JSON文件以可讀取的格式將數(shù)據(jù)存儲為文本。Json是一種JavaScript對象表示法。R可以使用rjson包讀取JSON文件。

安裝rjson軟件包

在R控制臺中，您可以發(fā)出以下命令來安裝rjson包。

install.packages("rjson")

準(zhǔn)備輸入數(shù)據(jù)源

通過將以下數(shù)據(jù)復(fù)制到文本編輯器(如記事本)中創(chuàng)建一個JSON文件。 使用.json擴展名保存文件，并將文件類型選為所有文件(*.*)。創(chuàng)建一個文件：input.json，其內(nèi)容如下所示 -

{ 
   "ID":["1","2","3","4","5","6","7","8" ],
   "Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],
   "Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],

   "StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",
      "7/30/2013","6/17/2014"],
   "Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}

讀取JSON文件

R使用JSON()函數(shù)讀取JSON文件的內(nèi)容，它作為列表存儲在R中。參考以下代碼 -

# Load the package required to read JSON files.
library("rjson")

# Give the input file name to the function.
result <- fromJSON(file = "input.json")

# Print the result.
print(result)

當(dāng)我們執(zhí)行上述代碼時，會產(chǎn)生以下結(jié)果 -

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

$Name
[1] "Rick"     "Dan"      "Michelle" "Ryan"     "Gary"    
[6] "Nina"     "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" 
[5] "3/27/2015"  "5/21/2013"  "7/30/2013"  "6/17/2014" 

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

將JSON轉(zhuǎn)換為數(shù)據(jù)幀

我們可以將上述提取的數(shù)據(jù)通過使用as.data.frame()函數(shù)轉(zhuǎn)換為R數(shù)據(jù)幀，以便進(jìn)一步分析。

# Load the package required to read JSON files.
library("rjson")

# Give the input file name to the function.
result <- fromJSON(file = "input.json")

# Convert JSON file to a data frame.
json_data_frame <- as.data.frame(result)

print(json_data_frame)

當(dāng)我們執(zhí)行上述代碼時，會產(chǎn)生以下結(jié)果 -

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