| Title: | Obtaining Star Databases from Flat Tables |
| Version: | 2.5.2 |
| Description: | Data in multidimensional systems is obtained from operational systems and is transformed to adapt it to the new structure. Frequently, the operations to be performed aim to transform a flat table into a ROLAP (Relational On-Line Analytical Processing) star database. The main objective of the package is to allow the definition of these transformations easily. The implementation of the multidimensional database obtained can be exported to work with multidimensional analysis tools on spreadsheets or relational databases. |
| License: | MIT + file LICENSE |
| URL: | https://josesamos.github.io/rolap/, https://github.com/josesamos/rolap |
| BugReports: | https://github.com/josesamos/rolap/issues |
| Depends: | R (≥ 4.1.0) |
| Imports: | dm, dplyr, methods, purrr, readr, rlang, sf, snakecase, tibble, tidyr, tidyselect, tools, utils, when, xlsx |
| Suggests: | DBI, dbplyr, DiagrammeR, DiagrammeRsvg, knitr, lubridate, magrittr, maps, pander, pivottabler, RMariaDB, rmarkdown, RSQLite, stringr, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-GB |
| LazyData: | true |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-05-21 10:48:29 UTC; jsamos |
| Author: | Jose Samos  [aut,
cre],
Universidad de Granada [cph] |
| Maintainer: | Jose Samos <jsamos@ugr.es> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-22 05:10:02 UTC |
Add custom column
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
ft |
A |
name |
A string, new column name. |
definition |
A function that returns a table column. |
Value
A flat_table object.
See Also
Other flat table transformation functions:
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
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)
Add dimension instances
Description
Add dimension instances
Usage
add_dimension_instances(db, name, table)
Arguments
db |
A |
name |
A string, dimension name. |
table |
A table of new instances. |
Value
A star_database object.
For each row, add a vector of values
Description
For each row, add a vector of values
Usage
add_dput_column(v, column)
Arguments
v |
A |
column |
A string, name of the column to include a vector of values. |
Value
A tibble, rows of a dimension table.
A star_operation object row is added with a new operation
Description
A star_operation object row is added with a new operation
Usage
add_operation(op, op_name, name = NULL, details = NULL, details2 = NULL)
Arguments
op |
A |
op_name |
A string, operation name. |
name |
A string, element name. |
details |
A vector of strings, operation details. |
details2 |
A vector of strings, operation additional details. |
Value
A star_operation object.
Add the surrogate key from a dimension table to the instances table.
Description
Add the surrogate key from a dimension table to the instances table.
Usage
## S3 method for class 'dimension_table'
add_surrogate_key(dimension_table, instances)
Arguments
dimension_table |
A |
instances |
A |
Value
A tibble.
Apply filter dimension
Description
Select the instances of the dimensions that meet the defined conditions.
Usage
apply_filter_dimension(db, sq)
Arguments
db |
A |
sq |
A |
Apply select dimension
Description
Select dimensions and attributes.
Usage
apply_select_dimension(db, sq)
Arguments
db |
A |
sq |
A |
Apply select fact
Description
Select the facts, measures and define the aggregation functions.
Usage
apply_select_fact(db, sq)
Arguments
db |
A |
sq |
A |
Save as GeoPackage
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
gl |
A |
dir |
A string. |
name |
A string, file name. |
keep_all_variables_na |
A boolean, keep rows with all variables NA. |
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())
Generate csv files with fact and dimension tables
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
db |
A |
dir |
A string, name of a dir. |
type |
An integer, 1: uses "." for the decimal point and a comma for the separator; 2: uses a comma for the decimal point and a semicolon for the separator. |
Value
A string, name of a dir.
See Also
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())
Generate a dm class with fact and dimension tables
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
db |
A |
pk_facts |
A boolean, include primary key in fact tables. |
fk |
A boolean, include foreign key in fact tables. |
Value
A dm object.
See Also
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()
Get a geolayer object
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
db |
An |
dimension |
A string, dimension name. |
attribute |
A vector, attribute names. |
geometry |
A string, geometry name. |
include_nrow_agg |
A boolean, include default measure. |
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")
Generate a geomultistar::multistar object
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
db |
A |
Value
A geomultistar::multistar object.
See Also
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()
Generate tables in a relational database
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
db |
A |
con |
A |
overwrite |
A boolean, allow overwriting tables in the database. |
Value
Invisible NULL.
See Also
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)
Generate a list of tibbles of flat tables
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
db |
A |
Value
A list of tibble
See Also
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()
Get a star database from a flat table
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
ft |
A |
schema |
A |
Value
A star_database object.
See Also
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)
Generate a list of tibbles with fact and dimension tables
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
db |
A |
Value
A list of tibble
See Also
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()
Generate a xlsx file with fact and dimension tables
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
db |
A |
file |
A string, name of a file. |
Value
A string, name of a file.
See Also
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())
Cancel deployment
Description
Cancel deployment
Usage
cancel_deployment(db, name)
## S3 method for class 'star_database'
cancel_deployment(db, name)
Arguments
db |
A |
name |
A string, name of the deployment. |
Value
A star_database object.
See Also
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")
Check a geoattribute geometry instances.
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
db |
A |
dimension |
A string, dimension name. |
attribute |
A vector, attribute names. |
geometry |
A string, geometry name ('point' or 'polygon'). |
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")
Check the result of joining a flat table with a lookup table
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
ft |
A |
fk_attributes |
A vector of strings, attribute names. |
lookup |
A |
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
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)
Checks the refresh of the selected star database from the given database
Description
Checks the refresh operation of the selected star database from the given database. Once this operation is carried out, the results can be consulted on the new instances in dimensions or existing instances in the facts.
Usage
check_refesh(db, refresh_db)
Arguments
db |
A |
refresh_db |
A |
Value
A list of facts and dimensions, first facts, then dimensions.
Conform dimensions
Description
Generate a dimension from a list of dimensions with the same schema.
Usage
conform_dimensions(to_conform)
Arguments
to_conform |
A |
Value
A dimension_table object.
Create constellation
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
name |
A string. |
... |
|
Value
A star_database object.
See Also
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)
Transform coordinates to point geometry
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
table |
A |
lon_lat |
A vector, name of longitude and latitude attributes. |
crs |
A coordinate reference system: integer with the EPSG code, or character with proj4string. |
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"))
Default disconnect function
Description
Disconnect function that is used if no other is indicated in the parameter of the deploy function.
Usage
default_disconnect(con)
Arguments
con |
A |
Value
TRUE, invisibly.
Define dimension in a star_schema object.
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
schema |
A |
dimension |
A |
name |
A string, name of the dimension. |
attributes |
A vector of attribute names. |
scd_nk |
A vector of attribute names, scd natural key. |
scd_t0 |
A vector of attribute names, scd T0 attributes. |
scd_t1 |
A vector of attribute names, scd T1 attributes. |
scd_t2 |
A vector of attribute names, scd T2 attributes. |
scd_t3 |
A vector of attribute names, scd T3 attributes. |
scd_t6 |
A vector of attribute names, scd T6 attributes. |
is_when |
A boolean, is when dimension. |
... |
When dimension configuration parameters. |
Value
A star_schema object.
See Also
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)
Define facts in a star_schema object.
