Running ‘testthat.R’ [109s/171s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(ggseqplot)
  > 
  > test_check("ggseqplot")
  
  TraMineR stable version 2.2-6 (Built: 2023-03-18)
  Website: http://traminer.unige.ch
  Please type 'citation("TraMineR")' for citation information.
  
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        > 37 hours
       2  B           B        19-36 hours
       3  C           C        1-18 hours
       4  D           D        no work
   [>] 300 sequences in the data set
   [>] min/max sequence length: 12/12
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] sum of weights: 330.07 - min/max: 0/6.02881860733032
   [>] 300 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] 300 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        > 37 hours
       2  B           B        19-36 hours
       3  C           C        1-18 hours
       4  D           D        no work
   [>] 300 sequences in the data set
   [>] min/max sequence length: 12/12
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        > 37 hours
       2  B           B        19-36 hours
       3  C           C        1-18 hours
       4  D           D        no work
   [>] 300 sequences in the data set
   [>] min/max sequence length: 12/12
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        > 37 hours
       2  B           B        19-36 hours
       3  C           C        1-18 hours
       4  D           D        no work
   [>] 300 sequences in the data set
   [>] min/max sequence length: 12/12
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        > 37 hours
       2  B           B        19-36 hours
       3  C           C        1-18 hours
       4  D           D        no work
   [>] 300 sequences in the data set
   [>] min/max sequence length: 12/12
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] 100 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] sum of weights: 111.62 - min/max: 0/4.17260217666626
   [>] 100 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] 100 sequences with 8 distinct states
   [>] creating a 'sm' with a substitution cost of 2
   [>] creating 8x8 substitution-cost matrix using 2 as constant value
   [>] 76 distinct  sequences 
   [>] min/max sequence lengths: 16/16
   [>] computing distances using the LCS metric
   [>] elapsed time: 0.04 secs
   [>] computing state distribution for 100 sequences ...
   [>] Using k=12 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.3064171
   [>] Pseudo/medoid-based-F statistic: 4.001018, p-value: 7.736543e-05
   [>] Using k=12 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.45323
   [>] Pseudo/medoid-based-F statistic: 6.63138, p-value: 5.825085e-08
   [>] Using k=12 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.45323
   [>] Pseudo/medoid-based-F statistic: 6.63138, p-value: 5.825085e-08
   [>] Using k=12 frequency groups with grp.meth='first'
   [>] Pseudo/medoid-based-R2: 0.4620155
   [>] Pseudo/medoid-based-F statistic: 6.870317, p-value: 3.09994e-08
   [>] Using k=11 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.4604651
   [>] Pseudo/medoid-based-F statistic: 7.59569, p-value: 1.205403e-08
   [>] Using k=10 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.448062
   [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08
   [>] Using k=10 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.448062
   [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08
  Scale for y is already present.
  Adding another scale for y, which will replace the existing scale.
   [>] Using k=10 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.448062
   [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08
   [>] Using k=10 frequency groups with grp.meth='prop'
   [>] Pseudo/medoid-based-R2: 0.448062
   [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] 100 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] 100 sequences with 8 distinct states
   [>] creating a 'sm' with a substitution cost of 2
   [>] creating 8x8 substitution-cost matrix using 2 as constant value
   [>] 76 distinct  sequences 
   [>] min/max sequence lengths: 16/16
   [>] computing distances using the LCS metric
   [>] elapsed time: 0.035 secs
   [>] number of objects (sum of weights): 100
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=27% (threshold=25%)
   [>] 76 distinct sequence(s)
   [>] number of objects (sum of weights): 100
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=27% (threshold=25%)
   [>] 76 distinct sequence(s)
   [>] number of objects (sum of weights): 7
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 1 representative(s) selected, coverage=29% (threshold=25%)
   [>] 6 distinct sequence(s)
   [>] number of objects (sum of weights): 3
   [>] max. distance: 24
   [>] neighborhood radius: 2.4
   [>] 1 representative(s) selected, coverage=33% (threshold=25%)
   [>] 3 distinct sequence(s)
   [>] number of objects (sum of weights): 4
