Package 'starschemar'

Description

Transforms numeric type attributes of dimensions into character type. In a star_schema numerical data are measurements that are situated in the facts. Numerical data in dimensions are usually codes, day, week, month or year numbers. There are tools that consider any numerical data to be a measurement, for this reason it is appropriate to transform the numerical data of dimensions into character data.

Usage

character_dimensions(st, length_integers = list(), NA_replacement_value = NULL)

## S3 method for class 'star_schema'
character_dimensions(st, length_integers = list(), NA_replacement_value = NULL)

Arguments

Details

It allows indicating the amplitude for some fields, filling with zeros on the left. This is useful to make the alphabetical order of the result correspond to the numerical order.

It also allows indicating the literal to be used in case the numerical value is not defined.

If a role playing dimension has been defined, the transformation is performed on it.

Value

A star_schema object.

See Also

Other star schema and constellation definition functions: constellation(), role_playing_dimension(), snake_case(), star_schema()

Examples

st <- star_schema(mrs_age_test, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("date", "week", "year")
  ) |>
  character_dimensions(length_integers = list(week = 2),
                       NA_replacement_value = "Unknown")

Description

Creates a constellation object from a list of star_schema objects. All dimensions with the same name in the star schemas have to be conformable.

Usage

constellation(lst, name = NULL)

Arguments

Value

A constellation object.

See Also

Other star schema and constellation definition functions: character_dimensions(), role_playing_dimension(), snake_case(), star_schema()

Examples

ct <- constellation(list(st_mrs_age, st_mrs_cause), name = "mrs")

Description

Once we have refined the format or content of facts and dimensions, we can obtain a multistar. A multistar only distinguishes between general and conformed dimensions, each dimension has its own data. It can contain multiple fact tables.

Usage

constellation_as_multistar(ct)

## S3 method for class 'constellation'
constellation_as_multistar(ct)

Arguments

Value

A multistar object.

See Also

Other results export functions: constellation_as_tibble_list(), multistar_as_flat_table(), star_schema_as_flat_table(), star_schema_as_multistar(), star_schema_as_tibble_list()

Examples

ms <- ct_mrs |>
  constellation_as_multistar()

Description

Once we have refined the format or content of facts and dimensions, we can obtain a tibble list with them. Role playing dimensions can be optionally included.

Usage

constellation_as_tibble_list(ct, include_role_playing = FALSE)

## S3 method for class 'constellation'
constellation_as_tibble_list(ct, include_role_playing = FALSE)

Arguments

Value

A list of tibble objects.

See Also

Other results export functions: constellation_as_multistar(), multistar_as_flat_table(), star_schema_as_flat_table(), star_schema_as_multistar(), star_schema_as_tibble_list()

Examples

tl <- ct_mrs |>
  constellation_as_tibble_list()

tl <- ct_mrs |>
  constellation_as_tibble_list(include_role_playing = TRUE)

Description

Constellation for the Mortality Reporting System considering age and cause classification.

Usage

ct_mrs

Format

A constellation object.

Examples

# Defined by:

ct_mrs <- constellation(list(st_mrs_age, st_mrs_cause), name = "mrs")

Description

Constellation for the Mortality Reporting System considering age and cause classification data test.

Usage

ct_mrs_test

Format

A constellation object.

Examples

# Defined by:

ct_mrs_test <-
  constellation(list(st_mrs_age_test, st_mrs_cause_test), name = "mrs_test")

Description

To define a dimension in a dimensional_model object, we have to define its name and the set of attributes that make it up.

Usage

define_dimension(st, name = NULL, attributes = NULL)

## S3 method for class 'dimensional_model'
define_dimension(st, name = NULL, attributes = NULL)

Arguments

Details

To get a star schema (a star_schema object) we need a flat table (implemented through a tibble) and a dimensional_model object. The definition of dimensions in the dimensional_model object is made from the flat table column names. Using the dput function we can list the column names of the flat table so that we do not have to type their names.

Value

A dimensional_model object.

See Also

Other star definition functions: define_fact(), dimensional_model()

Examples

# dput(colnames(mrs_age))
#
# c(
#   "Reception Year",
#   "Reception Week",
#   "Reception Date",
#   "Data Availability Year",
#   "Data Availability Week",
#   "Data Availability Date",
#   "Year",
#   "WEEK",
#   "Week Ending Date",
#   "REGION",
#   "State",
#   "City",
#   "Age Range",
#   "Deaths"
# )

dm <- dimensional_model() |>
  define_dimension(name = "When",
                   attributes = c("Week Ending Date",
                                  "WEEK",
                                  "Year")) |>
  define_dimension(name = "When Available",
                   attributes = c("Data Availability Date",
                                  "Data Availability Week",
                                  "Data Availability Year")) |>
  define_dimension(name = "Where",
                   attributes = c("REGION",
                                  "State",
                                  "City")) |>
  define_dimension(name = "Who",
                   attributes = c("Age Range"))

Description

To define facts in a dimensional_model object, the essential data is a name and a set of measurements that can be empty (does not have explicit measurements). Associated with each measurement, an aggregation function is required, which by default is SUM.

Usage

define_fact(
  st,
  name = NULL,
  measures = NULL,
  agg_functions = NULL,
  nrow_agg = "nrow_agg"
)

## S3 method for class 'dimensional_model'
define_fact(
  st,
  name = NULL,
  measures = NULL,
  agg_functions = NULL,
  nrow_agg = "nrow_agg"
)

Arguments

Details

To get a star schema (a star_schema object) we need a flat table (implemented through a tibble) and a dimensional_model object. The definition of facts in the dimensional_model object is made from the flat table column names. Using the dput function we can list the column names of the flat table so that we do not have to type their names.

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

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

Value

A dimensional_model object.

