Parallelization in R (tutorial) - r

I've got a question kinda basic. It's the first time I use parallel package in R
I'm running a function you could find here. Well, this is not exactly the function but is very similar.
So my function is running on the WTI oil price dataset (see down below) and since p_max and q_max are 12 it takes like 3 min on my PC.
So I am trying to parallelize this process:
this is my code so far:
n.cores <- round(detectCores()/2)
cl <- makeCluster(n.cores)
clusterEvalQ(cl, {library(dplyr); library(forecast)})
clusterExport(cl, c("wti_", "which_arima"))
start_time <- Sys.time()
a <- parApply(cl = cl,
wti_,
function(x)
wti_models <- which_arima(x,p_max = 12, d_max = 1, q_max = 12, expl = F))
end_time <- Sys.time()
stopCluster(cl)
end_time - start_time
this gets me an error
So this is what I know:
I am creating a new environment only with forecast and dplyrpackages
In this environment I am using the wti_ data frame and the which_arima function.
The desired result as you could see in the link is a dataframe, so my question is: how can I apply the function over a dataset, but using the parallelization.
Like how do I say to R, "apply this function, with this data, these libraries, and return my dataframe"
data:
structure(c(3.59131383137997, 3.58693674637687, 3.50782953019081,
3.43581773710991, 3.40722540614203, 3.42200436261882, 3.48904753923734,
3.47452426198165, 3.46675753126697, 3.46568380114955, 3.49807117713751,
3.51687930816857, 3.46335166796688, 3.41285698665032, 3.3790196588941,
3.43353497596237, 3.38675587458917, 3.41877857802065, 3.4351468995404,
3.43645947831124, 3.40956358119916, 3.38513513606487, 3.40276099733318,
3.40963956656788, 3.42328064788982, 3.44117985614171, 3.44875660306423,
3.43353497596237, 3.40231141639508, 3.3889354116122, 3.34201790329997,
3.34511517255433, 3.34481173886808, 3.32549686098232, 3.33946614154227,
3.31876553349471, 3.2719744390575, 3.34603588846443, 3.36637904646734,
3.37366507547086, 3.30585112515203, 3.28962252754128, 3.27225558783058,
3.28979015878231, 3.2868499768523, 3.33280253315514, 3.39774845550964,
3.34281846838198, 3.15856777800092, 2.77168686901602, 2.56117445699168,
2.56878239388438, 2.72383440052745, 2.58909523968371, 2.42867520384351,
2.69982721445759, 2.67193554872065, 2.67205375315245, 2.72614311872445,
2.82219484828974, 2.95059244815988, 2.91370487551786, 2.93858161230468,
2.93740122860658, 2.95149846325297, 2.98463470799191, 3.04069683959108,
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Related

Plot VAR fitted values with original data R

Suppose you have a matrix of time series, where each column is a time series.
Now, suppose you take second differences of this matrix, (so the data is stationary), using this code:
var_train_data <- diff(var_train, differences = 2)
and then you estimate this model:
var_1 <- VAR(y=var_train_data, lag=9, type="const")
Recall that this function VARcomes from vars package.
How can you plot the original data and the fitted(var_1) data? Is there a function to return the fitted data from the VAR to "level"?
I want to do this but with a VAR.
Thanks in advance!!
