错误：仅在具有所有数值变量且在大型数据集上具有ddply的数据框上定义
[英]Error: only defined on a data frame with all numeric variables with ddply on large dataset
问题描述
I'm trying to calculate sums and means on a very large dataset (~22000 records) for several parameters (eg Er_Count, Mn_Count) by month, year , Survey ID and Grid ID. 
我正在尝试按月，年，调查ID和网格ID在几个参数（例如Er_Count，Mn_Count）的非常大的数据集（〜22000条记录）上计算总和和均值。 I tried this code initially to get overall sums: 我最初尝试使用此代码来获得总和：
dlply(Effort_All,c("Er_Count","Mn_Count","Bp_Count"),sum)
And received the following error: Error: only defined on a data frame with all numeric variables Since I cannot even get overall sums, I am unable to get statistics by the specific variables either. 并收到以下错误：错误：仅在具有所有数字变量的数据帧上定义由于我什至无法获得总和，因此我也无法通过特定变量来获取统计信息。 Do I need to split the data in some manner? 我是否需要以某种方式拆分数据？
I have included a sample dataset of 25 records below. 我在下面包括了25条记录的样本数据集。
structure(list(Grid_ID = structure(c(527L, 92L, 331L, 395L, 934L,
93L), .Label = c("1", "1,000", "1,001", "1,002", "1,003", "1,004",
"1,005", "1,006", "1,007", "1,008", "1,009", "1,010", "1,011",
"1,012", "1,013", "1,014", "1,015", "1,016", "1,017", "1,018",
"1,019", "1,020", "1,021", "1,022", "1,023", "1,024", "1,025",
"1,026", "1,027", "1,028", "1,029", "1,030", "1,031", "1,032",
"1,033", "1,034", "1,035", "1,036", "1,037", "1,038", "1,039",
"1,040", "1,041", "1,042", "1,043", "1,044", "1,045", "1,046",
"1,047", "1,048", "1,049", "1,050", "1,051", "1,052", "1,053",
"1,054", "1,055", "1,056", "1,057", "1,058", "1,059", "1,060",
"1,061", "10", "100", "101", "102", "103", "104", "105", "106",
"107", "108", "109", "11", "110", "111", "112", "113", "114",
"115", "116", "117", "118", "119", "12", "120", "121", "122",
"123", "124", "125", "126", "127", "128", "129", "13", "130",
"131", "132", "133", "134", "135", "136", "137", "138", "139",
"14", "140", "141", "142", "143", "144", "145", "146", "147",
"148", "149", "15", "150", "151", "152", "153", "154", "155",
"156", "157", "158", "159", "16", "160", "161", "162", "163",
"164", "165", "166", "167", "168", "169", "17", "170", "171",
"172", "173", "174", "175", "176", "177", "178", "179", "18",
"180", "181", "182", "183", "184", "185", "186", "187", "188",
"189", "19", "190", "191", "192", "193", "194", "195", "196",
"197", "198", "199", "2", "20", "200", "201", "202", "203", "204",
"205", "206", "207", "208", "209", "21", "210", "211", "212",
"213", "214", "215", "216", "217", "218", "219", "22", "220",
"221", "222", "223", "224", "225", "226", "227", "228", "229",
"23", "230", "231", "232", "233", "234", "235", "236", "237",
"238", "239", "24", "240", "241", "242", "243", "244", "245",
"246", "247", "248", "249", "25", "250", "251", "252", "253",
"254", "255", "256", "257", "258", "259", "26", "260", "261",
"262", "263", "264", "265", "266", "267", "268", "269", "27",
"270", "271", "272", "273", "274", "275", "276", "277", "278",
"279", "28", "280", "281", "282", "283", "284", "285", "286",
"287", "288", "289", "29", "290", "291", "292", "293", "294",
"295", "296", "297", "298", "299", "3", "30", "300", "301", "302",
"303", "304", "305", "306", "307", "308", "309", "31", "310",
"311", "312", "313", "314", "315", "316", "317", "318", "319",
"32", "320", "321", "322", "323", "324", "325", "326", "327",
"328", "329", "33", "330", "331", "332", "333", "334", "335",
