Package 'rolap'

Description

Add a column returned by a function that takes the data of the flat table as a parameter.

Usage

add_custom_column(ft, name, definition)

## S3 method for class 'flat_table'
add_custom_column(ft, name = NULL, definition)

Arguments

Value

A flat_table object.

See Also

flat_table

Other flat table transformation functions: remove_instances_without_measures(), replace_empty_values(), replace_string(), replace_unknown_values(), select_attributes(), select_instances_by_comparison(), select_instances(), select_measures(), separate_measures(), transform_attribute_format(), transform_from_values(), transform_to_attribute(), transform_to_measure(), transform_to_values()

Examples

f <- function(table) {
  paste0(table$City, ' - ', table$State)
}

ft <- flat_table('ft_num', ft_num) |>
  add_custom_column(name = 'city_state', definition = f)

Description

To port databases to other work environments it is useful to be able to export them as csv files, as this function does.

Usage

as_csv_files(db, dir, type)

## S3 method for class 'star_database'
as_csv_files(db, dir = NULL, type = 1)

Arguments

Value

A string, name of a dir.

See Also

star_database

Other star database exportation functions: as_dm_class(), as_multistar(), as_rdb(), as_single_tibble_list(), as_tibble_list(), as_xlsx_file(), draw_tables()

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
tl1 <- db1 |>
  as_csv_files()

db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()

ct <- constellation("MRS", db1, db2)
d <- ct |>
  as_csv_files(dir = tempdir())

Description

To port databases to other work environments it is useful to be able to export them as a dm class, as this function does, in this way it can be saved directly in a DBMS.

Usage

as_dm_class(db, pk_facts, fk)

## S3 method for class 'star_database'
as_dm_class(db, pk_facts = TRUE, fk = TRUE)

Arguments

Value

A dm object.

See Also

star_database

Other star database exportation functions: as_csv_files(), as_multistar(), as_rdb(), as_single_tibble_list(), as_tibble_list(), as_xlsx_file(), draw_tables()

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
dm1 <- db1 |>
  as_dm_class()

db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()

ct <- constellation("MRS", db1, db2)
dm <- ct |>
  as_dm_class()

Description

From a star_database with at least one geoattribute, we obtain a geolayer object that allows us to select the data to obtain a vector layer with geographic information.

Usage

as_geolayer(db, dimension, attribute, geometry, include_nrow_agg)

## S3 method for class 'star_database'
as_geolayer(
  db,
  dimension = NULL,
  attribute = NULL,
  geometry = NULL,
  include_nrow_agg = FALSE
)

Arguments

Details

If only one geographic attribute is defined, it is not necessary to indicate the dimension or the attribute. By default, polygon geometry is considered.

Value

A geolayer object.

See Also

Other query functions: as_GeoPackage(), filter_dimension(), get_layer(), get_variable_description(), get_variables(), run_query(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

gl_polygon <- mrs_db_geo |>
  as_geolayer()

gl_point <- mrs_db_geo |>
  as_geolayer(geometry = "point")

Description

Save the geolayer (geographic information layer) and the variables layer in a file in GeoPackage format to be able to work with other tools.

Usage

as_GeoPackage(gl, dir, name, keep_all_variables_na)

## S3 method for class 'geolayer'
as_GeoPackage(gl, dir = NULL, name = NULL, keep_all_variables_na = FALSE)

Arguments

Details

If the file name is not indicated, it defaults to the name of the geovariable.

By default, rows that are NA for all variables are eliminated.

The GeoPackage format only allows defining a maximum of 1998 columns. If the number of variables and columns in the geographic layer exceeds this number, it cannot be saved in this format.

Value

A string, file name.

See Also

Other query functions: as_geolayer(), filter_dimension(), get_layer(), get_variable_description(), get_variables(), run_query(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

gl <- mrs_db_geo |>
  as_geolayer()

f <- gl |>
  as_GeoPackage(dir = tempdir())

Description

In order to be able to use the query and integration functions with geographic information offered by the geomultistar package, we can obtain a multistar object from a star database or a constellation.

Usage

as_multistar(db)

## S3 method for class 'star_database'
as_multistar(db)

Arguments

Value

A geomultistar::multistar object.

See Also

star_database

Other star database exportation functions: as_csv_files(), as_dm_class(), as_rdb(), as_single_tibble_list(), as_tibble_list(), as_xlsx_file(), draw_tables()

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
ms1 <- db1 |>
  as_multistar()

db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()

ct <- constellation("MRS", db1, db2)
ms <- ct |>
  as_multistar()

Description

Given a connection to a relational database, it stores the facts and dimensions in the form of tables. Tables can be overwritten.

Usage

as_rdb(db, con, overwrite)

## S3 method for class 'star_database'
as_rdb(db, con, overwrite = FALSE)

Arguments

Value

Invisible NULL.

See Also

star_database

Other star database exportation functions: as_csv_files(), as_dm_class(), as_multistar(), as_single_tibble_list(), as_tibble_list(), as_xlsx_file(), draw_tables()

Examples

my_db <- DBI::dbConnect(RSQLite::SQLite())

db <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
db |>
  as_rdb(my_db)

DBI::dbDisconnect(my_db)

Description

Allows you to transform a star database into a flat table. If we have a constellation, it returns a list of flat tables.

Usage

as_single_tibble_list(db)

## S3 method for class 'star_database'
as_single_tibble_list(db)

Arguments

Value

A list of tibble

See Also

star_database

Other star database exportation functions: as_csv_files(), as_dm_class(), as_multistar(), as_rdb(), as_tibble_list(), as_xlsx_file(), draw_tables()

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
tl1 <- db1 |>
  as_single_tibble_list()

db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()

ct <- constellation("MRS", db1, db2)
tl <- ct |>
  as_single_tibble_list()

Description

Obtain a star database from the flat table and a star schema.

Usage

as_star_database(ft, schema)

## S3 method for class 'flat_table'
as_star_database(ft, schema)

Arguments

Value

A star_database object.

See Also

star_database

Other flat table definition functions: flat_table(), get_table(), get_unknown_value_defined(), get_unknown_values(), read_flat_table_file(), read_flat_table_folder()

Examples

db <- flat_table('ft_num', ft_num) |>
  as_star_database(mrs_cause_schema)

Description

To port databases to other work environments it is useful to be able to export them as a list of tibbles, as this function does.

Usage

as_tibble_list(db)

## S3 method for class 'star_database'
as_tibble_list(db)

Arguments

Value

A list of tibble

See Also

star_database

Other star database exportation functions: as_csv_files(), as_dm_class(), as_multistar(), as_rdb(), as_single_tibble_list(), as_xlsx_file(), draw_tables()

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
tl1 <- db1 |>
  as_tibble_list()

db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()

ct <- constellation("MRS", db1, db2)
tl <- ct |>
  as_tibble_list()

Description

To port databases to other work environments it is useful to be able to export them as a xlsx file, as this function does.

Usage

as_xlsx_file(db, file)

## S3 method for class 'star_database'
as_xlsx_file(db, file = NULL)

Arguments

Value

A string, name of a file.

See Also

star_database

Other star database exportation functions: as_csv_files(), as_dm_class(), as_multistar(), as_rdb(), as_single_tibble_list(), as_tibble_list(), draw_tables()

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
tl1 <- db1 |>
  as_xlsx_file()

db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()

ct <- constellation("MRS", db1, db2)
f <- ct |>
  as_xlsx_file(file = tempfile())

Description

Cancel deployment

Usage

cancel_deployment(db, name)

## S3 method for class 'star_database'
cancel_deployment(db, name)

Arguments

Value

A star_database object.

See Also

star_database

Other star database deployment functions: deploy(), get_deployment_names(), load_star_database()

Examples

mrs_rdb_file <- tempfile("mrs", fileext = ".rdb")
mrs_sqlite_file <- tempfile("mrs", fileext = ".sqlite")

mrs_sqlite_connect <- function() {
  DBI::dbConnect(RSQLite::SQLite(),
                 dbname = mrs_sqlite_file)
}

mrs_db <- mrs_db |>
  deploy(
    name = "mrs",
    connect = mrs_sqlite_connect,
    file = mrs_rdb_file
  )

mrs_db <- mrs_db |>
  cancel_deployment(name = "mrs")

Description

Get unrelated instances of a geoattribute for a geometry.

Usage

check_geoattribute_geometry(db, dimension, attribute, geometry)

## S3 method for class 'star_database'
check_geoattribute_geometry(
  db,
  dimension = NULL,
  attribute = NULL,
  geometry = "polygon"
)

Arguments

Details

We obtain the values of the dimension attribute that do not have an associated geographic element of the indicated geometry.