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
schema |
A |
facts |
A |
name |
A string, name of the fact. |
measures |
A vector of measure names. |
agg_functions |
A vector of aggregation function names, each one for its corresponding measure. If none is indicated, the default is SUM. Additionally they can be MAX or MIN. |
nrow_agg |
A string, name of a new measure that represents the COUNT of rows aggregated for each resulting row. |
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
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)
Define geoattribute of a dimension
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
db |
A |
dimension |
A string, dimension name. |
attribute |
A vector, attribute names. |
from_layer |
A |
by |
a vector of correspondence of attributes of the dimension with the
|
from_attribute |
A vector, attribute names. |
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")
)
Define geoattribute from a layer
Description
Define geoattribute from a layer
Usage
define_geoattribute_from_layer(
db,
dimension = NULL,
attribute = NULL,
geoatt = NULL,
from_layer = NULL,
by = NULL
)
Arguments
db |
A |
dimension |
A string, dimension name. |
attribute |
A string, attribute name. |
geoatt |
A string, geoattribute name. |
from_layer |
A |
by |
a vector of correspondence of attributes of the dimension with the
|
Value
A star_database object.
Delete in stars all operations found
Description
Delete in stars all operations found
Usage
delete_all_operations_found(stars, op)
Arguments
stars |
A list of |
op |
A |
Value
A list of star_database objects.
Delete an operation
Description
Delete an operation
Usage
delete_operation(op, op_name, name = NULL, details = NULL, details2 = NULL)
Arguments
op |
A |
op_name |
A string, operation name. |
name |
A string, element name. |
details |
A vector of strings, operation details. |
details2 |
A vector of strings, operation additional details. |
Value
op A star_operation object.
Delete a set of operations
Description
Delete a set of operations
Usage
delete_operation_set(op, op2)
Arguments
op |
A |
op2 |
A |
Value
op A star_operation object.
Deploy a star database in a relational database
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
db |
A |
name |
A string, name of the deployment. |
connect |
A function that returns a |
disconnect |
A function that receives a |
file |
A string, name of the file to store the object. |
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
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
)
dimension_schema S3 class
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
name |
A string, name of the dimension. |
attributes |
A vector of attribute names. |
scd_nk |
A vector of attribute names, scd natural key. |
scd_t0 |
A vector of attribute names, scd T0 attributes. |
scd_t1 |
A vector of attribute names, scd T1 attributes. |
scd_t2 |
A vector of attribute names, scd T2 attributes. |
scd_t3 |
A vector of attribute names, scd T3 attributes. |
scd_t6 |
A vector of attribute names, scd T6 attributes. |
is_when |
A boolean, is when dimension. |
... |
When dimension configuration parameters. |
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
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"
)
)
dimension_table S3 class
Description
A dimension_table object is created, we have to define its
surrogate key.
Usage
dimension_table(name = NULL, attributes = NULL, instances = NULL)
Arguments
name |
A string, dimension name. |
attributes |
A vector of strings, attributes names. |
instances |
A flat table with the dimension instances. |
Value
A dimension_table object.
Draw tables
Description
Draw the tables of the ROLAP star diagrams.
Usage
draw_tables(db)
## S3 method for class 'star_database'
draw_tables(db)
Arguments
db |
A |
Value
An object with a print() method.
See Also
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()
fact_schema S3 class
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
name |
A string, name of the fact. |
measures |
A vector of measure names. |
agg_functions |
A vector of aggregation function names, each one for its corresponding measure. If none is indicated, the default is SUM. Additionally they can be MAX or MIN. |
nrow_agg |
A string, name of a new measure that represents the COUNT of rows aggregated for each resulting row. |
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
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"
)
fact_table S3 class
Description
A fact_table object is created, we have to get its
surrogate keys.
Usage
fact_table(
name = NULL,
surrogate_keys = NULL,
agg = NULL,
dim_int_names = NULL,
instances = NULL
)
Arguments
name |
A string, fact name. |
surrogate_keys |
A vector of strings, surrogate key names. |
agg |
A vector of strings, aggregation functions. |
dim_int_names |
A vector of strings, internal names of dimensions. |
instances |
A flat table with the fact instances. |
Value
A fact_table object.
Filter dimension
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
sq |
A |
name |
A string, name of the dimension. |
... |
Conditions, defined in exactly the same way as in |
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")
From attributes, leave only these contained in dimensions
Description
From attributes, leave only these contained in dimensions
Usage
filter_geo_attributes(db)
Arguments
db |
A |
Value
A list of geodimensions.
From geodimensions, leave only contained in vector of names
Description
From geodimensions, leave only contained in vector of names
Usage
filter_geo_dimensions(db, dim)
Arguments
db |
A |
dim |
A vector of strings, dimension names. |
Value
A list of geodimensions.
From rpd dimensions, leave only contained in vector of names.
Description
From rpd dimensions, leave only contained in vector of names.
Usage
filter_rpd_dimensions(db, names)
Arguments
db |
A |
names |
A vector of strings, dimension names. |
Value
A list of vectors of dimension names.
flat_table S3 class
Description
Creates a flat_table object.
Usage
flat_table(name = NULL, instances, unknown_value = NULL)
Arguments
name |
A string. |
instances |
A |
unknown_value |
A string, value used to replace empty and NA values in attributes. |
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
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)
Mortality Reporting System
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
See Also
Other mrs example data:
ft_age,
ft_age_rpd,
ft_cause_rpd,
ft_num,
mrs_db,
mrs_db_geo,
mrs_ft,
mrs_ft_new
Mortality Reporting System by Age Group
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
See Also
Other mrs example data:
ft,
ft_age_rpd,
ft_cause_rpd,
ft_num,
mrs_db,
mrs_db_geo,
mrs_ft,
mrs_ft_new
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\\)", ""))
}
Mortality Reporting System by Age
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
See Also
Other mrs example data:
ft,
ft_age,
ft_cause_rpd,
ft_num,
mrs_db,
mrs_db_geo,
mrs_ft,
mrs_ft_new
Mortality Reporting System by Cause
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
See Also
Other mrs example data:
ft,
ft_age,
ft_age_rpd,
ft_num,
mrs_db,
mrs_db_geo,
mrs_ft,
mrs_ft_new
Mortality Reporting System with numerical measures
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
See Also
Other mrs example data:
ft,
ft_age,
ft_age_rpd,
ft_cause_rpd,
mrs_db,
mrs_db_geo,
mrs_ft,
mrs_ft_new
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`))
Generate refresh sql
Description
Generate sql code for the first refresh operation.
Usage
generate_refresh_sql(refresh)
Arguments
refresh |
A list of operations over tables. |
Value
A vector of strings.
Generate table sql delete
Description
Generate sql code for deleting instances in a table.
Usage
generate_table_sql_delete(table, instances)
Arguments
table |
A string, table name. |
instances |
A |
Value
A vector of strings.
Generate table sql insert
Description
Generate sql code for inserting a table.
Usage
generate_table_sql_insert(table, instances)
Arguments
table |
A string, table name. |
instances |
A |
Value
A string.
Generate table sql update
Description
Generate sql code for updating a table.
Usage
generate_table_sql_update(table, surrogate_keys, instances)
Arguments
table |
A string, table name. |
surrogate_keys |
A string. |
instances |
A |
Value
A vector of strings.
Get aggregate functions
Description
Get aggregate functions
Usage
## S3 method for class 'fact_schema'
get_agg_functions(schema)
Arguments
schema |
A |
Value
A vector of strings.
Gets the operations performed on a dimension in all star_database objects
Description
Gets the operations performed on a dimension in all star_database objects
Usage
get_all_dimension_operations(op_name, name, stars)
Arguments
op_name |
A string, operation name. |
name |
A string, element name. |
stars |
A list of |
Value
A star_operations object.
Get the names of the attributes
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
db |
A |
name |
A string, dimension name. |
ordered |
A boolean, sort names alphabetically. |
as_definition |
A boolean, get the names as a vector definition in R. |
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
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()
Get attribute names
Description
Get the attribute names.
Usage
## S3 method for class 'dimension_schema'
get_attribute_names_schema(schema)
Arguments
schema |
A |
Value
A string.
