Upon creation of an informant object (with the create_informant() function), there are two sections containing properties: (1) 'table' and (2) 'columns'. The 'columns' section is initialized with the table's column names and their types (as _type). Beyond that, it is useful to provide details about the nature of each column and we can do that with the info_columns() function. A single column (or multiple columns) is targeted, and then a series of named arguments (in the form entry_name = "The *info text*.") serves as additional information for the column or columns.

info_columns(x, columns, ..., .add = TRUE)



An informant object of class ptblank_informant.


The column or set of columns to focus on. Can be defined as a column name in quotes (e.g., "<column_name>"), one or more column names in vars() (e.g., vars(<column_name>)), or with a select helper (e.g., starts_with("date")).


Information entries as a series of named arguments. The names refer to subsection titles within COLUMN -> <COLUMN_NAME> and the RHS contains the info text (informational text that can be written as Markdown and further styled with Text Tricks).


Should new text be added to existing text? This is TRUE by default; setting to FALSE replaces any existing text for a property.


A ptblank_informant object.

Info Text

The info text that's used for any of the info_*() functions readily accepts Markdown formatting, and, there are a few Text Tricks that can be used to spice up the presentation. Markdown links written as < link url > or [ link text ]( link url ) will get nicely-styled links. Any dates expressed in the ISO-8601 standard with parentheses, "(2004-12-01)", will be styled with a font variation (monospaced) and underlined in purple. Spans of text can be converted to label-style text by using: (1) double parentheses around text for a rectangular border as in ((label text)), or (2) triple parentheses around text for a rounded-rectangular border like (((label text))).

CSS style rules can be applied to spans of info text with the following form:

[[ info text ]]<< CSS style rules >>

As an example of this in practice suppose you'd like to change the color of some text to red and make the font appear somewhat thinner. A variation on the following might be used:

"This is a [[factor]]<<color: red; font-weight: 300;>> value."

The are quite a few CSS style rules that can be used to great effect. Here are a few you might like:

  • color: <a color value>; (text color)

  • background-color: <a color value>; (the text's background color)

  • text-decoration: (overline | line-through | underline);

  • text-transform: (uppercase | lowercase | capitalize);

  • letter-spacing: <a +/- length value>;

  • word-spacing: <a +/- length value>;

  • font-style: (normal | italic | oblique);

  • font-weight: (normal | bold | 100-900);

  • font-variant: (normal | bold | 100-900);

  • border: <a color value> <a length value> (solid | dashed | dotted);

In the above examples, 'length value' refers to a CSS length which can be expressed in different units of measure (e.g., 12px, 1em, etc.). Some lengths can be expressed as positive or negative values (e.g., for letter-spacing). Color values can be expressed in a few ways, the most common being in the form of hexadecimal color values or as CSS color names.


A pointblank informant can be written to YAML with yaml_write() and the resulting YAML can be used to regenerate an informant (with yaml_read_informant()) or perform the 'incorporate' action using the target table (via yaml_informant_incorporate()). The way that information on table columns is represented in YAML works like this: info text goes into subsections of YAML keys named for the columns, which are themselves part of the top-level columns key. Here is an example of how several calls of info_columns() are expressed in R code and how the result corresponds to the YAML representation.

# R statement
informant %>% 
    columns = "date_time",
    info = "*info text* 1."
  ) %>%
    columns = "date",
    info = "*info text* 2."
  ) %>%
    columns = "item_count",
    info = "*info text* 3. Statistics: {snippet_1}."
  ) %>%
    columns = vars(date, date_time),
    info = "UTC time."

# YAML representation
    _type: POSIXct, POSIXt
    info: '*info text* 1. UTC time.'
    _type: Date
    info: '*info text* 2. UTC time.'
    _type: integer
    info: '*info text* 3. Statistics: {snippet_1}.'

Subsections represented as column names are automatically generated when creating an informant. Within these, there can be multiple subsections used for holding info text on each column. The subsections used across the different columns needn't be the same either, the only commonality that should be enforced is the presence of the _type key (automatically updated at every incorporate() invocation).

It's safest to use single quotation marks around any info text if directly editing it in a YAML file. Note that Markdown formatting and info snippet placeholders (shown here as {snippet_1}, see info_snippet() for more information) are preserved in the YAML. The Markdown to HTML conversion is done when printing an informant (or invoking get_informant_report() on an informant) and the processing of snippets (generation and insertion) is done when using the incorporate() function. Thus, the source text is always maintained in the YAML representation and is never written in processed form.


Function ID


See also

Other Information Functions: info_section(), info_snippet(), info_tabular(), snip_highest(), snip_list(), snip_lowest(), snip_stats()


# Create a pointblank `informant` # object with `create_informant()`; # we specify a `read_fn` with the # `~` followed by a statement that # gets the `small_table` dataset informant <- create_informant( read_fn = ~ small_table, tbl_name = "small_table", label = "An example." ) # We can add *info text* to describe # the columns in the table with multiple # `info_columns()` calls; the *info text* # calls are additive to existing content # in subsections informant <- informant %>% info_columns( columns = vars(a), info = "In the range of 1 to 10. (SIMPLE)" ) %>% info_columns( columns = starts_with("date"), info = "Time-based values (e.g., `Sys.time()`)." ) %>% info_columns( columns = "date", info = "The date part of `date_time`. (CALC)" ) # Upon printing the `informant` object, we see # the additions made to the 'Columns' section if (interactive()) { # The `informant` object can be written to # a YAML file with the `yaml_write()` # function; then, information can # be directly edited or modified yaml_write( informant = informant, filename = "informant.yml" ) # The YAML file can then be read back # into an informant object with the # `yaml_read_informant()` function informant <- yaml_read_informant( filename = "informant.yml" ) }