Connect to the MicroStrategy environment.

Description

Allows to establish, renew, check status and close the connection with MicroStrategy Intelligence Server.

Public fields

Methods

Public methods

• Connection$new()

• Connection$connect()

• Connection$delegate()

• Connection$get_identity_token()

• Connection$close()

• Connection$renew()

• Connection$status()

• Connection$clone()

Method new()

Establishes new connection with MicroStrategy Intelligence Server.

Usage

Connection$new(
  base_url,
  username = NULL,
  password = NULL,
  project_name = NULL,
  project_id = NULL,
  login_mode = 1,
  ssl_verify = TRUE,
  proxies = NULL,
  identity_token = NULL,
  verbose = TRUE
)

Arguments

Returns

A new "Connection" object.

Method connect()

Establishes new connection with MicroStrategy Intelligence Server, or renews active connection.

Usage

Connection$connect()

Method delegate()

Delegates identity token to get authentication token and connect to MicroStrategy Intelligence Server

Usage

Connection$delegate()

Method get_identity_token()

Gets identity token using existing authentication token

Usage

Connection$get_identity_token()

Method close()

Closes a connection with MicroStrategy REST API.

Usage

Connection$close()

Method renew()

Renews connection with MicroStrategy REST API.

Usage

Connection$renew()

Method status()

Displays status of the connection with MicroStrategy REST API.

Usage

Connection$status()

Method clone()

The objects of this class are cloneable with this method.

Usage

Connection$clone(deep = FALSE)

Arguments

Examples

## Not run: 
# Create a connection object.
connection = Connection$new(base_url, username, password, project_name)

# Connect or renew connection.
connection$connect()

# Check connection status.
connection$status()

# Renew connection to reset timeout counter.
connection$renew()

# Close connection.
connection$close()

## End(Not run)

Extract a MicroStrategy cube into a R Data.Frame

Description

Access, filter, publish, and extract data from MicroStrategy in-memory cubes

Create a Cube object to load basic information on a cube dataset. Specify subset of cube to be fetched through apply_filters() and clear_filters(). Fetch dataset through to_dataframe() method.

Public fields

Methods

Public methods

• Cube$new()

• Cube$to_dataframe()

• Cube$apply_filters()

• Cube$clear_filters()

• Cube$get_attr_elements()

• Cube$update()

• Cube$save_as()

• Cube$clone()

Method new()

Initialize an instance of a cube.

Usage

Cube$new(connection, cube_id, instance_id = NULL, parallel = FALSE)

Arguments

Method to_dataframe()

Extract contents of a cube into a R Data Frame.

Usage

Cube$to_dataframe(limit = NULL, multi_df = FALSE, callback = function(x, y) {
})

Arguments

Returns

Dataframe with data fetched from the given Cube.

Method apply_filters()

Apply filters on the cube data so only the chosen attributes, metrics, and attribute elements are retrieved from the Intelligence Server.

Usage

Cube$apply_filters(
  attributes = NULL,
  metrics = NULL,
  attr_elements = NULL,
  operator = "In"
)

Arguments

Method clear_filters()

Clear previously set filters, allowing all attributes, metrics, and attribute elements to be retrieved.

Usage

Cube$clear_filters()

Method get_attr_elements()

Load all attribute elements of the Cube. Accessible via Cube$attr_elements Fetching attriubte elements will also allow for validating attriute elements by the filter object.

Usage

Cube$get_attr_elements(limit = 50000, verbose = TRUE)

Arguments

Method update()

Update single-table cube easily with the data frame stored in the Cube instance (cube$dataframe). Before the update, make sure that the data frame has been modified.

Usage

Cube$update(update_policy = "update")

Arguments

Method save_as()

Creates a new single-table cube with the data frame stored in the Cube instance (cube$dataframe). Before the update, make sure that the data exists.

Usage

Cube$save_as(name, description = NULL, folder_id = NULL, table_name = NULL)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Cube$clone(deep = FALSE)

Arguments

Examples

## Not run: 
# Create a connection object.
connection = Connection$new(base_url, username, password, project_name)

# Create a cube object.
my_cube <- Cube$new(connection=conn, cube_id="...")

# See attributes and metrics in the report.
my_cube$attributes
my_cube$metrics
my_cube$attr_elements

# Specify attributes and metrics (columns) to be fetched.
my_cube$apply_filters(attributes = my_report$attributes[1:2],
                         metrics = my_report$metrics[1:2])

# See the selection of attributes, metrics and attribute elements.
my_cube$selected_attributes
my_cube$selected_metrics
my_cube$selected_attr_elements

# Clear filtering to load a full dataset.
my_cube$clear_filters()

# Fetch data from the Intelligence Server.
my_cube$to_dataframe()

# See the dataframe.
my_cube$dataframe

## End(Not run)

Create, update, delete and certify MicroStrategy datasets

Description

When creating a new dataset, provide a dataset name and an optional description. When updating a pre-existing dataset, provide the dataset identifier. Tables are added to the dataset in an iterative manner using 'add_table()'.