If there is only one geoattribute defined, neither the dimension nor the attribute must be indicated.

Value

A tibble.

See Also

Other star database geographic attributes: define_geoattribute(), get_geoattribute_geometries(), get_geoattributes(), get_layer_geometry(), get_point_geometry(), summarize_layer()

Examples

db <- mrs_db |>
  define_geoattribute(
    dimension = "where",
    attribute = "state",
    from_layer = us_layer_state,
    by = "STUSPS"
  )

instances <- check_geoattribute_geometry(db,
                                         dimension = "where",
                                         attribute = "state")

Description

Before joining a flat table with a lookup table we can check the result to determine if we need to adapt the values of some instances or add new elements to the lookup table. This function returns the values of the foreign key of the flat table that do not correspond to the primary key of the lookup table.

Usage

check_lookup_table(ft, fk_attributes, lookup)

## S3 method for class 'flat_table'
check_lookup_table(ft, fk_attributes = NULL, lookup)

Arguments

Details

If no attributes are indicated, those that form the primary key of the lookup table are considered in the flat table.

Value

A tibble with attribute values.

See Also

flat_table

Other flat table join functions: get_pk_attribute_names(), join_lookup_table(), lookup_table()

Examples

lookup <- flat_table('iris', iris) |>
  lookup_table(
    measures = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
    measure_agg = c('MAX', 'MIN', 'SUM', 'MEAN')
  )
values <- flat_table('iris', iris) |>
  check_lookup_table(lookup = lookup)

Description

Creates a constellation from a list of star_database objects. A constellation is also represented by a star_database object. All dimensions with the same name in the star schemas have to be conformable (share the same structure, even though they have different instances).

Usage

constellation(name = NULL, ...)

Arguments

Value

A star_database object.

See Also

as_tibble_list, as_dm_class

Examples

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()
ct1 <- constellation("MRS", db1, db2)


db3 <- star_database(mrs_cause_schema_rpd, ft_cause_rpd) |>
  role_playing_dimension(
    rpd = "When",
    roles = c("When Available", "When Received")
  )

db4 <- star_database(mrs_age_schema_rpd, ft_age_rpd) |>
  role_playing_dimension(
    rpd = "When Arrived",
    roles = c("When Available")
  )
ct2 <- constellation("MRS", db3, db4)

Description

From the coordinates defined in fields such as latitude and longitude, it returns a layer of points.

Usage

coordinates_to_point(table, lon_lat = c("intptlon", "intptlat"), crs = NULL)

Arguments

Details

If we start from a geographic layer, it initially transforms it into a table.

The CRS of the new layer is indicated. If a CRS is not indicated, it considers the layer's CRS by default and, if it is not a layer, it considers 4326 CRS (WGS84).

Value

A sf object.

Examples

us_state_point <-
  coordinates_to_point(us_layer_state,
                       lon_lat = c("INTPTLON", "INTPTLAT"))

Description

Dimensions are part of a star_schema object. They can be defined directly as a dimension_schema object or giving the name and a set of attributes.

Usage

define_dimension(
  schema,
  dimension,
  name,
  attributes,
  scd_nk,
  scd_t0,
  scd_t1,
  scd_t2,
  scd_t3,
  scd_t6,
  is_when,
  ...
)

## S3 method for class 'star_schema'
define_dimension(
  schema,
  dimension = NULL,
  name = NULL,
  attributes = NULL,
  scd_nk = NULL,
  scd_t0 = NULL,
  scd_t1 = NULL,
  scd_t2 = NULL,
  scd_t3 = NULL,
  scd_t6 = NULL,
  is_when = FALSE,
  ...
)

Arguments

Value

A star_schema object.

See Also

star_database

Other star schema definition functions: define_facts(), dimension_schema(), fact_schema(), star_schema()

Examples

s <- star_schema() |>
  define_dimension(
    name = "when",
    attributes = c(
      "Week Ending Date",
      "WEEK",
      "Year"
    )
  )

s <- star_schema()
d <- dimension_schema(
  name = "when",
  attributes = c(
    "Week Ending Date",
    "WEEK",
    "Year"
  )
)
s <- s |>
  define_dimension(d)

Description

Facts are part of a star_schema object. They can be defined directly as a fact_schema object or giving the name and a set of measures that can be empty (does not have explicit measures).

Usage

define_facts(schema, facts, name, measures, agg_functions, nrow_agg)

## S3 method for class 'star_schema'
define_facts(
  schema,
  facts = NULL,
  name = NULL,
  measures = NULL,
  agg_functions = NULL,
  nrow_agg = NULL
)

Arguments

Details

Associated with each measurement there is an aggregation function that can be SUM, MAX or MIN. AVG is not considered among the possible aggregation functions: The reason is that calculating AVG by considering subsets of data does not necessarily yield the AVG of the total data.

An additional measurement corresponding to the COUNT of aggregated rows is added which, together with SUM, allows us to obtain the mean if needed.

Value

A star_schema object.

See Also

star_database

Other star schema definition functions: define_dimension(), dimension_schema(), fact_schema(), star_schema()

Examples

s <- star_schema() |>
  define_facts(
    name = "mrs_cause",
    measures = c(
      "Pneumonia and Influenza Deaths",
      "Other Deaths"
    )
  )

s <- star_schema()
f <- fact_schema(
  name = "mrs_cause",
  measures = c(
    "Pneumonia and Influenza Deaths",
    "Other Deaths"
  )
)
s <- s |>
  define_facts(f)

Description

Define a set of attributes as a dimension's geoattribute. The set of attribute values must uniquely designate the instances of the given geographic layer.

Usage

define_geoattribute(db, dimension, attribute, from_layer, by, from_attribute)

## S3 method for class 'star_database'
define_geoattribute(
  db,
  dimension = NULL,
  attribute = NULL,
  from_layer = NULL,
  by = NULL,
  from_attribute = NULL
)

Arguments

Details

The definition can be done in two ways: Associates the instances of the attributes with the instances of a geographic layer or defines it from the geometry of previously defined geographic attributes.

Multiple attributes can be specified in the attribute parameter, the geographical attribute is the combination of all of them.

If defined from a layer (from_layer parameter), additionally the attributes used for the join between the tables (dimension and layer tables) must be indicated (by parameter).

If defined from another attribute, it should have the same or finer granularity, to obtain the result by grouping its instances. The considered attribute can be the pair that defines longitude and latitude.

If other geographic information has previously been associated with that attribute, the new information is considered and previous instances for which no new information is provided are also added.

If the geometry provided is polygons, a point layer is also generated.

Value

A star_database object.

See Also

Other star database geographic attributes: check_geoattribute_geometry(), get_geoattribute_geometries(), get_geoattributes(), get_layer_geometry(), get_point_geometry(), summarize_layer()

Examples

db <- mrs_db |>
  define_geoattribute(
    dimension = "where",
    attribute = "state",
    from_layer = us_layer_state,
    by = "STUSPS"
  ) |>
  define_geoattribute(
    dimension = "where",
    attribute = "region",
    from_attribute = "state"
  )  |>
  define_geoattribute(
    dimension = "where",
    attribute = "city",
    from_attribute = c("long", "lat")
  )

Description

To deploy the star database, we must indicate a name for the deployment, a connection function and a disconnection function from the database. If it is the first deployment, we must also indicate the name of a local file where the star database will be stored.

Usage

deploy(db, name, connect, disconnect, file)

## S3 method for class 'star_database'
deploy(db, name, connect, disconnect = NULL, file = NULL)

Arguments

Details

If the disconnection function consists only of calling DBI::dbDisconnect(con), there is no need to indicate it, it is taken by default.

As a result, it exports the tables from the star database to the connection database and from now on will keep them updated with each periodic refresh. Additionally, it will also keep a copy of the star database updated on file, which can be used when needed.

Value

A star_database object.

See Also

star_database

Other star database deployment functions: cancel_deployment(), get_deployment_names(), load_star_database()

Examples

mrs_rdb_file <- tempfile("mrs", fileext = ".rdb")
mrs_sqlite_file <- tempfile("mrs", fileext = ".sqlite")

mrs_sqlite_connect <- function() {
  DBI::dbConnect(RSQLite::SQLite(),
                 dbname = mrs_sqlite_file)
}

mrs_db <- mrs_db |>
  deploy(
    name = "mrs",
    connect = mrs_sqlite_connect,
    file = mrs_rdb_file
  )

Description

A dimension_schema object is created, we have to define its name and the set of attributes that make it up.