Get attribute names
Description
Get the attribute names.
Usage
## S3 method for class 'star_schema'
get_attribute_names_schema(schema)
Arguments
schema |
A |
Value
A string.
get default unknown value
Description
get default unknown value
Usage
get_default_unknown_value()
Value
A string.
Get the names of the facts of a star database
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
db |
A |
Value
A vector of strings, fact names.
See Also
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()
Get the names of the dimensions of a star database
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
db |
A |
star |
A string or integer, star database name or index in constellation. |
Value
A vector of strings, dimension names.
See Also
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()
Get dimension table
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
db |
A |
name |
A string, dimension name. |
Value
A tibble, dimension table.
See Also
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")
Get existing fact instances
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
sdbu |
A |
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
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()
Get fact name
Description
Get fact name
Usage
## S3 method for class 'fact_schema'
get_fact_name(schema)
Arguments
schema |
A |
Value
A string.
Get the names of the facts of a star database
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
db |
A |
Value
A vector of strings, fact names.
See Also
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()
Get geoattribute geometries
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
db |
A |
dimension |
A string, dimension name. |
attribute |
A vector, attribute names. |
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"
)
Get geoattribute name
Description
Get the name of the geoattribute from a vector of attribute names
Usage
get_geoattribute_name(attribute)
Arguments
attribute |
A vector, attribute names. |
Value
A string.
Get geoattributes
Description
For each dimension, get a list of available geoattributes.
Usage
get_geoattributes(db)
## S3 method for class 'star_database'
get_geoattributes(db)
Arguments
db |
A |
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()
Get geographic information layer
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
gl |
A |
keep_all_variables_na |
A boolean, keep rows with all variables NA. |
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()
Get layer from attribute
Description
Gets the geographic layer associated with the from_attribute at the level of the indicated attributes.
Usage
get_layer_from_attribute(
db,
dimension = NULL,
attribute = NULL,
from_attribute = NULL
)
Arguments
db |
A |
dimension |
A string, dimension name. |
attribute |
A string, attribute name. |
from_attribute |
A string, attribute name. |
Value
A star_database object.
Get layer geometry
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
layer |
A |
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)
Get lookup tables
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
sdbu |
A |
Value
A list of flat_table objects.
See Also
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()
Get the names of the measures
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
db |
A |
name |
A string, dimension name. |
ordered |
A boolean, sort names alphabetically. |
as_definition |
A boolean, get the names as a vector definition in R. |
Value
A vector of strings or a string, measure names.
See Also
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()
Get measure names
Description
Get the names of the measures defined in the fact schema.
Usage
## S3 method for class 'fact_schema'
get_measure_names_schema(schema)
Arguments
schema |
A |
Value
A vector of strings.
Get measure names
Description
Get the names of the measures defined in the fact schema.
Usage
## S3 method for class 'star_schema'
get_measure_names_schema(schema)
Arguments
schema |
A |
Value
A vector of strings.
Get new dimension instances
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
sdbu |
A |
Value
A list of tibble objects.
See Also
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()
A star_operation object row is returned, the one following the actual given
Description
A star_operation object row is returned, the one following the actual given
Usage
get_next_operation(op, op_name, name = NULL, actual = NULL)
Arguments
op |
A |
op_name |
A string, operation name. |
name |
A string, element name. |
actual |
A |
Value
A data frame.
Get number of rows aggregate column
Description
Get number of rows aggregate column
Usage
## S3 method for class 'fact_schema'
get_nrow_agg(schema)
Arguments
schema |
A |
Value
A string.
Get the names of the primary key attributes of a flat table
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
ft |
A |
as_definition |
A boolean, as the definition of the vector in R. |
Value
A vector of strings or a tibble, attribute names.
See Also
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()
Get point geometry
Description
Obtain point geometry from polygon geometry.
Usage
get_point_geometry(layer)
Arguments
layer |
A |
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)
Get the names of the role playing dimensions
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
db |
A |
Value
A list of vector of strings with dimension names.
See Also
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()
Get rpd dimensions of a dimension
Description
Get rpd dimensions of a dimension
Usage
get_rpd_dimensions(db, name)
Arguments
db |
A |
name |
A string, dimension name. |
Value
A vector of dimension names.
Get similar attribute values combination
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
db |
A |
name |
A string, dimension name. |
attributes |
A vector of strings, attribute names. |
exclude_numbers |
A boolean, exclude numbers from comparison. |
col_as_vector |
A string, name of the column to include a vector of values. |
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
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()
Get similar values for individual attributes
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
db |
A |
name |
A vector of strings, dimension names. |
attributes |
A vector of strings, attribute names. |
exclude_numbers |
A boolean, exclude numbers from comparison. |
col_as_vector |
A string, name of the column to include a vector of values. |
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
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()
Get similar values in a table
Description
Get similar values in a table
Usage
get_similar_values_table(table, attributes, exclude_numbers, col_as_vector)
Arguments
table |
A |
attributes |
A vector of strings, attribute names. |
exclude_numbers |
A boolean, exclude numbers from comparison. |
col_as_vector |
A string, name of the column to include a vector of values. |
Value
A vector of tibble objects with similar instances.
Get star database
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
db |
A |
name |
A string, star database name (fact name). |
Value
A star_database object.
See Also
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])
Get star query schema
Description
Obtain the star database schema to perform queries.
Usage
get_star_query_schema(db)
Arguments
db |
A |
Value
A star database schema, list of fact and dimension schemes.
Get star schema
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
sdbu |
A |
Value
A star_schema object.
See Also
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()
Get surrogate key names
Description
Get the names of the surrogate keys defined in the dimension table.
Usage
## S3 method for class 'dimension_table'
get_surrogate_key(dimension_table)
Arguments
dimension_table |
A |
Value
A vector of strings.
Get the table of the flat table
Description
Obtain the table of a flat table.
Usage
get_table(ft)
## S3 method for class 'flat_table'
get_table(ft)
Arguments
ft |
A |
Value
A tibble, the table.
See Also
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()
Get the names of the tables of a star database
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
db |
A |
Value
A vector of strings, table names.
See Also
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()
Get transformation function code
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
sdbu |
A |
Value
A vector of strings.
See Also
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()
Get transformation function file
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
sdbu |
A |
file |
A string, file name. |
Value
A string, file name.
See Also
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()
Get unique attribute values
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
db |
A |
name |
A string, dimension name. |
attributes |
A vector of strings, attribute names. |
col_as_vector |
A string, name of the column to include a vector of values. |
Details
If we work on a star database, a dimension must be indicated.
Value
A vector of tibble objects with unique instances.
See Also
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()
Get unique values in a table
Description
Get unique values in a table
Usage
get_unique_values_table(table, attributes, col_as_vector)
Arguments
table |
A |
attributes |
A vector of strings, attribute names. |
col_as_vector |
A string, name of the column to include a vector of values. |
Value
A vector of tibble objects with similar instances.
Get the unknown value defined
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
ft |
A |
Value
A string.
See Also
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()
Get unknown attribute values
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
ft |
A |
attributes |
A vector of strings, attribute names. |
col_as_vector |
A string, name of the column to include a vector of values. |
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
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()
Get variable description
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
gl |
A |
name |
A string vector. |
only_values |
A boolean, add names to component values. |
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()
Get the variables layer
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
gl |
A |
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()
Group table instances by keys aggregating the measures using the corresponding aggregation function.
Description
Group table instances by keys aggregating the measures using the corresponding aggregation function.
Usage
group_by_keys(table, keys, measures, agg_functions, nrow_agg)
Arguments
table |
A |
keys |
A vector of strings, key names to group by. |
measures |
A vector of strings, measures to aggregate. |
agg_functions |
A vector of strings, aggregate functions. |
nrow_agg |
A string, name of a new column to count the number of rows aggregated. |
Value
A tibble.
