我正在尝试在 R 中解析此数据类型，以便在 Tableau 中获得可视化(尝试使用 Tableau，但它非常复杂，并且没有给出好的结果)。
下面是我尝试解析的数据类型的示例:
{"010010208022":215,"010010205002":195,"010010208012":184,"010010208021":165,"010010206001":132,"010010205003":110,"010010211001":100,"010010208024":93,"010010201002":91,"010010205001":84,"010010210002":82,"010510310002":75,"010010203001":74}
基本上这只是一个条目。我有三列这种类型。我正在尝试解析每个条目并对数字进行求和(在每个冒号之后)，以便为三个变量中的每一个获取一个新列，其中保存每个条目的数字总和。
这是为了在 Tableau 中使用它来使用这些总计来获得可视化效果。
我拥有的数据是一个 Excel 工作表，其中包含三个在 Excel 中以文本形式存储的变量，这些变量具有上述数据类型。
R 中导入的数据集如下所示:
> dput(head(dataset))
structure(list(census_block_group = c(10010201001, 10010201002,
10010202001, 10010202002, 10010203001, 10010203002), date_range_start = c(1538352000,
1538352000, 1538352000, 1538352000, 1538352000, 1538352000),
date_range_end = c(1541030400, 1541030400, 1541030400, 1541030400,
1541030400, 1541030400), raw_visit_count = c(54544, 19583,
24552, 61089, 41139, 91053), raw_visitor_count = c(14460,
2351, 3020, 7123, 5132, 20907), visitor_home_cbgs = c("{\"010010208022\":215,\"010010205002\":195,\"010010208012\":184,\"010010208021\":165,\"010010206001\":132,\"010010205003\":110,\"010010211001\":100,\"010010208024\":93,\"010010201002\":91,\"010010205001\":84,\"010010210002\":82,\"010510310002\":75,\"010010203001\":74,\"010010208011\":73,\"010010204002\":62,\"010510309011\":61,\"010510309012\":61,\"010010207001\":58,\"010010209004\":58,\"010010207002\":55,\"010010209002\":54,\"010010208023\":53,\"011250112001\":53}",
"{\"010010208022\":143,\"010010208021\":140,\"010010205002\":116,\"010010206001\":87,\"010010208012\":83,\"010010205003\":56}",
"{\"010010208022\":193,\"010010205002\":170,\"010010208012\":112,\"010010206001\":96,\"010010203001\":84,\"010010205003\":82,\"010010208021\":74,\"010010208024\":71,\"010010205001\":67,\"010010201002\":65,\"010010204002\":55,\"010010208011\":55}",
"{\"010010205002\":326,\"010010208022\":287,\"010010208012\":184,\"010010206001\":170,\"010010208021\":162,\"010010208024\":145,\"010010205003\":141,\"010010205001\":125,\"010010203001\":121,\"010510310002\":113,\"010010204002\":99,\"010010211001\":96,\"010010207001\":95,\"010010201002\":94,\"010510309011\":94,\"010010208011\":88,\"010010209004\":79,\"010510309012\":78,\"010510310001\":72,\"010010207002\":70,\"010010210002\":69,\"010010208023\":67,\"010510301001\":58,\"010510313001\":57,\"010010202001\":56,\"010010209002\":56,\"010510311002\":52}",
"{\"010010208022\":247,\"010010205002\":235,\"010010208024\":135,\"010010208012\":121,\"010010205003\":116,\"010010206001\":111,\"010010208021\":99,\"010010209004\":91,\"010010204002\":86,\"010010205001\":83,\"010010201002\":62,\"010010207002\":61,\"010510310002\":60,\"010010209002\":58,\"010010207001\":56,\"010010208023\":56,\"010010208011\":55,\"010510309011\":54,\"010010204001\":52}",
"{\"010010205002\":391,\"010010208022\":328,\"010010208012\":181,\"010010208024\":174,\"010010206001\":169,\"010010205003\":161,\"010510310002\":160,\"010010208021\":158,\"010010203001\":152,\"010010205001\":138,\"010010204002\":130,\"010510309011\":129,\"010510309012\":128,\"010010207001\":113,\"010510310001\":105,\"010010209004\":99,\"010010201002\":96,\"010010211001\":93,\"010510313001\":89,\"010010207002\":85,\"010010208011\":85,\"010510301001\":81,\"010510311002\":78,\"010010209002\":76,\"010010210002\":71,\"010010208023\":70,\"010010202002\":63,\"010510307012\":60,\"010010204001\":59,\"010010209001\":56,\"010010202001\":54,\"010010204003\":54,\"010510301004\":53,\"010010206002\":53,\"010510309023\":51,\"010510301003\":50}"