Usage

dimension_schema(
  name = NULL,
  attributes = NULL,
  scd_nk = NULL,
  scd_t0 = NULL,
  scd_t1 = NULL,
  scd_t2 = NULL,
  scd_t3 = NULL,
  scd_t6 = NULL,
  is_when = FALSE,
  ...
)

Arguments

Details

A dimension_schema object is part of a star_schema object, defines a dimension of the star schema.

Value

A dimension_schema object.

See Also

star_database

Other star schema definition functions: define_dimension(), define_facts(), fact_schema(), star_schema()

Examples

d <- dimension_schema(
  name = "when",
  attributes = c(
    "Week Ending Date",
    "WEEK",
    "Year"
  )
)

Description

Draw the tables of the ROLAP star diagrams.

Usage

draw_tables(db)

## S3 method for class 'star_database'
draw_tables(db)

Arguments

Value

An object with a print() method.

See Also

star_database

Other star database exportation functions: as_csv_files(), as_dm_class(), as_multistar(), as_rdb(), as_single_tibble_list(), as_tibble_list(), as_xlsx_file()

Examples

db <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()

db |>
  draw_tables()

Description

A fact_schema object is created, the essential data is a name and a set of measures that can be empty (does not have explicit measures). It is part of a star_schema object, defines the facts of the star schema.

Usage

fact_schema(
  name = NULL,
  measures = NULL,
  agg_functions = NULL,
  nrow_agg = NULL
)

Arguments

Details

Associated with each measure there is an aggregation function that can be SUM, MAX or MIN. AVG is not considered among the possible aggregation functions: The reason is that calculating AVG by considering subsets of data does not necessarily yield the AVG of the total data.

An additional measure corresponding to the COUNT of aggregated rows is added which, together with SUM, allows us to obtain the AVG if needed.

Value

A fact_schema object.

See Also

star_database

Other star schema definition functions: define_dimension(), define_facts(), dimension_schema(), star_schema()

Examples

f <- fact_schema(
  name = "mrs_cause",
  measures = c(
    "Pneumonia and Influenza Deaths",
    "Other Deaths"
  )
)

f <- fact_schema(
  name = "mrs_cause",
  measures = c(
    "Pneumonia and Influenza Deaths",
    "Other Deaths"
  ),
  agg_functions = c(
    "MAX",
    "SUM"
  ),
  nrow_agg = "Nrow"
)

Description

Allows you to define selection conditions for dimension rows.

Usage

filter_dimension(sq, name, ...)

## S3 method for class 'star_query'
filter_dimension(sq, name = NULL, ...)

Arguments

Details

Conditions can be defined on any attribute of the dimension (not only on attributes selected in the query for the dimension). The selection is made based on the function dplyr::filter. Conditions are defined in exactly the same way as in that function.

Value

A star_query object.

See Also

Other query functions: as_GeoPackage(), as_geolayer(), get_layer(), get_variable_description(), get_variables(), run_query(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

sq <- mrs_db |>
  star_query() |>
  filter_dimension(name = "when", week <= " 3") |>
  filter_dimension(name = "where", city == "Cambridge")

Description

Creates a flat_table object.

Usage

flat_table(name = NULL, instances, unknown_value = NULL)

Arguments

Details

The objective is to allow the transformation of flat tables.

We indicate the name of the flat table and we can also give the value that will be used to replace NA or empty values.

Value

A flat_table object.

See Also

star_database

Other flat table definition functions: as_star_database(), get_table(), get_unknown_value_defined(), get_unknown_values(), read_flat_table_file(), read_flat_table_folder()

Examples

ft <- flat_table('iris', iris)

ft <- flat_table('ft_num', ft_num)

Description

Selection of 20 rows from the 122 Cities Mortality Reporting System.

Usage

ft

Format

A tibble.

Details

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, mortality figures by age group and cause, considered separately, are included. In the cause, only a distinction is made between pneumonia or influenza and others.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

mrs_cause_schema

Other mrs example data: ft_age_rpd, ft_age, ft_cause_rpd, ft_num, mrs_db_geo, mrs_db, mrs_ft_new, mrs_ft

Description

Selection data from the 122 Cities Mortality Reporting System by age group.

Usage

ft_age

Format

A tibble.

Details

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, mortality figures by age group and cause, considered separately, are included.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

mrs_age_schema

Other mrs example data: ft_age_rpd, ft_cause_rpd, ft_num, ft, mrs_db_geo, mrs_db, mrs_ft_new, mrs_ft

Examples

# The operations to obtain it from the `ft` data set are:

if (rlang::is_installed("stringr")) {
  ft_age <- ft |>
    dplyr::select(-`Pneumonia and Influenza Deaths`, -`All Deaths`) |>
    tidyr::gather("Age", "All Deaths", 7:11) |>
    dplyr::mutate(`All Deaths` = as.integer(`All Deaths`)) |>
    dplyr::mutate(Age = stringr::str_replace(Age, " \\(all cause deaths\\)", ""))
}

Description

Selection of data from the 122 Cities Mortality Reporting System by age group, for the first 9 weeks of 1962 and 4 cities.

Usage

ft_age_rpd

Format

A tibble.

Details

The original dataset begins in 1962. For each week, in 122 US cities, mortality figures by age group and cause, considered separately, are included (i.e., the combination of age group and cause is not included). In the cause, only a distinction is made between pneumonia or influenza and others.

Two additional dates have been generated, which were not present in the original dataset.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

mrs_age_schema

Other mrs example data: ft_age, ft_cause_rpd, ft_num, ft, mrs_db_geo, mrs_db, mrs_ft_new, mrs_ft

Description

Selection of data from the 122 Cities Mortality Reporting System by cause, for the first 9 weeks of 1962 and 4 cities.

Usage

ft_cause_rpd

Format

A tibble.

Details

The original dataset begins in 1962. For each week, in 122 US cities, mortality figures by age group and cause, considered separately, are included (i.e., the combination of age group and cause is not included). In the cause, only a distinction is made between pneumonia or influenza and others.

Two additional dates have been generated, which were not present in the original dataset.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

mrs_cause_schema

Other mrs example data: ft_age_rpd, ft_age, ft_num, ft, mrs_db_geo, mrs_db, mrs_ft_new, mrs_ft

Description

Selection of 20 rows from the 122 Cities Mortality Reporting System. Measures have been defined as integer values.

Usage

ft_num

Format

A tibble.

Details

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, mortality figures by age group and cause, considered separately, are included. In the cause, only a distinction is made between pneumonia or influenza and others.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

mrs_cause_schema

Other mrs example data: ft_age_rpd, ft_age, ft_cause_rpd, ft, mrs_db_geo, mrs_db, mrs_ft_new, mrs_ft

Examples

# The operations to obtain it from the `ft` data set are:

ft_num <- ft |>
  dplyr::mutate(`Pneumonia and Influenza Deaths` = as.integer(`Pneumonia and Influenza Deaths`)) |>
  dplyr::mutate(`All Deaths` = as.integer(`All Deaths`))

Description

Obtain the names of the attributes in a flat table or a dimension in a star database.

Usage

## S3 method for class 'flat_table'
get_attribute_names(db, name = NULL, ordered = FALSE, as_definition = FALSE)

get_attribute_names(db, name, ordered, as_definition)

## S3 method for class 'star_database'
get_attribute_names(db, name, ordered = FALSE, as_definition = FALSE)

Arguments

Details

If indicated, names can be obtained in alphabetical order or as a vector definition in R

Value

A vector of strings or a string, attribute names.

See Also

star_database, flat_table

Other star database and flat table functions: get_measure_names.flat_table(), get_similar_attribute_values.flat_table(), get_similar_attribute_values_individually.flat_table(), get_unique_attribute_values.flat_table(), replace_attribute_values.flat_table(), set_attribute_names.flat_table(), set_measure_names.flat_table(), snake_case.flat_table()

Examples

names <- star_database(mrs_cause_schema, ft_num) |>
  get_attribute_names(name = "where")

names <- flat_table('iris', iris) |>
  get_attribute_names()

Description

Obtain the names of the facts of a star database.

Usage

get_deployment_names(db)

## S3 method for class 'star_database'
get_deployment_names(db)

Arguments

Value

A vector of strings, fact names.