Group instances of a dimension
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
db |
A |
name |
A string, dimension name. |
Value
A star_database object.
See Also
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")
Group facts
Description
Once the external keys have been possibly replaced, group the rows of facts.
Usage
group_facts(db)
Arguments
db |
A |
Refresh a star database in a constellation
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
db |
A |
sdbu |
A |
existing_instances |
A string, operation to be carried out on the instances of already existing facts. The possible values are: "ignore", "replace", "group" and "delete". |
replace_transformations |
A boolean, replace the |
... |
internal test parameters. |
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
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)
Integrate two geodimensions
Description
Integrate two geodimensions
Usage
integrate_geo_dimensions(gd1, gd2)
Arguments
gd1 |
A geodimension. |
gd2 |
A geodimension. |
Value
A geodimension.
Interpret operation
Description
operation, name, details, details2 "add_custom_column", name, as.character(list(definition))
Usage
interpret_operation_add_custom_column(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Details
f <- function(...) g <- as.character(list(f)) h <- eval(parse(text = g))
Value
A flat table.
Interpret operation
Description
Interpret operation
Usage
interpret_operation_flat_table(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name "group_dimension_instances", name)
Usage
interpret_operation_group_dimension_instances(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "join_lookup_table", fk_attributes, pos)
Usage
interpret_operation_join_lookup_table(ft, op, lookup_tables, file, last_op)
Arguments
ft |
flat table |
op |
operation |
lookup_tables |
lookup tables |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "lookup_table", pk_attributes, c(attributes, '|', attribute_agg), c(measures, '|', measure_agg)
Usage
interpret_operation_lookup_table(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, "remove_instances_without_measures")
Usage
interpret_operation_remove_instances_without_measures(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "replace_attribute_values", attributes, old, new) "replace_attribute_values", c(name, "|", attributes), old, new)
Usage
interpret_operation_replace_attribute_values(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "replace_empty_values", attributes, empty_values
Usage
interpret_operation_replace_empty_values(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "replace_string", attributes, string, replacement)
Usage
interpret_operation_replace_string(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "replace_unknown_values", attributes, value)
Usage
interpret_operation_replace_unknown_values(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "role_playing_dimension", rpd, roles, att_names)
Usage
interpret_operation_role_playing_dimension(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name "select_attributes", attributes)
Usage
interpret_operation_select_attributes(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "select_instances", not, attributes, unlist(values)
Usage
interpret_operation_select_instances(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "select_instances_by_comparison", c(not, n_ele_set), unlist(attributes), c(unlist(comparisons), unlist(values))
Usage
interpret_operation_select_instances_by_comparison(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details "select_measures", measures, na_rm
Usage
interpret_operation_select_measures(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "separate_measures", measures, c(name, names), na_rm)
Usage
interpret_operation_separate_measures(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "set_attribute_names", name, old, new)
Usage
interpret_operation_set_attribute_names(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "set_measure_names", name, old, new)
Usage
interpret_operation_set_measure_names(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, "snake_case")
Usage
interpret_operation_snake_case(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "star_database", names(db$schemas), unknown_value)
Usage
interpret_operation_star_database(ft, op, schema, file, last_op)
Arguments
ft |
flat table |
op |
operation |
schema |
multidimensional schema |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "transform_attribute_format", attributes, c(width, decimal_places), c(k_sep, decimal_sep, space_filling)
Usage
interpret_operation_transform_attribute_format(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, "transform_from_values", attribute)
Usage
interpret_operation_transform_from_values(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "transform_to_attribute", measures, c(width, decimal_places), c(k_sep, decimal_sep)
Usage
interpret_operation_transform_to_attribute(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "transform_to_measure", attributes, k_sep, decimal_sep
Usage
interpret_operation_transform_to_measure(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
Interpret operation
Description
operation, name, details, details2 "transform_to_values", attribute, measure, c(id_reverse, na_rm))
Usage
interpret_operation_transform_to_values(ft, op, file, last_op)
Arguments
ft |
flat table |
op |
operation |
file |
file to write the code |
last_op |
A boolean, is the last operation? |
Value
A flat table.
check if a string is empty
Description
check if a string is empty
Usage
is_empty_string(string)
Arguments
string |
A string. |
Value
A boolean.
A star_operation is new?
Description
A star_operation is new?
Usage
is_new_operation(op, op_name, name = NULL, details = NULL, details2 = NULL)
Arguments
op |
A |
op_name |
A string, operation name. |
name |
A string, element name. |
details |
A vector of strings, operation details. |
details2 |
A vector of strings, operation additional details. |
Value
A boolean.
Is a scd dimension
Description
Is a scd dimension
Usage
is_scd(schema)
Arguments
schema |
A |
Value
A boolean.
Join a flat table with a lookup table
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
ft |
A |
fk_attributes |
A vector of strings, attribute names. |
lookup |
A |
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
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)
Get line last operation
Description
Get line last operation
Usage
line_last_op(last_op)
Arguments
last_op |
A boolean, is the last operation? |
Value
A string
Load star_database (from a RDS file)
Description
Load star_database (from a RDS file)
Usage
load_star_database(file)
Arguments
file |
A string, name of the file that stores the object. |
Value
A star_database object.
See Also
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)
Transform a flat table into a look up table
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
ft |
A |
pk_attributes |
A vector of strings, attribute names. |
attributes |
A vector of strings, rest of attribute names. |
attribute_agg |
A vector of strings, attribute aggregation functions. |
measures |
A vector of strings, measure names. |
measure_agg |
A vector of strings, measure aggregation functions. |
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
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')
)
Star schema for Mortality Reporting System by Age
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
Other mrs example schema:
mrs_age_schema_rpd,
mrs_cause_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_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"))
Star schema for Mortality Reporting System by Age with additional dates
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
Other mrs example schema:
mrs_age_schema,
mrs_cause_schema,
mrs_cause_schema_rpd
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"
)
))
Star schema for Mortality Reporting System by Cause
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
Other mrs example schema:
mrs_age_schema,
mrs_age_schema_rpd,
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)
Star schema for Mortality Reporting System by Cause with additional dates
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
Other mrs example schema:
mrs_age_schema,
mrs_age_schema_rpd,
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"
)
))
Constellation generated from MRS file
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
See Also
Other mrs example data:
ft,
ft_age,
ft_age_rpd,
ft_cause_rpd,
ft_num,
mrs_db_geo,
mrs_ft,
mrs_ft_new
Constellation generated from MRS file through a query and with geographic information
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
See Also
Other mrs example data:
ft,
ft_age,
ft_age_rpd,
ft_cause_rpd,
ft_num,
mrs_db,
mrs_ft,
mrs_ft_new
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"
)
Flat table generated from MRS file
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
See Also
Other mrs example data:
ft,
ft_age,
ft_age_rpd,
ft_cause_rpd,
ft_num,
mrs_db,
mrs_db_geo,
mrs_ft_new
Flat table generated from MRS file
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
See Also
Other mrs example data:
ft,
ft_age,
ft_age_rpd,
ft_cause_rpd,
ft_num,
mrs_db,
mrs_db_geo,
mrs_ft
Multiple value key
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
tb |
A |
col_as_vector |
A string, name of the column to include a vector of values. |
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)
Name with nexus
Description
Given a name, if it ends in "/" the nexus is the empty string, otherwise it is "/". Add the nexus.
Usage
name_with_nexus(name)
Arguments
name |
A string. |
Value
A string.
multistar S3 class
Description
Internal low-level constructor that creates new objects with the correct structure.