Public fields

Methods

Public methods

• Dataset$new()

• Dataset$add_table()

• Dataset$create()

• Dataset$update()

• Dataset$publish()

• Dataset$publish_status()

• Dataset$delete()

• Dataset$certify()

• Dataset$clone()

Method new()

Interface for creating, updating, and deleting MicroStrategy in-memory datasets.

Usage

Dataset$new(
  connection,
  name = NULL,
  description = NULL,
  dataset_id = NULL,
  verbose = TRUE
)

Arguments

Details

When creating a new dataset, provide a dataset name and an optional description. When updating a pre-existing dataset, provide the dataset identifier. Tables are added to the dataset in an iterative manner using 'add_table()'.

Returns

A new 'Datasets' object

Method add_table()

Add a data.frame to a collection of tables which are later used to update the MicroStrategy dataset

Usage

Dataset$add_table(
  name,
  data_frame,
  update_policy,
  to_metric = NULL,
  to_attribute = NULL
)

Arguments

Details

Add tables to the dataset in an iterative manner using 'add_table()'.

Method create()

Create a new dataset.

Usage

Dataset$create(
  folder_id = NULL,
  auto_upload = TRUE,
  auto_publish = TRUE,
  chunksize = 1e+05
)

Arguments

Method update()

Updates an existing dataset with new data.

Usage

Dataset$update(chunksize = 1e+05, auto_publish = TRUE)

Arguments

Method publish()

Publish the uploaded data to the selected dataset. A dataset can be published just once.

Usage

Dataset$publish()

Method publish_status()

Check the status of data that was uploaded to a dataset.

Usage

Dataset$publish_status()

Returns

Response status code

Method delete()

Delete a dataset that was previously created using the REST API.

Usage

Dataset$delete()

Returns

Response object from the Intelligence Server acknowledging the deletion process.

Method certify()

Certify a dataset that was previously creted using the REST API

Usage

Dataset$certify()

Returns

Response object from the Intelligence Server acknowledging the certification process.

Method clone()

The objects of this class are cloneable with this method.

Usage

Dataset$clone(deep = FALSE)

Arguments

Examples

## Not run: 
# Create data frames
df1 <- data.frame("id" = c(1, 2, 3, 4, 5),
                  "first_name" = c("Jason", "Molly", "Tina", "Jake", "Amy"),
                  "last_name" = c("Miller", "Jacobson", "Turner", "Milner", "Cooze"))

df2 <- data.frame("id" = c(1, 2, 3, 4, 5),
                  "age" = c(42, 52, 36, 24, 73),
                  "state" = c("VA", "NC", "WY", "CA", "CA"),
                  "salary" = c(50000, 100000, 75000, 85000, 250000))

# Create a list of tables containing one or more tables and their names
my_dataset <- Dataset$new(connection=conn, name="HR Analysis")
my_dataset$add_table("Employees", df1, "add")
my_dataset$add_table("Salaries", df2, "add")
my_dataset$create()

# By default Dataset$create() will upload the data to the Intelligence Server and publish the
 dataset.
# If you just want to create the dataset but not upload the row-level data, use
Dataset$create(auto_upload=FALSE)

# followed by
Dataset$update()
Dataset$publish()

# When the source data changes and users need the latest data for analysis and reporting in
# MicroStrategy, mstrio allows you to update the previously created dataset.

ds <- Dataset$new(connection=conn, dataset_id="...")
ds$add_table(name = "Stores", data_frame = stores_df, update_policy = 'update')
ds$add_table(name = "Sales", data_frame = stores_df, update_policy = 'upsert')
ds$update(auto_publish=TRUE)

# By default Dataset$update() will upload the data to the Intelligence Server and publish the
 dataset.
# If you just want to update the dataset but not publish the row-level data, use
Dataset$update(auto_publish=FALSE)

# By default, the raw data is transmitted to the server in increments of 100,000 rows. On very
# large datasets (>1 GB), it is beneficial to increase the number of rows transmitted to the
# Intelligence Server with each request. Do this with the chunksize parameter:

ds$update(chunksize = 500000)

# If you want to cerfify an existing dataset, use
ds$certify()

## End(Not run)

Create filters that may be passed to MicroStrategy Report/Cube objects.

Description

Pass ids of all objects and selected objects (attributes, metrics and elements) and the generate the requested objects and view filter body for the pull Data API.

Public fields

Methods

Public methods

• Filter$new()

• Filter$select()

• Filter$select_attr_elements()

• Filter$clear()

• Filter$filter_body()

• Filter$clone()

Method new()

Initialize a filter instance

Usage

Filter$new(attributes, metrics, attr_elements = NULL)

Arguments

Method select()

Select attribute/metric/attr_element objects to filter data. Selecting empty lists or vectors is supported and means that all objects (attributes/metrics) are deselected. This method eliminates duplicates and provides type validation for attributes/metrics and optionally attribute elements (if attr_elements are fetched before)

Usage

Filter$select(object_id, object_type = "automatic")

Arguments

Method select_attr_elements()

Usage

Filter$select_attr_elements(attribute_elements)

Method clear()

Usage

Filter$clear(object = "all")

Method filter_body()

Usage

Filter$filter_body()

Method clone()

The objects of this class are cloneable with this method.

