CODEWITHSUNDEEP
rpanelplyrdata.tablelag
slackline
slackline

Reputation: 2417

Lagged variables in panels with ddply

I'm trying to generate precision change (based on estimated confidence intervals) in what is in essence a panel data set.

So as a simple example here's the function I've written and applying it to a non-sensical example....

precision.gain <- function(x){
  x        <- ts(x, start=x[1])
  x.length <- seq(length = length(x))
  x.lag    <- lag(x, -1)
  x.gain   <- ((x - x.lag) * 100) / x
  x.gain   <- c(NA, x.gain)
  x.gain
}
t <- data.frame(x=1:20)
t <- cbind(t, precision.gain(t$x))
t
x precision.gain(t$x)
1   1                  NA
2   2           50.000000
3   3           33.333333
4   4           25.000000
5   5           20.000000 
6   6           16.666667
7   7           14.285714
8   8           12.500000
9   9           11.111111
10 10           10.000000
11 11            9.090909
12 12            8.333333
13 13            7.692308
14 14            7.142857
15 15            6.666667
16 16            6.250000
17 17            5.882353
18 18            5.555556
19 19            5.263158
20 20            5.000000

That works and is great, but I'm having trouble (or more likely mis-understanding) how to then (t?)apply this to my data frame a sample of which is....

subset(results.normal.sum, n2 > 20 & n2 < 30, select=c(sd2, n2, ci.width1))
    sd2 n2 ci.width1
11  0.4 22 0.6528714
12  0.4 24 0.6167015
13  0.4 26 0.5895856
14  0.4 28 0.5658297
46  0.6 22 0.6529126
47  0.6 24 0.6196544
48  0.6 26 0.5922061
49  0.6 28 0.5642688
81  0.8 22 0.6513849
82  0.8 24 0.6194468
83  0.8 26 0.5923094
84  0.8 28 0.5636396
116 1.0 22 0.6522927
117 1.0 24 0.6191043
118 1.0 26 0.5900129
119 1.0 28 0.5652429
151 1.2 22 0.6518072
152 1.2 24 0.6193353
153 1.2 26 0.5892683
154 1.2 28 0.5632235
186 1.4 22 0.6527031
187 1.4 24 0.6191458
188 1.4 26 0.5899453
189 1.4 28 0.5640431
221 1.6 22 0.6521401
222 1.6 24 0.6191883
223 1.6 26 0.5893458
224 1.6 28 0.5637215
256 1.8 22 0.6512491
257 1.8 24 0.6180401
258 1.8 26 0.5905810
259 1.8 28 0.5647388
291 2.0 22 0.6515769
292 2.0 24 0.6183121
293 2.0 26 0.5896990
294 2.0 28 0.5663394

I've tried using ddply() from Hadley Wickham's plyr package.....

ddply(results.normal.sum, .(sd2), precision.gain, x=ci.width1)
Error in .fun(piece, ...) : unused argument(s) (piece)

Using tapply() directly I sort of get there, but it doesn't return a data frame which can be cbind()....

> tapply(results.normal.sum$ci.width1, sd2, precision.gain)
$`0.4`
 [1]          NA -771.332292  -68.852635  -30.514545  -19.877447  -14.515380
 [7]  -11.147183   -9.282641   -7.680418   -6.836209   -5.954992   -5.865053
[13]   -4.599158   -4.198409   -4.155838   -3.529773   -3.590234   -3.432364
[19]   -2.899601   -3.092533   -2.721967   -2.506706   -2.498318   -2.321500
[25]   -2.299822   -2.187855   -2.116990   -1.896162   -1.853487   -1.604902
[31]   -2.194138   -1.473042   -1.710051   -1.701994   -1.417754

$`0.6`
 [1]          NA -756.196418  -68.222048  -30.566420  -19.216860  -15.162929
 [7]  -10.645899   -9.628775   -7.326799   -7.178820   -5.770681   -5.367216
[13]   -4.634938   -4.951049   -3.949776   -3.761633   -3.326209   -3.387764
[19]   -3.009317   -3.074398   -2.397660   -2.678573   -2.626077   -2.268373
[25]   -2.426720   -1.956498   -2.119986   -1.859410   -1.992678   -1.707448
[31]   -1.991583   -1.595951   -1.765913   -1.415065   -1.655725
....