See Also

star_database

Other star database deployment functions: cancel_deployment(), deploy(), load_star_database()

Examples

mrs_rdb_file <- tempfile("mrs", fileext = ".rdb")
mrs_sqlite_file <- tempfile("mrs", fileext = ".sqlite")

mrs_sqlite_connect <- function() {
  DBI::dbConnect(RSQLite::SQLite(),
                 dbname = mrs_sqlite_file)
}

mrs_db <- mrs_db |>
  deploy(
    name = "mrs",
    connect = mrs_sqlite_connect,
    file = mrs_rdb_file
  )

names <- mrs_db |>
  get_deployment_names()

Description

Obtain the names of the dimensions of a star database.

Usage

get_dimension_names(db, star)

## S3 method for class 'star_database'
get_dimension_names(db, star = NULL)

Arguments

Value

A vector of strings, dimension names.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_table(), get_fact_names(), get_role_playing_dimension_names(), get_table_names(), group_dimension_instances(), role_playing_dimension(), star_database()

Examples

names <- star_database(mrs_cause_schema, ft_num) |>
  get_dimension_names()

Description

Get the table for the dimension indicated by its name.

Usage

get_dimension_table(db, name)

## S3 method for class 'star_database'
get_dimension_table(db, name = NULL)

Arguments

Value

A tibble, dimension table.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_names(), get_fact_names(), get_role_playing_dimension_names(), get_table_names(), group_dimension_instances(), role_playing_dimension(), star_database()

Examples

table <- star_database(mrs_cause_schema, ft_num) |>
  get_dimension_table("where")

Description

From the planned update, it obtains the instances of the update facts that are already included in the star database facts to be updated.

Usage

get_existing_fact_instances(sdbu)

## S3 method for class 'star_database_update'
get_existing_fact_instances(sdbu)

Arguments

Details

The most common thing is that refresh operations only include new instances in fact tables, but it may be the case that repeated instances appear: They may have different values in the measures, but the same values in the dimension foreign keys. When the update occurs, we need to determine what happens to these instances.

Value

A tibble object.

See Also

star_database

Other star database refresh functions: get_lookup_tables(), get_new_dimension_instances(), get_star_database(), get_star_schema(), get_transformation_code(), get_transformation_file(), incremental_refresh(), update_according_to()

Examples

f1 <-
  flat_table('ft_num', ft_cause_rpd[ft_cause_rpd$City != 'Cambridge' &
                                      ft_cause_rpd$WEEK != '4',]) |>
  as_star_database(mrs_cause_schema_rpd) |>
  role_playing_dimension(rpd = "When",
                         roles = c("When Available", "When Received"))
f2 <- flat_table('ft_num2', ft_cause_rpd[ft_cause_rpd$City != 'Bridgeport' &
                                           ft_cause_rpd$WEEK != '2',])
f2 <- f2 |>
  update_according_to(f1)
fact_instances <- f2 |>
  get_existing_fact_instances()

Description

Obtain the names of the facts of a star database.

Usage

get_fact_names(db)

## S3 method for class 'star_database'
get_fact_names(db)

Arguments

Value

A vector of strings, fact names.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_names(), get_dimension_table(), get_role_playing_dimension_names(), get_table_names(), group_dimension_instances(), role_playing_dimension(), star_database()

Examples

names <- star_database(mrs_cause_schema, ft_num) |>
  get_fact_names()

Description

For each geoattribute, get its geometries.

Usage

get_geoattribute_geometries(db, dimension, attribute)

## S3 method for class 'star_database'
get_geoattribute_geometries(db, dimension = NULL, attribute = NULL)

Arguments

Details

If the name of the dimension is not indicated, it is considered the first one that has geoattributes defined.

Value

A vector of strings.

See Also

Other star database geographic attributes: check_geoattribute_geometry(), define_geoattribute(), get_geoattributes(), get_layer_geometry(), get_point_geometry(), summarize_layer()

Examples

db <- mrs_db |>
  define_geoattribute(
    dimension = "where",
    attribute = "state",
    from_layer = us_layer_state,
    by = "STUSPS"
  )

geometries <- db |>
  get_geoattribute_geometries(
    dimension = "where",
    attribute = "state"
  )

Description

For each dimension, get a list of available geoattributes.

Usage

get_geoattributes(db)

## S3 method for class 'star_database'
get_geoattributes(db)

Arguments

Value

A list of dimension geoattributes.

See Also

Other star database geographic attributes: check_geoattribute_geometry(), define_geoattribute(), get_geoattribute_geometries(), get_layer_geometry(), get_point_geometry(), summarize_layer()

Examples

db <- mrs_db |>
  define_geoattribute(
    dimension = "where",
    attribute = "state",
    from_layer = us_layer_state,
    by = "STUSPS"
  )

attributes <- db |>
    get_geoattributes()

Description

Get the geographic information layer from a geolayer object.

Usage

get_layer(gl, keep_all_variables_na)

## S3 method for class 'geolayer'
get_layer(gl, keep_all_variables_na = FALSE)

Arguments

Details

By default, rows that are NA for all variables are eliminated.

Value

A sf object.

See Also

Other query functions: as_GeoPackage(), as_geolayer(), filter_dimension(), get_variable_description(), get_variables(), run_query(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

gl <- mrs_db_geo |>
  as_geolayer()

l <- gl |>
  get_layer()

Description

Get the geometry of a layer. It will only be valid if one of the two geometries is interpreted: point or polygon.

Usage

get_layer_geometry(layer)

Arguments

Value

A string.

See Also

Other star database geographic attributes: check_geoattribute_geometry(), define_geoattribute(), get_geoattribute_geometries(), get_geoattributes(), get_point_geometry(), summarize_layer()

Examples

geometry <- get_layer_geometry(us_layer_state)

Description

From the planned update, it obtains the lookup tables used to define the data.

Usage

get_lookup_tables(sdbu)

## S3 method for class 'star_database_update'
get_lookup_tables(sdbu)

Arguments

Value

A list of flat_table objects.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_new_dimension_instances(), get_star_database(), get_star_schema(), get_transformation_code(), get_transformation_file(), incremental_refresh(), update_according_to()

Examples

f1 <- flat_table('ft_num', ft_cause_rpd) |>
  as_star_database(mrs_cause_schema_rpd)
f2 <- flat_table('ft_num2', ft_cause_rpd) |>
  update_according_to(f1)
ft <- f2 |>
  get_lookup_tables()

Description

Obtain the names of the measures in a flat table or in a star database.

Usage

## S3 method for class 'flat_table'
get_measure_names(db, name = NULL, ordered = FALSE, as_definition = FALSE)

get_measure_names(db, name, ordered, as_definition)

## S3 method for class 'star_database'
get_measure_names(db, name = NULL, ordered = FALSE, as_definition = FALSE)

Arguments

Value

A vector of strings or a string, measure names.

See Also

star_database, flat_table

Other star database and flat table functions: get_attribute_names.flat_table(), get_similar_attribute_values.flat_table(), get_similar_attribute_values_individually.flat_table(), get_unique_attribute_values.flat_table(), replace_attribute_values.flat_table(), set_attribute_names.flat_table(), set_measure_names.flat_table(), snake_case.flat_table()

Examples

names <- star_database(mrs_cause_schema, ft_num) |>
  get_measure_names()

names <- flat_table('iris', iris) |>
  get_measure_names()

Description

From the planned update, it obtains the instances of the update dimensions that are not included in the star database dimensions to be updated.

Usage

get_new_dimension_instances(sdbu)

## S3 method for class 'star_database_update'
get_new_dimension_instances(sdbu)

Arguments

Value

A list of tibble objects.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_lookup_tables(), get_star_database(), get_star_schema(), get_transformation_code(), get_transformation_file(), incremental_refresh(), update_according_to()

Examples

f1 <-
  flat_table('ft_num', ft_cause_rpd[ft_cause_rpd$City != 'Cambridge' &
                                      ft_cause_rpd$WEEK != '4',]) |>
  as_star_database(mrs_cause_schema_rpd) |>
  role_playing_dimension(rpd = "When",
                         roles = c("When Available", "When Received"))
f2 <- flat_table('ft_num2', ft_cause_rpd[ft_cause_rpd$City != 'Bridgeport' &
                                           ft_cause_rpd$WEEK != '2',])
f2 <- f2 |>
  update_according_to(f1)
dim_instances <- f2 |>
  get_new_dimension_instances()

Description

Obtain the names of the attributes that form the primary key of a flat table, if defined.

Usage

get_pk_attribute_names(ft, as_definition)

## S3 method for class 'flat_table'
get_pk_attribute_names(ft, as_definition = FALSE)

Arguments

Value

A vector of strings or a tibble, attribute names.