See Also

Other star definition functions: define_dimension(), dimensional_model()

Examples

# dput(colnames(mrs_age))
#
# c(
#   "Reception Year",
#   "Reception Week",
#   "Reception Date",
#   "Data Availability Year",
#   "Data Availability Week",
#   "Data Availability Date",
#   "Year",
#   "WEEK",
#   "Week Ending Date",
#   "REGION",
#   "State",
#   "City",
#   "Age Range",
#   "Deaths"
# )

dm <- dimensional_model() |>
  define_fact(
    name = "mrs_age",
    measures = c("Deaths"),
    agg_functions = c("SUM"),
    nrow_agg = "nrow_agg"
  )

dm <- dimensional_model() |>
  define_fact(
    name = "mrs_age",
    measures = c("Deaths")
  )

dm <- dimensional_model() |>
  define_fact(name = "Factless fact")

Description

An empty dimensional_model object is created in which definition of facts and dimensions can be added.

Usage

dimensional_model()

Details

To get a star schema (a star_schema object) we need a flat table (implemented through a tibble) and a dimensional_model object. The definition of facts and dimensions in the dimensional_model object is made from the flat table columns. Each attribute can only appear once in the definition.

Value

A dimensional_model object.

See Also

star_schema

Other star definition functions: define_dimension(), define_fact()

Examples

dm <- dimensional_model()

Description

An empty dimensional_query object is created where you can select fact measures, dimension attributes and filter dimension rows.

Usage

dimensional_query(ms = NULL)

Arguments

Value

A dimensional_query object.

See Also

Other query functions: filter_dimension(), run_query(), select_dimension(), select_fact()

Examples

# ms_mrs <- ct_mrs |>
#  constellation_as_multistar()

# dq <- dimensional_query(ms_mrs)

Description

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

Usage

dm_mrs_age

Format

A dimensional_model object.

Examples

# Defined by:

dm_mrs_age <- dimensional_model() |>
  define_fact(
    name = "mrs_age",
    measures = c(
      "Deaths"
    ),
    agg_functions = c(
      "SUM"
    ),
    nrow_agg = "nrow_agg"
  ) |>
  define_dimension(
    name = "when",
    attributes = c(
      "Week Ending Date",
      "WEEK",
      "Year"
    )
  ) |>
  define_dimension(
    name = "when_available",
    attributes = c(
      "Data Availability Date",
      "Data Availability Week",
      "Data Availability Year"
    )
  ) |>
  define_dimension(
    name = "where",
    attributes = c(
      "REGION",
      "State",
      "City"
    )
  ) |>
  define_dimension(
    name = "who",
    attributes = c(
      "Age Range"
    )
  )

Description

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

Usage

dm_mrs_cause

Format

A dimensional_model object.

Examples

# Defined by:

dm_mrs_cause <- dimensional_model() |>
  define_fact(
    name = "mrs_cause",
    measures = c(
      "Pneumonia and Influenza Deaths",
      "Other Deaths"
    ),
  ) |>
  define_dimension(
    name = "when",
    attributes = c(
      "Week Ending Date",
      "WEEK",
      "Year"
    )
  ) |>
  define_dimension(
    name = "when_received",
    attributes = c(
      "Reception Date",
      "Reception Week",
      "Reception Year"
    )
  ) |>
  define_dimension(
    name = "when_available",
    attributes = c(
      "Data Availability Date",
      "Data Availability Week",
      "Data Availability Year"
    )
  ) |>
  define_dimension(
    name = "where",
    attributes = c(
      "REGION",
      "State",
      "City"
    )
  )

Description

Export the selected attributes of a dimension, without repeated combinations, to enrich the dimension.

Usage

enrich_dimension_export(st, name = NULL, attributes = NULL)

## S3 method for class 'star_schema'
enrich_dimension_export(st, name = NULL, attributes = NULL)

Arguments

Details

If it is a role dimension they cannot be exported, you have to work with the associated role playing dimension.

Value

A tibble object.

See Also

Other dimension enrichment functions: enrich_dimension_import(), enrich_dimension_import_test()

Examples

tb <-
  enrich_dimension_export(st_mrs_age,
                          name = "when_common",
                          attributes = c("week", "year"))

Description

For a dimension of a star schema a tibble is attached. This contains dimension attributes and new attributes. If values associated with all rows in the dimension are included in the tibble, the dimension is enriched with the new attributes.

Usage

enrich_dimension_import(st, name = NULL, tb)

## S3 method for class 'star_schema'
enrich_dimension_import(st, name = NULL, tb)

Arguments

Details

Role dimensions cannot be directly enriched. If a role playing dimension is enriched, the new attributes are also added to the associated role dimensions.

Value

A star_schema object.

See Also

Other dimension enrichment functions: enrich_dimension_export(), enrich_dimension_import_test()

Examples

tb <-
  enrich_dimension_export(st_mrs_age,
                          name = "when_common",
                          attributes = c("week", "year"))

# Add new columns with meaningful data (these are not), possibly exporting
# data to a file, populating it and importing it.
tb <- tibble::add_column(tb, x = "x", y = "y", z = "z")

st <- enrich_dimension_import(st_mrs_age, name = "when_common", tb)

Description

For a dimension of a star schema a tibble is attached. This contains dimension attributes and new attributes. If values associated with all rows in the dimension are included in the tibble, the dimension is enriched with the new attributes. This function checks that there are values for all instances. Returns the dimension instances that do not match the imported data.

Usage

enrich_dimension_import_test(st, name = NULL, tb)

## S3 method for class 'star_schema'
enrich_dimension_import_test(st, name = NULL, tb)

Arguments

Value

A dimension object.

See Also

Other dimension enrichment functions: enrich_dimension_export(), enrich_dimension_import()

Examples

tb <-
  enrich_dimension_export(st_mrs_age,
                          name = "when_common",
                          attributes = c("week", "year"))

# Add new columns with meaningful data (these are not), possibly exporting
# data to a file, populating it and importing it.
tb <- tibble::add_column(tb, x = "x", y = "y", z = "z")[-1, ]

tb2 <- enrich_dimension_import_test(st_mrs_age, name = "when_common", tb)

Description

Allows you to define selection conditions for dimension rows.

Usage

filter_dimension(dq, name = NULL, ...)

## S3 method for class 'dimensional_query'
filter_dimension(dq, 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 dimensional_query object.

See Also

Other query functions: dimensional_query(), run_query(), select_dimension(), select_fact()

Examples

dq <- dimensional_query(ms_mrs) |>
  filter_dimension(name = "when", when_happened_week <= "03") |>
  filter_dimension(name = "where", city == "Boston")

Description

Filter fact rows based on dimension conditions in a star schema. Dimensions remain unchanged.

