   10   20   30   40   50   60   70   80   90   100
3_a   10   20   30   40   50   60   70   80   90   100

Actual:

, , xpos

    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
1_C   10   20   30   40   50   60   70   80   90   100
2_C   10   20   30   40   50   60   70   80   90   100
3_C   10   20   30   40   50   60   70   80   90   100
1_b   30   60   90  120  150  180  210  240  270   300
2_b   30   60   90  120  150  180  210  240  270   300
3_b   30   60   90  120  150  180  210  240  270   300
1_a   20   40   60   80  100  120  140  160  180   200
2_a   20   40   60   80  100  120  140  160  180   200
3_a   20   40   60   80  100  120  140  160  180   200

I.e. the condition A is imported in the position of condition C.

Analysis:

This is caused by a difference how dplyr::count (here) and order (here) sort mixed case data.

Example:

dt <- dt %>% mutate(mt_id = paste0(sbj,"_",condition))

dt[order(dt$mt_id),]    # Produces order a,b,C
dplyr::count(dt,mt_id) # Produces order C,a,b

[PascalKieslich]

Thanks a lot for discovering this - and for providing the reproducible example and detailed description. I have fixed this issue now so that the original order of the trajectories is preserved.

Relating to your example above, the imported trajectory array now corresponds to the expected array:

...

, , xpos

    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
1_C   30   60   90  120  150  180  210  240  270   300
2_C   30   60   90  120  150  180  210  240  270   300
3_C   30   60   90  120  150  180  210  240  270   300
1_b   20   40   60   80  100  120  140  160  180   200
2_b   20   40   60   80  100  120  140  160  180   200
3_b   20   40   60   80  100  120  140  160  180   200
1_a   10   20   30   40   50   60   70   80   90   100
2_a   10   20   30   40   50   60   70   80   90   100
3_a   10   20   30   40   50   60   70   80   90   100

...