大きなデータ フレームを扱っており、ピボット テーブル タイプの関数を実行したいと考えています。reshape2 パッケージを使用しようとしましたが、何らかの理由で溶融データ フレームが再形成されません。
私はこのようなフレームを取りたいです:
County Industry Type Variable Value
LA Plumbing Tax Rev 1000
LA Plumbing No tax Emp 100
LA Plumbing Tax Pay 500
そして、それを（Typeで集計）にします：
Plumbing Tailors
County Rev Emp Pay Rev Emp Pay
LA 1000 100 500 1000 50 65
次のコードを実行しています。
dcast(m.data, county ~ variable + industry)
しかし、データフレームをまったく変更していません。私はどこを台無しにしていますか？
編集：
この問題についてもう少し情報を含めています。溶けたデータ フレームに到達する前に、必要な場所にデータを移動するために非常に悪いクリーンアップを行っています。以下のコードは理想的ではなく、実際に修正する必要があることはわかっていますが、基本的には複数の CSV ファイル (同じ列名を持つ) をアップロードし、それらを結合し、いくつかの値を再コーディングし、いくつかの列を削除し、データのサブセットを選択し、それを溶融フレームに入れ、dcast を使用して再形成しようとしています。特定の値を再コーディングするコードを削除しましたが、その部分は問題なく動作しているようです。コードの一部を次に示します。
data1 <- read.table("census_data_r_1.csv",header=TRUE,sep=",",stringsAsFactors=FALSE)
data2 <- read.table("census_data_r_2.csv",header=TRUE,sep=",", stringsAsFactors=FALSE)
fulldata <- rbind(data1,data2)
delete <- c("GEO.id","GEO.id2","NAICS.id","OPTAX.id","YEAR.id")
fulldata <- fulldata[, !(names(fulldata) %in% delete)]
colnames(fulldata) <- c("county","industry","tax_type","firms","revenue","payroll","num_employees","non_emp_firms","non_emp_firms_rev")
fulldata[c("firms","revenue","payroll","num_employees","non_emp_firms","non_emp_firms_rev")] <- recode.variables(fulldata[c("firms","revenue","payroll","num_employees","non_emp_firms","non_emp_firms_rev")],"'N' -> 'Nothing';'D' -> 'Withheld';'b' -> 20;'c' -> 100;'e' -> 250;'a' -> 10;'g' -> 1000;'f' -> 500;'Q' -> 'No Rev Collected';'h' -> 2500;'i' -> 5000;'j' -> 10000;'l' -> 50000;'k' -> 25000;'S' -> 'Bad Data';'m' -> 100000;")
fulldata.sub <- subset(fulldata, subset = (tax_type %in% c('Total', 'All establishments')) & (!(revenue %in% c('Nothing', 'Withheld','No Rev Collected'))) & (!(non_emp_firms %in% c('Nothing','Withheld'))))
m.data <- melt(fulldata.sub, id.vars = 1:3)
dcast(m.data, county ~ variable, sum)
今、私は次のエラーが発生しています:
構造のエラー (順序付け、dim = ns) : ディム [製品 18300] がオブジェクト [0] の長さと一致しません
からの出力dput(head(fulldata.sub,40)):
structure(list(county = c("Autauga County, Alabama", "Autauga County, Alabama",
"Autauga County, Alabama", "Autauga County, Alabama", "Autauga County, Alabama",
"Autauga County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Baldwin County, Alabama", "Baldwin County, Alabama", "Baldwin County, Alabama",
"Barbour County, Alabama", "Barbour County, Alabama"), industry = c("Rental and leasing services",
"Professional, scientific, and technical services", "Professional, scientific, and technical services",
"Accounting, tax preparation, bookkeeping, and payroll services",
"Accounting, tax preparation, bookkeeping, and payroll services",
"Architectural, engineering, and related services", "Real estate and rental and leasing",
"Real estate", "Lessors of real estate", "Offices of real estate agents and brokers",
"Offices of real estate agents and brokers", "Activities related to real estate",
"Real estate property managers", "Offices of real estate appraisers",
"Consumer goods rental", "Accounting, tax preparation, bookkeeping, and payroll services",
"Accounting, tax preparation, bookkeeping, and payroll services",
"Offices of certified public accountants", "Tax preparation services",
"Architectural, engineering, and related services", "Architectural services",
"Engineering services", "Specialized design services", "Computer systems design and related services",
"Computer systems design and related services", "Management, scientific, and technical consulting services",
"Advertising, public relations, and related services", "Veterinary services",
"Administrative and support and waste management and remediation services",
"Administrative and support services", "Employment services",
"Business support services", "Investigation and security services",
"Services to buildings and dwellings", "Exterminating and pest control services",
"Janitorial services", "Landscaping services", "Waste management and remediation services",
