A example of how to create an informant for the `revenue` table (hosted on a PostgreSQL DB)

tests_informant-revenue-postgres.R

library(pointblank)
library(here)
library(intendo)

# Note: while this table is hosted on a database, the table can be obtained
# from the {intendo} package (https://github.com/rich-iannone/intendo)

informant_revenue_postgres <- 
  create_informant(
    read_fn = 
      ~ db_tbl(
        db = "postgres", dbname = "intendo", table = "revenue",
        user = "PG_P_DB_USER", password = "PG_P_DB_PASS",
        host = "134.122.40.123"
      ),
    tbl_name = "intendo::revenue", 
    label = "The **intendo** revenue table."
  ) %>%
  info_tabular(description = "This table contains the daily revenue data for
               Intendo's **Super Jetroid** game. All data is for the complete
               year of 2015. Each row represents a single revenue by a user
               `user_id` in a particular session `session_id`. Revenue could be
               earned through ad views (where `type == 'ad'`) or through in-app
               purchases.") %>%
  info_columns("user_id", info = "This is the User ID field.") %>%
  info_columns("session_id", info = "This is the Session ID field.") %>%
  info_columns(ends_with("id"),
               info = "ID fields like this one are unique.") %>%
  info_columns("time", info = "This is a date-time field.") %>%
  info_columns("time", info = "Even though it's a character column, the
               times are in ISO-8601 format.") %>%
  info_columns("name", 
               info = "These contain the names of buyable products.") %>%
  info_columns("name", info = "Currently these products are {names}.") %>%
  info_columns("size", info = "The `size` refers to the relative size of
               the product. Ads are always `NULL` but products like `gold`
               and `gems` have a size value.") %>%
  info_columns("type", info = "These contain the names of buyable products.
               Currently there are the following types: {types}.") %>%
  info_snippet("types", fn = snip_list(column = "type")) %>%
  info_snippet("names", fn = snip_list(column = "name", limit = Inf)) %>%
  info_columns("price", info = "The price (in USD) for the product in
               the `name` column. This value will always be greater than
               the corresponding `revenue` value (30% higher).") %>%
  info_columns("revenue", info = "The reported revenue (in USD) for the product.
               The value may change up to 3-4 weeks after the sale date due
               to processing of refunds.") %>%
  info_columns("revenue", info = "The revenue total is ${revenue_total}.") %>%
  info_columns(vars(price, revenue), info = "(PARTNER)") %>%
  info_snippet("revenue_total",
               fn = ~ . %>%
                 dplyr::summarize(total = sum(revenue, na.rm = TRUE)) %>%
                 dplyr::pull(total)) %>%
  info_columns(vars(name, type),
               `person responsible` = "Rita Mercer (r.mercer@example.com)") %>%
  info_columns("time",
               TODO = "Ensure that this becomes a `DATETIME` column.") %>%
  info_section("source", 
               database = "Data table hosted in a *PostgreSQL* database on
               Digital Ocean (`134.122.40.123`). Email roy@example.com for
               access info.",
               repo = "Original datasets available in the
               [intendo repo](https://github.com/rich-iannone/intendo)") %>%
  incorporate()

informant_revenue_postgres

get_informant_report(informant_revenue_postgres, size = "standard")

get_informant_report(informant_revenue_postgres, size = "small")

x_write_disk(
  informant_revenue_postgres,
  filename = "informant_revenue_postgres.rds",
  path = here::here("tests/manual_tests")
)