StoredProcedure：SQL Server 存储过程：类生成器

StoredProcedure：生成 SQLServer 存储过程对象和（可选）包含用于创建存储过程的查询的 .sql 文件。 StoredProcedure$registrationVec 包含表示创建存储过程所需的查询的字符串

用法

  StoredProcedure (func, spName, ..., filePath = NULL ,dbName = NULL,
  connectionString = NULL, batchSeparator = "GO")

参数

func

有效 R 函数或有效 R 函数的字符串名称：1) 函数所依赖的所有变量都应在函数内定义或作为输入参数提供。 在输入参数中，最多可以有 1 个数据帧 2) 函数应返回数据帧、命名列表或 NULL。 列表中最多只能有一个数据帧。

spName

一个字符串，用于指定存储过程的名称。

...

存储过程的可选输入和输出参数；必须是 InputData、InputParameter 或 outputParameter 类的对象。

filePath

一个字符串，用于指定要在其中创建 .sql 的目录的路径。 如果为 NULL，则不生成 .sql 文件。

dbName

一个字符串，用于指定要使用的数据库的名称。

connectionString

一个字符串，用于指定连接字符串。

batchSeparator

所需 SQL 批处理分隔符（仅在定义 filePath 时相关）

值

SQLServer 存储过程对象

示例

 ## Not run:

 ############# Example 1 #############
 # etl1 - reads from and write directly to the database
 etl1 <- function() {
   # The query to get the data
   qq <- "select top 10000 ArrDelay,CRSDepTime,DayOfWeek from AirlineDemoSmall"
   # The connection string
   conStr <- paste("Driver={ODBC Driver 13 for SQL Server};Server=.;Database=RevoTestDB;",
                 "Trusted_Connection=Yes;", sep = "")
   # The data source - retrieves the data from the database
   dsSqls <- RxSqlServerData(sqlQuery=qq, connectionString=conStr)
   # The destination data source
   dsSqls2 <- RxSqlServerData(table ="cleanData",  connectionString = conStr)
   # A transformation function
   transformFunc <- function(data) {
     data$CRSDepHour <- as.integer(trunc(data$CRSDepTime))
     return(data)
   }
   # The transformation variables
   transformVars <- c("CRSDepTime")
   rxDataStep(inData = dsSqls,
              outFile = dsSqls2,
              transformFunc=transformFunc,
              transformVars=transformVars,
              overwrite = TRUE)
   return(NULL)
 }
 # Create a StoredProcedure object
 sp_ds_ds <- StoredProcedure(etl1, "spTest",
                        filePath = ".", dbName ="RevoTestDB")
 # Define a connection string
 conStr <- paste("Driver={ODBC Driver 13 for SQL Server};Server=.;Database=RevoTestDB;",
                 "Trusted_Connection=Yes;", sep = "")
 # register the stored procedure with a database
 registerStoredProcedure(sp_ds_ds, conStr)
 # execute the stored procedure
 executeStoredProcedure(sp_ds_ds, connectionString = conStr)


 ############# Example 2 #############
 # train 1 takes a data frame with clean data and outputs a model
 train1 <- function(in_df) {
   in_df[,"DayOfWeek"] <- factor(in_df[,"DayOfWeek"], levels=c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"))
   # The model formula
   formula <- ArrDelay ~ CRSDepTime + DayOfWeek + CRSDepHour:DayOfWeek
   # Train the model
   rxSetComputeContext("local")
   mm <- rxLinMod(formula, data=in_df)
   mm <- rxSerializeModel(mm)
   return(list("mm" = mm))
 }
 # create InpuData Object for an input parameter that is a data frame
 # note: if the input parameter is not a data frame use InputParameter object
 id <- InputData(name = "in_df",
                defaultQuery = paste0("select top 10000 ArrDelay,CRSDepTime,",
                                      "DayOfWeek,CRSDepHour from cleanData"))
 # create an OutputParameter object for the variable inside the return list
 # note: if that variable is a data frame use OutputData object
 out <- OutputParameter("mm", "raw")

 # connections string
 conStr <- paste0("Driver={ODBC Driver 13 for SQL Server};Server=.;Database=RevoTestDB;",
                  "Trusted_Connection=Yes;")
 # create the stored procedure object
 sp_df_op <- StoredProcedure("train1", "spTest1", id, out,
                        filePath = ".")
 # register the stored procedure with the database
 registerStoredProcedure(sp_df_op, conStr)

 # get the linear model
 model <- executeStoredProcedure(sp_df_op, connectionString = conStr)
 mm <- rxUnserializeModel(model$params$op1)
## End(Not run)