Here is the data:
> dput(var_train)
structure(c(8.59225428063812, 8.61632381521795, 8.48941975098913,
8.42616336163893, 8.4656261753598, 8.49981580266834, 8.50658473928686,
8.54846279771184, 8.58669260125764, 8.56316032696837, 8.52323490346238,
8.48186742854092, 8.48127581622944, 8.49729119076164, 8.49055049683316,
8.58344453055748, 8.66539149708416, 7.9357662604667, 6.05291235942189,
6.03890804924141, 5.98225560802266, 5.97240914057282, 5.94306180787981,
6.20137810965131, 6.12654161955374, 5.94624694111468, 5.85594359899009,
5.80529348520438, 5.8381279297813, 5.94886540621632, 5.89095336551159,
5.78449385503938, 5.80588769759335, 5.70948049577085, 5.67528501375454,
5.52325929980363, 5.31085745753021, 5.33500450854624, 5.3938341365814,
5.45958551414416, 5.62663959880732, 5.67152161243798, 5.87292229474808,
6.13320939934395, 6.0539450308861, 5.91620206260743, 5.98431404307914,
5.97698522584315, 6.09421451539306, 6.16657264938364, 6.27079937807193,
6.32011405804798, 6.31492735684061, 6.33107810311843, 6.44029387009958,
6.5049295418109, 6.68300329197134, 7.30512711870751, 7.48990882366175,
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6.06003704039333, 5.96216446139643, 5.8926960131323, 5.91466063648335,
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6.01266675475859, 6.15176510875538, 6.27125844671076, 6.34813949104671,
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6.65109576407555, 6.55243995455944, 6.52108609966874, 6.61791214295492,
6.73192688164199, 6.82964630649759, 6.7830420348085, 6.67163010253559,
7.35778808496937, 7.68222895818785, 7.65345178297661, 7.75628546245401,
7.62338589734873, 18.0052565534027, 18.0283161481762, 18.0354203734597,
18.071119409619, 18.1057986145642, 18.1434464627582, 18.1679613320011,
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18.2718727763864, 18.3010161442641, 18.3291390076371, 18.3572538287519,
18.3507204046193, 18.3600370069532, 18.3939643481435, 18.4165259145903,
18.4908471052973, 18.4867143437776, 18.4804562806922, 18.469238614477,
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18.6623309458927, 18.6585086859232, 18.6627389755133, 18.6664151651325,
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18.7436675451662, 18.7459556596228, 18.7514145615817, 18.7664618044321,
18.8918838160788, 18.8524507901715, 18.8267408416858, 18.8351746759483,
18.8659203733722, 18.8520280137673, 18.8947341990718, 18.8888147953761,
18.8994685106982, 18.9232996525635, 18.8866047503417, 18.9024896904205,
19.0233652386019, 18.987660272875, 18.9544232700339, 18.9334369249929,
18.9449183299094, 18.9631673064947, 18.9933965789268, 18.9872237290741,
18.9873356136058, 19.0123636641398, 19.0206652835947, 19.0270219591515,
19.1598215152463, 19.1409817254047, 19.1376727583276, 19.1107669946426,
19.1241264422661, 19.1370331827589, 19.1912655465102, 19.2115823692113,
19.2158835209359, 19.2414814594526, 19.2701126456528, 19.2843375695361,
19.457274554838, 19.3897327048706, 19.3892248817594, 19.4122218249334,
19.4394604950819, 19.4541631046363, 19.4997359433774, 19.5176831040473,
19.5230181490842, 19.5320146650821, 19.5251016863462, 19.5528663129766,
19.7183358456188, 19.6474484090812, 19.6534432674726, 19.6576920553381,
19.6789279209334, 19.7218625314866, 19.7970056245357, 19.8212720524189,
19.8242227530495, 19.8192388840771, 19.8436023860703, 19.881222941016,
20.0436302759402, 19.9570453168158, 19.9591040082, 19.9740784817898,
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1.12183475077377, 1.12994175229071, 1.12956170931204, 1.12199732095331,
1.11645064755987, 1.12481242467782, 1.13066151473637, 1.13028712061827,
1.12694056065497, 1.12382226475179, 1.12352013167586, 1.13391069257413,
1.14763982976838, 1.14481816405703, 1.14852949174863, 1.14182560351963,
1.14086563926171, 1.14491904045717, 1.14897189333479, 1.14616964486707,
1.15074750127031, 1.14681353487065, 1.11151754535415, 1.10497749493861,