Usage
new_multistar(fl = list(), dl = list())
Arguments
fl |
A |
dl |
A |
Details
It only distinguishes between general and conformed dimensions, each dimension has its own data. It can contain multiple fact tables.
Value
A multistar object.
Prepare the instances table implemented by a tibble to join
Description
Transform all fields in the instances table to character type and replace
the NA values to facilitate the join operation.
Usage
prepare_to_join(table, unknown_value)
Arguments
table |
A |
unknown_value |
A string, value used to replace NA values in dimensions. |
Value
A tibble.
Purge instances of a dimension
Description
Delete instances of a dimension that are not referenced in the facts.
Usage
purge_dimension(db, dim)
Arguments
db |
A |
dim |
A string, dimension name. |
Value
A tibble, dimension table.
Purge instances of dimensions
Description
Delete instances of dimensions that are not referenced in the facts.
Usage
purge_dimension_instances(db)
Arguments
db |
A |
Value
A star_database object.
Purge instances of dimensions
Description
Delete instances of dimensions that are not referenced in the facts.
Usage
purge_dimension_instances_star_database(db)
Arguments
db |
A |
Value
A star_database object.
Import flat table file
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
name |
A string, flat table name. |
file |
A string, name of a text file. |
sep |
Column separator character. |
page |
A string, name of the new field in which to include the name of the file. |
unknown_value |
A string, value used to replace empty and NA values in attributes. |
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
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)
Import all flat table files in a folder
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
name |
A string, flat table name. |
folder |
A string, folder name. |
sep |
Column separator character. |
page |
A string, name of the new field in which to include the name of the file. |
unknown_value |
A string, value used to replace empty and NA values in attributes. |
same_columns |
A boolean, indicates whether all tables have the same columns in the same order. |
snake_case |
A boolean, indicates if we want to transform the names of the columns to snake case. |
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
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)
Get line last operation
Description
Get line last operation
Usage
reformat_file(out_file, function_name)
Arguments
out_file |
A string, file name. |
function_name |
A string, name of the function to generate in the file. |
Value
A string
Refresh deployments
Description
Generate sql code for the first refresh operation.
Usage
refresh_deployments(db, internal)
Arguments
db |
A |
internal |
A boolean. |
Value
A star_database object.
Remove instance if all measures are na
Description
Remove instance if all measures are na
Usage
remove_all_measures_na(table, measures)
Arguments
table |
A |
measures |
A vector of strings, measure names. |
Remove duplicate dimension rows
Description
After selecting only a few columns of the dimensions, there may be rows with duplicate values. We eliminate duplicates and adapt facts to the new dimensions.
Usage
remove_duplicate_dimension_rows(db)
Arguments
db |
A |
Remove instances without measures
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
ft |
A |
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
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()
Replace instance values
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
db |
A |
name |
A string, dimension name. |
attributes |
A vector of strings, attribute names. |
old |
A vector of values. |
new |
A vector of values. |
Value
A flat_table or star_database object.
See Also
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')
)
Replace empty values with the unknown value
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
ft |
A |
attributes |
A vector of names. |
empty_values |
A vector of values that correspond to empty values. |
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
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
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 empty values with the unknown value
Description
Replace empty values with the unknown value
Usage
replace_empty_values_table(
table,
attributes = NULL,
empty_values = NULL,
unknown_value
)
Arguments
table |
A |
attributes |
A vector of names. |
empty_values |
A vector of values that correspond to empty values. |
unknown_value |
A string. |
Value
A tibble object.
Replace names
Description
Replace names
Usage
replace_names(original, old, new)
Arguments
original |
A string, original names. |
old |
A vector of names to replace. |
new |
A vector of names, new names. |
Value
A vector of strings, names replaced.
Replace strings
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
ft |
A |
attributes |
A vector of strings, attribute names. |
string |
A character string to replace. |
replacement |
A replacement for matched string. |
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
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')
)
Replace unknown values with the given value
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
ft |
A |
attributes |
A vector of names. |
value |
A value. |
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
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")
Define a role playing dimension and its associated dimensions
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
db |
A |
rpd |
A string, dimension name (role playing dimension). |
roles |
A vector of strings, dimension names (dimension roles). |
rpd_att_names |
A boolean, common attribute names taken from rpd dimension. |
att_names |
A vector of strings, common attribute names. |
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
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"))
Transform role playing dimensions in constellation
Description
Transform role playing dimensions in constellation
Usage
rpd_in_constellation(db)
Arguments
db |
A |
Value
A constellation object.
Run query
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
db |
A |
sq |
A |
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)
Do all fact tables have the same granularity?
Description
Do all fact tables have the same granularity?
Usage
same_granularity_facts(db, names)
Arguments
db |
A |
names |
A vector of strings, fact names. |
Value
A boolean.
Select attributes of a flat table
Description
Select only the indicated attributes from the flat table.
Usage
select_attributes(ft, attributes)
## S3 method for class 'flat_table'
select_attributes(ft, attributes)
Arguments
ft |
A |
attributes |
A vector of names. |
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_instances(),
select_instances_by_comparison(),
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) |>
select_attributes(attributes = c('Species'))
ft <- flat_table('ft_num', ft_num) |>
select_attributes(attributes = c('Year', 'WEEK', 'Week Ending Date'))
Select dimension
Description
To add a dimension in a star_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(sq, name, attributes)
## S3 method for class 'star_query'
select_dimension(sq, name = NULL, attributes = NULL)
Arguments
sq |
A |
name |
A string, name of the dimension. |
attributes |
A vector of attribute names. |
Value
A star_query object.
See Also
Other query functions:
as_GeoPackage(),
as_geolayer(),
filter_dimension(),
get_layer(),
get_variable_description(),
get_variables(),
run_query(),
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")
Select fact
Description
To define the fact to be consulted, its name is indicated, optionally, a vector of names of selected measures, another of aggregation functions and another of new names for measures are also indicated.
Usage
select_fact(sq, name, measures, agg_functions, new, nrow_agg)
## S3 method for class 'star_query'
select_fact(
sq,
name = NULL,
measures = NULL,
agg_functions = NULL,
new = NULL,
nrow_agg = NULL
)
Arguments
sq |
A |
name |
A string, name of the fact. |
measures |
A vector of measure names. |
agg_functions |
A vector of aggregation function names, each one for its corresponding measure. They can be SUM, MAX or MIN. |
new |
A vector of measure new names. |
nrow_agg |
A string, name of a new measure that represents the COUNT of rows aggregated for each resulting row. |
Details
If there is only one fact table, it is the one that is considered if no name is indicated.
If no aggregation function is given, those defined for the measures are considered.
If no new names are given, the original names will be considered. If the aggregation function is different from the one defined by default, it will be included as a prefix to the name.
Value
A star_query object.
See Also
Other query functions:
as_GeoPackage(),
as_geolayer(),
filter_dimension(),
get_layer(),
get_variable_description(),
get_variables(),
run_query(),
select_dimension(),
set_layer(),
set_variables(),
star_query()
Examples
sq <- mrs_db |>
star_query()
sq_1 <- sq |>
select_fact(
name = "mrs_age",
measures = "all_deaths",
agg_functions = "MAX"
)
sq_2 <- sq |>
select_fact(name = "mrs_age",
measures = "all_deaths")
sq_3 <- sq |>
select_fact(name = "mrs_age")
Select instances of a flat table by value
Description
Select only the indicated instances from the flat table.
Usage
select_instances(ft, not, attributes, values)
## S3 method for class 'flat_table'
select_instances(ft, not = FALSE, attributes = NULL, values)
Arguments
ft |
A |
not |
A boolean. |
attributes |
A vector of names. |
values |
A list of value vectors. |
Details
Several values can be indicated for attributes (performs an OR operation) or several attributes and a value for each one (performs an AND operation).