Usage

filter_fact_rows(st, name = NULL, ...)

## S3 method for class 'star_schema'
filter_fact_rows(st, name = NULL, ...)

Arguments

Details

Filtered rows can be deleted using the incremental_refresh_star_schema function.

Value

A star_schema object.

See Also

Other incremental refresh functions: get_star_schema(), get_star_schema_names(), incremental_refresh_constellation(), incremental_refresh_star_schema(), purge_dimensions_constellation(), purge_dimensions_star_schema()

Examples

st <- st_mrs_age |>
  filter_fact_rows(name = "when", week <= "03") |>
  filter_fact_rows(name = "where", city == "Bridgeport")

st2 <- st_mrs_age |>
  incremental_refresh_star_schema(st, existing = "delete")

Description

Estimation of the long-term health impacts of exposure to air pollution in London from 2016 to 2050.

Usage

ft_datagov_uk

Format

A tibble.

Details

The original dataset contains 68 files, corresponding to 34 London areas and 2 pollutants: pollutant and zone are indicated in the name of each file. Each file has several sheets with different variables. It has been transformed into a flat table considering a single variable and defining the area and the pollutant as columns.

Source

https://data.world/datagov-uk/fd864906-8456-46a8-9a01-0dcb2dbd87b9

Description

Classification of London's boroughs into zones and sub-regions.

Usage

ft_london_boroughs

Format

A tibble.

Source

https://en.wikipedia.org/wiki/List_of_sub-regions_used_in_the_London_Plan

Description

City, state and county for US cities. It only includes those that appear in the Mortality Reporting System.

Usage

ft_usa_city_county

Format

A tibble.

Source

https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html

Description

Name and abbreviation of US states.

Usage

ft_usa_states

Format

A tibble.

Source

https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html

Description

Get a conformed dimension of a constellation given its name.

Usage

get_conformed_dimension(ct, name)

## S3 method for class 'constellation'
get_conformed_dimension(ct, name)

Arguments

Value

A dimension_table object.

See Also

Other data cleaning functions: get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

d <- ct_mrs |>
  get_conformed_dimension("when")

Description

Get the names of the conformed dimensions of a constellation.

Usage

get_conformed_dimension_names(ct)

## S3 method for class 'constellation'
get_conformed_dimension_names(ct)

Arguments

Value

A vector of dimension names.

See Also

Other data cleaning functions: get_conformed_dimension(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

d <- ct_mrs |>
  get_conformed_dimension_names()

Description

Get a dimension of a star schema given its name.

Usage

get_dimension(st, name)

## S3 method for class 'star_schema'
get_dimension(st, name)

Arguments

Details

Role dimensions can be obtained but not role playing dimensions. Role dimensions get their instances of role playing dimensions.

Value

A dimension_table object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

d <- st_mrs_age |>
  get_dimension("when")

Description

Get the name of attributes in a dimension.

Usage

get_dimension_attribute_names(st, name)

## S3 method for class 'star_schema'
get_dimension_attribute_names(st, name)

Arguments

Value

A vector of attribute names.

See Also

Other rename functions: get_measure_names(), rename_dimension(), rename_dimension_attributes(), rename_fact(), rename_measures()

Examples

attribute_names <-
  st_mrs_age |> get_dimension_attribute_names("when")

Description

Get the names of the dimensions of a star schema.

Usage

get_dimension_names(st)

## S3 method for class 'star_schema'
get_dimension_names(st)

Arguments

Details

Role playing dimensions are not considered.

Value

A vector of dimension names.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

dn <- st_mrs_age |>
  get_dimension_names()

Description

Get the name of measures in facts.

Usage

get_measure_names(st)

## S3 method for class 'star_schema'
get_measure_names(st)

Arguments

Value

A vector of measure names.

See Also

Other rename functions: get_dimension_attribute_names(), rename_dimension(), rename_dimension_attributes(), rename_fact(), rename_measures()

Examples

measure_names <-
  st_mrs_age |> get_measure_names()

Description

Get a star schema of a constellation given its name.

Usage

get_star_schema(ct, name)

## S3 method for class 'constellation'
get_star_schema(ct, name)

Arguments

Value

A dimension_table object.

See Also

Other incremental refresh functions: filter_fact_rows(), get_star_schema_names(), incremental_refresh_constellation(), incremental_refresh_star_schema(), purge_dimensions_constellation(), purge_dimensions_star_schema()

Examples

d <- ct_mrs |>
  get_star_schema("mrs_age")

Description

Get the names of the star schemas in a constellation.

Usage

get_star_schema_names(ct)

## S3 method for class 'constellation'
get_star_schema_names(ct)

Arguments

Value

A vector of star schema names.

See Also

Other incremental refresh functions: filter_fact_rows(), get_star_schema(), incremental_refresh_constellation(), incremental_refresh_star_schema(), purge_dimensions_constellation(), purge_dimensions_star_schema()

Examples

d <- ct_mrs |>
  get_star_schema_names()

Description

Incrementally refresh a star schema in a constellation with the content of a new star schema that is integrated into the first.

Usage

incremental_refresh_constellation(ct, st, existing = "ignore")

## S3 method for class 'constellation'
incremental_refresh_constellation(ct, st, existing = "ignore")

Arguments

Details

Once the dimensions are integrated, if there are records in the fact table whose keys match the new ones, new ones can be ignored, they can be replaced by new ones, all of them can be grouped using the aggregation functions, or they can be deleted. Therefore, the possible values of the existing parameter are: "ignore", "replace", "group" or "delete".

Value

A constellation object.

See Also

Other incremental refresh functions: filter_fact_rows(), get_star_schema(), get_star_schema_names(), incremental_refresh_star_schema(), purge_dimensions_constellation(), purge_dimensions_star_schema()

Examples

ct <- ct_mrs |>
  incremental_refresh_constellation(st_mrs_age_w10, existing = "replace")

ct <- ct_mrs |>
  incremental_refresh_constellation(st_mrs_cause_w10, existing = "group")

Description

For a dimension, given the primary key of two records, it adds an update to the set of updates that modifies the combination of values of the rest of attributes of the first record so that they become the same as those of the second.

