R语言做为BI中ETL的工具

R语言做为BI中ETL的工具，增删改

R语言提供了强大的R_package与各类数据库进行数据交互。
外加其强大数据变换清洗函数，为ＥＴＬ提供一条方便快捷的道路。
RODBC
ROracal
RMysql
Rmongodb http://mirrors.ustc.edu.cn/CRAN/web/packages/rmongodb/vignettes/rmongodb_cheat_sheet.pdf

• step1 新建链接con，并查看其信息

library(RODBC) 
con<-odbcConnect("LI")
con

RODBC Connection 1
Details:
  case=nochange
  DSN=LI
  UID=
  Trusted_Connection=Yes
  APP=RStudio
  WSID=LIYI-PC

• step2 引入数据集USArrests

data(USArrests)
head(USArrests)
           Murder Assault UrbanPop Rape
Alabama      13.2     236       58 21.2
Alaska       10.0     263       48 44.5
Arizona       8.1     294       80 31.0
Arkansas      8.8     190       50 19.5
California    9.0     276       91 40.6
Colorado      7.9     204       78 38.7

• step3 将USArrests保存到sqlserver

sqlSave(con, USArrests)

sqlSave(channel, dat, tablename = NULL, append = FALSE,
        rownames = TRUE, colnames = FALSE, verbose = FALSE,
        safer = TRUE, addPK = FALSE, typeInfo, varTypes,
        fast = TRUE, test = FALSE, nastring = NULL)
channel      数据库链接通道
dat              data frame.要存入的数据集
tablename  character 数据库中表名
index          character 索引列的名字
append       logical逻辑变量 ，是否数据集添加/写入已存在的表
rownames  logical 逻辑变量 or character字符串,若是是logical，表示是否把rowname这个字符串做为数据库表首列列名，若是是字符串，则将新字符串做为表首列列名
colnames  logical 逻辑变量 是否将数据集的列名保留做为表的首行 (谨慎更改，可能致使，列名变成数据第一行，各列的数据类型所有变为varchar(255)
verbose   display statements as they are sent to the server?
safer     logical逻辑变量.若是真，生成一个新表，不在已存在的表后添加。若是假，强制删除已存在的同名表并新建，或者删除表中已存在的数据，覆盖写入
addPK     logical逻辑变量,是否将首列做为主键
typeInfo  list 数据框中数据类型。包括character ,double ,integer
varTypes  an optional named character vector giving the DBMSs datatypes to be used for some (or all) of the columns if a table is to be created.可选项涉及各列数据类型转换，由于数据库中的数据类型比R语言中的要多不少。
fast      logical. If false, write data a row at a time. If true, use a parametrized INSERT INTO or UPDATE query to write all the data in one operation. 逻辑变量，若是F，数据将一次一行地写入，若是为Ture，将用到变量插入INSERT INTO，或者UPDATE 将数据一次性写入
nastring     optional character string to be used for writing NAs to the database. 选择哪一种字符串，将缺失项在数据库中填充

getSqlTypeInfo("Microsoft SQL Server")
$double
[1] "float"

$integer
[1] "int"

$character
[1] "varchar(255)"

$logical
[1] "varchar(5)"

sqlSave(con, USArrests,rownames = "city", addPK = T) # 原没有列名的rownames更名为city,并设置首列为主键key

sqlSave(con, USArrests,'USA2',rownames = "city", addPK = T,fast=T,test=T)  #注意此操做可能在数据库中create名为USA2的空表