"336", "337", "338", "339", "34", "340", "341", "342", "343",
"344", "345", "346", "347", "348", "349", "35", "350", "351",
"352", "353", "354", "355", "356", "357", "358", "359", "36",
"360", "361", "362", "363", "364", "365", "366", "367", "368",
"369", "37", "370", "371", "372", "373", "374", "375", "376",
"377", "378", "379", "38", "380", "381", "382", "383", "384",
"385", "386", "387", "388", "389", "39", "390", "391", "392",
"393", "394", "395", "396", "397", "398", "399", "4", "40", "400",
"401", "402", "403", "404", "405", "406", "407", "408", "409",
"41", "410", "411", "412", "413", "414", "415", "416", "417",
"418", "419", "42", "420", "421", "422", "423", "424", "425",
"426", "427", "428", "429", "43", "430", "431", "432", "433",
"434", "435", "436", "437", "438", "439", "44", "440", "441",
"442", "443", "444", "445", "446", "447", "448", "449", "45",
"450", "451", "452", "453", "454", "455", "456", "457", "458",
"459", "46", "460", "461", "462", "463", "464", "465", "466",
"467", "468", "469", "47", "470", "471", "472", "473", "474",
"475", "476", "477", "478", "479", "48", "480", "481", "482",
"483", "484", "485", "486", "487", "488", "489", "49", "490",
"491", "492", "493", "494", "495", "496", "497", "498", "499",
"5", "50", "500", "501", "502", "503", "504", "505", "506", "507",
"508", "509", "51", "510", "511", "512", "513", "514", "515",
"516", "517", "518", "519", "52", "520", "521", "522", "523",
"524", "525", "526", "527", "528", "529", "53", "530", "531",
"532", "533", "534", "535", "536", "537", "538", "539", "54",
"540", "541", "542", "543", "544", "545", "546", "547", "548",
"549", "55", "550", "551", "552", "553", "554", "555", "556",
"557", "558", "559", "56", "560", "561", "562", "563", "564",
"565", "566", "567", "568", "569", "57", "570", "571", "572",
"573", "574", "575", "576", "577", "578", "579", "58", "580",
"581", "582", "583", "584", "585", "586", "587", "588", "589",
"59", "590", "591", "592", "593", "594", "595", "596", "597",
"598", "599", "6", "60", "600", "601", "602", "603", "604", "605",
"606", "607", "608", "609", "61", "610", "611", "612", "613",
"614", "615", "616", "617", "618", "619", "62", "620", "621",
"622", "623", "624", "625", "626", "627", "628", "629", "63",
"630", "631", "632", "633", "634", "635", "636", "637", "638",
"639", "64", "640", "641", "642", "643", "644", "645", "646",
"647", "648", "649", "65", "650", "651", "652", "653", "654",
"655", "656", "657", "658", "659", "66", "660", "661", "662",
"663", "664", "665", "666", "667", "668", "669", "67", "670",
"671", "672", "673", "674", "675", "676", "677", "678", "679",
"68", "680", "681", "682", "683", "684", "685", "686", "687",
"688", "689", "69", "690", "691", "692", "693", "694", "695",
"696", "697", "698", "699", "7", "70", "700", "701", "702", "703",
"704", "705", "706", "707", "708", "709", "71", "710", "711",
"712", "713", "714", "715", "716", "717", "718", "719", "72",
"720", "721", "722", "723", "724", "725", "726", "727", "728",
"729", "73", "730", "731", "732", "733", "734", "735", "736",
"737", "738", "739", "74", "740", "741", "742", "743", "744",
"745", "746", "747", "748", "749", "75", "750", "751", "752",
"753", "754", "755", "756", "757", "758", "759", "76", "760",
"761", "762", "763", "764", "765", "766", "767", "768", "769",
"77", "770", "771", "772", "773", "774", "775", "776", "777",
