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Map sets rows to columns (query based, take name of table). — blocks_to_rowrecs_q • cdata

Transform data facts from rows into additional columns using SQL and controlTable.

blocks_to_rowrecs_q(
  tallTable,
  keyColumns,
  controlTable,
  my_db,
  ...,
  columnsToCopy = NULL,
  tempNameGenerator = mk_tmp_name_source("mvtcq"),
  strict = FALSE,
  controlTableKeys = colnames(controlTable)[[1]],
  checkNames = TRUE,
  checkKeys = FALSE,
  showQuery = FALSE,
  defaultValue = NULL,
  dropDups = TRUE,
  temporary = FALSE,
  resultName = NULL,
  incoming_qualifiers = NULL,
  outgoing_qualifiers = NULL,
  executeQuery = TRUE
)

name of table containing data to be mapped (db/Spark data)

character list of column defining row groups

table specifying mapping (local data frame)

db handle

force later arguments to be by name.

character list of column names to copy

a tempNameGenerator from cdata::mk_tmp_name_source()

logical, if TRUE check control table name forms

character, which column names of the control table are considered to be keys.

logical, if TRUE check names

logical, if TRUE check keying of tallTable

if TRUE print query

if not NULL literal to use for non-match values.

logical if TRUE suppress duplicate columns (duplicate determined by name, not content).

logical, if TRUE make result temporary.

character, name for result table.

optional named ordered vector of strings carrying additional db hierarchy terms, such as schema.

optional named ordered vector of strings carrying additional db hierarchy terms, such as schema.

logical, if TRUE execute the query and return result.

wide table built by mapping key-grouped tallTable rows to one row per group

This is using the theory of "fluid data"n (https://github.com/WinVector/cdata), which includes the principle that each data cell has coordinates independent of the storage details and storage detail dependent coordinates (usually row-id, column-id, and group-id) can be re-derived at will (the other principle is that there may not be "one true preferred data shape" and many re-shapings of data may be needed to match data to different algorithms and methods).

The controlTable defines the names of each data element in the two notations: the notation of the tall table (which is row oriented) and the notation of the wide table (which is column oriented). controlTable[ , 1] (the group label) cross colnames(controlTable) (the column labels) are names of data cells in the long form. controlTable[ , 2:ncol(controlTable)] (column labels) are names of data cells in the wide form. To get behavior similar to tidyr::gather/spread one builds the control table by running an appropriate query over the data.

Some discussion and examples can be found here: https://winvector.github.io/FluidData/FluidData.html and here https://github.com/WinVector/cdata.

build_pivot_control_q, blocks_to_rowrecs

if (requireNamespace("DBI"
, quietly 
= TRUE) &&
  requireNamespace("RSQLite"
, quietly 
= TRUE)) {
  my_db <- DBI::dbConnect(RSQLite::SQLite()
, 
":memory:")
  # pivot example
  d <- data.frame(
meas 
= c('AUC'
, 
'R2')
, val 
= c(0.6
, 
0.2))
  rquery::rq_copy_to(my_db
, 
'd'
, 
d
, temporary 
= TRUE)
  cT <- build_pivot_control_q('d'
, columnToTakeKeysFrom
= 'meas'
, columnToTakeValuesFrom
= 'val'
, my_db 
= my_db)
  tab <- blocks_to_rowrecs_q('d'
, keyColumns 
= NULL
, controlTable 
= cT
, my_db 
= my_db)
  qlook(my_db
, 
tab)
  DBI::dbDisconnect(my_db)
}
#> table `mvtcq_04050014603667178285_0000000000` SQLiteConnection 
#>  nrow: 1 
#> 'data.frame':	1 obs. of  2 variables:
#>  $ AUC: num 0.6
#>  $ R2 : num 0.2

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