With the Information Management workflow, table information can be synthesized, giving us a snapshot of a data table we care to collect information on. The pointblank informant is given a series of information functions to define portions of information about a table. This info text can pertain to individual columns, the table as a whole, and whatever additional information makes sense for your organization. We can even glean little snippets of information from the target table and mix them into the info text to make the overall information more dynamic.

## Starting with a Simple Example

This workflow is somewhat similar in concept and usage to the Data Quality Reporting Workflow (VALID-I). Instead of an agent, we use an informant and we get that informant with the create_informant() function. We point the informant to a target table and this can be a data frame, a tibble, a database table (as a tbl_dbi object), or a Spark DataFrame (as a tbl_spark object).

The small_table dataset is included in the pointblank package. It’s a small, uninteresting dataset but it’s useful for simple examples. This is what it looks like:

small_table
## # A tibble: 13 × 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

Let’s use the small_table object and see what results from introducing it to the informant.

informant <-
create_informant(
tbl = small_table,
tbl_name = "small_table",
label = "Example No. 1"
)

Printing the informant will show us the automatically-generated information on the small_table dataset, adding the Columns section.

informant

Alternatively we can get the same report with get_informant_report() and have access to additional output options, like producing a narrower version of the output.

get_informant_report(informant, size = "small")

Either way, what we get in the initial reporting is very basic. What should be done next is to add information with the following set of info_*() functions:

• info_tabular(): Add information that focuses on aspects of the data table as a whole
• info_columns(): Add information that focuses on aspects of a data table’s columns
• info_section(): Add information that focuses on some key aspect of the data table

Let’s try adding some information with each of these functions and then look at the resulting report.

informant <-
create_informant(
tbl = small_table,
tbl_name = "small_table",
label = "Example No. 2"
) %>%
info_tabular(
description = "This table is included in the **pointblank** pkg."
) %>%
info_columns(
columns = "date_time",
info = "This column is full of timestamps."
) %>%
info_section(
section_name = "further information",
examples and documentation = "Examples for how to use the info_*() functions
(and many more) are available at the
[**pointblank** site](https://rich-iannone.github.io/pointblank/)."
)

informant

As can be seen, the report is a bit more filled out with information. The TABLE and COLUMNS sections are in their prescribed order and the new section we named FURTHER INFORMATION follows those (and it has one subsection called EXAMPLES and DOCUMENTATION). Let’s explore how each of the three different info_*() functions work.

## The TABLE Section and info_tabular()

The info_tabular() function adds information to the TABLE section. We use named arguments to define subsection names and their content. In the previous example info_tabular(description = "This table is included in the **pointblank** pkg.") was used to make the DESCRIPTION subsection (all section titles are automatically capitalized), and the info text is the Markdown text "This table is included in the **pointblank** pkg.". We can define as many subsections to the TABLE section as we need, either in the same info_tabular() call or across multiple calls. If we want to use more complicated subsection names than a single word, then we can enclose that text in back ticks. For example, why not put some emoji in your subsection name?

informant %>%
info_tabular(🔄 updates = "This table is not regularly updated.")

This is an excerpt of the complete report, showing just the report header and the TABLE section.

The TABLE section is a great place to put all the information about the table that needs to be front and center. Examples of some useful topics for this section might include:

• a high-level summary of the table, stating its purpose and importance
• what each row of the table represents
• the main users of the table within an organization
• description of how the table is generated
• information on the frequency of updates

## The COLUMNS Section and info_columns()

The section that follows the TABLE section is COLUMNS. This section provides an opportunity to describe each table column in as much detail as necessary. Here, individual columns serve as subsections (automatically generated upon using create_informant()) and there can be subsections within each column as well.