Binding: 'city' DataType 12, ColSize 255
Binding: 'Murder' DataType 6, ColSize 53
Binding: 'Assault' DataType 4, ColSize 10
Binding: 'UrbanPop' DataType 4, ColSize 10
Binding: 'Rape' DataType 6, ColSize 53
Parameters:
no: 1: city Alabama/***/no: 2: Murder 13.2/***/no: 3: Assault 236/***/no: 4: UrbanPop 58/***/no: 5: Rape 21.2/***/
no: 1: city Alaska/***/no: 2: Murder 10/***/no: 3: Assault 263/***/no: 4: UrbanPop 48/***/no: 5: Rape 44.5/***/
no: 1: city Arizona/***/no: 2: Murder 8.1/***/no: 3: Assault 294/***/no: 4: UrbanPop 80/***/no: 5: Rape 31/***/
no: 1: city Arkansas/***/no: 2: Murder 8.8/***/no: 3: Assault 190/***/no: 4: UrbanPop 50/***/no: 5: Rape 19.5/***/
no: 1: city California/***/no: 2: Murder 9/***/no: 3: Assault 276/***/no: 4: UrbanPop 91/***/no: 5: Rape 40.6/***/
no: 1: city Colorado/***/no: 2: Murder 7.9/***/no: 3: Assault 204/***/no: 4: UrbanPop 78/***/no: 5: Rape 38.7/***/
no: 1: city Connecticut/***/no: 2: Murder 3.3/***/no: 3: Assault 110/***/no: 4: UrbanPop 77/***/no: 5: Rape 11.1/***/
no: 1: city Delaware/***/no: 2: Murder 5.9/***/no: 3: Assault 238/***/no: 4: UrbanPop 72/***/no: 5: Rape 15.8/***/
no: 1: city Florida/***/no: 2: Murder 15.4/***/no: 3: Assault 335/***/no: 4: UrbanPop 80/***/no: 5: Rape 31.9/***/
no: 1: city Georgia/***/no: 2: Murder 17.4/***/no: 3: Assault 211/***/no: 4: UrbanPop 60/***/no: 5: Rape 25.8/***/
no: 1: city Hawaii/***/no: 2: Murder 5.3/***/no: 3: Assault 46/***/no: 4: UrbanPop 83/***/no: 5: Rape 20.2/***/
no: 1: city Idaho/***/no: 2: Murder 2.6/***/no: 3: Assault 120/***/no: 4: UrbanPop 54/***/no: 5: Rape 14.2/***/
no: 1: city Illinois/***/no: 2: Murder 10.4/***/no: 3: Assault 249/***/no: 4: UrbanPop 83/***/no: 5: Rape 24/***/
no: 1: city Indiana/***/no: 2: Murder 7.2/***/no: 3: Assault 113/***/no: 4: UrbanPop 65/***/no: 5: Rape 21/***/
no: 1: city Iowa/***/no: 2: Murder 2.2/***/no: 3: Assault 56/***/no: 4: UrbanPop 57/***/no: 5: Rape 11.3/***/
no: 1: city Kansas/***/no: 2: Murder 6/***/no: 3: Assault 115/***/no: 4: UrbanPop 66/***/no: 5: Rape 18/***/
# 此处省略10000字

sqlColumns    Enquire about the column structure of tables on an ODBC database connection. 访问数据库表的结构

columnsenquire<-sqlColumns(con,'USA2')

str(columnsenquire)

str(columnsenquire)
'data.frame':   5 obs. of  29 variables:
 $ TABLE_CAT                           : chr  "master" "master" "master" "master" ...
 $ TABLE_SCHEM                         : chr  "dbo" "dbo" "dbo" "dbo" ...
 $ TABLE_NAME                          : chr  "USA2" "USA2" "USA2" "USA2" ...
 $ COLUMN_NAME                         : chr  "city" "Murder" "Assault" "UrbanPop" ...
 $ DATA_TYPE                           : int  12 6 4 4 6
 $ TYPE_NAME                           : chr  "varchar" "float" "int" "int" ...
 $ COLUMN_SIZE                         : int  255 53 10 10 53
 $ BUFFER_LENGTH                       : int  255 8 4 4 8
 $ DECIMAL_DIGITS                      : int  NA NA 0 0 NA
 $ NUM_PREC_RADIX                      : int  NA 2 10 10 2
 $ NULLABLE                            : int  0 1 1 1 1
 $ REMARKS                             : chr  NA NA NA NA ...
 $ COLUMN_DEF                          : chr  NA NA NA NA ...
 $ SQL_DATA_TYPE                       : int  12 6 4 4 6
 $ SQL_DATETIME_SUB                    : int  NA NA NA NA NA
 $ CHAR_OCTET_LENGTH                   : int  255 NA NA NA NA
 $ ORDINAL_POSITION                    : int  1 2 3 4 5
 $ IS_NULLABLE                         : chr  "NO" "YES" "YES" "YES" ...
 $ SS_IS_SPARSE                        : int  0 0 0 0 0
 $ SS_IS_COLUMN_SET                    : int  0 0 0 0 0
 $ SS_IS_COMPUTED                      : int  0 0 0 0 0
 $ SS_IS_IDENTITY                      : int  0 0 0 0 0
 $ SS_UDT_CATALOG_NAME                 : chr  NA NA NA NA ...
 $ SS_UDT_SCHEMA_NAME                  : chr  NA NA NA NA ...
 $ SS_UDT_ASSEMBLY_TYPE_NAME           : chr  NA NA NA NA ...
 $ SS_XML_SCHEMACOLLECTION_CATALOG_NAME: chr  NA NA NA NA ...
 $ SS_XML_SCHEMACOLLECTION_SCHEMA_NAME : chr  NA NA NA NA ...
 $ SS_XML_SCHEMACOLLECTION_NAME        : chr  NA NA NA NA ...
 $ SS_DATA_TYPE                        : chr  "39" "109" "38" "38" ...

