HRSデータをワイドフォーマットからロングフォーマットに再形成し、時間変数を作成する

次のデータセット（Weight = WとHeight = H以外の約25個の変数を含む）があり、すべて10年に渡っています。

現在、次の形式で時間インデックスはありません。

df <- structure(list(data = structure(1:4, .Label = c("Ind_1", "Ind_2", 
"Ind_3", "Ind_4"), class = "factor"), r1weight = c(56, 76, 87, 64
), r2weight = c(57, 75, 88, 66), r3weight = c(56, 76, 87, 65), r4weight = c(56L, 
73L, 85L, 63L), r5weight = c(55L, 77L, 84L, 65L), r1height = c(151L, 163L, 
173L, 153L), r2height = c(154L, 164L, NA, 154L), r3height = c(NA, 165L, NA, 
152L), r4height = c(153L, 162L, 172L, 154L), r5height = c(152,161,171,154)), class = 
"data.frame", row.names = c(NA, 
 -4L)) 

  data  r1w r2w r3w r4w r5w r1h r2h r3h r4h r5h
1 Ind_1  56  57  56  56  55 151 154  NA 153 152
2 Ind_2  76  75  76  73  77 163 164 165 162 161
3 Ind_3  87  88  87  85  84 173  NA  NA 172 171
4 Ind_4  64  66  65  63  65 153 154 152 154 154`

時間変数を追加して長い形式に変形する必要があります。うまくいけば、このようなものが得られます。

dflong <- structure(list(time = structure(1:20, .Label = c("1", "2", 
     "3", "4", "5", "1","2","3","4","5", "1","2","3","4","5","1","2","3","4","5"), 
     class = "factor"), Ind = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4), W = c(56,57,56,56,55,76,75,76,73,77,87,88,87,85,84,64,66,65,63,65),
     H = c(151,154,NA,153,152,163,164,165,162,161,173,NA,NA,172,171,153,154,152,154,154)), class = "data.frame", row.names = c(NA, -20L))

見える

   time Ind  W   H
1     1   1 56 151
2     2   1 57 154
3     3   1 56  NA
4     4   1 56 153
5     5   1 55 152
6     1   2 76 163
7     2   2 75 164
8     3   2 76 165
9     4   2 73 162
10    5   2 77 161
11    1   3 87 173
12    2   3 88  NA
13    3   3 87  NA
14    4   3 85 172
15    5   3 84 171
16    1   4 64 153
17    2   4 66 154
18    3   4 65 152
19    4   4 63 154
20    5   4 65 154`

reshape2-コマンドを使用しようとしましたが、これまでのところ次のようになっています。

library(reshape2)
dflong <- melt(df,id.vars = c("idhhpn",r1w-r10w, r1h-r10h (help writing compactly),
     time(needs help constructing) )`

「r1w、r2w、r3w」を書きたくありませんが、r1weight-r10weightに似ているので、25個の変数すべてに対して10個すべての時間インスタンスを書く必要はありません。

これまでのところ、私はこの点に到達しました

次のコードを使用して

melt <- melt(setDT(HRSdata), measure = patterns("idhhpn", "srhlt", "highbp", "diabetes", "cancer", "lungev", "heartp", "strokev", "psychev", "arth", "obese", "agey", "marpart", "male", "black", "hispan", "logass", "logdebt", "atotal", "debt", "lths", "hsorged", "somehs", "scorAA", "bachelor", "graduate", "works62", "works65", "momagey", "dadagey", "dadalive", "momalive", "vigact3", "smokesn"), 
     value.name = c("idhhpn", "srhlt", "highbp", "diabetes", "cancer", "lungev", "heartp", "strokev", "psychev", "arth", "obese", "agey", "marpart", "male", "black", "hispan", "logass", "logdebt", "atotal", "debt", "lths", "hsorged", "somehs", "scorAA", "bachelor", "graduate", "works62", "works65", "momagey", "dadagey", "dadalive", "momalive", "vigact3", "smokesn"), 
     variable.name = "time")[, 
      idhhpn := as.integer(sub("\\D+", "", HRSdata))][order(idhhpn)][, .(time, idhhpn, srhlt, highbp, diabetes, cancer, lungev, heartp, strokev, psychev, arth, obese, agey, marpart, male, black, hispan, logass, logdebt, atotal, debt, lths, hsorged, somehs, scorAA, bachelor, graduate, works62, works65, momagey, dadagey, dadalive, momalive, vigact3, smokesn        )]