Usage

match_records(updates, dimension, old, new)

## S3 method for class 'record_update_set'
match_records(updates, dimension, old, new)

Arguments

Details

Primary keys are only used to get the combination of values easily. The update is defined exclusively from the rest of values.

It is especially useful when it is detected that two records should be only one: Two have been generated due to some data error.

Value

A record_update_set object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

dim_names <- st_mrs_age |>
    get_dimension_names()

where <- st_mrs_age |>
  get_dimension("where")

# head(where, 2)

updates <- record_update_set() |>
  match_records(dimension = where,
                old = 1,
                new = 2)

Description

Given a list of dimension record update operations, they are applied on the conformed dimensions of the constellation object. Update operations must be defined with the set of functions available for that purpose.

Usage

modify_conformed_dimension_records(ct, updates = record_update_set())

## S3 method for class 'constellation'
modify_conformed_dimension_records(ct, updates = record_update_set())

Arguments

Details

When dimensions are defined, records can be detected that must be modified as part of the data cleaning process: frequently to unify two or more records due to data errors or missing data. This is not immediate because facts must be adapted to the new set of dimension instances.

This operation allows us to unify records and automatically propagate modifications to facts in star schemas.

Value

A constellation object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

ct <- ct_mrs |>
  modify_conformed_dimension_records(updates_st_mrs_age)

Description

Given a list of dimension record update operations, they are applied on the dimensions of the star_schema object. Update operations must be defined with the set of functions available for that purpose.

Usage

modify_dimension_records(st, updates = record_update_set())

## S3 method for class 'star_schema'
modify_dimension_records(st, updates = record_update_set())

Arguments

Details

When dimensions are defined, records can be detected that must be modified as part of the data cleaning process: frequently to unify two or more records due to data errors or missing data. This is not immediate because facts must be adapted to the new set of dimension instances.

This operation allows us to unify records and automatically propagate modifications to facts.

The list of update operations can be applied repeatedly to new data received to be incorporated into the star_schema object.

Value

A star_schema object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), record_update_set(), update_record(), update_selection(), update_selection_general()

Examples

st <- st_mrs_age |>
  modify_dimension_records(updates_st_mrs_age)

Description

Selection of data from the 122 Cities Mortality Reporting System, for the first 11 weeks of 1962.

Usage

mrs

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.

Source

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

Description

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

Usage

mrs_age

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

Description

Selection of data from the 2 Cities Mortality Reporting System by age group, for the first 3 weeks of 1962.

Usage

mrs_age_test

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

Description

Selection of data from the 3 Cities Mortality Reporting System by age group, for week 4 of 1962. It also includes some isolated data from previous weeks that is supposed to be corrections for data errors.

Usage

mrs_age_w_test

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

Description

Selection of data from the 122 Cities Mortality Reporting System by age group, for week 10 of 1962. It also includes some isolated data from previous weeks that is supposed to be corrections for data errors.

Usage

mrs_age_w10

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

Description

Selection of data from the 122 Cities Mortality Reporting System by age group, for week 11 of 1962. It also includes some isolated data from previous weeks that is supposed to be corrections for data errors.

Usage

mrs_age_w11

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

Description

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

Usage

mrs_cause

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

Description

Selection of data from the 2 Cities Mortality Reporting System by cause, for the first 3 weeks of 1962.

Usage

mrs_cause_test

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

Description

Selection of data from the 3 Cities Mortality Reporting System by cause, for week 4 of 1962. It also includes some isolated data from previous weeks that is supposed to be additional data not considered before.

Usage

mrs_cause_w_test

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

Description

Selection of data from the 122 Cities Mortality Reporting System by cause, for week 10 of 1962. It also includes some isolated data from previous weeks that is supposed to be additional data not considered before.

Usage

mrs_cause_w10

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

Description

Selection of data from the 122 Cities Mortality Reporting System by cause, for week 11 of 1962. It also includes some isolated data from previous weeks that is supposed to be additional data not considered before.

Usage

mrs_cause_w11

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

Description

Multistar for the Mortality Reporting System considering age and cause classification. It is the result obtained in the vignette.

Usage

ms_mrs

Format

A multistar object.

Examples

# Defined by:

ms_mrs <- ct_mrs |>
  constellation_as_multistar()

Description

Multistar for the Mortality Reporting System considering age and cause classification data test.

Usage

ms_mrs_test

Format

A multistar object.

Examples

# Defined by:

ms_mrs_test <- ct_mrs_test |>
  constellation_as_multistar()

Description

We can obtain a flat table, implemented using a tibble, from a multistar (which can be the result of a query). If it only has one fact table, it is not necessary to provide its name.

Usage

multistar_as_flat_table(ms, fact = NULL)

## S3 method for class 'multistar'
multistar_as_flat_table(ms, fact = NULL)

Arguments

Value

A tibble.

See Also

Other results export functions: constellation_as_multistar(), constellation_as_tibble_list(), star_schema_as_flat_table(), star_schema_as_multistar(), star_schema_as_tibble_list()

Examples

ft <- ms_mrs |>
  multistar_as_flat_table(fact = "mrs_age")

ms <- dimensional_query(ms_mrs) |>
  select_dimension(name = "where",
                   attributes = c("city", "state")) |>
  select_dimension(name = "when",
                   attributes = c("when_happened_year")) |>
  select_fact(name = "mrs_age",
              measures = c("n_deaths")) |>
  select_fact(
    name = "mrs_cause",
    measures = c("pneumonia_and_influenza_deaths", "other_deaths")
  ) |>
  filter_dimension(name = "when", when_happened_week <= "03") |>
  filter_dimension(name = "where", city == "Boston") |>
  run_query()

ft <- ms |>
  multistar_as_flat_table()

Description

Delete instances of dimensions not related to facts in a constellation.

Usage

purge_dimensions_constellation(ct)

## S3 method for class 'constellation'
purge_dimensions_constellation(ct)

Arguments

Value

A constellation object.