The interesting thing about the information provided here via info_columns() is that the information is additive. We can make multiple calls of info_columns() and disperse common pieces of info text to multiple columns and append the text to any existing. Let’s take a look at this in practice with the far more interesting palmerpenguins::penguins dataset. Here, we fill in information for each column by adapting documentation from the palmerpenguins package.

informant_pp <-
create_informant(
tbl = palmerpenguins::penguins,
tbl_name = "penguins",
label = "The penguins dataset from the **palmerpenguins** 📦."
) %>%
info_columns(
columns = "species",
ℹ️ = "A factor denoting penguin species (*Adélie*, *Chinstrap*, and *Gentoo*)."
) %>%
info_columns(
columns = "island",
ℹ️ = "A factor denoting island in Palmer Archipelago, Antarctica
(*Biscoe*, *Dream*, or *Torgersen*)."
) %>%
info_columns(
columns = "bill_length_mm",
ℹ️ = "A number denoting bill length"
) %>%
info_columns(
columns = "bill_depth_mm",
ℹ️ = "A number denoting bill depth"
) %>%
info_columns(
columns = "flipper_length_mm",
ℹ️ = "An integer denoting flipper length"
) %>%
info_columns(
columns = ends_with("mm"),
ℹ️ = "(in units of millimeters)."
) %>%
info_columns(
columns = "body_mass_g",
ℹ️ = "An integer denoting body mass (grams)."
) %>%
info_columns(
columns = "sex",
ℹ️ = "A factor denoting penguin sex (\"female\", \"male\")."
) %>%
info_columns(
columns = "year",
ℹ️ = "The study year (e.g., 2007, 2008, 2009)."
)

informant_pp

We are able to provide subsections with the name ℹ️ and, furthermore, use tidyselect functions like ends_with() to append info text to a common subsection that exists across multiple columns. This was useful for stating the units which were common across three columns: bill_length_mm, bill_depth_mm, and flipper_length_mm. The following tidyselect functions are available in pointblank to make this process easier:

• starts_with(): Match columns that start with a prefix.
• ends_with(): Match columns that end with a suffix.
• contains(): Match columns that contain a literal string.
• matches(): Perform matching with a regular expression.
• everything(): Select all columns.

## Extra Sections and info_section()

For all that information that we can’t or don’t want to put in the TABLE or COLUMNS sections, we can place it extra sections (and their subsections) with the info_section() function. These sections go below the aforementioned sections in the order of creation. Let’s put together some extra sections that further describe the palmerpenguins::penguins dataset.

informant_pp <-
informant_pp %>%
info_section(
section_name = "source",
"References" = c(
"- Adélie penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural
size measurements and isotopic signatures of foraging among adult male and female
Palmer Station, 2007-2009 ver 5. Environmental Data Initiative
<https://doi.org/10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f>",

"- Gentoo penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural
size measurements and isotopic signatures of foraging among adult male and female
Gentoo penguin (Pygoscelis papua) nesting along the Palmer Archipelago near Palmer
Station, 2007-2009 ver 5. Environmental Data Initiative
<https://doi.org/10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689>",

"- Chinstrap penguins: Palmer Station Antarctica LTER and K. Gorman. 2020.
Structural size measurements and isotopic signatures of foraging among adult male
and female Chinstrap penguin (Pygoscelis antarcticus) nesting along the Palmer
Archipelago near Palmer Station, 2007-2009 ver 6. Environmental Data Initiative
"
),
"Note" = "
Originally published in: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual
Dimorphism and Environmental Variability within a Community of Antarctic Penguins
(Genus Pygoscelis). PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081
"
)

informant_pp

This is an excerpt of the complete report, showing just the SOURCE section and the footer.

What other types of information go well in these separate sections? Some ideas are:

• any info related to the source of the data table (e.g., references, background, etc.)
• definitions/explanations of terms used above
• persons responsible for the data table, perhaps with contact information
• further details on how the table is produced
• any important issues with the table and notes on upcoming changes
• links to other information artifacts that pertain to the table
• information to do with the report itself (how meta!); this might include things like the update history, persons responsible, instructions on how to contribute, etc.

Really, the sky is the limit here! We can incorporate a great deal of useful information here and use this process to generate reports on all of the important tables within an organization. But there’s more that can be done. To do that you have to go on to the the next phase (the Advanced Information Management article).

You have to go all the way now, total transformation. Do you think you’re ready?