R语言数据接口

R语言数据接口

R语言处理的数据一般从外部导入，因此需要数据接口来读取各种格式化的数据

CSV

# 获得data是一个数据帧
data = read.csv("input.csv")
# 可以使用类似于SQL的where查询
retval = subset(data,dept == "IT" & salary > 600)
print(retval)
# 写入文件
# row.names=FALSE是为了去除额外的行号
write.csv(retval,"output.csv", row.names = FALSE)

Excel

安装xlsx时，要依赖rJava，和xlsxjars

在安装完之后要要建立和java的连接

sudo R CMD javareconf

配置完后在terminal中运行没有问题，但在Rgui和RStudio中均报错

dlopen(/Library/Frameworks/R.framework/Versions/3.4/Resources/library/rJava/libs/rJava.so, 6): Library not loaded: @rpath/libjvm.dylib
  Referenced from: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rJava/libs/rJava.so
  Reason: image not found

博主也尚未找到原因

因为Excel效果跟CSV一致，建议使用CSV

#需要在terminal中运行
library("xlsx")
data = read.xlsx("data.xlsx", sheetIndex = 1)
print(data)

Binary

二进制文件不能结构化存储，但不容易被查看，也是一种数据接口(虽然博主从没用过)

# 写入二进制文件
writeData = colnames(read.csv('input.csv'))
# 创建文件句柄
print(writeData)
writeFile = file('data.dat','wb')
# 写入文件
# 二进制文件只能写矢量对象
writeBin(writeData,writeFile)
close(writeFile)

# 读取二进制文件
readFile = file('data.dat','rb')
# 读取18个字节，内容解析为character否则默认转成数字
readData = readBin(readFile, character(), n = 18)
print(readData)
close(readFile)

[1] "id"         "name"       "salary"     "start_date" "dept"
[1] "id"         "name"       "salary"     "start_date" "dept"

XML

library(XML)
xmlToList('data.xml')[1:3]
xmlToDataFrame('data.xml')

$EMPLOYEE
$EMPLOYEE$ID
[1] "1"

$EMPLOYEE$NAME
[1] "Rick"

$EMPLOYEE$SALARY
[1] "623.3"

$EMPLOYEE$STARTDATE
[1] "1/1/2012"

$EMPLOYEE$DEPT
[1] "IT"


$EMPLOYEE
$EMPLOYEE$ID
[1] "2"

$EMPLOYEE$NAME
[1] "Dan"

$EMPLOYEE$SALARY
[1] "515.2"

$EMPLOYEE$STARTDATE
[1] "9/23/2013"

$EMPLOYEE$DEPT
[1] "Operations"


$EMPLOYEE
$EMPLOYEE$ID
[1] "3"

$EMPLOYEE$NAME
[1] "Michelle"

$EMPLOYEE$SALARY
[1] "611"

$EMPLOYEE$STARTDATE
[1] "11/15/2014"

$EMPLOYEE$DEPT
[1] "IT"


  ID     NAME SALARY  STARTDATE       DEPT
1  1     Rick  623.3   1/1/2012         IT
2  2      Dan  515.2  9/23/2013 Operations
3  3 Michelle    611 11/15/2014         IT
4  4     Ryan    729  5/11/2014         HR
5  5     Gary 843.25  3/27/2015    Finance
6  6     Nina    578  5/21/2013         IT
7  7    Simon  632.8  7/30/2013 Operations
8  8     Guru  722.5  6/17/2014    Finance

JSON

library(rjson)
result = fromJSON(file = 'data.json')
result #默认方法的JSON对象
as.data.frame(result) #转为数据帧

$ID
[1] "1" "2" "3" "4" "5" "6" "7" "8"

$Name
[1] "Rick"     "Dan"      "Michelle" "Ryan"     "Gary"     "Nina"    
[7] "Simon"    "Guru"    

$Salary
[1] "623.3"  "515.2"  "611"    "729"    "843.25" "578"    "632.8" 
[8] "722.5" 

$StartDate
[1] "1/1/2012"   "9/23/2013"  "11/15/2014" "5/11/2014"  "3/27/2015" 
[6] "5/21/2013"  "7/30/2013"  "6/17/2014" 

$Dept
[1] "IT"         "Operations" "IT"         "HR"         "Finance"   
[6] "IT"         "Operations" "Finance"   


  ID     Name Salary  StartDate       Dept
1  1     Rick  623.3   1/1/2012         IT
2  2      Dan  515.2  9/23/2013 Operations
3  3 Michelle    611 11/15/2014         IT
4  4     Ryan    729  5/11/2014         HR
5  5     Gary 843.25  3/27/2015    Finance
6  6     Nina    578  5/21/2013         IT
7  7    Simon  632.8  7/30/2013 Operations
8  8     Guru  722.5  6/17/2014    Finance

Web数据

web中的数据就是要做网络爬虫了，这个以后单独讨论。

数据库

R语言可以连接数据库，连接方式与其他编程语言非常相似，下面以MySQL为例介绍

library(RMySQL)
# 建立数据库连接手柄
con = dbConnect(MySQL(),
                user = 'user',
                password = 'password',
                dbname = 'dbname',
                host = 'host'
                )
# 解决中文编码问题
# 统一使用utf8
dbSendQuery(con, "SET NAMES utf8")

# 列出表格
dbListTables(con)
# 执行SQL，result接收数据入口
result = dbSendQuery(con, "select * from card")
df = fetch(result) #返回一个dataFrame
print(head(df))

  card_ID card_name   card_workplace card_type
1    CD01      张敏         浙江大学      教师
2    CD02    刘鹏飞         浙江大学      学生
3    CD03      李想 杭州电子科技大学      职工
4    CD04      李四         南京大学      学生
5    CD05      李楠             腾讯  社会人士、
6    CD09      张三         阿里巴巴  社会人士

小结

博主认为，数据接口的优点就是把采集数据和分析数据的过程解耦合，让每个模块处理擅长的问题。