), visitor_work_cbgs = c("{\"010010208022\":233,\"011250112001\":106,\"011010001001\":70,\"011010002001\":70,\"010510313001\":63,\"010010209004\":59,\"010010205002\":54}",
"{\"010010208022\":223}", "{\"010010208022\":258}", "{\"010010208022\":262,\"011010002001\":90,\"010510313001\":87,\"011010001001\":86,\"010010209004\":72,\"010010205002\":68,\"010510311001\":57,\"010010202001\":50}",
"{\"010010208022\":265,\"010010209004\":66,\"010510313001\":59,\"011010001001\":57,\"011010002001\":55,\"010010202001\":54}",
"{\"010010208022\":278,\"011010002001\":110,\"011010001001\":106,\"010510313001\":104,\"010510311001\":82,\"010010209004\":80,\"010010205002\":78,\"011010009002\":61,\"011010054072\":59,\"010010202001\":54,\"011010053021\":53,\"010010207002\":50}"
), distance_from_home = c(45094, 7346, 6977, 13528, 11597,
16162), related_same_day_brand = c("[\"Winn Dixie\",\"Chick-fil-A\",\"walmart\"]",
"[\"Winn Dixie\",\"walmart\",\"Chick-fil-A\"]", "[\"America's Thrift Store\",\"Winn Dixie\",\"Chick-fil-A\"]",
"[\"Winn Dixie\",\"walmart\",\"America's Thrift Store\",\"Chick-fil-A\",\"Dollar General\"]",
"[\"America's Thrift Store\",\"walmart\",\"Chick-fil-A\"]",
"[\"Winn Dixie\",\"walmart\",\"America's Thrift Store\"]"
), related_same_month_brand = c("[\"walmart\",\"Dollar General\",\"Chick-fil-A\",\"America's Thrift Store\",\"mcdonalds\",\"Zaxby's\",\"Jack's Family Restaurants\",\"Winn Dixie\",\"Waffle House\",\"Chevron\"]",
"[\"walmart\",\"America's Thrift Store\",\"Chick-fil-A\",\"Dollar General\",\"mcdonalds\",\"Winn Dixie\",\"Zaxby's\",\"Wendy's\",\"Sonic\",\"Taco Bell\"]",
"[\"walmart\",\"America's Thrift Store\",\"Dollar General\",\"Chick-fil-A\",\"mcdonalds\",\"Winn Dixie\",\"Zaxby's\",\"Sonic\",\"Wendy's\",\"Taco Bell\"]",
"[\"walmart\",\"Dollar General\",\"Chick-fil-A\",\"mcdonalds\",\"America's Thrift Store\",\"Winn Dixie\",\"Zaxby's\",\"Sonic\",\"Wendy's\",\"Publix Super Markets\"]",
"[\"walmart\",\"Dollar General\",\"mcdonalds\",\"America's Thrift Store\",\"Chick-fil-A\",\"Winn Dixie\",\"Zaxby's\",\"Wendy's\",\"Taco Bell\",\"Sonic\"]",
"[\"America's Thrift Store\",\"Dollar General\",\"walmart\",\"Winn Dixie\",\"Chick-fil-A\",\"Zaxby's\",\"Waffle House\",\"Jack's Family Restaurants\",\"Publix Super Markets\",\"Sonic\"]"
), top_brands = c("[]", "[]", "[]", "[\"Alabama DMV\",\"Fox's Pizza Den\",\"America's Thrift Store\",\"CrossFit\",\"Habitat for Humanity\"]",
"[]", "[\"Winn Dixie\",\"Planet Fitness\",\"America's Thrift Store\",\"Dollar General\",\"fred's\",\"Church's Chicken\",\"Advance Auto Parts\",\"Sunoco\",\"Boost Mobile\",\"Factory Connection\"]"
), popularity_by_hour = c("[1840,1666,1562,1541,1735,1977,3045,6630,3445,3681,3529,3894,4224,4292,4531,6463,5084,5378,4814,4162,3394,2918,2615,2074]",
"[2201,2137,2121,2108,2118,2175,2465,4458,1723,1664,1483,1471,1585,1586,1740,3185,2166,2566,2441,2339,2359,2319,2300,2255]",
"[1280,1233,1220,1199,1206,1288,1887,6141,2267,2196,2028,2059,2239,2337,2793,4935,2234,2685,2327,2052,1829,1616,1433,1340]",
"[1518,1326,1272,1199,1285,1539,2641,7010,4374,4681,4997,5268,5662,5346,5733,7468,6253,6882,5971,4754,3619,2757,2292,1799]",