1.10963378437214, 1.12415745716768, 1.17507535290893, 1.20285968503846,
1.22784769136553, 1.23940795216891, 1.254741010879, 1.29442450660416,
1.30428779451896, 1.31314618462517, 1.32544236970695, 1.33728107423435,
1.34408499591568, 1.34199331033196, 1.34027541040719, 1.33616830504407,
1.33360421057602, 1.33332422301893, 1.34717794252774, 1.3502492092262,
1.35168291803248, 1.35827816606688, 1.36772644852242, 1.36755741578293,
1.36926148542701, 1.37264481021763, 1.37322962601678, 1.37643913938007,
1.37906284181634, 1.37644362054554, 1.38911039237937, 1.39412557349575,
1.40094895608589, 1.40630864159528, 1.40823485306921, 1.4138446752069,
1.42340582796496, 1.43641264727375, 1.43605231080207, 1.44839810240334,
1.45451041581127, 1.46166006472498, 1.46774816064695, 1.46930608347752,
1.47885183796249, 1.49059366171423, 1.49849145403671, 1.51209667142067,
1.5250141727637, 1.5392257264567, 1.55144303632514, 1.56488453313021,
1.58308777691125, 1.59737589266492, 1.60896279958586, 1.62553339664661,
1.63594174408691, 1.65233080464302, 1.67114336171075, 1.6897476078746,
1.71673790971729, 1.74453973794979, 1.76317526009814, 1.79187692264759,
1.84186982937622, 1.9460629324144, 2.05986108970089, 2.06767436493269,
2.0783176148561, 2.08271855277262, 2.09358626977224, 2.09674958523685,
2.11582742548029, 2.12810020369675, 2.13596929171732, 2.13972610568317,
2.14456803530813, 2.15013985201827, 2.16007349878874, 2.17165498940627,
2.18057666565755, 2.19162746118342, 2.20308765886345, 2.21304799942168,
2.22367586966847, 2.23629862083737, 2.24751866055731, 2.26100586740225,
2.40972893063106, 2.60366275683037, 2.68572993101095, 2.70501080420283,
2.6676315643757, 2.6479269687206, 2.64641010174172, 2.69966594490103,
2.69665303568271, 2.71396750774502, 2.71900427132191, 2.72876269360869,
2.76276620421252, 2.76620189252239, 2.74632816231219, 2.74196673817286,
2.72905831066292, 2.75190757584346, 2.77801573354251, 2.84089580821293,
2.85681823660541, 2.84754572013613, 2.85858396073969, 2.86184353545653,
2.86958309986952, 2.94279115543111, 2.98631808884879, 3.00648449252989,
3.00620698598987, 3.15207693676406, 3.27614511764022, 3.32011714920345,
3.39367422894347, 3.64822360464499, 3.61835354049394, 3.59374251055335,
3.63237359915986, 3.62209957896007, 3.64554153297999, 3.71611226971083,
3.76031231050606, 3.80307769833913, 3.77959145461296, 3.74772344909971,
3.95072671083008, 4.03652777624058, 4.06630193640976, 4.08838169421096,
4.09074775372752), .Dim = c(192L, 7L), .Dimnames = list(NULL,
c("EMBI+", "M2 (pesos)", "Commodity Price index", "emae",
"gasto programas SS", "recursos tributarios", "ex_rate")), .Tsp = c(2004,
2019.91666666667, 12), class = c("mts", "ts", "matrix"))

Seasonal adjustment in R via 13ARIMA-SEATS

I have downloaded some raw data on inflation which said in the description that it has not been seasonally adjusted. Therefore I assumed that there is some seasonality present. I did not check if this is indeed true. What is the common way of checking a time-series for the presence of seasonality?
Nevertheless, I still tried to apply the procedure, but the before and after result are exactly the same; nothing has changed.
The code:
if (!require("tis")) {install.packages("tis"); library('tis')} # Load time series library
if (!require("seasonal")) {install.packages("seasonal"); library('seasonal')}
inflation.start <- c(1960,1)
inflation.end <- c(2018,1)
inflation.raw <- "rawData/germany_inflation.csv"
inflation.table <- read.table(inflation.raw, skip = 1, header = F, sep = ',', stringsAsFactors = F)
inflation.ger <- ts(inflation.table[,2], start = inflation.start, frequency = 4)
# ts.plot(inflation.ger)
inflation.seasadj <- final(seas(as.ts(naWindow(inflation.ger),freq=4))) # seasonal adjustment
inflation.seasadj.ger <- ts(inflation.seasadj, start = inflation.start, frequency = 4)
So apparently, inflation.ger and inflation.seasadj.ger are the exact same time-series. What could have gone wrong?