• step4 查询数据

sqlQuery(con,'select * from USArrests')

# 注意此时第一列的名字已经为city了
             city Murder Assault UrbanPop Rape
1         Alabama   13.2     236       58 21.2
2          Alaska   10.0     263       48 44.5
3         Arizona    8.1     294       80 31.0
4        Arkansas    8.8     190       50 19.5
5      California    9.0     276       91 40.6
6        Colorado    7.9     204       78 38.7
7     Connecticut    3.3     110       77 11.1
8        Delaware    5.9     238       72 15.8
9         Florida   15.4     335       80 31.9
10        Georgia   17.4     211       60 25.8
11         Hawaii    5.3      46       83 20.2
12          Idaho    2.6     120       54 14.2
13       Illinois   10.4     249       83 24.0
14        Indiana    7.2     113       65 21.0
...

对于sql语句多是以‘XX’ 结尾则须要用以下形式来进行查询

sqlQuery(con,paste('select * from USArrests',
         "where city = 'Alabama'"))

city Murder Assault UrbanPop Rape
1 Alabama   13.2     236       58 21.2

可是对于Update，如下倒是失效的

a<-paste("update [master].[dbo].[USArrests]",
         "set Murder =13.2","where city ='Alabama'")
sqlQuery(con,a)   # 失效
sqlUpdate(con,a)  # 失效

sqlUpdate()  sqlUpdate(channel, dat, tablename = NULL, index = NULL,
          verbose = FALSE, test = FALSE, nastring = NULL,
          fast = TRUE) 
不能进行脚本语句直接更新，可是能够进行以下操做
foo <- cbind(city=row.names(USArrests), USArrests)[1:3, c(1,3)]
 foo
           city Assault
Alabama Alabama     236
Alaska   Alaska     263
Arizona Arizona     294
 foo[1,2] <- 200
foo
           city Assault
Alabama Alabama     200
Alaska   Alaska     263
Arizona Arizona     294
流程是先选定要更新的行列，将值更新，而后再将值update入库

实例以下

temp<-sqlQuery(con,paste('select * from USArrests',
"where city = 'Alabama'"))
temp
     city Murder Assault UrbanPop Rape
1 Alabama   13.2     300       58 21.2
  str(temp)
'data.frame':   1 obs. of  5 variables:
 $ city    : Factor w/ 1 level "Alabama": 1
 $ Murder  : num 13.2
 $ Assault : num 300
 $ UrbanPop: int 58
 $ Rape    : num 21.2
  temp[1,]
[1] 300

  temp[1,3]<-200
  sqlUpdate(con, temp, "USArrests")
  sqlQuery(con,paste('select * from USArrests',
"where city = 'Alabama'"))
     city Murder Assault UrbanPop Rape
1 Alabama   13.2     200       58 21.2

sqlFetch(con, "USArrests", rownames = "city", max = 20,rows_at_time = 10)

实践后发现，单单对于简单的ETL，sqlQuery,sqlUpdate是足够了，
写一些for循环+list.files/liset.dir+reshape/ddplyr/tidyr（进行数据筛选，清洗，变换）,
对于脚本是否执行的问题，能够写日志文件，对ETL过程进行检测。

• step5 构建日志文件
  1. 监督机制，对于每次要写入/更新的数据进行count（originaldata），保存数据，而后与每次执行写入/变动的行数Rows进行比对，比对结果result为逻辑变量。将三者连同 执行时间，写成一个数据框，并在数据中新建对应的日志表，对执行结果进行监督。（对于追踪Rows没有找到很好的解决方法，send a letter ...)
  2. 暴力美学，对于sqlSave / sqlQuery 的执行结果进行追踪，成功为“1”,失败则"ON Errors"
• step6 快速操做数据库

sqlClear deletes all the rows of the table sqtable. #清楚表中数据

sqlDrop removes the table sqtable (if permitted). #删除表

sqlClear(channel, sqtable, errors = TRUE)

sqlDrop(channel, sqtable, errors = TRUE)