"778", "779", "78", "780", "781", "782", "783", "784", "785",
"786", "787", "788", "789", "79", "790", "791", "792", "793",
"794", "795", "796", "797", "798", "799", "8", "80", "800", "801",
"802", "803", "804", "805", "806", "807", "808", "809", "81",
"810", "811", "812", "813", "814", "815", "816", "817", "818",
"819", "82", "820", "821", "822", "823", "824", "825", "826",
"827", "828", "829", "83", "830", "831", "832", "833", "834",
"835", "836", "837", "838", "839", "84", "840", "841", "842",
"843", "844", "845", "846", "847", "848", "849", "85", "850",
"851", "852", "853", "854", "855", "856", "857", "858", "859",
"86", "860", "861", "862", "863", "864", "865", "866", "867",
"868", "869", "87", "870", "871", "872", "873", "874", "875",
"876", "877", "878", "879", "88", "880", "881", "882", "883",
"884", "885", "886", "887", "888", "889", "89", "890", "891",
"892", "893", "894", "895", "896", "897", "898", "899", "9",
"90", "900", "901", "902", "903", "904", "905", "906", "907",
"908", "909", "91", "910", "911", "912", "913", "914", "915",
"916", "917", "918", "919", "92", "920", "921", "922", "923",
"924", "925", "926", "927", "928", "929", "93", "930", "931",
"932", "933", "934", "935", "936", "937", "938", "939", "94",
"940", "941", "942", "943", "944", "945", "946", "947", "948",
"949", "95", "950", "951", "952", "953", "954", "955", "956",
"957", "958", "959", "96", "960", "961", "962", "963", "964",
"965", "966", "967", "968", "969", "97", "970", "971", "972",
"973", "974", "975", "976", "977", "978", "979", "98", "980",
"981", "982", "983", "984", "985", "986", "987", "988", "989",
"99", "990", "991", "992", "993", "994", "995", "996", "997",
"998", "999"), class = "factor"), ER_Groups = c(2, 2, 2, 3, 5,
6), Er_Count = c(60, 75, 14, 12, 8, 26), Mn_Count = c(30, 9, 6, 33,
7, 12), Bp_Groups = c(1, 2, 1, 1, 0, 1), Bp_Count = c(3, 3, 2,
5, 0, 6), Mn_Groups = c(1, 1, 3, 1, 0, 0), Month = c(10L, 6L,
12L, 4L, 2L, 4L), Year = c(2000L, 2001L, 2009L, 2004L, 2002L,
2001L), SurveyID = structure(c(16L, 24L, 93L, 56L, 34L, 22L), .Label = c("199708HS",
"199808HS", "199908HS", "199909SSLQ", "199910SSL", "199911SSL",
"200001SSLQ", "200002SSL", "200003SSLQ", "200004SSLQ", "200005SSL",
"200006SSL", "200007SSL", "200008HS", "200008SSL", "200009SSL",
"200010SSL", "200011SSL", "200101SSL", "200102SSL", "200103SSL",
"200104SSL", "200105SSL", "200106SSL", "200107SSL", "200108HS",
"200108SSL", "200109SSL", "200110SSL", "200111SSL", "200112SSL",
"200201SSL", "200202SSL", "200203SSL", "200204SSL", "200205SSL",
"200206SSL", "200207SSL", "200208HS", "200208SSL", "200210SSL",
"200211SSL", "200212SSL", "200301SSL", "200302SSL", "200303SSL",
"200304SSL", "200305SSL", "200306SSL", "200307SSL", "200309SSL",
"200310SSL", "200311SSL", "200312SSL", "200403SSL", "200404SSL",
"200405SSL", "200406SSL", "200407SSL", "200408HS", "200408SSL",
"200409SSL", "200505SSL", "200506SSL", "200507SSL", "200510SSL",
"200512SSL", "200603SSL", "200609SSL", "200612SSL", "200709GAP07",
"200710GAP07", "200712GAP07", "200802GAP07", "200803GAP07", "200804GAP07",
"200805GAP07", "200806GAP07", "200807GAP07", "200808GAP07", "200809GAP08",
"200810GAP08", "200812GAP08", "200901GAP08", "200903GAP08", "200904GAP08",
"200905GAP08", "200906GAP08", "200907GAP08", "200908GAP08", "200909GAP08",
"200910GAP09", "200912GAP09", "201001GAP09", "201002GAP09", "201003GAP09",