See Also

Other incremental refresh functions: filter_fact_rows(), get_star_schema(), get_star_schema_names(), incremental_refresh_constellation(), incremental_refresh_star_schema(), purge_dimensions_star_schema()

Examples

ct <- ct_mrs |>
  purge_dimensions_constellation()

Description

Delete instances of dimensions not related to facts in a star schema.

Usage

purge_dimensions_star_schema(st)

## S3 method for class 'star_schema'
purge_dimensions_star_schema(st)

Arguments

Value

A star_schema object.

See Also

Other incremental refresh functions: filter_fact_rows(), get_star_schema(), get_star_schema_names(), incremental_refresh_constellation(), incremental_refresh_star_schema(), purge_dimensions_constellation()

Examples

st <- st_mrs_age |>
  purge_dimensions_star_schema()

Description

A record_update_set object is created. Stores updates on dimension records.

Usage

record_update_set()

Details

Each update is made up of a dimension name, an old value set, and a new value set.

When the update is applied, all the dimension records that have the combination of old values are modified with the new values provided.

Value

A record_update_set object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), update_record(), update_selection(), update_selection_general()

Examples

updates <- record_update_set()

Description

Set new name for a dimension.

Usage

rename_dimension(st, name, new_name)

## S3 method for class 'star_schema'
rename_dimension(st, name, new_name)

Arguments

Value

A star_schema object.

See Also

Other rename functions: get_dimension_attribute_names(), get_measure_names(), rename_dimension_attributes(), rename_fact(), rename_measures()

Examples

st <- st_mrs_age |>
  rename_dimension(name = "when", new_name = "when_happened")

Description

Set new names of some attributes in a dimension.

Usage

rename_dimension_attributes(st, name, attributes, new_names)

## S3 method for class 'star_schema'
rename_dimension_attributes(st, name, attributes, new_names)

Arguments

Value

A star_schema object.

See Also

Other rename functions: get_dimension_attribute_names(), get_measure_names(), rename_dimension(), rename_fact(), rename_measures()

Examples

st <-
  st_mrs_age |> rename_dimension_attributes(
    name = "when",
    attributes = c("week", "year"),
    new_names = c("w", "y")
  )

Description

Set new name for facts.

Usage

rename_fact(st, name)

## S3 method for class 'star_schema'
rename_fact(st, name)

Arguments

Value

A star_schema object.

See Also

Other rename functions: get_dimension_attribute_names(), get_measure_names(), rename_dimension(), rename_dimension_attributes(), rename_measures()

Examples

st <- st_mrs_age |> rename_fact("age")

Description

Set new names of some measures in facts.

Usage

rename_measures(st, measures, new_names)

## S3 method for class 'star_schema'
rename_measures(st, measures, new_names)

Arguments

Value

A star_schema object.

See Also

Other rename functions: get_dimension_attribute_names(), get_measure_names(), rename_dimension(), rename_dimension_attributes(), rename_fact()

Examples

st <-
  st_mrs_age |> rename_measures(measures = c("deaths"),
                                 new_names = c("n_deaths"))

Description

Given a list of star_schema dimension names, all with the same structure, a role playing dimension with the indicated name and attributes is generated. The original dimensions become role dimensions defined from the new role playing dimension.

Usage

role_playing_dimension(st, dim_names, name = NULL, attributes = NULL)

## S3 method for class 'star_schema'
role_playing_dimension(st, dim_names, name = NULL, attributes = NULL)

Arguments

Details

After definition, all role dimensions have the same virtual instances (those of the role playing dimension). The foreign keys in facts are adapted to this new situation.

Value

A star_schema object.

See Also

Other star schema and constellation definition functions: character_dimensions(), constellation(), snake_case(), star_schema()

Examples

st <- star_schema(mrs_age, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("Date", "Week", "Year")
  )

st <- star_schema(mrs_cause, dm_mrs_cause) |>
  role_playing_dimension(
    dim_names = c("when", "when_received", "when_available"),
    name = "when_common",
    attributes = c("date", "week", "year")
  )

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(dq, unify_by_grain = TRUE)

## S3 method for class 'dimensional_query'
run_query(dq, unify_by_grain = TRUE)

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 dimensional_query object.

See Also

Other query functions: dimensional_query(), filter_dimension(), select_dimension(), select_fact()

Examples

ms <- dimensional_query(ms_mrs) |>
  select_dimension(name = "where",
                   attributes = c("city", "state")) |>
  select_dimension(name = "when",
                   attributes = c("when_happened_year")) |>
  select_fact(
    name = "mrs_age",
    measures = c("n_deaths"),
    agg_functions = c("MAX")
  ) |>
  select_fact(
    name = "mrs_cause",
    measures = c("pneumonia_and_influenza_deaths", "other_deaths")
  ) |>
  filter_dimension(name = "when", when_happened_week <= "03") |>
  filter_dimension(name = "where", city == "Boston") |>
  run_query()

Description

To add a dimension in a dimensional_query object, we have to define its name and a subset of the dimension attributes. If only the name of the dimension is indicated, it is considered that all its attributes should be added.

Usage

select_dimension(dq, name = NULL, attributes = NULL)

## S3 method for class 'dimensional_query'
select_dimension(dq, name = NULL, attributes = NULL)

Arguments

Value

A dimensional_query object.

See Also

Other query functions: dimensional_query(), filter_dimension(), run_query(), select_fact()

Examples

dq <- dimensional_query(ms_mrs) |>
  select_dimension(name = "where",
                  attributes = c("city", "state")) |>
  select_dimension(name = "when")

Description

To define the fact to be consulted, its name is indicated, optionally, a vector of names of selected measures and another of aggregation functions are also indicated.

Usage

select_fact(dq, name = NULL, measures = NULL, agg_functions = NULL)

## S3 method for class 'dimensional_query'
select_fact(dq, name = NULL, measures = NULL, agg_functions = NULL)

Arguments

Details

If the name of any measure is not indicated, only the one corresponding to the number of aggregated rows is included, which is always included.

If no aggregation function is included, those defined for the measures are considered.

Value

A dimensional_query object.