"[3318,3261,3232,3212,3213,3309,4079,7363,3271,3115,3113,3187,3305,3445,3601,7069,4687,5433,5104,4775,4394,3905,3744,3429]",
"[2109,1738,1979,2393,2579,2953,4381,8526,5665,5653,5859,6539,7119,7046,6950,9672,8220,9003,7618,6015,4763,3713,3230,2629]"
), popularity_by_day = c("{\"Monday\":7542,\"Tuesday\":8610,\"Wednesday\":9274,\"Thursday\":7473,\"Friday\":8060,\"Saturday\":7222,\"Sunday\":6363}",
"{\"Monday\":3160,\"Tuesday\":3167,\"Wednesday\":3353,\"Thursday\":2721,\"Friday\":2807,\"Saturday\":2315,\"Sunday\":2060}",
"{\"Monday\":3688,\"Tuesday\":4192,\"Wednesday\":4221,\"Thursday\":3876,\"Friday\":3887,\"Saturday\":2846,\"Sunday\":1842}",
"{\"Monday\":9091,\"Tuesday\":9653,\"Wednesday\":10016,\"Thursday\":8397,\"Friday\":9017,\"Saturday\":7919,\"Sunday\":6996}",
"{\"Monday\":6126,\"Tuesday\":6681,\"Wednesday\":6811,\"Thursday\":5935,\"Friday\":6251,\"Saturday\":5275,\"Sunday\":4060}",
"{\"Monday\":13680,\"Tuesday\":13569,\"Wednesday\":14006,\"Thursday\":11815,\"Friday\":13480,\"Saturday\":13697,\"Sunday\":10806}"
)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))
将 Excel 导入 R 后，变量存储为字符。
然后我运行 toJSON 命令将它们转换为 JSON 类型。在 toJSON 命令之后运行 typeof 命令时，结果再次为 character。
我现在得到的错误与在列表(res)上运行的 sum 命令有关。我得到的错误是:
Error in sum(unlist(res_home_visitor)) :
invalid 'type' (character) of argument
unlist命令的结果是:
visitor_home_cbgs1 "{\"010010208022\":215,\"010010205002\":195,\"010010208012\":184,\"010010208021\":165,\"010010206001\":132,\"010010205003\":110,\"010010211001\":100,\"010010208024\":93"
visitor_home_cbgs2
"{\"010010208022\":143,\"010010208021\":140,\"010010205002\":116,\"010010206001\":87,\"010010208012\":83,\"010010205003\":56}"
现在我是编码新手，我已经查找了上述数据类型，但我不确定它是否被称为关联数组或 JSON(因此是问题中的标签)。
我研究和尝试的是使用Tableau中的split函数。但是，它会产生问题，因为条目的大小不相同，并且许多条目都很大，因此 split 函数将创建很多列，这会导致 Tableau 中的滞后和很多麻烦(由于对列数等等..)
** 这是我想要得到的示例:
{"010479561003":52,"010479561002":52, "110478541030":33}
对于上述条目，总数应为:52+52+33= 137
这个总数 (137) 将被放入一个名为“总数”的新列中，这是可视化所需的。
最佳答案
您有一个 data.frame，其中某些列是 JSON。据我了解，您想独立处理每一行。因此，您可以逐行对 fromJSON 的结果进行求和。
在此示例中，我使用 library(dplyr) 来
rowwise()- 将代码应用于每一行mutate()- 创建一个新列，它是sum( unlist( fromJSON() ) ) 的结果
library(dplyr)
library(jsonlite)
res <- dataset %>%
rowwise() %>%
mutate(visitor_home_cbgs_sum = sum( unlist (jsonlite::fromJSON( visitor_home_cbgs ) ) ) ) %>%
mutate(visitor_work_cbgs_sum = sum( unlist (jsonlite::fromJSON( visitor_work_cbgs ) ) ) )
现在我们可以通过仅选择原始 JSON 列和新创建的 sum 列来查看结果
res %>%
select(visitor_home_cbgs, visitor_home_cbgs_sum, visitor_work_cbgs, visitor_work_cbgs_sum)
# Source: local data frame [6 x 4]
# Groups: <by row>
#
# # A tibble: 6 x 4
# visitor_home_cbgs visitor_home_cbg… visitor_work_cbgs visitor_work_cbg…
# <chr> <int> <chr> <int>
# 1 "{\"010010208022\":215,\"01001020500… 2188 "{\"010010208022\":233,\"0112501120… 655
# 2 "{\"010010208022\":143,\"01001020802… 625 "{\"010010208022\":223}" 223
# 3 "{\"010010208022\":193,\"01001020500… 1124 "{\"010010208022\":258}" 258
# 4 "{\"010010205002\":326,\"01001020802… 3054 "{\"010010208022\":262,\"0110100020… 772
# 5 "{\"010010208022\":247,\"01001020500… 1838 "{\"010010208022\":265,\"0100102090… 556
# 6 "{\"010010205002\":391,\"01001020802… 4093 "{\"010010208022\":278,\"0110100020… 1115
在此示例中，您只有两列要更新，因此我将两个 mutate() 语句保留在其中，以明确其工作原理。
如果您有许多/未知数量的 JSON 值列，您可能需要考虑另一种方法。
关于r - 在R中如何解析这个数据类型？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/56212785/