提取、格式化和分离已存储在数据帧列中的JSON
如何解析和处理已经存在于数据帧中的JSON 样本数据:提取、格式化和分离已存储在数据帧列中的JSON,json,r,jsonlite,Json,R,Jsonlite,如何解析和处理已经存在于数据帧中的JSON 样本数据: df <- data.frame( id = c("x1", "x2"), y = c('[{"Property":"94","Value":"Error"},{"Property":"C1","Value":"Found Match"},{"Property":"C2","Value":"Address Mismatch"}]', '[{"Property":"81","Value":"XYZ"},{"Proper
df <- data.frame(
id = c("x1", "x2"),
y = c('[{"Property":"94","Value":"Error"},{"Property":"C1","Value":"Found Match"},{"Property":"C2","Value":"Address Mismatch"}]', '[{"Property":"81","Value":"XYZ"},{"Property":"D1","Value":"Blah Blah"},{"Property":"Z2","Value":"Email Mismatch"}]')
)
df使用jsonlite
和tidyverse:
library(tidyverse)
library(jsonlite)
df %>% mutate(y = map(y, ~fromJSON(as.character(.x)))) %>% unnest()
# Source: local data frame [6 x 3]
#
# id Property Value
# <fctr> <chr> <chr>
# 1 x1 94 Error
# 2 x1 C1 Found Match
# 3 x1 C2 Address Mismatch
# 4 x2 81 XYZ
# 5 x2 D1 Blah Blah
# 6 x2 Z2 Email Mismatch
或者只使用dplyr
和jsonlite
df %>% rowwise() %>% do(data.frame(id = .$id, fromJSON(as.character(.$y))))
do.call(rbind,
Map(function(id, y){data.frame(id, fromJSON(as.character(y)))},
df$id, df$y))
或者只使用base R和jsonlite
df %>% rowwise() %>% do(data.frame(id = .$id, fromJSON(as.character(.$y))))
do.call(rbind,
Map(function(id, y){data.frame(id, fromJSON(as.character(y)))},
df$id, df$y))
所有返回的内容都相同,因此请选择对您最有意义的内容。请包括您尝试的代码和出现的任何错误。我们可以使用库(dplyr);绑定行(lappy(setNames(as.list(df$y),df$id),函数(x)fromJSON(as.character(x)),.id=“id”)