See Also

Other query functions: dimensional_query(), filter_dimension(), run_query(), select_dimension()

Examples

dq <- dimensional_query(ms_mrs) |>
  select_fact(
    name = "mrs_age",
    measures = c("n_deaths"),
    agg_functions = c("MAX")
  )

dq <- dimensional_query(ms_mrs) |>
  select_fact(name = "mrs_age",
             measures = c("n_deaths"))

dq <- dimensional_query(ms_mrs) |>
  select_fact(name = "mrs_age")

Description

Transform fact, dimension, measurement, and attribute names according to the snake case style.

Usage

snake_case(st)

## S3 method for class 'star_schema'
snake_case(st)

Arguments

Details

This style is suitable if we are going to work with databases.

Value

A star_schema object.

See Also

Other star schema and constellation definition functions: character_dimensions(), constellation(), role_playing_dimension(), star_schema()

Examples

st <- star_schema(mrs_age, dm_mrs_age) |>
  snake_case()

st <- star_schema(mrs_age, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("Date", "Week", "Year")
  ) |>
  snake_case()

Description

Star Schema for the Mortality Reporting System considering the age classification.

Usage

st_mrs_age

Format

A star_schema object.

Examples

# Defined by:

st_mrs_age <- star_schema(mrs_age, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("date", "week", "year")
  ) |>
  snake_case() |>
  character_dimensions(NA_replacement_value = "Unknown",
                       length_integers = list(week = 2))

Description

Star Schema for the Mortality Reporting System considering the age classification data test.

Usage

st_mrs_age_test

Format

A star_schema object.

Examples

# Defined by:

st_mrs_age_test <- star_schema(mrs_age_test, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("date", "week", "year")
  ) |>
  snake_case() |>
  character_dimensions(NA_replacement_value = "Unknown",
                       length_integers = list(week = 2))

Description

Star Schema for the Mortality Reporting System considering the age classification data test, for week 4 of 1962. It also includes some isolated data from previous weeks that is supposed to be corrections for data errors.

Usage

st_mrs_age_w_test

Format

A star_schema object.

Examples

# Defined by:

st_mrs_age_w_test <- star_schema(mrs_age_w_test, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("date", "week", "year")
  ) |>
  snake_case() |>
  character_dimensions(NA_replacement_value = "Unknown",
                       length_integers = list(week = 2))

Description

Star Schema for the Mortality Reporting System considering the age classification data, for week 10 of 1962. It also includes some isolated data from previous weeks that is supposed to be corrections for data errors.

Usage

st_mrs_age_w10

Format

A star_schema object.

Examples

# Defined by:

st_mrs_age_w10 <- star_schema(mrs_age_w10, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("date", "week", "year")
  ) |>
  snake_case() |>
  character_dimensions(NA_replacement_value = "Unknown",
                       length_integers = list(week = 2))

Description

Star Schema for the Mortality Reporting System considering the age classification data, for week 11 of 1962. It also includes some isolated data from previous weeks that is supposed to be corrections for data errors.

Usage

st_mrs_age_w11

Format

A star_schema object.

Examples

# Defined by:

st_mrs_age_w11 <- star_schema(mrs_age_w11, dm_mrs_age) |>
  role_playing_dimension(
    dim_names = c("when", "when_available"),
    name = "When Common",
    attributes = c("date", "week", "year")
  ) |>
  snake_case() |>
  character_dimensions(NA_replacement_value = "Unknown",
                       length_integers = list(week = 2))

Description

Star Schema for the Mortality Reporting System considering the cause classification.

Usage

st_mrs_cause

Format

A star_schema object.

Examples

# Defined by:

st_mrs_cause <- star_schema(mrs_cause, dm_mrs_cause) |>
  snake_case() |>
  character_dimensions(
    NA_replacement_value = "Unknown",
    length_integers = list(
      week = 2,
      data_availability_week = 2,
      reception_week = 2
    )
  ) |>
  role_playing_dimension(
    dim_names = c("when", "when_received", "when_available"),
    name = "when_common",
    attributes = c("date", "week", "year")
  )

Description

Star Schema for the Mortality Reporting System considering the cause classification data test.

Usage

st_mrs_cause_test

Format

A star_schema object.

Examples

# Defined by:

st_mrs_cause_test <- star_schema(mrs_cause_test, dm_mrs_cause) |>
  snake_case() |>
  character_dimensions(
    NA_replacement_value = "Unknown",
    length_integers = list(
      week = 2,
      data_availability_week = 2,
      reception_week = 2
    )
  ) |>
  role_playing_dimension(
    dim_names = c("when", "when_received", "when_available"),
    name = "when_common",
    attributes = c("date", "week", "year")
  )

Description

Star Schema for the Mortality Reporting System considering the cause classification data test, for week 4 of 1962. It also includes some isolated data from previous weeks that is supposed to be additional data not considered before.

Usage

st_mrs_cause_w_test

Format

A star_schema object.

Examples

# Defined by:

st_mrs_cause_w_test <- star_schema(mrs_cause_w_test, dm_mrs_cause) |>
  snake_case() |>
  character_dimensions(
    NA_replacement_value = "Unknown",
    length_integers = list(
      week = 2,
      data_availability_week = 2,
      reception_week = 2
    )
  ) |>
  role_playing_dimension(
    dim_names = c("when", "when_received", "when_available"),
    name = "when_common",
    attributes = c("date", "week", "year")
  )

Description

Star Schema for the Mortality Reporting System considering the cause classification data, for week 10 of 1962. It also includes some isolated data from previous weeks that is supposed to be additional data not considered before.

Usage

st_mrs_cause_w10

Format

A star_schema object.

Examples

# Defined by:

st_mrs_cause_w10 <- star_schema(mrs_cause_w10, dm_mrs_cause) |>
  snake_case() |>
  character_dimensions(
    NA_replacement_value = "Unknown",
    length_integers = list(
      week = 2,
      data_availability_week = 2,
      reception_week = 2
    )
  ) |>
  role_playing_dimension(
    dim_names = c("when", "when_received", "when_available"),
    name = "when_common",
    attributes = c("date", "week", "year")
  )

Description

Star Schema for the Mortality Reporting System considering the cause classification data, for week 11 of 1962. It also includes some isolated data from previous weeks that is supposed to be additional data not considered before.

