用 R 解析 JSON

我对 R 相当陌生，但是越是使用它，我就越能看到它在 SAS 或 SPSS 上的真正强大。在我看来，其中一个主要的好处就是能够从网络上获取和分析数据。我想这是可能的(甚至可能是直接的) ，但是我希望解析在 Web 上公开可用的 JSON 数据。我不是一个程序员的任何延伸，所以任何帮助和指导，您可以提供将非常感谢。即使你给我指出一个基本的工作示例，我也可以完成它。

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最佳答案

RJSONIO from Omegahat is another package which provides facilities for reading and writing data in JSON format.

rjson does not use S4/S3 methods and so is not readily extensible, but still useful. Unfortunately, it does not used vectorized operations and so is too slow for non-trivial data. Similarly, for reading JSON data into R, it is somewhat slow and so does not scale to large data, should this be an issue.

Update (new Package 2013-12-03):

jsonlite: This package is a fork of the RJSONIO package. It builds on the parser from RJSONIO but implements a different mapping between R objects and JSON strings. The C code in this package is mostly from the RJSONIO Package, the R code has been rewritten from scratch. In addition to drop-in replacements for fromJSON and toJSON, the package has functions to serialize objects. Furthermore, the package contains a lot of unit tests to make sure that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.

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Here is the missing example

library(rjson)
url <- 'http://someurl/data.json'
document <- fromJSON(file=url, method='C')
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For the record, rjson and RJSONIO do change the file type, but they don't really parse per se. For instance, I receive ugly MongoDB data in JSON format, convert it with rjson or RJSONIO, then use unlist and tons of manual correction to actually parse it into a usable matrix.

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The jsonlite package is easy to use and tries to convert json into data frames.

Example:

library(jsonlite)


# url with some information about project in Andalussia
url <- 'https://api.stackexchange.com/2.2/badges?order=desc&sort=rank&site=stackoverflow'


# read url and convert to data.frame
document <- fromJSON(txt=url)
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The function fromJSON() in RJSONIO, rjson and jsonlite don't return a simple 2D data.frame for complex nested json objects.

To overcome this you can use tidyjson. It takes in a json and always returns a data.frame. It is currently not availble in CRAN, you can get it here: https://github.com/sailthru/tidyjson

Update: tidyjson is now available in cran, you can install it directly using install.packages("tidyjson")

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Try below code using RJSONIO in console

library(RJSONIO)
library(RCurl)




json_file = getURL("https://raw.githubusercontent.com/isrini/SI_IS607/master/books.json")


json_file2 = RJSONIO::fromJSON(json_file)


head(json_file2)

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