Func-methods {PivotalR} | R Documentation |
db.obj
objects as the
argumentFunctions that apply onto
db.obj
objects
## S4 method for signature 'db.obj' exp(x) ## S4 method for signature 'db.obj' abs(x) ## S4 method for signature 'db.obj' log(x, ...) ## S4 method for signature 'db.obj' log10(x) ## S4 method for signature 'db.obj' sign(x) ## S4 method for signature 'db.obj' sqrt(x) ## S4 method for signature 'db.obj' factorial(x) ## S4 method for signature 'db.obj' sin(x) ## S4 method for signature 'db.obj' cos(x) ## S4 method for signature 'db.obj' tan(x) ## S4 method for signature 'db.obj' asin(x) ## S4 method for signature 'db.obj' acos(x) ## S4 method for signature 'db.obj' atan(x) ## S4 method for signature 'db.obj,db.obj' atan2(y, x) ## S4 method for signature 'db.obj,numeric' atan2(y, x) ## S4 method for signature 'numeric,db.obj' atan2(y, x)
x,y |
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... |
Extra parameters. Not implemented. |
db.Rquery
object, which contains the SQL query
that computes the operations.
A meaningful expression is generated only when the
.col.data_type
is "numeric", otherwise a "NULL" value is
generated.
Author: Predictive Analytics Team at Pivotal Inc.
Maintainer: Frank McQuillan, Pivotal Inc. fmcquillan@pivotal.io
db.Rquery
contains a SQL query that does the
operations.
## Not run: ## get the help for a method ## help("+,db.obj,db.obj-method") ## set up the database connection ## Assume that .port is port number and .dbname is the database name cid <- db.connect(port = .port, dbname = .dbname) ## create a table from the example data.frame "abalone" delete("abalone", conn.id = cid) x <- as.db.data.frame(abalone, "abalone", conn.id = cid) x$rings <- exp(x$rings) # change the values x$area <- log((x$length + 1) * (x$height + 1)) # add a new column lk(x$area, 10) # view the actual values computed in database fit <- madlib.lm(rings ~ area, data = x) db.disconnect(cid, verbose = FALSE) ## End(Not run)