If the parameter not is true, the reported values are those that are not
included.
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances_by_comparison(),
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) |>
select_instances(attributes = c('Species'),
values = c('versicolor', 'virginica'))
ft <- flat_table('ft_num', ft_num) |>
select_instances(
not = TRUE,
attributes = c('Year', 'WEEK'),
values = list(c('1962', '2'), c('1964', '2'))
)
Select instances of a flat table by comparison
Description
Select only the indicated instances from the flat table by comparison.
Usage
select_instances_by_comparison(ft, not, attributes, comparisons, values)
## S3 method for class 'flat_table'
select_instances_by_comparison(
ft,
not = FALSE,
attributes = NULL,
comparisons,
values
)
Arguments
ft |
A |
not |
A boolean. |
attributes |
A list of name vectors. |
comparisons |
A list of comparison operator vectors. |
values |
A list of value vectors. |
Details
The elements of the three parameter lists correspond (all three must have the same structure and length or be of length 1). AND is performed for each combination of attribute, operator and value within each element of each list and OR between elements of the lists.
If the parameter not is true, the negation operation will be applied to the
result.
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
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) |>
select_instances_by_comparison(attributes = 'Species',
comparisons = '>=',
values = 'v')
ft <- flat_table('ft_num', ft_num) |>
select_instances_by_comparison(
not = FALSE,
attributes = c('Year', 'Year', 'WEEK'),
comparisons = c('>=', '<=', '=='),
values = c('1962', '1964', '2')
)
ft <- flat_table('ft_num', ft_num) |>
select_instances_by_comparison(
not = FALSE,
attributes = c('Year', 'Year', 'WEEK'),
comparisons = c('>=', '<=', '=='),
values = list(c('1962', '1964', '2'),
c('1962', '1964', '4'))
)
Select measures of a flat table
Description
Select only the indicated measures from the flat table.
Usage
select_measures(ft, measures, na_rm)
## S3 method for class 'flat_table'
select_measures(ft, measures = NULL, na_rm = TRUE)
Arguments
ft |
A |
measures |
A vector of names. |
na_rm |
A boolean, remove rows from output where all measure values are NA. |
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
separate_measures(),
transform_attribute_format(),
transform_from_values(),
transform_to_attribute(),
transform_to_measure(),
transform_to_values()
Examples
ft <- flat_table('iris', iris) |>
select_measures(measures = c('Sepal.Length', 'Sepal.Width'))
Separate measures in flat tables
Description
Separate groups of measures into different flat tables. For each group we must indicate a name. If we indicate more names than groups of measures, the measures not included in other groups are also included in a new group.
Usage
separate_measures(ft, measures, names, na_rm)
## S3 method for class 'flat_table'
separate_measures(ft, measures = NULL, names = NULL, na_rm = TRUE)
Arguments
ft |
A |
measures |
A list of string vectors, groups of measure names. |
names |
A list of string, measure group names. |
na_rm |
A boolean, remove rows from output where all measure values are NA. |
Details
A list of flat tables is returned. It assign the names to the result list.
Value
A list of flat_table objects.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
select_measures(),
transform_attribute_format(),
transform_from_values(),
transform_to_attribute(),
transform_to_measure(),
transform_to_values()
Examples
lft <- flat_table('iris', iris) |>
separate_measures(
measures = list(
c('Petal.Length'),
c('Petal.Width'),
c('Sepal.Length')
),
names = c('PL', 'PW', 'SL', 'SW')
)
Rename attributes
Description
Rename attributes in a flat table or a dimension in a star database.
Usage
## S3 method for class 'flat_table'
set_attribute_names(db, name = NULL, old = NULL, new)
set_attribute_names(db, name, old, new)
## S3 method for class 'star_database'
set_attribute_names(db, name, old = NULL, new)
Arguments
db |
A |
name |
A string, dimension name. |
old |
A vector of names. |
new |
A vector of names. |
Details
To rename the attributes there are three possibilities: 1) give only one vector with the new names for all the attributes; 2) a vector of old names and another of new names that must correspond; 3) a vector of new names whose names are the old names they replace.
Value
A flat_table or star_database object.
See Also
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(),
replace_attribute_values.flat_table(),
set_measure_names.flat_table(),
snake_case.flat_table()
Examples
db <- star_database(mrs_cause_schema, ft_num) |>
set_attribute_names(
name = "where",
new = c(
"Region",
"State",
"City"
)
)
db <- star_database(mrs_cause_schema, ft_num) |>
set_attribute_names(name = "where",
old = "REGION",
new = "Region")
new <- "Region"
names(new) <- "REGION"
db <- star_database(mrs_cause_schema, ft_num) |>
set_attribute_names(name = "where",
new = new)
ft <- flat_table('iris', iris) |>
set_attribute_names(
old = 'Species',
new = 'species')
new <- "species"
names(new) <- "Species"
ft <- flat_table('iris', iris) |>
set_attribute_names(
new = new)
Set geographic layer
Description
If for some reason we modify the geographic layer, for example, to add a new
calculated variable, we can set that layer to become the new geographic layer
of the geolayer object using this function.
Usage
set_layer(gl, layer)
## S3 method for class 'geolayer'
set_layer(gl, layer)
Arguments
gl |
A |
layer |
A |
Value
A geolayer object.
See Also
Other query functions:
as_GeoPackage(),
as_geolayer(),
filter_dimension(),
get_layer(),
get_variable_description(),
get_variables(),
run_query(),
select_dimension(),
select_fact(),
set_variables(),
star_query()
Examples
gl <- mrs_db_geo |>
as_geolayer()
l <- gl |>
get_layer()
l$tpc_001 <- l$var_002 * 100 / l$var_001
gl <- gl |>
set_layer(l)
Rename measures
Description
Rename measures in a flat table or in facts in a star database.
Usage
## S3 method for class 'flat_table'
set_measure_names(db, name = NULL, old = NULL, new)
set_measure_names(db, name, old, new)
## S3 method for class 'star_database'
set_measure_names(db, name = NULL, old = NULL, new)
Arguments
db |
A |
name |
A string, fact name. |
old |
A vector of names. |
new |
A vector of names. |
Details
To rename the measures there are three possibilities: 1) give only one vector with the new names for all the measures; 2) a vector of old names and another of new names that must correspond; 3) a vector of new names whose names are the old names they replace.
Value
A flat_table or star_database object.
See Also
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(),
replace_attribute_values.flat_table(),
set_attribute_names.flat_table(),
snake_case.flat_table()
Examples
db <- star_database(mrs_cause_schema, ft_num) |>
set_measure_names(
new = c(
"Pneumonia and Influenza",
"All",
"Rows Aggregated"
)
)
ft <- flat_table('iris', iris) |>
set_measure_names(
old = c('Petal.Length', 'Petal.Width', 'Sepal.Length', 'Sepal.Width'),
new = c('pl', 'pw', 'ls', 'sw'))
new <- c('pl', 'pw', 'ls', 'sw')
names(new) <- c('Petal.Length', 'Petal.Width', 'Sepal.Length', 'Sepal.Width')
ft <- flat_table('iris', iris) |>
set_measure_names(
new = new)
Set variables layer
Description
The variables layer includes the names and description through various fields of the variables contained in the reports.
Usage
set_variables(gl, variables, keep_all_variables_na)
## S3 method for class 'geolayer'
set_variables(gl, variables, keep_all_variables_na = FALSE)
Arguments
gl |
A |
variables |
A |
keep_all_variables_na |
A boolean, keep rows with all variables NA. |
Details
When we set the variables layer, after filtering it, the data layer is also filtered keeping only the variables from the variables layer.
