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We can get an informative summary table from a collective of agents by using the get_multiagent_report() function. Information from multiple agent can be provided in three very forms: (1) the Long Display (stacked reports), (2) the Wide Display (a comparison report), (3) as a tibble with packed columns.


  display_table = TRUE,
  display_mode = c("long", "wide"),
  title = ":default:",
  lang = NULL,
  locale = NULL



A multiagent object of class ptblank_multiagent.


Should a display table be generated? If TRUE (the default) a display table for the report will be shown in the Viewer. If FALSE then a tibble will be returned.


If we are getting a display table, should the agent data be presented in a "long" or "wide" form? The default is "long" but when comparing multiple runs where the target table is the same it might be preferable to choose "wide".


Options for customizing the title of the report when display_table = TRUE. The default is the keyword ":default:" which produces generic title text. If no title is wanted, then the ":none:" keyword option can be used. Another keyword option is ":tbl_name:", and that presents the name of the table as the title for the report (this can only be used when display_mode = "long"). Aside from keyword options, text can be provided for the title and glue::glue() calls can be used to construct the text string. If providing text, it will be interpreted as Markdown text and transformed internally to HTML. To circumvent such a transformation, use text in I() to explicitly state that the supplied text should not be transformed.


Reporting language

scalar<character> // default: NULL (optional)

The language to use for the long or wide report forms. By default, NULL will preserve any language set in the component reports. The following options will force the same language across all component reports: English ("en"), French ("fr"), German ("de"), Italian ("it"), Spanish ("es"), Portuguese ("pt"), Turkish ("tr"), Chinese ("zh"), Russian ("ru"), Polish ("pl"), Danish ("da"), Swedish ("sv"), and Dutch ("nl").


Locale for value formatting

scalar<character> // default: NULL (optional)

An optional locale ID to use for formatting values in the long or wide report forms (according the locale's rules). Examples include "en_US" for English (United States) and "fr_FR" for French (France); more simply, this can be a language identifier without a country designation, like "es" for Spanish (Spain, same as "es_ES"). This locale option will override any previously set locale values.


A gt table object if display_table = TRUE or a tibble if display_table = FALSE.

The Long Display

When displayed as "long" the multiagent report will stack individual agent reports in a single document in the order of the agents in the multiagent object.

Each validation plan (possibly with interrogation info) will be provided and the output for each is equivalent to calling get_agent_report() on each of the agents within the multiagent object.

The Wide Display

When displayed as "wide" the multiagent report will show data from individual agents as columns, with rows standing as validation steps common across the agents.

Each validation step is represented with an icon (standing in for the name of the validation function) and the associated SHA1 hash. This is a highly trustworthy way for ascertaining which validation steps are effectively identical across interrogations. This way of organizing the report is beneficial because different agents may have used different steps and we want to track the validation results where the validation step doesn't change but the target table does (i.e., new rows are added, existing rows are updated, etc.).

The single table from this display mode will have the following columns:

  • STEP: the SHA1 hash for the validation step, possibly shared among several interrogations.

  • subsequent columns: each column beyond STEP represents a separate interrogation from an agent object. The time stamp for the completion of each interrogation is shown as the column label.


Let's walk through several theoretical data quality analyses of an extremely small table. that table is called small_table and we can find it as a dataset in this package.

#> # A tibble: 13 x 8
#>    date_time           date           a b             c      d e     f    
#>    <dttm>              <date>     <int> <chr>     <dbl>  <dbl> <lgl> <chr>
#>  1 2016-01-04 11:00:00 2016-01-04     2 1-bcd-345     3  3423. TRUE  high 
#>  2 2016-01-04 00:32:00 2016-01-04     3 5-egh-163     8 10000. TRUE  low  
#>  3 2016-01-05 13:32:00 2016-01-05     6 8-kdg-938     3  2343. TRUE  high 
#>  4 2016-01-06 17:23:00 2016-01-06     2 5-jdo-903    NA  3892. FALSE mid  
#>  5 2016-01-09 12:36:00 2016-01-09     8 3-ldm-038     7   284. TRUE  low  
#>  6 2016-01-11 06:15:00 2016-01-11     4 2-dhe-923     4  3291. TRUE  mid  
#>  7 2016-01-15 18:46:00 2016-01-15     7 1-knw-093     3   843. TRUE  high 
#>  8 2016-01-17 11:27:00 2016-01-17     4 5-boe-639     2  1036. FALSE low  
#>  9 2016-01-20 04:30:00 2016-01-20     3 5-bce-642     9   838. FALSE high 
#> 10 2016-01-20 04:30:00 2016-01-20     3 5-bce-642     9   838. FALSE high 
#> 11 2016-01-26 20:07:00 2016-01-26     4 2-dmx-010     7   834. TRUE  low  
#> 12 2016-01-28 02:51:00 2016-01-28     2 7-dmx-010     8   108. FALSE low  
#> 13 2016-01-30 11:23:00 2016-01-30     1 3-dka-303    NA  2230. TRUE  high

To set failure limits and signal conditions, we designate proportional failure thresholds to the warn, stop, and notify states using action_levels().

al <- 
    warn_at = 0.05,
    stop_at = 0.10,
    notify_at = 0.20

We will create four different agents and have slightly different validation steps in each of them. In the first, agent_1, eight different validation steps are created and the agent will interrogate the small_table.

agent_1 <-
    tbl = small_table,
    label = "An example.",
    actions = al
  ) %>%
    columns = date_time,
    value = vars(date),
    na_pass = TRUE
  ) %>%
    columns = b, 
    value = vars(g),
    na_pass = TRUE
  ) %>%
  rows_distinct() %>%
    columns = d, 
    value = vars(d),
    na_pass = TRUE
  ) %>%
    columns = c, 
    left = vars(a), right = vars(d)
  ) %>%
    columns = c,
    left = 10, right = 20,
    na_pass = TRUE
  ) %>%
  rows_distinct(columns = d, e, f) %>%
  col_is_integer(columns = a) %>%

The second agent, agent_2, retains all of the steps of agent_1 and adds two more (the last of which is inactive).

agent_2 <- 
  agent_1 %>%
  col_exists(columns = date, date_time) %>%
    columns = b, 
    regex = "[0-9]-[a-z]{3}-[0-9]{3}",
    active = FALSE
  ) %>%

The third agent, agent_3, adds a single validation step, removes the fifth one, and deactivates the first.

agent_3 <- 
  agent_2 %>%
    columns = f,
    set = c("low", "mid", "high")
  ) %>%
  remove_steps(i = 5) %>%
  deactivate_steps(i = 1) %>%

The fourth and final agent, agent_4, reactivates steps 1 and 10, and removes the sixth step.

agent_4 <-
  agent_3 %>%
  activate_steps(i = 1) %>%
  activate_steps(i = 10) %>%
  remove_steps(i = 6) %>%

While all the agents are slightly different from each other, we can still get a combined report of them by creating a 'multiagent'.

multiagent <-
    agent_1, agent_2, agent_3, agent_4

Calling multiagent in the console prints the multiagent report. But we can generate a "ptblank_multiagent_report" object with the get_multiagent_report() function and specify options for layout and presentation.

By default, get_multiagent_report() gives you a long report with agent reports being stacked. Think of this "long" option as the serial mode of agent reports. However if we want to view interrogation results of the same table over time, the wide view may be preferable. In this way we can see whether the results of common validation steps improved or worsened over consecutive interrogations of the data.

report_wide <- 
    display_mode = "wide"

This image was generated from the first code example in the `get_multiagent_report()` help file.

Function ID


See also

Other The multiagent: create_multiagent(), read_disk_multiagent()