Calcul de plusieurs fonctions sur plusieurs trames de données effondrées

Question

Je suis assez nouveau pour R et j'ai essayé de comprendre comment utiliser les nombreuses fonctions vectorielles disponibles. Beaucoup d'autres réponses offrent une partie d'une solution mais je n'arrive pas à obtenir une combinaison pour fonctionner. Quelques conseils seraient bien.

J'ai un dataframe des résultats de la négociation d'actions avec plusieurs colonnes, je ne l'ai montré deux ici:

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") 

je voudrais appliquer plusieurs fonctions à cette trame de données après l'effondrement et appliquent également les mêmes fonctions aux sous-ensembles de cette base de données; spécifiquement les métiers à but lucratif et les métiers de pertes. J'ai jusqu'à présent adopté l'approche de la force brute avec beaucoup de répétitions:

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$Trade.Profit.Loss.Perc >= 0 
Profits = Trades[logP,] 
logL = Trades$Trade.Profit.Loss.Perc < 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 <- with (Trades, tapply(Trades$Trade.Profit.Loss.Perc,list(T_ATRVE.cut),length)) 
Profits_Num <- with (Profits, tapply(Profits$Trade.Profit.Loss.Perc,list(P_ATRVE.cut),length)) 
Losses_Num <- with (Losses, tapply(Losses$Trade.Profit.Loss.Perc,list(L_ATRVE.cut),length)) 


# Mean Return 
Trades_Mean <- with (Trades, tapply(Trades$Trade.Profit.Loss.Perc,list(T_ATRVE.cut),mean)) 
Profits_Mean <- with (Profits, tapply(Profits$Trade.Profit.Loss.Perc,list(P_ATRVE.cut),mean)) 
Losses_Mean <- 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) 

J'ai regardé différentes questions à l'aide du paquet plyr ainsi que les permutations de la fonction lapply avec vector lists et les fonctions mais ne peut pas les faire fonctionner. Je vois les composants mais je ne peux pas les coller ensemble! .......

Après avoir appliqué le code condensé de Ramnath i obtenir la sortie suivante:

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 

Je voudrais la sortie de regarder quelque chose comme ça, avec ou sans colonnes supplémentaires:

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 

Answer 1

Voici mon approche. Il utilise la fantastique bibliothèque plyr de Hadley Wickham.

library(plyr) 
Trades = read.csv("Trades.csv") 

# define breaks to bin the data 
breaks = c(0:9, 32) 

# define profitable and ATRVE.cut variables 
Trades = transform(Trades, 
    profitable = ifelse(Trade.Profit.Loss.Perc >=0, 1, 0), 
    ATRVE.cut = cut(Entry.ATRVE, breaks, right = FALSE) 
) 

# split data frame by ATRVE.cut and profitable and apply functions 
Trades_Summary = ddply(Trades, .(ATRVE.cut, profitable), summarize, 
    Num = length(Trade.Profit.Loss.Perc), 
    Mean = mean(Trade.Profit.Loss.Perc) 
) 

print(Trades_Summary) 

Cela produit la sortie

ATRVE.cut profitable Num  Mean 
1  [0,1)   0 6 -5.383333 
2  [1,2)   0 3 -13.883333 

EDIT: Pour formater la sortie comme souhaité par l'OP, voici quelques lignes supplémentaires

p0 = subset(Trades_Summary, profitable == 0) 
p1 = subset(Trades_Summary, profitable == 1) 
merge(p0, p1, by = 'ATRVE.cut') 

Cela produit la sortie

ATRVE.cut profitable.x Num.x Mean.x  profitable.y 
1  [0,1)   0  2 -1.305000   1 
2  [1,2)   0 30 -5.135667   1 
3  [2,3)   0 40 -6.994250   1 
4  [3,4)   0 27 -8.513333   1 
    Num.y Mean.y 
1  4 13.36250 
2 39 15.67385 
3 36 15.48306 
4 21 15.05571