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POCalculating multiple functions on multiple collapsed data frames
 

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  1. POCalculating multiple functions on multiple collapsed data frames
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    <p>I'm fairly new to R and have been trying to get my head around the more elegant ways to use the many vector based functions available. Many of the other answers offer part of a solution but I can't seem to get a combination to work. Some guidance would be nice.</p> <p>I have a dataframe of stock trading results with multiple columns, i've only shown two here:</p> <pre><code>structure(list(Trade.Profit.Loss.Perc = c(4.25, 0.32, 3.57, -14.06, -11.68, 0, 3.03, -22.76, 19.25, 0, 7.2, 1.56, 4.1, -2.27, -9.68, 7.14, 1.41, -4.96, -7.95, 17.59, -9.09, 4.41, -4.92, -3.75, -13.58, -2.44, -3.79, -6.08, -6.63, 7.06, 74.29, -7.64, 45.53, -11.42, 13.6, -6.47, 18.77, 20.69, -9.67, -4.64, 93.88, 11.96, 0, 3.33, -18.27, 0, -8.5, 7.92, 50.86, 10, 24.39, -9.68, -3.91, -14.6, 3.03, -13.43, -2.09, -0.5, 16.67, 14.52, 0.62, 6, -7.28, -0.47, -4.01, 17.4, 12.41, 24.63, 4.71, -15.02, -1.68, 41.13, 0.33, 0, -6.49, 8.51, -4.29, -2.28, 7.51, 32.27, -9.7, 151.57, 2.31, 0.48, 31.49, 2.46, 6.61, -0.22, 11.19, -7.63, -4.56, 2.48, -0.62, -6.25, 50, -3.2, 27.33, -6.25, 36.03, 29, 11.69, -2.34, -3.77, -1.45, -9.76, -5.68, 17.62, -2.7, -1.6, 42.36, -5.41, 0, 7.69, 15.6, -2.6, -9.4, -4.75, 5.36, 1.07, 24.32, 14.58, 3.64, -29.17, -8.27, 8.68, -11.56, -3.57, -2.4, -1.36, -16.98, 3.75, -4.2, -8.62, -6.19, -6.31, -11.38, -4.65, 35.74, -6.59, -3.5, -12.53, 18.18, 1.77, -8.14, -1.75, -0.75, 5.56, 58.54, -6.19, -7.31, 4.39, 3.25, 2.52, -2.33, -5.3, 27.09, -1.11, -37.14, -7.26, -1.02, 0, -3.14, 6.56, -7.81, -13.04, 22.38, -1.36, -1.2, -7.49, -9.09, 11.68, -8.46, -4.42, -1.54, -2.32, 1.2, 0, 4.65, -5.75, -2.5, 11.74, 7.52, 12.37, 1.22, 14.18, -10.47, 20.63, -6.28, -0.01, 16.67, 3.12, 5.9, 9.96, 3.45, 38.64, -1.98, 0.71, 6.1, 29.41), Entry.ATRVE = c(2.022, 2.031, 3.979, 3.478, 2.75, 1.784, 1.971, 2.904, 1.69, 1.268, 2.624, 1.37, 2.531, 3.379, 1.871, 1.989, 3.235, 1.26, 2.423, 0.907, 2.406, 3.459, 1.328, 2.885, 3.803, 3.506, 1.738, 2.245, 2.279, 3.661, 2.256, 1.633, 1.777, 3.558, 2.336, 1.746, 1.896, 3.075, 3.812, 2.489, 3.372, 3.295, 2.022, 1.215, 2.007, 2.644, 3.605, 1.576, 2.596, 3.212, 1.4, 3.031, 2.916, 2.254, 2.19, 3.661, 2.046, 2.537, 3.246, 2.091, 2.762, 2.742, 1.345, 1.91, 1.293, 1.348, 2.842, 2, 2.672, 1.779, 2.068, 3.433, 3.006, 3.525, 3.575, 3.36, 2.168, 1.688, 2.567, 0.835, 1.652, 1.422, 3.11, 2.454, 2.27, 1.199, 2.571, 1.85, 3.092, 1.932, 1.397, 1.859, 3.141, 2.821, 3.549, 1.594, 1.941, 2.526, 2.274, 1.723, 2.058, 3.126, 2.941, 1.953, 2.55, 1.909, 2.715, 2.394, 3.152, 2.691, 2.063, 3.163, 1.034, 1.059, 0.362, 1.979, 2.075, 1.087, 0.893, 1.56, 2.121, 3.507, 2.813, 2.56, 1.459, 2.314, 3.137, 1.798, 2.551, 3.36, 1.41, 1.083, 2.641, 2.703, 3.341, 3.831, 2.336, 2.856, 1.906, 2.14, 3.138, 1.047, 2.043, 2.467, 1.328, 1.092, 1.821, 1.972, 3.94, 2.412, 2.314, 1.668, 0.626, 1.862, 3.442, 2.479, 2.598, 3.307, 1.433, 3.45, 3.47, 3.082, 1.978, 2.764, 3.775, 1.908, 2.695, 2.571, 1.934, 3.324, 2.411, 1.573, 2.579, 2.34, 3.684, 2.008, 3.225, 1.435, 3.283, 1.993, 1.754, 1.063, 1.208, 1.903, 1.312, 2.875, 2.676, 