在 R 中读取嵌套的 JSON答案

【问题标题】：Read nested JSON in R在 R 中读取嵌套的 JSON
【发布时间】：2019-12-20 16:09:38
【问题描述】：

我有一个像这样的 JSON 对象。这是来自股票市场的 JSON。

{
    "Meta Data": {
        "1. Information": "Daily Time Series with Splits and Dividend Events",
        "2. Symbol": "NSE:20MICRONS",
        "3. Last Refreshed": "2019-12-20",
        "4. Output Size": "Full size",
        "5. Time Zone": "US/Eastern"
    },
    "Time Series (Daily)": {
        "2019-12-20": {
            "1. open": "33.5000",
            "2. high": "33.5000",
            "3. low": "32.4000",
            "4. close": "32.4500",
            "5. adjusted close": "32.4500",
            "6. volume": "12737",
            "7. dividend amount": "0.0000",
            "8. split coefficient": "1.0000"
        },
        "2019-12-19": {
            "1. open": "32.0000",
            "2. high": "32.6500",
            "3. low": "31.7500",
            "4. close": "32.3000",
            "5. adjusted close": "32.3000",
            "6. volume": "13320",
            "7. dividend amount": "0.0000",
            "8. split coefficient": "1.0000"
        }
            }
}

我想创建如下格式的数据框

有没有简单的方法可以做到这一点？

【问题讨论】：

你看过this post吗？
你尝试了什么，你在哪里卡住了？

标签： r dplyr tidyr jsonlite rjson

【解决方案1】：

可能有更优雅的方法，但这应该能够处理任何长度的Time Series (Daily)

library(jsonlite)
# json string saved to jsonInput
jsonRough <- fromJSON(jsonInput)
jsonFlat <- lapply(seq_along(jsonRough$`Time Series (Daily)`), 
                   function(x) c(jsonRough$`Meta Data`,
                                 Date = names(jsonRough$`Time Series (Daily)`[x]),
                                 jsonRough$`Time Series (Daily)`[[x]]))
df1 <- fromJSON(toJSON(jsonFlat))
names(df1) <- sub("^\\d+\\.\\s+", "", names(df1))

> df1[,c(-1, -4, -5)]
         Symbol Last Refreshed       Date    open    high     low   close adjusted close volume dividend amount split coefficient
1 NSE:20MICRONS     2019-12-20 2019-12-20 33.5000 33.5000 32.4000 32.4500        32.4500  12737          0.0000            1.0000
2 NSE:20MICRONS     2019-12-20 2019-12-19 32.0000 32.6500 31.7500 32.3000        32.3000  13320          0.0000            1.0000

【讨论】：