Usage

st_mrs_cause_w11

Format

A star_schema object.

Examples

# Defined by:

st_mrs_cause_w11 <- star_schema(mrs_cause_w11, dm_mrs_cause) |>
  snake_case() |>
  character_dimensions(
    NA_replacement_value = "Unknown",
    length_integers = list(
      week = 2,
      data_availability_week = 2,
      reception_week = 2
    )
  ) |>
  role_playing_dimension(
    dim_names = c("when", "when_received", "when_available"),
    name = "when_common",
    attributes = c("date", "week", "year")
  )

Description

Creates a star_schema object from a flat table (implemented by a tibble) and a dimensional_model object.

Usage

star_schema(ft, sd)

Arguments

Details

Transforms the flat table data according to the facts and dimension definitions of the dimensional_model object. Each dimension is generated with a surrogate key which is a foreign key in facts.

Facts only contain measurements and foreign keys.

Value

A star_schema object.

See Also

dimensional_model

Other star schema and constellation definition functions: character_dimensions(), constellation(), role_playing_dimension(), snake_case()

Examples

st <- star_schema(mrs_age, dm_mrs_age)

Description

Once we have refined the format or content of facts and dimensions, we can again obtain a flat table, implemented using a tibble, from a star schema.

Usage

star_schema_as_flat_table(st)

## S3 method for class 'star_schema'
star_schema_as_flat_table(st)

Arguments

Value

A tibble.

See Also

Other results export functions: constellation_as_multistar(), constellation_as_tibble_list(), multistar_as_flat_table(), star_schema_as_multistar(), star_schema_as_tibble_list()

Examples

ft <- st_mrs_age |>
  star_schema_as_flat_table()

Description

Once we have refined the format or content of facts and dimensions, we can obtain a multistar. A multistar only distinguishes between general and conformed dimensions, each dimension has its own data. It can contain multiple fact tables.

Usage

star_schema_as_multistar(st)

## S3 method for class 'star_schema'
star_schema_as_multistar(st)

Arguments

Value

A multistar object.

See Also

Other results export functions: constellation_as_multistar(), constellation_as_tibble_list(), multistar_as_flat_table(), star_schema_as_flat_table(), star_schema_as_tibble_list()

Examples

ms <- st_mrs_age |>
  star_schema_as_multistar()

Description

Once we have refined the format or content of facts and dimensions, we can obtain a tibble list with them. Role playing dimensions can be optionally included.

Usage

star_schema_as_tibble_list(st, include_role_playing = FALSE)

## S3 method for class 'star_schema'
star_schema_as_tibble_list(st, include_role_playing = FALSE)

Arguments

Value

A list of tibble objects.

See Also

Other results export functions: constellation_as_multistar(), constellation_as_tibble_list(), multistar_as_flat_table(), star_schema_as_flat_table(), star_schema_as_multistar()

Examples

tl <- st_mrs_age |>
  star_schema_as_tibble_list()

tl <- st_mrs_age |>
  star_schema_as_tibble_list(include_role_playing = TRUE)

Description

Transformations that allow obtaining star schemas from flat tables.

Details

From flat tables star schemas can be defined that can form constellations (star schema and constellation definition functions). Dimensions contain data without duplicates, operations to do data cleaning can be applied on them (data cleaning functions). Dimensions can be enriched by adding additional columns, sometimes using functions, others explicitly defined by the user (dimension enrichment functions). When new data is obtained, it is necessary to refresh the existing data with them by means of incremental refresh operations or delete data that is no longer necessary (incremental refresh functions). Finally, the results obtained can be exported to be consulted with other tools (results export functions) or through the defined query functions (query functions).

Star schema and constellation definition

Starting from a flat table, a dimensional model is defined specifying the attributes that make up each of the dimensions and the measurements in the facts. The result is a dimensional_model object. It is carried out through the following dimensional model definition functions:

• dimensional_model()

• define_dimension()

• define_fact()

A star schema is defined from a flat table and a dimensional model definition. Once defined, a star schema can be transformed by defining role playing dimensions, changing the writing style of element names or the type of dimension attributes. These operations are carried out through the following star schema definition and transformation functions:

• star_schema()

• role_playing_dimension()

• snake_case()

• character_dimensions()

Once a star schema is defined, we can rename its elements. It is necessary to be able to rename attributes of dimensions and measures of facts because the definition operations only allowed us to select columns of a flat table. For completeness also dimensions and facts can be renamed. To carry out these operations, the following star schema rename functions are available:

• rename_dimension()

• get_dimension_attribute_names()

• rename_dimension_attributes()

• rename_fact()

• get_measure_names()

• rename_measures()

Based on various star schemas, a constellation can be defined in which star schemas share common dimensions. Dimensions with the same name must be shared. It is defined by the following constellation definition function:

• constellation()

Data cleaning

Once the star schemas and constellations are defined, data cleaning operations can be carried out on dimensions. There are three groups of functions: one to obtain dimensions of star schemas and constellations; another to define data cleaning operations over dimensions; and one more to apply operations to star schemas or constellations.

Obtaining dimensions:

• get_dimension_names()

• get_dimension()

• get_conformed_dimension_names()

• get_conformed_dimension()

Update definition functions:

• record_update_set()

• match_records()

• update_record()

• update_selection()

• update_selection_general()

Modification application functions:

• modify_dimension_records()

• modify_conformed_dimension_records()

Dimension enrichment

To enrich a dimension with new attributes related to others already included in it, first, we export the attributes on which the new ones depend, then we define the new attributes, and import the table with all the attributes to be added to the dimension.