"201004GAP09", "201005GAP09", "201006GAP09", "201007GAP09", "201008GAP09",
"201009GAP09", "201010GAP09", "201011GAP09", "201101GAP09", "201102GAP09",
"201103GAP09", "201104GAP09", "201106GAP09", "201108GAP09", "201109GAP09",
"201111GAP09", "201201GAP09", "201203GAP09", "201205GAP09", "201207GAP09",
"201208GAP09", "201211GAP09", "201301GAP09", "201303GAP09", "201305GAP09",
"201307GAP09", "201309GAP09", "201311GAP09"), class = "factor"),
Er_Group_Density = c(4, 9, 12, 4, 1, 0), Mn_Group_Density = c(3,
1, 1, 1, 0, 2), Bp_Group_Density = c(1, 2, 1, 0, 1, 0), Er_Count_Density = c(50,
14, 12, 9, 6, 4), Mn_Count_Density = c(9, 5, 2, 3, 2, 0), Bp_Count_Density = c(2,
3, 0, 4, 1, 0)), .Names = c("Grid_ID", "ER_Groups", "Er_Count",
"Mn_Count", "Bp_Groups", "Bp_Count", "Mn_Groups", "Month", "Year",
"SurveyID", "Er_Group_Density", "Mn_Group_Density", "Bp_Group_Density",
"Er_Count_Density", "Mn_Count_Density", "Bp_Count_Density"), row.names = c(2770L,
4421L, 17348L, 11263L, 6736L, 3974L), class = "data.frame")
2 个解决方案
解决方案1
2 已采纳 2014-08-01 23:31:57
There are a number of ways to get statistics by group. 有多种方法可以按组获取统计信息。 I'll assume you have a bias for plyr, since your example uses it. 我假设您对plyr有偏见，因为您的示例使用了它。
Remember that dlply() splits the data into smaller dataframes by the grouping variables, then it applies the requested function to each of the smaller dataframes. 请记住，dlply（）通过分组变量将数据拆分为较小的数据帧，然后将请求的函数应用于每个较小的数据帧。 Therefore the function you pass should operate on a whole dataframe. 因此，您传递的函数应在整个数据帧上运行。 sum() does not do this. sum（）不会这样做。 You can write your own function, though. 不过，您可以编写自己的函数。
Based on your description, what you want is something like this 根据您的描述，您想要的是这样的
myfun <- function(x) colSums(x[, c("Er_Count", "Mn_Count", "Bp_Count")])
dlply(Effort_All, c("Month", "Year", "Grid_ID", "SurveyID"), myfun)
Remember that the second argument to dlply() is the set of variables used for grouping. 请记住，dlply（）的第二个参数是用于分组的一组变量。 Not sure why you want the output as a list. 不知道为什么要将输出显示为列表。 Would it be easier to read if you used ddply (with the same arguments)? 如果您使用ddply（具有相同的参数）会更容易阅读吗？
Other approaches include using sqldf() or something like lapply(). 其他方法包括使用sqldf（）或类似lapply（）的方法。
=============== EDIT: Other approaches ============= ==============编辑：其他方法==============
sqldf is always very easy to read and understand: sqldf总是很容易阅读和理解：
output <- sqldf('select Month,Year,Grid_ID,SurveyID,
sum(Er_Count) as ercount,
sum(Mn_Count) as mncount,
sum(Bp_Count) as bpcount
from Effort_All
group by Month, Year, Grid_ID, SurveyID')
lapply works pretty much the same way as dlply. lapply的工作方式与dlply几乎相同。 Just different arguments. 只是不同的论点。
解决方案2
0 2014-08-02 07:03:26
Also, you could use colwise from plyr 另外，您可以使用colwise的plyr
dlply(Effort_All, .(Month, Year, Grid_ID, SurveyID), colwise(sum, .(Er_Count, Mn_Count, Bp_Count)))
Or summarise_each from dplyr 或summarise_each从dplyr
library(dplyr)
Effort_All%>%
group_by(Month, Year, Grid_ID, SurveyID) %>%
summarise_each(funs(sum), Er_Count, Mn_Count, Bp_Count)
#Source: local data frame [6 x 7]
#Groups: Month, Year, Grid_ID
# Month Year Grid_ID SurveyID Er_Count Mn_Count Bp_Count
# 1 2 2002 884 200203SSL 8 7 0
# 2 4 2001 126 200104SSL 26 12 6
# 3 4 2004 399 200404SSL 12 33 5
# 4 6 2001 125 200106SSL 75 9 3
# 5 10 2000 517 200009SSL 60 30 3
# 6 12 2009 340 200912GAP09 14 6 2
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