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_layer(),
get_variable_description(),
get_variables(),
run_query(),
select_dimension(),
select_fact(),
set_layer(),
star_query()
Examples
gl <- mrs_db_geo |>
as_geolayer()
v <- gl |>
get_variables()
v <- v |>
dplyr::filter(year == '1966' | year == '2016')
gl_sel <- gl |>
set_variables(v)
Share dimension instance operations between all star_database objects
Description
Share dimension instance operations between all star_database objects
Usage
share_dimension_instance_operations(stars, dim_freq)
Arguments
stars |
A list of |
dim_freq |
Dimension frequency table. |
Value
A list of star_database objects.
Share the given dimensions in the database
Description
Share the given dimensions in the database
Usage
share_dimensions(db, dims)
Arguments
db |
|
dims |
Vector of dimension names. |
Value
A star_database object.
From a vector of dimensions, leave only one of each rpd.
Description
From a vector of dimensions, leave only one of each rpd.
Usage
simplify_rpd_dimensions(db, names)
Arguments
db |
A |
names |
A vector of strings, dimension names. |
Value
A vector of dimension names.
Transform names according to the snake case style
Description
For flat tables, transform attribute and measure names according to the snake case style. For star databases, transform fact, dimension, measures, and attribute names according to the snake case style.
Usage
## S3 method for class 'flat_table'
snake_case(db)
snake_case(db)
## S3 method for class 'star_database'
snake_case(db)
Arguments
db |
A |
Details
This style is suitable if we are going to work with databases.
Value
A flat_table or star_database object.
See Also
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(),
replace_attribute_values.flat_table(),
set_attribute_names.flat_table(),
set_measure_names.flat_table()
Examples
db <- star_database(mrs_cause_schema, ft_num) |>
snake_case()
ft <- flat_table('iris', iris) |>
snake_case()
Transform names according to the snake case style
Description
Transform names according to the snake case style
Usage
## S3 method for class 'dimension_table'
snake_case_table(table)
Arguments
table |
A |
Value
A dimension_table object.
Transform names according to the snake case style
Description
Transform names according to the snake case style
Usage
## S3 method for class 'fact_table'
snake_case_table(table)
Arguments
table |
A |
Value
A fact_table object.
star_database S3 class
Description
A star_database object is created from a star_schema object and a flat
table that contains the data from which database instances are derived.
Usage
star_database(schema, instances, unknown_value = NULL)
Arguments
schema |
A |
instances |
A flat table to define the database instances according to the schema. |
unknown_value |
A string, value used to replace NA values in dimensions. |
Details
Measures and measures of the star_schema must correspond to the names of
the columns of the flat table.
Since NA values cause problems when doing Join operations between tables, you can indicate the value that will be used to replace them before doing these operations. If none is indicated, a default value is taken.
Value
A star_database object.
See Also
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(),
role_playing_dimension()
Examples
db <- star_database(mrs_cause_schema, ft_num)
Creates a star_database adding previous operations
Description
Creates a star_database adding previous operations
Usage
star_database_with_previous_operations(
schema,
instances,
unknown_value = NULL,
operations = NULL,
lookup_tables = NULL
)
Arguments
schema |
A |
instances |
A flat table to define the database instances according to the schema. |
unknown_value |
A string, value used to replace NA values in dimensions. |
operations |
A list of operations. |
lookup_tables |
A list of lookup tables. |
Value
A star_database object.
star_operation S3 class
Description
A star_operation object is created.
Usage
star_operation()
Details
A star_operation object is part of a star_schema object, defines
operations of the star schema.
Value
A star_operation object.
star_query S3 class
Description
An empty star_query object is created where we can select facts and
measures, dimensions, dimension attributes and filter dimension rows.
Usage
star_query(db)
## S3 method for class 'star_database'
star_query(db)
Arguments
db |
A |
Value
A star_query object.
See Also
Other query functions:
as_GeoPackage(),
as_geolayer(),
filter_dimension(),
get_layer(),
get_variable_description(),
get_variables(),
run_query(),
select_dimension(),
select_fact(),
set_layer(),
set_variables()
Examples
sq <- mrs_db |>
star_query()
star_schema S3 class
Description
An empty star_schema object is created in which definition of facts
and dimensions can be added.
Usage
star_schema()
Details
To get a star database (a star_database object) we need a flat table
and a star_schema object. The definition of facts and dimensions in
the star_schema object is made from the flat table columns.
Value
A star_schema object.
See Also
Other star schema definition functions:
define_dimension(),
define_facts(),
dimension_schema(),
fact_schema()
Examples
s <- star_schema()
Get the representation to output
Description
Get the representation to output
Usage
string_or_null(value, last = FALSE)
Arguments
value |
A string |
last |
A boolean |
Value
A string
Transforms string into a vector of strings.
Description
Transforms string into a vector of strings.
Usage
string_to_vector(str)
Arguments
str |
A string. |
Value
A vector of strings.
Summarize geometry of a layer
Description
Groups the geometric elements of a layer according to the values of the indicated attribute.
Usage
summarize_layer(layer, attribute)
Arguments
layer |
A |
attribute |
A string, attribute name. |
Value
A sf object.
See Also
Other star database geographic attributes:
check_geoattribute_geometry(),
define_geoattribute(),
get_geoattribute_geometries(),
get_geoattributes(),
get_layer_geometry(),
get_point_geometry()
Examples
layer <-
summarize_layer(us_layer_state, "REGION")
Transform attribute format
Description
Transforms numeric attributes adapting their format as indicated.
Usage
transform_attribute_format(
ft,
attributes,
width,
decimal_places,
k_sep,
decimal_sep,
space_filling
)
## S3 method for class 'flat_table'
transform_attribute_format(
ft,
attributes,
width = 1,
decimal_places = 0,
k_sep = NULL,
decimal_sep = NULL,
space_filling = TRUE
)
Arguments
ft |
A |
attributes |
A vector of strings, attribute names. |
width |
An integer, string length. |
decimal_places |
An integer, number of decimal places. |
k_sep |
A character, thousands separator used (It can not be changed). |
decimal_sep |
A character, decimal separator used (It can not be changed). |
space_filling |
A boolean, fill on the left with spaces (with '0' otherwise). |
Details
If a number > 1 is specified in the width parameter, at least that length
will be obtained in the result, padded with blanks on the left.
Value
ft A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
select_measures(),
separate_measures(),
transform_from_values(),
transform_to_attribute(),
transform_to_measure(),
transform_to_values()
Examples
ft <- flat_table('iris', iris) |>
transform_to_attribute(measures = "Sepal.Length", decimal_places = 2) |>
transform_attribute_format(
attributes = "Sepal.Length",
width = 5,
decimal_places = 1
)
Transform attribute values into measure names
Description
The values of an attribute will become measure names. There can only be one measure that will be from where the new defined measures take the values.
Usage
transform_from_values(ft, attribute)
## S3 method for class 'flat_table'
transform_from_values(ft, attribute = NULL)
Arguments
ft |
A |
attribute |
A string, attribute that stores the measures names. |
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
select_measures(),
separate_measures(),
transform_attribute_format(),
transform_to_attribute(),
transform_to_measure(),
transform_to_values()
Examples
ft <- flat_table('iris', iris) |>
transform_to_values(attribute = 'Characteristic',
measure = 'Value',
id_reverse = 'id')
ft <- ft |>
transform_from_values(attribute = 'Characteristic')
For each row, add a vector of values
Description
For each row, add a vector of values
Usage
transform_names(names, ordered, as_definition)
Arguments
names |
A vector of strings, names of attributes or measures. |
ordered |
A boolean, sort names alphabetically. |
as_definition |
A boolean, as the definition of the vector in R. |
Value
A vector of strings, attribute or measure names.