See Also

flat_table

Other flat table join functions: check_lookup_table(), join_lookup_table(), lookup_table()

Examples

ft <- flat_table('iris', iris) |>
  lookup_table(
    measures = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
    measure_agg = c('MAX', 'MIN', 'SUM', 'MEAN')
  )
names <- ft |>
  get_pk_attribute_names()

Description

Obtain point geometry from polygon geometry.

Usage

get_point_geometry(layer)

Arguments

Value

A sf object.

See Also

Other star database geographic attributes: check_geoattribute_geometry(), define_geoattribute(), get_geoattribute_geometries(), get_geoattributes(), get_layer_geometry(), summarize_layer()

Examples

layer <-
  get_point_geometry(us_layer_state)

Description

Role playing dimensions are defined in star_databases. When integrating several star_databases to form a constellation, role playing dimensions are also integrated. This function allows you to see the result.

Usage

get_role_playing_dimension_names(db)

## S3 method for class 'star_database'
get_role_playing_dimension_names(db)

Arguments

Value

A list of vector of strings with dimension names.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_names(), get_dimension_table(), get_fact_names(), get_table_names(), group_dimension_instances(), role_playing_dimension(), star_database()

Examples

db1 <- star_database(mrs_cause_schema_rpd, ft_cause_rpd) |>
  role_playing_dimension(
    rpd = "When",
    roles = c("When Available", "When Received")
  )

db2 <- star_database(mrs_age_schema_rpd, ft_age_rpd) |>
  role_playing_dimension(
    rpd = "When Arrived",
    roles = c("When Available")
  )
rpd <- constellation("MRS", db1, db2) |>
  get_role_playing_dimension_names()

Description

Get sets of attribute values for individual attributes that differ only by tildes, spaces, or punctuation marks. If no attributes are indicated, all are considered.

Usage

## S3 method for class 'flat_table'
get_similar_attribute_values_individually(
  db,
  name = NULL,
  attributes = NULL,
  exclude_numbers = FALSE,
  col_as_vector = NULL
)

get_similar_attribute_values_individually(
  db,
  name,
  attributes,
  exclude_numbers,
  col_as_vector
)

## S3 method for class 'star_database'
get_similar_attribute_values_individually(
  db,
  name = NULL,
  attributes = NULL,
  exclude_numbers = FALSE,
  col_as_vector = NULL
)

Arguments

Details

For star databases, if no dimension name is indicated, all dimensions are considered.

You can indicate that the numbers are ignored to make the comparison.

If a name is indicated in the col_as_vector parameter, it includes a column with the data in vector form to be used in other functions.

Value

A vector of tibble objects with similar instances.

See Also

star_database, flat_table

Other star database and flat table functions: get_attribute_names.flat_table(), get_measure_names.flat_table(), get_similar_attribute_values.flat_table(), get_unique_attribute_values.flat_table(), replace_attribute_values.flat_table(), set_attribute_names.flat_table(), set_measure_names.flat_table(), snake_case.flat_table()

Examples

instances <- star_database(mrs_cause_schema, ft_num) |>
  get_similar_attribute_values_individually(name = c("where", "when"))

instances <- star_database(mrs_cause_schema, ft_num) |>
  get_similar_attribute_values_individually()

ft <- flat_table('iris', iris)
ft$table$Species[20] <- "se.Tosa."
ft$table$Species[60] <- "Versicolor"
instances <- ft |>
  get_similar_attribute_values_individually()

Description

Get sets of attribute values that differ only by tildes, spaces, or punctuation marks, for the combination of the given set of attributes. If no attributes are indicated, they are all considered together.

Usage

## S3 method for class 'flat_table'
get_similar_attribute_values(
  db,
  name = NULL,
  attributes = NULL,
  exclude_numbers = FALSE,
  col_as_vector = NULL
)

get_similar_attribute_values(
  db,
  name,
  attributes,
  exclude_numbers,
  col_as_vector
)

## S3 method for class 'star_database'
get_similar_attribute_values(
  db,
  name = NULL,
  attributes = NULL,
  exclude_numbers = FALSE,
  col_as_vector = NULL
)

Arguments

Details

For star databases, a list of dimensions can be indicated, otherwise it considers all dimensions. If a dimension is indicated, a list of attributes to be considered in it can also be indicated.

You can indicate that the numbers are ignored to make the comparison.

If a name is indicated in the col_as_vector parameter, it includes a column with the data in vector form to be used in other functions.

Value

A vector of tibble objects with similar instances.

See Also

star_database, flat_table

Other star database and flat table functions: get_attribute_names.flat_table(), get_measure_names.flat_table(), get_similar_attribute_values_individually.flat_table(), get_unique_attribute_values.flat_table(), replace_attribute_values.flat_table(), set_attribute_names.flat_table(), set_measure_names.flat_table(), snake_case.flat_table()

Examples

instances <- star_database(mrs_cause_schema, ft_num) |>
  get_similar_attribute_values(name = "where")

db <- star_database(mrs_cause_schema, ft_num)
db$dimensions$where$table$City[2] <- " BrId  gEport "
instances <- db |>
  get_similar_attribute_values("where")

db <- star_database(mrs_cause_schema, ft_num)
db$dimensions$where$table$City[2] <- " BrId  gEport "
instances <- db |>
  get_similar_attribute_values("where",
    attributes = c("City", "State"),
    col_as_vector = "As a vector")

ft <- flat_table('iris', iris)
ft$table$Species[20] <- "se.Tosa."
ft$table$Species[60] <- "Versicolor"
instances <- ft |>
  get_similar_attribute_values()

Description

It obtains the star database: For updates, the one defined from the data; for constellations, the one indicated by the parameter.

Usage

get_star_database(db, name)

## S3 method for class 'star_database_update'
get_star_database(db, name = NULL)

## S3 method for class 'star_database'
get_star_database(db, name)

Arguments

Value

A star_database object.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_lookup_tables(), get_new_dimension_instances(), get_star_schema(), get_transformation_code(), get_transformation_file(), incremental_refresh(), update_according_to()

Examples

f1 <- flat_table('ft_num', ft_cause_rpd) |>
  as_star_database(mrs_cause_schema_rpd)
f2 <- flat_table('ft_num2', ft_cause_rpd) |>
  update_according_to(f1)
st <- f2 |>
  get_star_database()

db1 <- star_database(mrs_cause_schema, ft_num) |>
  snake_case()
db2 <- star_database(mrs_age_schema, ft_age) |>
  snake_case()
ct <- constellation("MRS", db1, db2)
names <- ct |>
  get_fact_names()
st <- ct |>
  get_star_database(names[1])

Description

From the planned update, it obtains the star schema used to define the data.

Usage

get_star_schema(sdbu)

## S3 method for class 'star_database_update'
get_star_schema(sdbu)

Arguments

Value

A star_schema object.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_lookup_tables(), get_new_dimension_instances(), get_star_database(), get_transformation_code(), get_transformation_file(), incremental_refresh(), update_according_to()

Examples

f1 <- flat_table('ft_num', ft_cause_rpd) |>
  as_star_database(mrs_cause_schema_rpd)
f2 <- flat_table('ft_num2', ft_cause_rpd) |>
  update_according_to(f1)
st <- f2 |>
  get_star_schema()

Description

Obtain the table of a flat table.

Usage

get_table(ft)

## S3 method for class 'flat_table'
get_table(ft)

Arguments

Value

A tibble, the table.

See Also

star_database

Other flat table definition functions: as_star_database(), flat_table(), get_unknown_value_defined(), get_unknown_values(), read_flat_table_file(), read_flat_table_folder()

Examples

table <- flat_table('iris', iris) |>
  get_table()

Description

Obtain the names of the tables of a star database.

Usage

get_table_names(db)

## S3 method for class 'star_database'
get_table_names(db)

Arguments

Value

A vector of strings, table names.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_names(), get_dimension_table(), get_fact_names(), get_role_playing_dimension_names(), group_dimension_instances(), role_playing_dimension(), star_database()

Examples

names <- star_database(mrs_cause_schema, ft_num) |>
  get_table_names()

Description

From the planned update, it obtains the function with the source code of the transformations performed on the original data in string vector format.

