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Print a TNA Data Object

Source: R/print.R
print.tna_data.Rd

Print a TNA Data Object

Usage

# S3 method for class 'tna_data'
print(x, data = "sequence", ...)

Arguments

x

A tna_data object.

data

A character string that defines the data to be printed tibble. Accepts either "sequence" (default) for wide format sequence data, "meta", for the wide format metadata, or "long" for the long format data.

...

Arguments passed to the tibble print method.

Value

x (invisibly).

See also

Other data: import_data(), import_onehot(), list_random_state_pools(), prepare_data(), random_group_tna(), random_tna(), random_tna_mmm(), simulate.group_tna(), simulate.tna()

Examples

res <- prepare_data(group_regulation_long, action = "Action", actor = "Actor",
time = "Time")
#> ── Preparing Data ──────────────────────────────────────────────────────────────
#> ℹ Input data dimensions: 27533 rows, 6 columns
#> ℹ First few time values: 2025-01-01 08:27:07.712698, 2025-01-01
#>   08:35:20.712698, and 2025-01-01 08:42:18.712698
#> ℹ Number of values to parse: 27533
#> ℹ Sample values: 2025-01-01 08:27:07.712698, 2025-01-01 08:35:20.712698, and
#>   2025-01-01 08:42:18.712698
#> ℹ Sample of parsed times: 2025-01-01 08:27:07.712698, 2025-01-01
#>   08:35:20.712698, and 2025-01-01 08:42:18.712698
#> ℹ Time threshold for new session: 900 seconds
#> ℹ Total number of sessions: 2000
#> ℹ Number of unique users: 2000
#> ℹ Total number of actions: 27533
#> ℹ Maximum sequence length: 26 actions
#> ℹ Time range: 2025-01-01 08:01:16.009382 to 2025-01-01 13:03:20.238288
print(res, which = "sequence")
#> # A tibble: 2,000 × 26
#>    Action_T1 Action_T2  Action_T3  Action_T4 Action_T5 Action_T6 Action_T7
#>    <chr>     <chr>      <chr>      <chr>     <chr>     <chr>     <chr>    
#>  1 cohesion  consensus  discuss    synthesis adapt     consensus plan     
#>  2 plan      emotion    consensus  discuss   synthesis adapt     emotion  
#>  3 consensus coregulate monitor    consensus plan      emotion   consensus
#>  4 monitor   emotion    plan       discuss   synthesis consensus discuss  
#>  5 discuss   emotion    cohesion   NA        NA        NA        NA       
#>  6 plan      plan       consensus  plan      plan      plan      plan     
#>  7 plan      discuss    coregulate NA        NA        NA        NA       
#>  8 plan      emotion    consensus  discuss   consensus plan      consensus
#>  9 discuss   consensus  NA         NA        NA        NA        NA       
#> 10 emotion   cohesion   discuss    synthesis NA        NA        NA       
#> # ℹ 1,990 more rows
#> # ℹ 19 more variables: Action_T8 <chr>, Action_T9 <chr>, Action_T10 <chr>,
#> #   Action_T11 <chr>, Action_T12 <chr>, Action_T13 <chr>, Action_T14 <chr>,
#> #   Action_T15 <chr>, Action_T16 <chr>, Action_T17 <chr>, Action_T18 <chr>,
#> #   Action_T19 <chr>, Action_T20 <chr>, Action_T21 <chr>, Action_T22 <chr>,
#> #   Action_T23 <chr>, Action_T24 <chr>, Action_T25 <chr>, Action_T26 <chr>
print(res, which = "meta")
#> # A tibble: 2,000 × 26
#>    Action_T1 Action_T2  Action_T3  Action_T4 Action_T5 Action_T6 Action_T7
#>    <chr>     <chr>      <chr>      <chr>     <chr>     <chr>     <chr>    
#>  1 cohesion  consensus  discuss    synthesis adapt     consensus plan     
#>  2 plan      emotion    consensus  discuss   synthesis adapt     emotion  
#>  3 consensus coregulate monitor    consensus plan      emotion   consensus
#>  4 monitor   emotion    plan       discuss   synthesis consensus discuss  
#>  5 discuss   emotion    cohesion   NA        NA        NA        NA       
#>  6 plan      plan       consensus  plan      plan      plan      plan     
#>  7 plan      discuss    coregulate NA        NA        NA        NA       
#>  8 plan      emotion    consensus  discuss   consensus plan      consensus
#>  9 discuss   consensus  NA         NA        NA        NA        NA       
#> 10 emotion   cohesion   discuss    synthesis NA        NA        NA       
#> # ℹ 1,990 more rows
#> # ℹ 19 more variables: Action_T8 <chr>, Action_T9 <chr>, Action_T10 <chr>,
#> #   Action_T11 <chr>, Action_T12 <chr>, Action_T13 <chr>, Action_T14 <chr>,
#> #   Action_T15 <chr>, Action_T16 <chr>, Action_T17 <chr>, Action_T18 <chr>,
#> #   Action_T19 <chr>, Action_T20 <chr>, Action_T21 <chr>, Action_T22 <chr>,
#> #   Action_T23 <chr>, Action_T24 <chr>, Action_T25 <chr>, Action_T26 <chr>
print(res, which = "long")
#> # A tibble: 2,000 × 26
#>    Action_T1 Action_T2  Action_T3  Action_T4 Action_T5 Action_T6 Action_T7
#>    <chr>     <chr>      <chr>      <chr>     <chr>     <chr>     <chr>    
#>  1 cohesion  consensus  discuss    synthesis adapt     consensus plan     
#>  2 plan      emotion    consensus  discuss   synthesis adapt     emotion  
#>  3 consensus coregulate monitor    consensus plan      emotion   consensus
#>  4 monitor   emotion    plan       discuss   synthesis consensus discuss  
#>  5 discuss   emotion    cohesion   NA        NA        NA        NA       
#>  6 plan      plan       consensus  plan      plan      plan      plan     
#>  7 plan      discuss    coregulate NA        NA        NA        NA       
#>  8 plan      emotion    consensus  discuss   consensus plan      consensus
#>  9 discuss   consensus  NA         NA        NA        NA        NA       
#> 10 emotion   cohesion   discuss    synthesis NA        NA        NA       
#> # ℹ 1,990 more rows
#> # ℹ 19 more variables: Action_T8 <chr>, Action_T9 <chr>, Action_T10 <chr>,
#> #   Action_T11 <chr>, Action_T12 <chr>, Action_T13 <chr>, Action_T14 <chr>,
#> #   Action_T15 <chr>, Action_T16 <chr>, Action_T17 <chr>, Action_T18 <chr>,
#> #   Action_T19 <chr>, Action_T20 <chr>, Action_T21 <chr>, Action_T22 <chr>,
#> #   Action_T23 <chr>, Action_T24 <chr>, Action_T25 <chr>, Action_T26 <chr>