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 1 representative(s) selected, coverage=25% (threshold=25%)
   [>] 4 distinct sequence(s)
   [>] number of objects (sum of weights): 11
   [>] max. distance: 22
   [>] neighborhood radius: 2.2
   [>] 2 representative(s) selected, coverage=27% (threshold=25%)
   [>] 11 distinct sequence(s)
   [>] number of objects (sum of weights): 7
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 2 representative(s) selected, coverage=29% (threshold=25%)
   [>] 7 distinct sequence(s)
   [>] number of objects (sum of weights): 7
   [>] max. distance: 24
   [>] neighborhood radius: 2.4
   [>] 1 representative(s) selected, coverage=29% (threshold=25%)
   [>] 7 distinct sequence(s)
   [>] number of objects (sum of weights): 12
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 2 representative(s) selected, coverage=25% (threshold=25%)
   [>] 12 distinct sequence(s)
   [>] number of objects (sum of weights): 11
   [>] max. distance: 24
   [>] neighborhood radius: 2.4
   [>] 3 representative(s) selected, coverage=27% (threshold=25%)
   [>] 11 distinct sequence(s)
   [>] number of objects (sum of weights): 14
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 2 representative(s) selected, coverage=29% (threshold=25%)
   [>] 14 distinct sequence(s)
   [>] number of objects (sum of weights): 15
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 2 representative(s) selected, coverage=27% (threshold=25%)
   [>] 15 distinct sequence(s)
   [>] number of objects (sum of weights): 9
   [>] max. distance: 22
   [>] neighborhood radius: 2.2
   [>] 2 representative(s) selected, coverage=33% (threshold=25%)
   [>] 9 distinct sequence(s)
  i You are trying to render a representative sequence plot for many groups.
    The resulting output (if produced at all) might be hard to decipher.
    Consider reducing the number of distinct groups.
  i You are trying to render a representative sequence plot for many groups using just one column.
    The resulting output (if produced at all) might be hard to decipher.
    Consider reducing the number of distinct groups or increase facet_ncol.
   [>] number of objects (sum of weights): 21
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=29% (threshold=25%)
   [>] 21 distinct sequence(s)
   [>] number of objects (sum of weights): 20
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 3 representative(s) selected, coverage=30% (threshold=25%)
   [>] 18 distinct sequence(s)
   [>] number of objects (sum of weights): 23
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 2 representative(s) selected, coverage=30% (threshold=25%)
   [>] 19 distinct sequence(s)
   [>] number of objects (sum of weights): 17
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 4 representative(s) selected, coverage=29% (threshold=25%)
   [>] 17 distinct sequence(s)
   [>] number of objects (sum of weights): 19
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 1 representative(s) selected, coverage=26% (threshold=25%)
   [>] 16 distinct sequence(s)
  i You are trying to render a representative sequence plot for many groups using just one column.
    The resulting output (if produced at all) might be hard to decipher.
    Consider reducing the number of distinct groups or increase facet_ncol.
   [>] number of objects (sum of weights): 100
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=27% (threshold=25%)
   [>] 76 distinct sequence(s)
   [>] number of objects (sum of weights): 100
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 11 representative(s) selected
   [>] 76 distinct sequence(s)
   [>] number of objects (sum of weights): 100
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=27% (threshold=25%)
   [>] 76 distinct sequence(s)
   [>] number of objects (sum of weights): 100
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=27% (threshold=25%)
   [>] 76 distinct sequence(s)
   [>] number of objects (sum of weights): 21
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=29% (threshold=25%)
   [>] 21 distinct sequence(s)
   [>] number of objects (sum of weights): 20
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 3 representative(s) selected, coverage=30% (threshold=25%)
   [>] 18 distinct sequence(s)
   [>] number of objects (sum of weights): 23
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 2 representative(s) selected, coverage=30% (threshold=25%)
   [>] 19 distinct sequence(s)
   [>] number of objects (sum of weights): 17
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 4 representative(s) selected, coverage=29% (threshold=25%)
   [>] 17 distinct sequence(s)
   [>] number of objects (sum of weights): 19
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 1 representative(s) selected, coverage=26% (threshold=25%)
   [>] 16 distinct sequence(s)
  i You are trying to render a representative sequence plot for many groups using just one column.
    The resulting output (if produced at all) might be hard to decipher.