Usage

get_transformation_code(sdbu)

## S3 method for class 'star_database_update'
get_transformation_code(sdbu)

Arguments

Value

A vector of strings.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_lookup_tables(), get_new_dimension_instances(), get_star_database(), get_star_schema(), get_transformation_file(), incremental_refresh(), update_according_to()

Examples

f1 <- flat_table('ft_num', ft_cause_rpd) |>
  as_star_database(mrs_cause_schema_rpd) |>
  replace_attribute_values(
    name = "When Available",
    old = c('1962', '11', '1962-03-14'),
    new = c('1962', '3', '1962-01-15')
  ) |>
  group_dimension_instances(name = "When")
f2 <- flat_table('ft_num2', ft_cause_rpd) |>
  update_according_to(f1)
code <- f2 |>
  get_transformation_code()

Description

From the planned update, it obtains the function with the source code of the transformations performed on the original data in file format.

Usage

get_transformation_file(sdbu, file)

## S3 method for class 'star_database_update'
get_transformation_file(sdbu, file = NULL)

Arguments

Value

A string, file name.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_lookup_tables(), get_new_dimension_instances(), get_star_database(), get_star_schema(), get_transformation_code(), incremental_refresh(), update_according_to()

Examples

f1 <- flat_table('ft_num', ft_cause_rpd) |>
  as_star_database(mrs_cause_schema_rpd) |>
  replace_attribute_values(
    name = "When Available",
    old = c('1962', '11', '1962-03-14'),
    new = c('1962', '3', '1962-01-15')
  ) |>
  group_dimension_instances(name = "When")
f2 <- flat_table('ft_num2', ft_cause_rpd) |>
  update_according_to(f1)
file <- f2 |>
  get_transformation_file()

Description

Get unique set of values for the given attributes. If no attributes are indicated, all are considered.

Usage

## S3 method for class 'flat_table'
get_unique_attribute_values(
  db,
  name = NULL,
  attributes = NULL,
  col_as_vector = NULL
)

get_unique_attribute_values(db, name, attributes, col_as_vector)

## S3 method for class 'star_database'
get_unique_attribute_values(
  db,
  name = NULL,
  attributes = NULL,
  col_as_vector = NULL
)

Arguments

Details

If we work on a star database, a dimension must be indicated.

Value

A vector of tibble objects with unique instances.

See Also

star_database, flat_table

Other star database and flat table functions: get_attribute_names.flat_table(), get_measure_names.flat_table(), get_similar_attribute_values.flat_table(), get_similar_attribute_values_individually.flat_table(), replace_attribute_values.flat_table(), set_attribute_names.flat_table(), set_measure_names.flat_table(), snake_case.flat_table()

Examples

instances <- star_database(mrs_cause_schema, ft_num) |>
  get_unique_attribute_values()

instances <- star_database(mrs_cause_schema, ft_num) |>
  get_unique_attribute_values(name = "where")

instances <- star_database(mrs_cause_schema, ft_num) |>
  get_unique_attribute_values("where",
    attributes = c("REGION", "State"))

instances <- flat_table('iris', iris) |>
  get_unique_attribute_values()

Description

Obtain the unknown value of a flat table.

Usage

get_unknown_value_defined(ft)

## S3 method for class 'flat_table'
get_unknown_value_defined(ft)

Arguments

Value

A string.

See Also

star_database

Other flat table definition functions: as_star_database(), flat_table(), get_table(), get_unknown_values(), read_flat_table_file(), read_flat_table_folder()

Examples

table <- flat_table('iris', iris) |>
  get_unknown_value_defined()

Description

Obtain the instances that have an empty or unknown value in any given attribute. If no attribute is given, all are considered.

Usage

get_unknown_values(ft, attributes, col_as_vector)

## S3 method for class 'flat_table'
get_unknown_values(ft, attributes = NULL, col_as_vector = NULL)

Arguments

Details

If a name is indicated in the col_as_vector parameter, it includes a column with the data in vector form to be used in other functions.

Value

A tibble with unknown values in instances.

See Also

star_database

Other flat table definition functions: as_star_database(), flat_table(), get_table(), get_unknown_value_defined(), read_flat_table_file(), read_flat_table_folder()

Examples

iris2 <- iris
iris2[10, 'Species'] <- NA
instances <- flat_table('iris', iris2) |>
  get_unknown_values()

Description

Obtain a description of the variables whose name is indicated. If no name is indicated, all are returned.

Usage

get_variable_description(gl, name, only_values)

## S3 method for class 'geolayer'
get_variable_description(gl, name = NULL, only_values = FALSE)

Arguments

Details

Using the parameter only_values, we can obtain only the combination of values or also the combination of names with values.

Value

A string vector.

See Also

Other query functions: as_GeoPackage(), as_geolayer(), filter_dimension(), get_layer(), get_variables(), run_query(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

gl <- mrs_db_geo |>
  as_geolayer()

vd <- gl |>
  get_variable_description()

Description

The variables layer includes the names and description through various fields of the variables contained in the geolayer.

Usage

get_variables(gl)

## S3 method for class 'geolayer'
get_variables(gl)

Arguments

Details

The way to select the variables we want to work with is to filter this layer and subsequently set it as the object's variables layer using the set_variables() function.

Value

A tibble object.

See Also

Other query functions: as_GeoPackage(), as_geolayer(), filter_dimension(), get_layer(), get_variable_description(), run_query(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

gl <- mrs_db_geo |>
  as_geolayer()

v <- gl |>
  get_variables()

Description

After changes in values in the instances of a dimension, groups the instances and, if necessary, also the related facts.

Usage

group_dimension_instances(db, name)

## S3 method for class 'star_database'
group_dimension_instances(db, name)

Arguments

Value

A star_database object.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_names(), get_dimension_table(), get_fact_names(), get_role_playing_dimension_names(), get_table_names(), role_playing_dimension(), star_database()

Examples

db <- star_database(mrs_cause_schema, ft_num) |>
  group_dimension_instances(name = "where")

Description

Incremental update of a star database from the star database generated with the new data.

Usage

incremental_refresh(db, sdbu, existing_instances, replace_transformations, ...)

## S3 method for class 'star_database'
incremental_refresh(
  db,
  sdbu,
  existing_instances = "ignore",
  replace_transformations = FALSE,
  ...
)

Arguments

Details

There may be data in the update that already exists in the facts: it is indicated what to do with it, replace it, group it, delete it or ignore it in the update.

If to obtain the update data we have had to perform new transformations (which were not necessary to obtain the star database), we can indicate that these are the new transformation operations for the star database. These operations are not applied to the star database, they will only be applied to new periodic updates.

Value

A star_database object.

See Also

star_database

Other star database refresh functions: get_existing_fact_instances(), get_lookup_tables(), get_new_dimension_instances(), get_star_database(), get_star_schema(), get_transformation_code(), get_transformation_file(), update_according_to()

Examples

db <-
  flat_table('ft_num', ft_cause_rpd[ft_cause_rpd$City != 'Cambridge' &
                                      ft_cause_rpd$WEEK != '4',]) |>
  as_star_database(mrs_cause_schema_rpd) |>
  role_playing_dimension(rpd = "When",
                         roles = c("When Available", "When Received"))
f2 <- flat_table('ft_num2', ft_cause_rpd[ft_cause_rpd$City != 'Bridgeport' &
                                           ft_cause_rpd$WEEK != '2',])
f2 <- f2 |>
  update_according_to(db)

db <- db |>
  incremental_refresh(f2)

Description

To join a flat table with a lookup table, the attributes of the first table that will be used in the operation are indicated. The lookup table must have the primary key previously defined.

Usage

join_lookup_table(ft, fk_attributes, lookup)

## S3 method for class 'flat_table'
join_lookup_table(ft, fk_attributes = NULL, lookup)

Arguments

Details

If no attributes are indicated, those that form the primary key of the lookup table are considered in the flat table.

Value

A flat_table object.

See Also

flat_table

Other flat table join functions: check_lookup_table(), get_pk_attribute_names(), lookup_table()

Examples

lookup <- flat_table('iris', iris) |>
  lookup_table(
    measures = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
    measure_agg = c('MAX', 'MIN', 'SUM', 'MEAN')
  )
ft <- flat_table('iris', iris) |>
  join_lookup_table(lookup = lookup)

Description

Load star_database (from a RDS file)

Usage

load_star_database(file)

Arguments

Value

A star_database object.

