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.").

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

Figures

Function ID

3-4

See also

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

Examples

# Take the `small_table` 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 informant <- create_informant( read_fn = ~ 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()
#> Error in rlang::eval_tidy(., env = caller_env(n = 1)): object 'test_table' not found
# We can print the `informant` object # to see the information report # Let's modify `test_table` 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()` will cause # the snippets to be reprocessed, and, # the info text to be updated informant <- informant %>% incorporate()
#> Error in incorporate(.): object 'informant' not found