"Lessors of real estate", "Legal services"), tax_type = c("Total",
"All establishments", "All establishments", "All establishments",
"All establishments", "All establishments", "Total", "Total",
"Total", "Total", "Total", "Total", "Total", "Total", "Total",
"All establishments", "All establishments", "All establishments",
"All establishments", "All establishments", "All establishments",
"All establishments", "All establishments", "All establishments",
"All establishments", "All establishments", "All establishments",
"All establishments", "Total", "Total", "Total", "Total", "Total",
"Total", "Total", "Total", "Total", "Total", "Total", "All establishments"
), firms = c("10", "61", "61", "14", "14", "10", "358", "312",
"77", "161", "161", "74", "52", "16", "28", "79", "79", "36",
"20", "77", "13", "37", "19", "27", "27", "63", "17", "26", "250",
"238", "26", "14", "17", "157", "16", "29", "96", "12", "11",
"19"), revenue = c("8433", "42285", "42285", "8581", "8581",
"5571", "266692", "201777", "59742", "104768", "104768", "37267",
"32141", "4615", "20691", "33203", "33203", "19805", "3160",
"39318", "10494", "21167", "6833", "12391", "12391", "21496",
"11097", "18388", "163661", "145935", "30746", "4048", "13849",
"77076", "9934", "15832", "47411", "17726", "1585", "6439"),
payroll = c("1641", "15473", "15473", "3506", "3506", "2229",
"59476", "47937", "4053", "30180", "30180", "13704", "11902",
"1674", "4854", "17298", "17298", "9718", "1122", "15263",
"3688", "8649", "908", "4429", "4429", "7335", "2634", "6073",
"67526", "62354", "19529", "1002", "6824", "27688", "3181",
"8632", "14434", "5172", "265", "1431"), num_employees = c("56",
"386", "386", "127", "127", "41", "1987", "1643", "160",
"1030", "1030", "453", "406", "42", "217", "491", "491",
"217", "138", "356", "69", "204", "45", "111", "111", "165",
"101", "282", "2807", "2686", "806", "53", "399", "1241",
"110", "399", "675", "121", "23", "36"), non_emp_firms = c("8",
"330", "330", "49", "49", "35", "2358", "2289", "648", "840",
"840", "801", "186", "32", "19", "208", "208", "20", "40",
"203", "21", "74", "107", "99", "99", "356", "82", "10",
"1452", "1435", "25", "153", "61", "982", "12", "526", "350",
"17", "40", "16"), non_emp_firms_rev = c("882", "10111",
"10111", "493", "493", "1280", "164778", "160968", "55888",
"33321", "33321", "71759", "25870", "1504", "692", "2961",
"2961", "533", "466", "9220", "889", "5387", "4448", "3235",
"3235", "14395", "10337", "602", "35998", "33953", "708",
"3991", "806", "18726", "329", "6246", "9974", "2045", "1978",
"488")), .Names = c("county", "industry", "tax_type", "firms",
"revenue", "payroll", "num_employees", "non_emp_firms", "non_emp_firms_rev"
), row.names = c(6L, 7L, 9L, 19L, 21L, 25L, 54L, 55L, 56L, 65L,
66L, 70L, 71L, 74L, 77L, 99L, 101L, 103L, 105L, 109L, 111L, 115L,
119L, 125L, 127L, 131L, 139L, 143L, 147L, 148L, 152L, 155L, 159L,
162L, 163L, 165L, 167L, 169L, 174L, 180L), class = "data.frame")
編集
>str(fulldata.sub) および str(m.data) からの出力を含むもう 1 つの編集
data.frame': 130098 obs. $ county :
Factor w/ 3237 level "Abbeville County, South Carolina",..: 121 121 121 121 121 121 121 121 131 131 ...
$ industry : Factor w/ 369 level "Accounting, tax prepare,簿記、給与サービス",..: 283 239 239 1 1 33 358 358 274 273 ...
$ tax_type : 4 レベルの係数 "すべての事業所",..: 4 1 1 1 1 1 1 1 4 4 . ..
$ 企業: num 10 61 61 14 14 10 4 4 358 312 ...
$ 収益: num 31466 21347 21347 31717 31717 ...
$ 給与: num 5521 4863 4863 13729 13729 ...
$ num_employees: 4 num_6 256 571 571 ...
$ non_emp_firms : num 3122 1887 1887 2486 2486 ...
$ non_emp_firms_rev: num 17550 96 96 12669 12669 ...
'data.frame': 780588 obs. $ county :
Factor w/ 3237 level "Abbeville County, South Carolina",..: 121 121 121 121 121 121 121 121 131 131 ...
$ industry: Factor w/ 369 level "Accounting, tax prepare,簿記、給与サービス",..: 283 239 239 1 1 33 358 358 274 273 ...
$ tax_type: 4 レベルの係数 "すべての事業所",..: 4 1 1 1 1 1 1 1 4 4 . ..
$ 変数: 6 レベルの因子 "会社","収益",..: 1 1 1 1 1 1 1 1 1 ...
$ 値: num 10 61 61 14 14 10 4 4 358 312 .. .