Usage

Filter$clone(deep = FALSE)

Arguments

Create the definition of multi-table and single-table datasets

Description

Create the definition of a dataset containing one or more tables. The definition includes the name and description of the dataset and the name and description of each table, attribute, and metric within the dataset.

Public fields

Methods

Public methods

• Model$new()

• Model$get_model()

• Model$get_name()

• Model$get_description()

• Model$get_folder_id()

• Model$get_tables()

• Model$get_attributes()

• Model$get_metrics()

• Model$clone()

Method new()

Usage

Model$new(tables, name, description = NULL, folder_id = NULL)

Method get_model()

Usage

Model$get_model()

Method get_name()

Usage

Model$get_name()

Method get_description()

Usage

Model$get_description()

Method get_folder_id()

Usage

Model$get_folder_id()

Method get_tables()

Usage

Model$get_tables()

Method get_attributes()

Usage

Model$get_attributes()

Method get_metrics()

Usage

Model$get_metrics()

Method clone()

The objects of this class are cloneable with this method.

Usage

Model$clone(deep = FALSE)

Arguments

Examples

## Not run: 
# Create data frames
df1 <- data.frame("id" = c(1, 2, 3, 4, 5),
                  "first_name" = c("Jason", "Molly", "Tina", "Jake", "Amy"),
                  "last_name" = c("Miller", "Jacobson", "Turner", "Milner", "Cooze"))

df2 <- data.frame("id" = c(1, 2, 3, 4, 5),
                  "age" = c(42, 52, 36, 24, 73),
                  "state" = c("VA", "NC", "WY", "CA", "CA"),
                  "salary" = c(50000, 100000, 75000, 85000, 250000))

# Create a list of tables containing one or more tables and their names
tables = list(list("table_name" = "employee_id",
                    "data_frame" = df1),
              list("table_name" = "employee_data",
                   "data_frame" = df2))

# Generate the data model
model <- Model$new(tables=tables, name="Employees", description="Employee Analytics Data")
model_info <- model$get_model()

## End(Not run)

Download api v2 parser

Description

Parse the JSON response from cube/report v2 download api and convert the data into a R data frame

Public fields

Active bindings

Methods

Public methods

• Parser$new()

• Parser$parse()

• Parser$to_dataframe()

• Parser$clone()

Method new()

Usage

Parser$new(response)

Method parse()

Usage

Parser$parse(response)

Method to_dataframe()

Usage

Parser$to_dataframe()

Method clone()

The objects of this class are cloneable with this method.

Usage

Parser$clone(deep = FALSE)

Arguments

Examples

## Not run: 
# Create a parser object
p <- Parser$new(response)

# Parse the first request
p$parse(response)

# Return the data frame
p$to_dataframe()

## End(Not run)

Extract a MicroStrategy report into a R Data.Frame

Description

Access, filter, publish, and extract data from in-memory reports. Create a Report object to load basic information on a report dataset. Specify subset of report to be fetched through Report$apply_filters() and Report$clear_filters() . Fetch dataset through Report$to_dataframe() method.

Public fields

Methods

Public methods

• Report$new()

• Report$to_dataframe()

• Report$apply_filters()

• Report$clear_filters()

• Report$get_attr_elements()

• Report$clone()

Method new()

Initialize an instance of a report.

Usage

Report$new(connection, report_id, instance_id = NULL, parallel = FALSE)

Arguments

Method to_dataframe()

Extract contents of a Report into a R Data Frame.

Usage

Report$to_dataframe(limit = NULL, callback = function(x, y) {
})

Arguments

Returns

Dataframe with data fetched from the given Report.

Method apply_filters()

Apply filters on the report data so only the chosen attributes, metrics, and attribute elements are retrieved from the Intelligence Server.

Usage

Report$apply_filters(
  attributes = NULL,
  metrics = NULL,
  attr_elements = NULL,
  operator = "In"
)

Arguments

Method clear_filters()

Clear previously set filters, allowing all attributes, metrics, and attribute elements to be retrieved.

Usage

Report$clear_filters()

Method get_attr_elements()

Load all attribute elements of the Report. Accessible via Report$attr_elements. Fetching attriubte elements will also allow for validating attriute elements by the filter object.

Usage

Report$get_attr_elements(limit = 50000, verbose = TRUE)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

Report$clone(deep = FALSE)

Arguments

Examples

## Not run: 
# Create a connection object.
connection = Connection$new(base_url, username, password, project_name)

# Create a report object.
my_report <- Report$new(connection, report_id)

# See attributes and metrics in the report.
my_report$attributes
my_report$metrics
my_report$attr_elements

# Specify attributes and metrics (columns) to be fetched.
my_report$apply_filters(attributes = my_report$attributes[1:2],
                           metrics = my_report$metrics[1:2])

# See the selection of attributes, metrics and attribute elements.
my_report$selected_attributes
my_report$selected_metrics
my_report$selected_attr_elements

# Clear filtering to load a full dataset.
my_report$clear_filters()

# Fetch data from the Intelligence Server.
my_report$to_dataframe()

# See the dataframe.
my_report$dataframe

## End(Not run)