    Consider reducing the number of distinct groups or increase facet_ncol.
   [>] number of objects (sum of weights): 21
   [>] max. distance: 32
   [>] neighborhood radius: 3.2
   [>] 3 representative(s) selected, coverage=29% (threshold=25%)
   [>] 21 distinct sequence(s)
   [>] number of objects (sum of weights): 20
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 3 representative(s) selected, coverage=30% (threshold=25%)
   [>] 18 distinct sequence(s)
   [>] number of objects (sum of weights): 23
   [>] max. distance: 30
   [>] neighborhood radius: 3
   [>] 2 representative(s) selected, coverage=30% (threshold=25%)
   [>] 19 distinct sequence(s)
   [>] number of objects (sum of weights): 17
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 4 representative(s) selected, coverage=29% (threshold=25%)
   [>] 17 distinct sequence(s)
   [>] number of objects (sum of weights): 19
   [>] max. distance: 28
   [>] neighborhood radius: 2.8
   [>] 1 representative(s) selected, coverage=26% (threshold=25%)
   [>] 16 distinct sequence(s)
  i You are trying to render a representative sequence plot for many groups using just one column.
    The resulting output (if produced at all) might be hard to decipher.
    Consider reducing the number of distinct groups or increase facet_ncol.
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] sum of weights: 330.07 - min/max: 0/6.02881860733032
   [>] 300 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] 8 distinct states appear in the data: 
       1 = 0
       2 = 1
       3 = 2
       4 = 3
       5 = 4
       6 = 5
       7 = 6
       8 = 7
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  0           0        Parent
       2  1           1        Left
       3  2           2        Married
       4  3           3        Left+Marr
       5  4           4        Child
       6  5           5        Left+Child
       7  6           6        Left+Marr+Child
       8  7           7        Divorced
   [>] 300 sequences in the data set
   [>] min/max sequence length: 16/16
   [>] found missing values ('NA') in sequence data
   [>] preparing 7 sequences
   [>] coding void elements with '%' and missing values with '*'
   [!!] 1 empty sequence(s) with index: 7
        may produce inconsistent results.
   [>] 4 distinct states appear in the data: 
       1 = A
       2 = B
       3 = C
       4 = D
   [>] state coding:
         [alphabet]  [label]  [long label] 
       1  A           A        A
       2  B           B        B
       3  C           C        C
       4  D           D        D
   [>] sum of weights: 60 - min/max: 0/29.3
   [>] 7 sequences in the data set
   [>] min/max sequence length: 0/13
   [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ...
   [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ...
   [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ...
   [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ...
   [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ...
   [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ...
   [>] computing transition probabilities for states A/B/C/D ...
   [>] computing transition probabilities for states A/B/C/D/* ...
   [>] computing transition probabilities for states A/B/C/D ...
  [ FAIL 1 | WARN 1039 | SKIP 0 | PASS 129 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-ggseqiplot.R:42:3'): check if output of ggseqiplot is ggplot ───
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
       ▆
    1. ├─testthat::expect_s3_class(ggseqiplot(ex1.seq), "ggplot") at test-ggseqiplot.R:42:2
    2. │ └─testthat::quasi_label(enquo(object), arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─ggseqplot::ggseqiplot(ex1.seq)
    5.   ├─base::suppressMessages(...)
    6.   │ └─base::withCallingHandlers(...)
    7.   ├─dplyr::rename(...)
    8.   ├─dplyr::as_tibble(...)
    9.   ├─dplyr::mutate(...)
   10.   ├─dplyr::group_by(...)
   11.   ├─dplyr::mutate(...)
   12.   ├─dplyr::select(...)
   13.   ├─dplyr::full_join(...)
   14.   └─dplyr:::full_join.data.frame(...)
   15.     └─dplyr:::join_mutate(...)
   16.       ├─base::`[<-`(`*tmp*`, new_rows, merge, value = `<tibble[,1]>`)
   17.       └─tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,1]>`)
   18.         └─tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   19.           └─tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   20.             ├─base::withCallingHandlers(...)
   21.             └─tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  
  [ FAIL 1 | WARN 1039 | SKIP 0 | PASS 129 ]
  Error: Test failures
  Execution halted