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)

Arguments

tallTable

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

keyColumns

character list of column defining row groups

controlTable

table specifying mapping (local data frame)

my_db

db handle

...

force later arguments to be by name.

columnsToCopy

character list of column names to copy

tempNameGenerator

a tempNameGenerator from cdata::mk_tmp_name_source()

strict

logical, if TRUE check control table name forms

controlTableKeys

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

checkNames

logical, if TRUE check names

checkKeys

logical, if TRUE check keying of tallTable

showQuery

if TRUE print query

defaultValue

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

dropDups

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

temporary

logical, if TRUE make result temporary.

resultName

character, name for result table.

incoming_qualifiers

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

outgoing_qualifiers

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

Value

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

Details

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.

See also

Examples

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_70292169253906552817_0000000000` SQLiteConnection #> nrow: 1 #> 'data.frame': 1 obs. of 2 variables: #> $ AUC: num 0.6 #> $ R2 : num 0.2