R JSON文件

JSON文件以人类可读格式将数据存储为文本。 Json代表JavaScript Object Notation。 R可以使用rjson包读取JSON文件。

安装rjson包

在R语言控制台中，您可以发出以下命令来安装rjson包。 

  1. install.packages("rjson")

输入数据

通过将以下数据复制到文本编辑器（如记事本）中来创建JSON文件。 使用.json扩展名保存文件，并将文件类型选择为所有文件（.）。

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

读取JSON文件

JSON文件由R使用来自JSON()的函数读取。 它作为列表存储在R中。

  1. # Load the package required to read JSON files.
  2. library("rjson")
  4. # Give the input file name to the function.
  5. result <- fromJSON(file = "input.json")
  7. # Print the result.
  8. print(result)

当我们执行上面的代码，它产生以下结果：

  1. $ID
  2. [1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"
  4. $Name
  5. [1] "Rick"     "Dan"      "Michelle" "Ryan"     "Gary"     "Nina"     "Simon"    "Guru"
  7. $Salary
  8. [1] "623.3"  "515.2"  "611"    "729"    "843.25" "578"    "632.8"  "722.5"
  10. $StartDate
  11. [1] "1/1/2012"   "9/23/2013"  "11/15/2014" "5/11/2014"  "3/27/2015"  "5/21/2013"
  12.    "7/30/2013"  "6/17/2014"
  14. $Dept
  15. [1] "IT"         "Operations" "IT"         "HR"         "Finance"    "IT"
  16.    "Operations" "Finance"

将JSON转换为数据帧

我们可以使用as.data.frame()函数将上面提取的数据转换为R语言数据帧以进行进一步分析。 

  1. # Load the package required to read JSON files.
  2. library("rjson")
  4. # Give the input file name to the function.
  5. result <- fromJSON(file = "input.json")
  7. # Convert JSON file to a data frame.
  8. json_data_frame <- as.data.frame(result)
  10. print(json_data_frame)

当我们执行上面的代码，它产生以下结果：

  1.        id,   name,     salary,    start_date,     dept
  2. 1       1     Rick       623.30    2012-01-01      IT
  3. 2       2     Dan        515.20    2013-09-23      Operations
  4. 3       3     Michelle 611.00    2014-11-15      IT
  5. 4       4     Ryan      729.00    2014-05-11      HR
  6. 5     NA    Gary       843.25    2015-03-27      Finance
  7. 6       6     Nina       578.00    2013-05-21      IT
  8. 7       7     Simon     632.80    2013-07-30      Operations
  9. 8       8     Guru       722.50    2014-06-17      Finance