Transform to attribute
Description
Transform measures into attributes. We can indicate if we want all the numbers in the result to have the same length and the number of decimal places.
Usage
transform_to_attribute(ft, measures, width, decimal_places, k_sep, decimal_sep)
## S3 method for class 'flat_table'
transform_to_attribute(
ft,
measures,
width = 1,
decimal_places = 0,
k_sep = ",",
decimal_sep = "."
)
Arguments
ft |
A |
measures |
A vector of strings, measure names. |
width |
An integer, string length. |
decimal_places |
An integer, number of decimal places. |
k_sep |
A character, indicates thousands separator. |
decimal_sep |
A character, indicates decimal separator. |
Details
If a number > 1 is specified in the width parameter, at least that length
will be obtained in the result, padded with blanks on the left.
Value
ft A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
select_measures(),
separate_measures(),
transform_attribute_format(),
transform_from_values(),
transform_to_measure(),
transform_to_values()
Examples
ft <- flat_table('iris', iris) |>
transform_to_attribute(
measures = "Sepal.Length",
width = 3,
decimal_places = 2
)
Transform to measure
Description
Transform attributes into measures.
Usage
transform_to_measure(ft, attributes, k_sep, decimal_sep)
## S3 method for class 'flat_table'
transform_to_measure(ft, attributes, k_sep = NULL, decimal_sep = NULL)
Arguments
ft |
A |
attributes |
A vector of strings, attribute names. |
k_sep |
A character, thousands separator to remove. |
decimal_sep |
A character, new decimal separator to use, if necessary. |
Details
We can indicate a thousands indicator to remove and a decimal separator to use. The only decimal separators considered are "." and ",".
Value
ft A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
select_measures(),
separate_measures(),
transform_attribute_format(),
transform_from_values(),
transform_to_attribute(),
transform_to_values()
Examples
ft <- flat_table('iris', iris) |>
transform_to_attribute(measures = "Sepal.Length", decimal_places = 2) |>
transform_to_measure(attributes = "Sepal.Length", decimal_sep = ".")
Transform measure names into attribute values
Description
Transforms the measure names into values of a new attribute. The values of the measures will become values of the new measure that is indicated.
Usage
transform_to_values(ft, attribute, measure, id_reverse, na_rm)
## S3 method for class 'flat_table'
transform_to_values(
ft,
attribute = NULL,
measure = NULL,
id_reverse = NULL,
na_rm = TRUE
)
Arguments
ft |
A |
attribute |
A string, new attribute that will store the measures names. |
measure |
A string, new measure that will store the measure value. |
id_reverse |
A string, name of a new attribute that will store the row id. |
na_rm |
A boolean, remove rows from output where the value column is NA. |
Details
If we wanted to perform the reverse operation later using the transform_from_values
function, we would need to uniquely identify each original row. By indicating
a value in the id_reverse parameter, an identifier is added that will allow
us to always carry out the inverse operation.
Value
A flat_table object.
See Also
Other flat table transformation functions:
add_custom_column(),
remove_instances_without_measures(),
replace_empty_values(),
replace_string(),
replace_unknown_values(),
select_attributes(),
select_instances(),
select_instances_by_comparison(),
select_measures(),
separate_measures(),
transform_attribute_format(),
transform_from_values(),
transform_to_attribute(),
transform_to_measure()
Examples
ft <- flat_table('iris', iris) |>
transform_to_values(attribute = 'Characteristic',
measure = 'Value')
ft <- flat_table('iris', iris) |>
transform_to_values(attribute = 'Characteristic',
measure = 'Value',
id_reverse = 'id')
Unify facts and dimensions in a flat table
Description
Unify facts and dimensions in a flat table
Usage
unify_facts_and_dimensions(db, dimension, include_nrow_agg)
Arguments
db |
A |
dimension |
A vector of strings, dimension names. |
include_nrow_agg |
A boolean. |
Value
A tibble.
Unify lists of dimension names if there are any in common
Description
Unify lists of dimension names if there are any in common
Usage
unify_rpd(rpd)
Arguments
rpd |
A list of strings (dimension names). |
Value
A list of strings (dimension names).
Update a flat table according to another structure
Description
Update a flat table with the operations of another structure based on a flat table.
Usage
update_according_to(ft, sdb, star, sdb_operations)
## S3 method for class 'flat_table'
update_according_to(ft, sdb, star = 1, sdb_operations = NULL)
Arguments
ft |
A |
sdb |
A |
star |
A string or integer, star database name or index in constellation. |
sdb_operations |
A |
Value
A star_database_update object.
See Also
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(),
incremental_refresh()
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)
Census of US States, by sex and age
Description
Census of US States, by sex and age, obtained from the United States Census Bureau (USCB), American Community Survey (ACS). Obtained from the variables defined in reports, classifying the concepts according to the defined subjects.
Usage
us_census_state
Format
A tibble.
Details
U.S. Census Bureau. “Government Units: US and State: Census Years 1942 - 2022.” Public Sector, PUB Public Sector Annual Surveys and Census of Governments, Table CG00ORG01, 2022, https://data.census.gov/table/GOVSTIMESERIES.CG00ORG01?q=census+state+year. Accessed on October 25, 2023.
Source
https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.2021.html
Geographic layer of US States
Description
Geographic layer with data from the States of the USA in polygon format, with simplified geometry so that it takes up less space.
Usage
us_layer_state
Format
A sf.
Details
It has been obtained from the geographic data included in the US census prepared by the U.S. Census Bureau.
Source
https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.2021.html
Validate attribute names
Description
Validate attribute names
Usage
validate_attributes(defined_attributes, attributes, repeated = FALSE)
Arguments
defined_attributes |
A vector of strings, defined attribute names. |
attributes |
A vector of strings, new attribute names. |
repeated |
A boolean, repeated attributes allowed. |
Value
A vector of strings, attribute names.
Validate dimension attributes
Description
Validate dimension attributes
Usage
validate_dimension_attributes(db, dimension, attributes)
Arguments
db |
A |
dimension |
A dimension name. |
attributes |
Attribute names. |
Value
A vector of strings, dimension names.
Validate dimension names
Description
Validate dimension names
Usage
validate_dimension_names(db, name)
Arguments
db |
A |
name |
A vector of strings, dimension names. |
Value
A vector of strings, dimension names.
Validate fact names
Description
Validate fact names
Usage
validate_facts(defined_facts, facts)
Arguments
defined_facts |
A vector of strings, defined fact names. |
facts |
A vector of strings, fact names. |
Value
A vector of strings, fact names.
Validate lookup parameters
Description
Validate lookup parameters
Usage
validate_lookup_parameters(ft, fk_attributes, lookup)
Arguments
ft |
A |
fk_attributes |
A vector of strings, attribute names. |
lookup |
A |
Value
A vector of strings, fk attribute names.
Validate measure names
Description
Validate measure names
Usage
validate_measures(defined_measures, measures)
Arguments
defined_measures |
A vector of strings, defined measure names. |
measures |
A vector of strings, measure names. |
Value
A vector of strings, measure names.
Validate names
Description
Validate names
Usage
validate_names(defined_names, names, concept = "name", repeated = FALSE)
Arguments
defined_names |
A vector of strings, defined attribute names. |
names |
A vector of strings, new attribute names. |
concept |
A string, treated concept. |
repeated |
A boolean, repeated names allowed. |
Value
A vector of strings, names.
vector to string for presentation
Description
vector to string for presentation
Usage
vector_presentation(vector)
Arguments
vector |
A vector |
Value
A string
Transforms a vector of strings into a string.
Description
Transforms a vector of strings into a string.
Usage
vector_to_string(vector)
Arguments
vector |
A vector of strings. |
Value
A string.