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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(), prepare_data(), 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 emotion   cohesion  discuss   synthesis NA         NA         NA       
#>  3 plan      consensus plan      NA        NA         NA         NA       
#>  4 discuss   discuss   consensus plan      cohesion   consensus  discuss  
#>  5 cohesion  consensus plan      plan      monitor    plan       consensus
#>  6 discuss   adapt     cohesion  consensus discuss    emotion    cohesion 
#>  7 discuss   emotion   cohesion  consensus coregulate coregulate plan     
#>  8 cohesion  plan      consensus plan      consensus  discuss    discuss  
#>  9 emotion   cohesion  emotion   plan      monitor    discuss    emotion  
#> 10 emotion   cohesion  consensus plan      plan       plan       plan     
#> # 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 emotion   cohesion  discuss   synthesis NA         NA         NA       
#>  3 plan      consensus plan      NA        NA         NA         NA       
#>  4 discuss   discuss   consensus plan      cohesion   consensus  discuss  
#>  5 cohesion  consensus plan      plan      monitor    plan       consensus
#>  6 discuss   adapt     cohesion  consensus discuss    emotion    cohesion 
#>  7 discuss   emotion   cohesion  consensus coregulate coregulate plan     
#>  8 cohesion  plan      consensus plan      consensus  discuss    discuss  
#>  9 emotion   cohesion  emotion   plan      monitor    discuss    emotion  
#> 10 emotion   cohesion  consensus plan      plan       plan       plan     
#> # 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 emotion   cohesion  discuss   synthesis NA         NA         NA       
#>  3 plan      consensus plan      NA        NA         NA         NA       
#>  4 discuss   discuss   consensus plan      cohesion   consensus  discuss  
#>  5 cohesion  consensus plan      plan      monitor    plan       consensus
#>  6 discuss   adapt     cohesion  consensus discuss    emotion    cohesion 
#>  7 discuss   emotion   cohesion  consensus coregulate coregulate plan     
#>  8 cohesion  plan      consensus plan      consensus  discuss    discuss  
#>  9 emotion   cohesion  emotion   plan      monitor    discuss    emotion  
#> 10 emotion   cohesion  consensus plan      plan       plan       plan     
#> # 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>