See Also

star_database

Other star database deployment functions: cancel_deployment(), deploy(), get_deployment_names()

Examples

mrs_rdb_file <- tempfile("mrs", fileext = ".rdb")
mrs_sqlite_file <- tempfile("mrs", fileext = ".sqlite")

mrs_sqlite_connect <- function() {
  DBI::dbConnect(RSQLite::SQLite(),
                 dbname = mrs_sqlite_file)
}

mrs_db <- mrs_db |>
  deploy(
    name = "mrs",
    connect = mrs_sqlite_connect,
    file = mrs_rdb_file
  )

mrs_db2 <- load_star_database(mrs_rdb_file)

Description

Checks that the given attributes form a primary key of the table. Otherwise, group the records so that they form a primary key. To carry out the groupings, aggregation functions for attributes and measures must be provided.

Usage

lookup_table(
  ft,
  pk_attributes,
  attributes,
  attribute_agg,
  measures,
  measure_agg
)

## S3 method for class 'flat_table'
lookup_table(
  ft,
  pk_attributes = NULL,
  attributes = NULL,
  attribute_agg = NULL,
  measures = NULL,
  measure_agg = NULL
)

Arguments

Details

If the table does not have measures, attributes with equal values are grouped without the need to indicate a grouping function.

If no attribute is indicated, all the attributes are considered to form the primary key.

Value

A flat_table object.

See Also

flat_table

Other flat table join functions: check_lookup_table(), get_pk_attribute_names(), join_lookup_table()

Examples

ft <- flat_table('iris', iris) |>
  lookup_table(
    measures = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
    measure_agg = c('MAX', 'MIN', 'SUM', 'MEAN')
  )

Description

Definition of schemas for facts and dimensions for the Mortality Reporting System considering the age classification.

Usage

mrs_age_schema

Format

A star_schema object.

Details

Dimension schemes can be defined using variables so that you do not have to repeat the definition in several multidimensional designs.

See Also

ft_age

Other mrs example schema: mrs_age_schema_rpd, mrs_cause_schema_rpd, mrs_cause_schema

Examples

# Defined by:

when <- dimension_schema(name = "When",
                         attributes = c("Year"))
where <- dimension_schema(name = "Where",
                          attributes = c("REGION",
                                         "State",
                                         "City"))
mrs_age_schema <- star_schema() |>
  define_facts(name = "MRS Age",
               measures = c("All Deaths")) |>
  define_dimension(when) |>
  define_dimension(where) |>
  define_dimension(name = "Who",
                   attributes = c("Age"))

Description

Definition of schemas for facts and dimensions for the Mortality Reporting System considering the cause classification with additional dates to be used as role playing dimensions..

Usage

mrs_age_schema_rpd

Format

A star_schema object.

See Also

ft_age_rpd

Other mrs example schema: mrs_age_schema, mrs_cause_schema_rpd, mrs_cause_schema

Examples

# Defined by:

mrs_age_schema_rpd <- star_schema() |>
  define_facts(fact_schema(
    name = "mrs_age",
    measures = c(
      "Deaths"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When",
    attributes = c(
      "Year",
      "WEEK",
      "Week Ending Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When Available",
    attributes = c(
      "Data Availability Year",
      "Data Availability Week",
      "Data Availability Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When Arrived",
    attributes = c(
      "Arrival Year",
      "Arrival Week",
      "Arrival Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "Who",
    attributes = c(
      "Age Range"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "where",
    attributes = c(
      "REGION",
      "State",
      "City"
    )
  ))

Description

Definition of schemas for facts and dimensions for the Mortality Reporting System considering the cause classification.

Usage

mrs_cause_schema

Format

A star_schema object.

Details

Dimension schemes can be defined using variables so that you do not have to repeat the definition in several multidimensional designs.

See Also

ft_num

Other mrs example schema: mrs_age_schema_rpd, mrs_age_schema, mrs_cause_schema_rpd

Examples

# Defined by:

when <- dimension_schema(name = "When",
                         attributes = c("Year"))
where <- dimension_schema(name = "Where",
                          attributes = c("REGION",
                                         "State",
                                         "City"))
mrs_cause_schema <- star_schema() |>
  define_facts(name = "MRS Cause",
               measures = c("Pneumonia and Influenza Deaths",
                            "All Deaths")) |>
  define_dimension(when) |>
  define_dimension(where)

Description

Definition of schemas for facts and dimensions for the Mortality Reporting System considering the cause classification with additional dates to be used as role playing dimensions..

Usage

mrs_cause_schema_rpd

Format

A star_schema object.

See Also

ft_cause_rpd

Other mrs example schema: mrs_age_schema_rpd, mrs_age_schema, mrs_cause_schema

Examples

# Defined by:

mrs_cause_schema_rpd <- star_schema() |>
  define_facts(fact_schema(
    name = "mrs_cause",
    measures = c(
      "Pneumonia and Influenza Deaths",
      "All Deaths"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When",
    attributes = c(
      "Year",
      "WEEK",
      "Week Ending Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When Available",
    attributes = c(
      "Data Availability Year",
      "Data Availability Week",
      "Data Availability Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When Received",
    attributes = c(
      "Reception Year",
      "Reception Week",
      "Reception Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "where",
    attributes = c(
      "REGION",
      "State",
      "City"
    )
  ))

Description

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, from the original file, we have stored in the package a file with the same format as the original file but that includes only 1% of its data, selected at random.

Usage

mrs_db

Format

A star_database.

Details

From these data the constellation in the vignette titled 'Obtaining and transforming flat tables' has been generated. This variable contains the defined constellation.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

Other mrs example data: ft_age_rpd, ft_age, ft_cause_rpd, ft_num, ft, mrs_db_geo, mrs_ft_new, mrs_ft

Description

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, from the original file, we have stored in the package a file with the same format as the original file but that includes only 1% of its data, selected at random.

Usage

mrs_db_geo

Format

A star_database.

Details

From these data the constellation in the vignette titled 'Obtaining and transforming flat tables' has been generated. This variable contains the defined constellation.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

Other mrs example data: ft_age_rpd, ft_age, ft_cause_rpd, ft_num, ft, mrs_db, mrs_ft_new, mrs_ft

Examples

# Defined by:

sq <- mrs_db |>
  star_query() |>
  select_dimension(name = "where",
                   attributes = "state") |>
  select_dimension(name = "when",
                   attributes = "year") |>
  select_fact(
    name = "mrs_age",
    measures = "all_deaths"
  )  |>
  select_fact(
    name = "mrs_cause",
    measures = "pneumonia_and_influenza_deaths"
  )

db <- mrs_db |>
  run_query(sq)

mrs_db_geo <- db |>
  define_geoattribute(
    dimension = "where",
    attribute = "state",
    from_layer = us_layer_state,
    by = "STUSPS"
  )

Description

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, from the original file, we have stored in the package a file with the same format as the original file but that includes only 1% of its data, selected at random.

Usage

mrs_ft

Format

A flat_table.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

Other mrs example data: ft_age_rpd, ft_age, ft_cause_rpd, ft_num, ft, mrs_db_geo, mrs_db, mrs_ft_new

Description

The original dataset covers from 1962 to 2016. For each week, in 122 US cities, from the original file, we have stored in the package a file with the same format as the original file but that includes only 0,1% of its data, selected at random to test the incremental refresh.

Usage

mrs_ft_new

Format

A flat_table.

Source

https://catalog.data.gov/dataset/deaths-in-122-u-s-cities-1962-2016-122-cities-mortality-reporting-system

See Also

Other mrs example data: ft_age_rpd, ft_age, ft_cause_rpd, ft_num, ft, mrs_db_geo, mrs_db, mrs_ft

Description

Gets the keys that have multiple values associated with them. The first field in the table is the key, the rest of fields are the values.

Usage

multiple_value_key(tb, col_as_vector = NULL)

Arguments

Details

If a name is indicated in the col_as_vector parameter, it includes a column with the data in vector form to be used in other functions.

Value

A tibble.

Examples

tb <- unique(ft[, c('WEEK', 'Week Ending Date')])
mvk <- multiple_value_key(tb)

Description

Reads a text file and creates a flat_table object. The file is expected to contain a flat table whose first row contains the name of the columns. All columns are considered to be of type String.

Usage

read_flat_table_file(name, file, sep = ",", page = NULL, unknown_value = NULL)

Arguments

Details

When multiple files are handled, the file name may contain information associated with the flat table, it could be the table page information if the name of a new field in which to store it is indicated in the page parameter.

We can also indicate the value that is used in the data with undefined values.

Value

A flat_table object.