The data:
dput(inflation.ger)
structure(c(2.22222222222224, 1.244019138756, 0.75973409306742,
1.80608365019013, 1.98487712665404, 2.64650283553874, 2.73327049952876,
3.36134453781511, 3.15106580166824, 2.39410681399631, 2.47706422018348,
3.25203252032522, 2.87511230907457, 2.42805755395685, 3.31244404655327,
2.27471566054242, 2.09606986899562, 2.72168568920105, 2.2530329289428,
2.05303678357573, 3.07955517536358, 3.84615384615386, 3.98305084745763,
4.10729253981556, 4.06639004149377, 3.04526748971191, 2.93398533007336,
2.49597423510466, 1.8341307814992, 1.75718849840257, 1.10847189231985,
1.49253731343286, 1.25293657008615, 1.33437990580847, 1.80109631949884,
1.62538699690402, 2.01082753286931, 2.09140201394268, 1.92307692307691,
2.89413556740292, 3.33586050037907, 3.5660091047041, 4.00000000000001,
4.44115470022204, 5.06236243580337, 5.78754578754579, 5.66037735849054,
5.31537916371368, 4.88826815642457, 5.47091412742381, 6.25000000000001,
6.46029609690444, 7.39014647137152, 6.9599474720946, 7.30446024563674,
7.45891276864728, 7.06757594544325, 6.99815837937383, 6.44578313253009,
5.88235294117648, 6.25361899247252, 6.08146873207113, 5.43293718166383,
5.22222222222223, 4.30517711171662, 3.78583017847487, 3.70370370370372,
3.80147835269271, 3.81400208986417, 3.85617509119333, 3.46790890269152,
3.00101729399797, 2.86864620030195, 2.50878073256398, 2.50125062531265,
2.91358024691356, 3.27788649706457, 4.650024473813, 5.31966813079548,
5.51823416506719, 5.92136428233066, 5.19176800748362, 5.14365152919371,
5.72987721691677, 5.81395348837209, 6.66963094708762, 7.13970912296167,
5.89247311827958, 5.36770921386304, 5.08545227177989, 4.648292883587,
4.06173842404549, 3.28920978740473, 3.13367711225704, 2.71226415094341,
2.84933645589384, 2.83495145631071, 1.88461538461538, 2.06659012629162,
2.31499051233397, 2.30362537764349, 2.076255190638, 1.57480314960629,
0.741839762611279, 0, -0.332840236686399, -0.922849760059066,
-0.515463917525782, 0.0369139904023732, 0.519480519480511, 0.968703427719836,
0.962250185048109, 1.14391143911439, 1.29198966408267, 1.69741697416972,
2.4193548387097, 2.88215979569498, 2.80612244897959, 3.01161103047896,
2.72011453113812, 2.3049645390071, 2.72952853598016, 3.02923564635434,
2.84251480582282, 3.29915972330998, 4.51351791825871, 5.50437317784258,
5.9108527131783, 6.09404990403073, 4.95750708215298, 3.33642261353105,
4.66605672461115, 4.38715513342377, 4.5434098065677, 4.30493273542601,
2.97202797202796, 2.77296360485268, 2.58175559380379, 2.45055889939811,
2.07979626485568, 1.7284991568297, 1.5520134228188, 1.46873688627781,
1.45530145530146, 1.45047658516367, 1.36307311028499, 1.53019023986766,
1.76229508196721, 1.55228758169937, 2.32273838630808, 2.11812627291242,
1.24848973016513, 1.36765888978277, 0.597371565113501, 0.438771439968094,
0.198886237072386, 0.515873015873038, 0.673000791765629, 0.953137410643369,
1.54823342596267, 1.10540860639556, 1.37632717263074, 1.73092053501181,
1.72009382329947, 2.53807106598985, 2.01706749418153, 1.6627996906419,
1.99846272098384, 1.29474485910129, 1.21673003802282, 1.17915557246102,
1.13036925395631, 0.789473684210527, 1.05184072126221, 1.16541353383457,
0.968703427719814, 1.86497575531517, 1.82156133828996, 2.0066889632107,
1.62361623616237, 1.24496521420726, 1.64293537787514, 1.67577413479055,
1.70660856935367, 1.84448462929475, 1.4727011494253, 1.28986026513793,
1.7850767583006, 2.05965909090908, 2.26548672566371, 3.0774672798019,
2.94633461943178, 2.88796102992346, 3.08065074420214, 1.61290322580647,
0.81771720613288, 0.270544470747383, -0.235057085292139, 0.405268490374862,
0.811084825954715, 1.11298482293424, 1.11073712554695, 1.37907837201482,
1.87730472678512, 2.00133422281521, 2.19707057256989, 2.22295952222959,
2.13886146758801, 1.86396337475473, 2.01954397394136, 2.01233365790325,
1.54639175257732, 1.50882825040129, 1.62835249042147, 1.33630289532296,
1.20558375634519, 1.07526881720429, 0.848256361922726, 0.50235478806906,
0.0313479623824208, 0.469336670838559, 0.124610591900319, 0.312402374258052,
0.250705108116592, 0.0934288383680993, 0.466708151835709, 1.12114606041732,
1.87558612066271, 1.68014934660859, 1.73428305977082, 1.66307360640589,
1.50352868978214), .Tsp = c(1960, 2017.75, 4), class = "ts")

Rollapply for rolling regression with different window sizes, save coefficients in matrixes

I'm trying to perform a rolling linear regression, whit overlapping windows and for different window sizes. I want to save the result in two tables, one with window size and slopes and one wiht window size a and intercepts.