• enrich_dimension_export()

• enrich_dimension_import()

Incremental refresh

When new data is obtained, an incremental refresh of the data can be carried out, both of the dimensions and of the facts. Incremental refresh can be applied to both star schema and constellation, using the following functions:

• incremental_refresh_star_schema()

• incremental_refresh_constellation()

Sometimes the data refresh consists of eliminating data that is no longer necessary, generally because it corresponds to a period that has stopped being analysed but it can also be for other reasons. This data can be selected using the following function:

• filter_fact_rows()

Once the fact data is removed (using the other incremental refresh functions), we can remove the data for the dimensions that are no longer needed using the following functions:

• purge_dimensions_star_schema()

• purge_dimensions_constellation()

Results export

Once the data has been properly structured and transformed, it can be exported to be consulted with other tools or with R. Various export formats have been defined, both for star schemas and for constellations, using the following functions:

• star_schema_as_flat_table()

• star_schema_as_multistar()

• star_schema_as_tibble_list()

• constellation_as_multistar()

• constellation_as_tibble_list()

• multistar_as_flat_table()

Query functions

There are many multidimensional query tools available. The exported data, once stored in files, can be used directly from them. Using the following functions, you can also perform basic queries from R on data in the multistar format:

• dimensional_query()

• select_fact()

• select_dimension()

• filter_dimension()

• run_query()

Description

For a dimension, given the primary key of one record, it adds an update to the set of updates that modifies the combination of values of the rest of attributes of the selected record so that they become those given.

Usage

update_record(updates = NULL, dimension, old, values = vector())

## S3 method for class 'record_update_set'
update_record(updates = NULL, dimension, old, values = vector())

Arguments

Details

Primary key is only used to get the combination of values easily. The update is defined exclusively from the rest of values.

Value

A record_update_set object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_selection(), update_selection_general()

Examples

dim_names <- st_mrs_age |>
    get_dimension_names()

where <- st_mrs_age |>
  get_dimension("where")

# head(where, 2)

updates <- record_update_set() |>
  update_record(
    dimension = where,
    old = 1,
    values = c("1", "CT", "Bridgeport")
  )

Description

For a dimension, given a vector of column names, a vector of old values and a vector of new values, it adds an update to the set of updates that modifies all the records that have the combination of old values in the columns with the new values in those same columns.

Usage

update_selection(
  updates = NULL,
  dimension,
  columns = vector(),
  old_values = vector(),
  new_values = vector()
)

## S3 method for class 'record_update_set'
update_selection(
  updates = NULL,
  dimension,
  columns = vector(),
  old_values = vector(),
  new_values = vector()
)

Arguments

Value

A record_update_set object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection_general()

Examples

dim_names <- st_mrs_age |>
    get_dimension_names()

where <- st_mrs_age |>
  get_dimension("where")

# head(where, 2)

updates <- record_update_set() |>
  update_selection(
    dimension = where,
    columns = c("city"),
    old_values = c("Bridgepor"),
    new_values = c("Bridgeport")
  )

Description

For a dimension, given a vector of column names, a vector of old values for those columns, another vector column names, and a vector of new values for those columns, it adds an update to the set of updates that modifies all the records that have the combination of old values in the first column vector with the new values in the second column vector.

Usage

update_selection_general(
  updates = NULL,
  dimension,
  columns_old = vector(),
  old_values = vector(),
  columns_new = vector(),
  new_values = vector()
)

## S3 method for class 'record_update_set'
update_selection_general(
  updates = NULL,
  dimension,
  columns_old = vector(),
  old_values = vector(),
  columns_new = vector(),
  new_values = vector()
)

Arguments

Value

A record_update_set object.

See Also

Other data cleaning functions: get_conformed_dimension(), get_conformed_dimension_names(), get_dimension(), get_dimension_names(), match_records(), modify_conformed_dimension_records(), modify_dimension_records(), record_update_set(), update_record(), update_selection()

Examples

dim_names <- st_mrs_age |>
    get_dimension_names()

where <- st_mrs_age |>
  get_dimension("where")

# head(where, 2)

updates <- record_update_set() |>
  update_selection_general(
    dimension = where,
    columns_old = c("state", "city"),
    old_values = c("CT", "Bridgepor"),
    columns_new = c("city"),
    new_values = c("Bridgeport")
  )

Description

Example of updates on some dimensions of the star schema for Mortality Reporting System by age.

Usage

updates_st_mrs_age

Format

A record_update_set object.

Examples

# Defined by:

(dim_names <- st_mrs_age |>
    get_dimension_names())

where <- st_mrs_age |>
  get_dimension("where")

when <- st_mrs_age |>
  get_dimension("when")

who <- st_mrs_age |>
  get_dimension("who")

updates_st_mrs_age <- record_update_set() |>
  update_selection_general(
    dimension = where,
    columns_old = c("state", "city"),
    old_values = c("CT", "Bridgepor"),
    columns_new = c("city"),
    new_values = c("Bridgeport")
  ) |>
  match_records(dimension = when,
                old = 37,
                new = 36) |>
  update_record(
    dimension = when,
    old = 73,
    values = c("1962-02-17", "07", "1962")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("<1 year"),
    new_values = c("1: <1 year")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("1-24 years"),
    new_values = c("2: 1-24 years")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("25-44 years"),
    new_values = c("3: 25-44 years")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("45-64 years"),
    new_values = c("4: 45-64 years")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("65+ years"),
    new_values = c("5: 65+ years")
  )

Description

Example of updates on some dimensions of the star schema for Mortality Reporting System by age test.

Usage

updates_st_mrs_age_test

Format

A record_update_set object.

Examples

# Defined by:

(dim_names <- st_mrs_age_test |>
    get_dimension_names())

where <- st_mrs_age_test |>
  get_dimension("where")

when <- st_mrs_age_test |>
  get_dimension("when")

who <- st_mrs_age_test |>
  get_dimension("who")

updates_st_mrs_age_test <- record_update_set() |>
  update_selection_general(
    dimension = where,
    columns_old = c("state", "city"),
    old_values = c("CT", "Bridgepor"),
    columns_new = c("city"),
    new_values = c("Bridgeport")
  ) |>
  match_records(dimension = when,
                old = 4,
                new = 3) |>
  update_record(
    dimension = when,
    old = 9,
    values = c("1962-01-20", "03", "1962")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("<1 year"),
    new_values = c("1: <1 year")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("1-24 years"),
    new_values = c("2: 1-24 years")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("25-44 years"),
    new_values = c("3: 25-44 years")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("45-64 years"),
    new_values = c("4: 45-64 years")
  ) |>
  update_selection(
    dimension = who,
    columns = c("age_range"),
    old_values = c("65+ years"),
    new_values = c("5: 65+ years")
  )