See Also

star_database

Other flat table definition functions: as_star_database(), flat_table(), get_table(), get_unknown_value_defined(), get_unknown_values(), read_flat_table_folder()

Examples

file <-
  system.file("extdata/mrs",
              "mrs_122_us_cities_1962_2016_new.csv",
              package = "rolap")

ft <- read_flat_table_file('mrs_new', file)

Description

Reads all text files in a folder and creates a flat_table object. Each file is expected to contain a flat table, all with the same structure, whose first row contains the name of the columns. All columns are considered to be of type String.

Usage

read_flat_table_folder(
  name,
  folder,
  sep = ",",
  page = NULL,
  unknown_value = NULL,
  same_columns = FALSE,
  snake_case = FALSE
)

Arguments

Details

When multiple files are handled, the file name may contain information associated with the flat table, it could be the table page information if the name of a new field in which to store it is indicated.

We can also indicate the value that is used in the data with undefined values.

In some situations all the files have the same structure but the column names may change slightly. In these cases it can be useful to transform the names to snake case or consider for all the files the names of the columns of the first one. These operations can be indicated by the corresponding parameters.

Value

A flat_table object.

See Also

star_database

Other flat table definition functions: as_star_database(), flat_table(), get_table(), get_unknown_value_defined(), get_unknown_values(), read_flat_table_file()

Examples

file <- system.file("extdata/mrs", package = "rolap")

ft <- read_flat_table_folder('mrs_new', file)

Description

Delete instances that have all measures undefined.

Usage

remove_instances_without_measures(ft)

## S3 method for class 'flat_table'
remove_instances_without_measures(ft)

Arguments

Value

A flat_table object.

See Also

flat_table

Other flat table transformation functions: add_custom_column(), replace_empty_values(), replace_string(), replace_unknown_values(), select_attributes(), select_instances_by_comparison(), select_instances(), select_measures(), separate_measures(), transform_attribute_format(), transform_from_values(), transform_to_attribute(), transform_to_measure(), transform_to_values()

Examples

ft <- flat_table('iris', iris) |>
  remove_instances_without_measures()

Description

Given the values of a possible instance, for that combination, replace them with the new data values.

Usage

## S3 method for class 'flat_table'
replace_attribute_values(db, name = NULL, attributes = NULL, old, new)

replace_attribute_values(db, name, attributes, old, new)

## S3 method for class 'star_database'
replace_attribute_values(db, name, attributes = NULL, old, new)

Arguments

Value

A flat_table or star_database object.

See Also

star_database, flat_table

Other star database and flat table functions: get_attribute_names.flat_table(), get_measure_names.flat_table(), get_similar_attribute_values.flat_table(), get_similar_attribute_values_individually.flat_table(), get_unique_attribute_values.flat_table(), set_attribute_names.flat_table(), set_measure_names.flat_table(), snake_case.flat_table()

Examples

db <- star_database(mrs_cause_schema, ft_num) |>
  replace_attribute_values(name = "where",
    old = c('1', 'CT', 'Bridgeport'),
    new = c('1', 'CT', 'Hartford'))

db <- star_database(mrs_cause_schema, ft_num) |>
  replace_attribute_values(name = "where",
                           attributes = c('REGION', 'State'),
                           old = c('1', 'CT'),
                           new = c('2', 'CT'))

ft <- flat_table('iris', iris) |>
  replace_attribute_values(
    attributes = 'Species',
    old = c('setosa'),
    new = c('versicolor')
  )

Description

Transforms the given attributes by replacing the empty values with the unknown value.

Usage

replace_empty_values(ft, attributes, empty_values)

## S3 method for class 'flat_table'
replace_empty_values(ft, attributes = NULL, empty_values = NULL)

Arguments

Details

In addition to the NA or empty values, those indicated (e.g., "-") can be considered as empty values.

Value

A flat_table object.

See Also

flat_table

Other flat table transformation functions: add_custom_column(), remove_instances_without_measures(), replace_string(), replace_unknown_values(), select_attributes(), select_instances_by_comparison(), select_instances(), select_measures(), separate_measures(), transform_attribute_format(), transform_from_values(), transform_to_attribute(), transform_to_measure(), transform_to_values()

Examples

iris2 <- iris
iris2[10, 'Species'] <- NA
ft <- flat_table('iris', iris2) |>
  replace_empty_values()

Description

Transforms the given attributes by replacing the string values with the replacement value.

Usage

replace_string(ft, attributes, string, replacement)

## S3 method for class 'flat_table'
replace_string(ft, attributes = NULL, string, replacement = NULL)

Arguments

Value

A flat_table object.

See Also

flat_table

Other flat table transformation functions: add_custom_column(), remove_instances_without_measures(), replace_empty_values(), replace_unknown_values(), select_attributes(), select_instances_by_comparison(), select_instances(), select_measures(), separate_measures(), transform_attribute_format(), transform_from_values(), transform_to_attribute(), transform_to_measure(), transform_to_values()

Examples

ft <- flat_table('iris', iris) |>
  replace_string(
    attributes = 'Species',
    string = c('set'),
    replacement = c('Set')
  )

Description

Transforms the given attributes by replacing unknown values in them with the given value.

Usage

replace_unknown_values(ft, attributes, value)

## S3 method for class 'flat_table'
replace_unknown_values(ft, attributes = NULL, value)

Arguments

Value

A flat_table object.

See Also

flat_table

Other flat table transformation functions: add_custom_column(), remove_instances_without_measures(), replace_empty_values(), replace_string(), select_attributes(), select_instances_by_comparison(), select_instances(), select_measures(), separate_measures(), transform_attribute_format(), transform_from_values(), transform_to_attribute(), transform_to_measure(), transform_to_values()

Examples

iris2 <- iris
iris2[10, 'Species'] <- NA
ft <- flat_table('iris', iris2) |>
  replace_empty_values() |>
  replace_unknown_values(value = "Not available")

Description

The same dimension can play several roles in relation to the facts. We can define the main dimension and the dimensions that play different roles.

Usage

role_playing_dimension(db, rpd, roles, rpd_att_names, att_names)

## S3 method for class 'star_database'
role_playing_dimension(db, rpd, roles, rpd_att_names = FALSE, att_names = NULL)

Arguments

Details

As a result, all the dimensions will have the same instances and, if we deem it necessary, also the same name of their attributes (except the surrogate key).

Value

A star_database object.

See Also

star_schema, flat_table

Other star database definition functions: get_dimension_names(), get_dimension_table(), get_fact_names(), get_role_playing_dimension_names(), get_table_names(), group_dimension_instances(), star_database()

Examples

s <- star_schema() |>
  define_facts(fact_schema(
    name = "mrs_cause",
    measures = c(
      "Pneumonia and Influenza Deaths",
      "All Deaths"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When",
    attributes = c(
      "Year",
      "WEEK",
      "Week Ending Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When Available",
    attributes = c(
      "Data Availability Year",
      "Data Availability Week",
      "Data Availability Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "When Received",
    attributes = c(
      "Reception Year",
      "Reception Week",
      "Reception Date"
    )
  )) |>
  define_dimension(dimension_schema(
    name = "where",
    attributes = c(
      "REGION",
      "State",
      "City"
    )
  ))

db <- star_database(s, ft_cause_rpd) |>
  role_playing_dimension(
    rpd = "When",
    roles = c("When Available", "When Received"),
    rpd_att_names = TRUE
  )

db <- star_database(s, ft_cause_rpd) |>
  role_playing_dimension("When",
                         c("When Available", "When Received"),
                         att_names = c("Year", "Week", "Date"))

Description

Once we have selected the facts, dimensions and defined the conditions on the instances, we can execute the query to obtain the result.

Usage

run_query(db, sq)

## S3 method for class 'star_database'
run_query(db, sq)

Arguments

Details

As an option, we can indicate if we do not want to unify the facts in the case of having the same grain.

Value

A star_database object.

See Also

Other query functions: as_GeoPackage(), as_geolayer(), filter_dimension(), get_layer(), get_variable_description(), get_variables(), select_dimension(), select_fact(), set_layer(), set_variables(), star_query()

Examples

sq <- mrs_db |>
  star_query() |>
  select_dimension(name = "where",
                   attributes = c("city", "state")) |>
  select_dimension(name = "when",
                   attributes = "year") |>
  select_fact(
    name = "mrs_age",
    measures = "all_deaths",
    agg_functions = "MAX"
  ) |>
  select_fact(
    name = "mrs_cause",
    measures = c("pneumonia_and_influenza_deaths", "all_deaths")
  ) |>
  filter_dimension(name = "when", week <= " 3") |>
  filter_dimension(name = "where", city == "Bridgeport")

mrs_db_2 <- mrs_db |>
  run_query(sq)