2.426, 0.997, 2.654, 1.323, 1.717, 2.547, 1.346, 2.055, 1.551, 2.215, 1.396, 3.314), X = c(0.177371087, 0.906809191, 0.208469544, 0.151138531, 0.345978372, 0.574636442, 0.385996104, 0.861229906, 0.141104879, 0.561812589, 0.340836813, 0.756585548, 0.058332616, 0.349425684, 0.932898281, 0.004165203, 0.41053952, 0.077378432, 0.058095648, 0.497867816, 0.216763889, 0.571999138, 0.800967707, 0.147823619, 0.568139526, 0.952335278, 0.414131303, 0.930967507, 0.951035818, 0.655865856, 0.363502228, 0.319418729, 0.803576908, 0.302248103, 0.374374328, 0.306277968, 0.562535965, 0.45865619, 0.528323919, 0.228827088, 0.090731023, 0.810054676, 0.040312554, 0.936651623, 0.613018811, 0.323526959, 0.938253046, 0.59613313, 0.096626581, 0.295683059, 0.691093101, 0.370450864, 0.953446954, 0.002676696, 0.036867464, 0.304420348, 0.108535358, 0.642157095, 0.683799239, 0.751778295, 0.394137427, 0.888028432, 0.008651793, 0.41384267, 0.004815106, 0.25080828, 0.022183931, 0.598519722, 0.867941507, 0.987371256, 0.56446045, 0.0576671, 0.32560138, 0.835096526, 0.585997663, 0.028029888, 0.982674378, 0.135163749, 0.717039075, 0.764402586, 0.648966357, 0.48131708, 0.364586775, 0.664295882, 0.594879264, 0.131346724, 0.153489409, 0.396639421, 0.240334462, 0.970626753, 0.872674761, 0.965853941, 0.018889313, 0.500850411, 0.020251346, 0.477868178, 0.441027497, 0.464538719, 0.650548595, 0.113164376, 0.953159401, 0.828417591, 0.909501277, 0.974955387, 0.005544144, 0.796553436, 0.82135113, 0.196713654, 0.161114125, 0.040137343, 0.60080166, 0.731497271, 0.484325649, 0.195530412, 0.133857393, 0.052481523, 0.52277393, 0.258780518, 0.305323357, 0.609780934, 0.873767561, 0.897915264, 0.803924978, 0.844458949, 0.551162924, 0.707426453, 0.756850677, 0.782465398, 0.034789665, 0.722527887, 0.430711179, 0.312993878, 0.04175475, 0.661157221, 0.377607192, 0.293913794, 0.208493996, 0.58177576, 0.828698155, 0.057039434, 0.211224526, 0.269573861, 0.169065249, 0.672225791, 0.673528094, 0.258476807, 0.027798473, 0.739185813, 0.77504509, 0.76611552, 0.60618332, 0.215831874, 0.660387201, 0.529556218, 0.948005457, 0.99885867, 0.774597927, 0.885260589, 0.947632843, 0.624328215, 0.253942724, 0.721110859, 0.74616445, 0.042866232, 0.166422973, 0.473853092, 0.411103767, 0.284882459, 0.672563947, 0.367742347, 0.947790843, 0.981166871, 0.646502822, 0.130204379, 0.069492267, 0.115210676, 0.126707369, 0.247698717, 0.757196762, 0.607707261, 0.621710909, 0.859949287, 0.437920926, 0.245296529, 0.645008787, 0.564204912, 0.800645722, 0.767176007, 0.874495379, 0.037927782, 0.595771062, 0.073163803, 0.518055909, 0.765729075, 0.646506897, 0.077860654, 0.990334821, 0.220478109, 0.196541822), profitable = c(1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1), ATRVE.cut = structure(c(3L, 3L, 4L, 4L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 4L, 2L, 2L, 4L, 2L, 3L, 1L, 3L, 4L, 2L, 3L, 4L, 4L, 2L, 3L, 3L, 4L, 3L, 2L, 2L, 4L, 3L, 2L, 2L, 4L, 4L, 3L, 4L, 4L, 3L, 2L, 3L, 3L, 4L, 2L, 3L, 4L, 2L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 3L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 3L, 1L, 2L, 2L, 4L, 3L, 3L, 2L, 3L, 2L, 4L, 2L, 2L, 2L, 4L, 3L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 4L, 3L, 2L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 4L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 