My data set is now saved as a xts object. I'm trying to make a for-loop to generate the different window-sizes and then do a rolling regression between the two variables, with a overlapping window using rollapply. Then I would like that the coefficients for every regression get saved as a row in a matrix, that can be built on, so that every window size corresponds to one row. I have looked at a lot of previous questions, but I can not get it right.
I have hourly data for 2 variables, this is the data for the x-variable:
> dput(Xq$x)
structure(c(339.76625, 176.7196875, 142.8063125, 118.5785625,
102.0514375, 86.01156251, 86.99050001, 85.0089375, 380.0010626,
2114.279375, 2157.76875, 2442.575, 2562.6375, 2537.7125, 2581.888125,
1947.7575, 834.7918751, 765.1525001, 989.698125, 1433.2, 429.9081251,
388.333125, 1500.995, 1523.816625, 1529.090126, 439.746875, 1029.03625,
1470.07825, 1454.844813, 1448.8455, 413.7550001, 334.03125, 566.7362501,
1609.184375, 1509.266876, 1403.1425, 1142.695625, 795.138125,
1562.881875, 933.190625, 549.085625, 500.135, 535.83, 444.3493751,
356.386875, 268.1875001, 141.1878125, 119.658875, 162.0425625,
153.6944375, 161.0025, 151.37025, 155.9567501, 123.64825, 145.664125,
141.4480625, 232.2009375, 913.6480001, 2227.08125, 1870.618751,
2190.606251, 1357.81625, 824.555625, 699.99, 703.695, 620.74625,
581.2375001, 393.391875, 349.96625, 188.5445625, 154.03125, 142.0825,
339.7915625, 139.8941875, 154.6279376, 171.6016875, 151.1387501,
159.7665, 168.62025, 180.7596875, 447.2746875, 1550.11625, 1887.275,
1446.883125, 699.590625, 983.9925, 1319.62875, 769.505625, 812.713125,
638.1437501, 585.8312501, 626.7875, 1924.293125, 629.855625,
349.88375, 1038.54875, 1588.82225, 548.3475, 1053.368125, 964.825625,
990.324375, 1327.160625, 395.5862501, 421.8154376, 1308.551875,
1334.045, 1633.408125, 886.2662501, 1618.7975, 1325.59625, 847.0693751,
667.501875, 594.0531251, 249.920625, 210.8869376, 224.3725001,
209.6325, 213.64875, 210.3175001, 194.54, 90.1085625, 71.12575001,
70.69412501, 69.6075, 68.404625, 67.88837501, 67.317125, 67.220375,
67.696625, 273.2732501, 606.475625, 1277.3425, 913.24875, 612.5462501,
571.42125, 503.27375, 316.68375, 208.7436251, 127.283, 126.12375,
102.504625, 78.7938125, 70.0183125, 69.97900001, 77.7586875,
76.60543751, 94.5235625, 103.0095625, 105.1938125, 103.8090625,
104.005625, 104.544875, 105.2036875, 104.6611875, 375.7004375,
793.7725001, 782.544375, 257.065625, 387.1487501, 571.1912501,
356.6406251, 243.404375, 220.698125, 182.5680626, 161.060625,
166.5178125, 137.9805, 129.6253125, 99.4291875, 75.88075001,
65.769875, 62.62937501, 59.697875, 57.68675, 56.67275, 56.69425001,
57.296, 638.95125, 1210.92875, 990.09, 907.895625, 1122.704375,
569.139375, 295.6725, 263.21, 236.3885625, 153.935, 148.0899375,
161.72675, 102.1535, 115.8895, 114.2579375, 95.0023125, 90.72425,
84.2715, 71.06631251, 63.39812501, 75.2416875, 73.9420625, 72.26237501,
