library(pointblank)
library(here)
informant_revenue_postgres <-
create_informant(
read_fn =
~ db_tbl(
table = "revenue",
dbname = "intendo",
dbtype = "postgres",
host = "134.122.40.123",
user = "PG_P_DB_USER",
password = "PG_P_DB_PASS"
),
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(
columns = "user_id",
info = "This is the User ID field."
) %>%
info_columns(
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(
columns = "time",
info = "This is a date-time field."
) %>%
info_columns(
columns = "time",
info = "Even though it's a character column, the times are in
ISO-8601 format."
) %>%
info_columns(
columns = "name",
info = "These contain the names of buyable products."
) %>%
info_columns(
columns = "name",
info = "Currently these products are {names}."
) %>%
info_columns(
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(
columns = "type",
info = "These contain the names of buyable products. Currently there
are the following types: {types}."
) %>%
info_snippet(
snippet_name = "types",
fn = snip_list(column = "type")
) %>%
info_snippet(
snippet_name = "names",
fn = snip_list(column = "name", limit = Inf)
) %>%
info_columns(
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(
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.
Summary statistics: {summary_stats_rev}."
) %>%
info_columns(
columns = "revenue",
info = "The revenue total is ${revenue_total}."
) %>%
info_columns(
columns = vars(price, revenue),
info = "((PARTNER))"
) %>%
info_snippet(
snippet_name = "summary_stats_rev",
fn = snip_stats(column = "revenue", type = "7num")
) %>%
info_snippet(
snippet_name = "revenue_total",
fn = ~ . %>%
dplyr::summarize(total = sum(revenue, na.rm = TRUE)) %>%
dplyr::pull(total)
) %>%
info_columns(
columns = vars(name, type),
`person responsible` = "Rita Mercer (r.mercer@example.com)"
) %>%
info_columns(
columns = "time",
TODO = "Ensure that this becomes a `DATETIME` column."
) %>%
info_section(
section_name = "source",
database = "Data table hosted in a *PostgreSQL* database on Digital
Ocean (`134.122.40.123`). Email [[roy@example.com]]<<color: green;>>
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")
)
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