I feel like I'm close but am missing or have misunderstood something.

I found a similar question here but just do not understand the answer/solution provided.

Thanks in advance for any help,

slackline

Upvotes: 1

Views: 620

Answers (1)

Ryogi
Ryogi

Reputation: 5617

If I guessed correctly what you need, the following is a solution that exploits the handy := operator in data.table.

First read the sample data:

testData <- textConnection("sd2 n2 ci.width1
11  0.4 22 0.6528714
12  0.4 24 0.6167015
13  0.4 26 0.5895856
14  0.4 28 0.5658297
46  0.6 22 0.6529126
47  0.6 24 0.6196544
48  0.6 26 0.5922061
49  0.6 28 0.5642688
81  0.8 22 0.6513849
82  0.8 24 0.6194468
83  0.8 26 0.5923094
84  0.8 28 0.5636396
116 1.0 22 0.6522927
117 1.0 24 0.6191043
118 1.0 26 0.5900129
119 1.0 28 0.5652429
151 1.2 22 0.6518072
152 1.2 24 0.6193353
153 1.2 26 0.5892683
154 1.2 28 0.5632235
186 1.4 22 0.6527031
187 1.4 24 0.6191458
188 1.4 26 0.5899453
189 1.4 28 0.5640431
221 1.6 22 0.6521401
222 1.6 24 0.6191883
223 1.6 26 0.5893458
224 1.6 28 0.5637215
256 1.8 22 0.6512491
257 1.8 24 0.6180401
258 1.8 26 0.5905810
259 1.8 28 0.5647388
291 2.0 22 0.6515769
292 2.0 24 0.6183121
293 2.0 26 0.5896990
294 2.0 28 0.5663394")

Then, put the data in a data.table and ...

library(data.table)
dt <- data.table(read.table(testData, header = TRUE))
dt[, list(n2, ci.width1, prec.gain = precision.gain(ci.width1)), by = sd2]

Here is the output

> dt[, list(n2, ci.width1, prec.gain = precision.gain(ci.width1)), by = sd2]
   sd2 n2 ci.width1 prec.gain
   0.4 22 0.6528714        NA
   0.4 24 0.6167015 -5.865058
   0.4 26 0.5895856 -4.599146
   0.4 28 0.5658297 -4.198419
   0.6 22 0.6529126        NA
   0.6 24 0.6196544 -5.367218
   0.6 26 0.5922061 -4.634924
   0.6 28 0.5642688 -4.951062
   0.8 22 0.6513849        NA
   0.8 24 0.6194468 -5.155907
   0.8 26 0.5923094 -4.581626
   0.8 28 0.5636396 -5.086548
     1 22 0.6522927        NA
     1 24 0.6191043 -5.360712
     1 26 0.5900129 -4.930638
     1 28 0.5652429 -4.382187
   1.2 22 0.6518072        NA
   1.2 24 0.6193353 -5.243024
   1.2 26 0.5892683 -5.102430
   1.2 28 0.5632235 -4.624239
   1.4 22 0.6527031        NA
   1.4 24 0.6191458 -5.419935
   1.4 26 0.5899453 -4.949696
   1.4 28 0.5640431 -4.592238
   1.6 22 0.6521401        NA
   1.6 24 0.6191883 -5.321774
   1.6 26 0.5893458 -5.063666
   1.6 28 0.5637215 -4.545560
   1.8 22 0.6512491        NA
   1.8 24 0.6180401 -5.373276
   1.8 26 0.5905810 -4.649506
   1.8 28 0.5647388 -4.575956
     2 22 0.6515769        NA
     2 24 0.6183121 -5.379937
     2 26 0.5896990 -4.852153
     2 28 0.5663394 -4.124664
cn sd2 n2 ci.width1 prec.gain

Upvotes: 1

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