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Getting little snippets of information from a table goes hand-in-hand with mixing those bits of info with your table info. Call info_snippet() to define a snippet and how you'll get that from the target table. The snippet definition is supplied either with a formula, or, with a pointblank-supplied snip_*() function. So long as you know how to interact with a table and extract information, you can easily define snippets for a informant object. And once those snippets are defined, you can insert them into the info text as defined through the other info_*() functions (info_tabular(), info_columns(), and info_section()). Use curly braces with just the snippet_name inside (e.g., "This column has {n_cat} categories.").

Usage

info_snippet(x, snippet_name, fn)

Arguments

x

An informant object of class ptblank_informant.

snippet_name

The name for snippet, which is used for interpolating the result of the snippet formula into info text defined by an info_*() function.

fn

A formula that obtains a snippet of data from the target table. It's best to use a leading dot (.) that stands for the table itself and use pipes to construct a series of operations to be performed on the table (e.g., ~ . %>% dplyr::pull(column_2) %>% max(na.rm = TRUE)). So long as the result is a length-1 vector, it'll likely be valid for insertion into some info text. Alternatively, a snip_*() function can be used here (these functions always return a formula that's suitable for all types of data sources).

Value

A ptblank_informant object.

Snip functions provided in pointblank

For convenience, there are several snip_*() functions provided in the package that work on column data from the informant's target table. These are:

As it's understood what the target table is, only the column in each of these functions is necessary for obtaining the resultant text.

YAML

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()). Snippets are stored in the YAML representation and here is is how they are expressed in both R code and in the YAML output (showing both the meta_snippets and columns keys to demonstrate their relationship here).

# R statement
informant %>% 
  info_columns(
    columns = "date_time",
    `Latest Date` = "The latest date is {latest_date}."
  ) %>%
  info_snippet(
    snippet_name = "latest_date",
    fn = ~ . %>% dplyr::pull(date) %>% max(na.rm = TRUE)
  ) %>%
  incorporate()

# YAML representation
meta_snippets:
  latest_date: ~. %>% dplyr::pull(date) %>% max(na.rm = TRUE)
...
columns:
  date_time:
    _type: POSIXct, POSIXt
    Latest Date: The latest date is {latest_date}.
  date:
    _type: Date
  item_count:
    _type: integer

Examples

Take the small_table dataset included in pointblank and assign it to test_table. We'll modify it later.

test_table <- small_table

Generate an informant object, add two snippets with info_snippet(), add information with some other info_*() functions and then incorporate() the snippets into the info text. The first snippet will be made with the expression ~ . %>% nrow() (giving us the number of rows in the dataset) and the second uses the snip_highest() function with column a (giving us the highest value in that column).

informant <- 
  create_informant(
    tbl = ~ test_table,
    tbl_name = "test_table",
    label = "An example."
  ) %>%
  info_snippet(
    snippet_name = "row_count",
    fn = ~ . %>% nrow()
  ) %>%
  info_snippet(
    snippet_name = "max_a",
    fn = snip_highest(column = "a")
  ) %>%
  info_columns(
    columns = vars(a),
    info = "In the range of 1 to {max_a}. ((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))"
  ) %>%
  info_section(
    section_name = "rows",
    row_count = "There are {row_count} rows available."
  ) %>%
  incorporate()

We can print the informant object to see the information report.

informant

Let's modify test_table with some dplyr to give it more rows and an extra column.

test_table <- 
  dplyr::bind_rows(test_table, test_table) %>%
  dplyr::mutate(h = a + c)

Using incorporate() on the informant object will cause the snippets to be reprocessed, and, the info text to be updated.

informant <- informant %>% incorporate()

informant

Function ID

3-5

See also