4L, 2L, 3L, 4L, 2L, 2L, 3L, 3L, 4L, 4L, 3L, 3L, 2L, 3L, 4L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 4L, 3L, 3L, 2L, 1L, 2L, 4L, 3L, 3L, 4L, 2L, 4L, 4L, 4L, 2L, 3L, 4L, 2L, 3L, 3L, 2L, 4L, 3L, 2L, 3L, 3L, 4L, 3L, 4L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 4L), .Label = c("[0,1)", "[1,2)", "[2,3)", "[3,4)", "[4,5)", "[5,6)", "[6,7)", "[7,8)", "[8,9)", "[9,32)"), class = "factor")), .Names = c("Trade.Profit.Loss.Perc", "Entry.ATRVE", "X", "profitable", "ATRVE.cut"), row.names = c(NA, -199L), class = "data.frame") </code></pre> <p>I would like to apply multiple functions to this dataframe after collapsing it and also apply the same functions to subsets of this dataframe; specifically the profit trades and loss trades. So far i have taken the brute force approach with much repetition:</p> <pre><code>Trades = read.csv("Trades.csv") #Bin the data according the breaks. breaks = seq(0,9,by=1) breaks = c(breaks,32) #Break Trades into Profits and Losses logP = Trades$Trade.Profit.Loss.Perc &gt;= 0 Profits = Trades[logP,] logL = Trades$Trade.Profit.Loss.Perc &lt; 0 Losses = Trades[logL,] #Collapse the dataframes by the ATRVE bins. T_ATRVE.cut = cut(Trades$Entry.ATRVE, breaks, right=FALSE) P_ATRVE.cut = cut(Profits$Entry.ATRVE, breaks, right=FALSE) L_ATRVE.cut = cut(Losses$Entry.ATRVE, breaks, right=FALSE) #Using Trades, on the specified column vector, use a bin list to calculate each bin statistic. #Number of Trades Trade_Num &lt;- with (Trades, tapply(Trades$Trade.Profit.Loss.Perc,list(T_ATRVE.cut),length)) Profits_Num &lt;- with (Profits, tapply(Profits$Trade.Profit.Loss.Perc,list(P_ATRVE.cut),length)) Losses_Num &lt;- with (Losses, tapply(Losses$Trade.Profit.Loss.Perc,list(L_ATRVE.cut),length)) # Mean Return Trades_Mean &lt;- with (Trades, tapply(Trades$Trade.Profit.Loss.Perc,list(T_ATRVE.cut),mean)) Profits_Mean &lt;- with (Profits, tapply(Profits$Trade.Profit.Loss.Perc,list(P_ATRVE.cut),mean)) Losses_Mean &lt;- with (Losses, tapply(Losses$Trade.Profit.Loss.Perc,list(L_ATRVE.cut),mean)) #Combine the results into one table. Results = cbind(Trade_Num, Profits_Num, Losses_Num, Trades_Mean, Profits_Mean, Losses_Mean) </code></pre> <p>I have looked at different questions using the <a href="https://stackoverflow.com/questions/6510390/collapse-data-frame-by-group-using-different-functions-on-each-variable">plyr</a> package as well as permutations of the lapply function with <a href="https://stackoverflow.com/questions/4791760/apply-a-function-to-each-data-frame">vector lists</a> and functions but can't make them work. I see the components but can't glue them together! .......</p> <p>After applying the condensed code from Ramnath i get the following output:</p> <pre><code> profitable ATRVE.cut Num Mean 1 0 [0,1) 2 -1.305000 2 0 [1,2) 30 -5.135667 3 0 [2,3) 40 -6.994250 4 0 [3,4) 27 -8.513333 5 1 [0,1) 4 13.362500 6 1 [1,2) 39 15.673846 7 1 [2,3) 36 15.483056 8 1 [3,4) 21 15.055714 </code></pre> <p>I'd like the output to look something like this, with or without extra columns:</p> <pre><code> profitable ATRVE.cut Num Mean Profitable Num Mean 1 0 [0,1) 2 -1.305000 1 4 13.362500 2 0 [1,2) 30 -5.135667 1 39 15.673846 3 0 [2,3) 40 -6.994250 1 36 15.483056 4 0 [3,4) 27 -8.513333 1 21 15.055714 </code></pre>
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