402.8870625, 1148.9775, 1058.86875, 896.47875, 890.9687501, 833.645625,
670.310625, 298.576875, 306.538125, 386.403125, 196.8343751,
174.0815, 156.6158126, 117.9345625, 105.095, 90.24362501, 87.08800001,
86.1628125, 105.3179375, 107.312625, 114.089, 104.85825, 95.9281875,
126.2874375, 529.7325626, 1533.605625, 1089.700625, 760.47, 1905.04125,
1744.460625, 1346.120625, 847.90625, 459.4625, 472.11, 474.5068751,
481.83875, 937.3618751, 267.1339376, 137.7895625, 124.0425625,
188.9461875, 108.8915, 111.67825, 84.319125, 127.1240625, 117.519125,
104.16225, 105.2490625, 158.5540625, 1215.278063, 1275.396875,
1301.14375, 1108.9225, 1280.390625, 1434.5225, 1038.926875, 599.3218751,
665.22875, 646.935, 487.604375, 236.8148126, 300.6626876, 830.2973126,
1286.612313, 403.0063751, 405.9081875, 156.5465625, 101.7661875,
100.2301875, 144.424625, 126.781125, 143.940625, 365.5354376,
849.313125, 1729.3175, 1762.6625, 1733.968751, 1275.4375, 1332.930625,
620.5625, 504.115625, 538.4556251, 433.5725, 337.094375, 225.183125,
184.7196251, 156.7545, 130.23325, 134.0796875, 145.196125, 153.243,
141.096375, 414.8042501, 304.7252501, 204.1889375, 129.7909375,
555.0276251, 1734.235625, 1780.209375, 1462.7825, 967.4743751,
1248.791875, 1001.655625, 662.92875, 546.801875, 393.72375, 308.85125,
259.985625, 280.439375, 274.1312501, 310.0375001, 270.3, 201.9288125,
219.6061875, 208.372625, 156.2023125, 162.5355626, 167.823875,
151.3928126, 151.42875, 293.7148125, 434.9101875, 943.9243751,
995.955, 614.055, 1387.864375, 1752.330626, 1238.1725, 1260.93,
565.383125, 253.179375, 275.66625, 280.4912501, 222.684375, 338.079875,
201.9365625, 136.7436875, 146.5479375, 142.794625, 150.3969375,
134.6478125, 88.22068751, 70.6445, 71.73737501, 243.176, 1559.0925,
1527.626876, 1260.648125, 1251.564375, 714.1075001, 1124.58,
901.6018751, 551.9075, 303.1025, 355.435625, 372.32375, 235.62125,
185.39475, 161.7701876, 155.4823751), index = structure(c(1438380000,
1438383600, 1438387200, 1438390800, 1438394400, 1438398000, 1438401600,
1438405200, 1438408800, 1438412400, 1438416000, 1438419600, 1438423200,
1438426800, 1438430400, 1438434000, 1438437600, 1438441200, 1438444800,
1438448400, 1438452000, 1438455600, 1438459200, 1438462800, 1438466400,
1438470000, 1438473600, 1438477200, 1438480800, 1438484400, 1438488000,
1438491600, 1438495200, 1438498800, 1438502400, 1438506000, 1438509600,
1438513200, 1438516800, 1438520400, 1438524000, 1438527600, 1438531200,
1438534800, 1438538400, 1438542000, 1438545600, 1438549200, 1438552800,
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1438599600, 1438603200, 1438606800, 1438610400, 1438614000, 1438617600,
1438621200, 1438624800, 1438628400, 1438632000, 1438635600, 1438639200,
1438642800, 1438646400, 1438650000, 1438653600, 1438657200, 1438660800,
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1438686000, 1438689600, 1438693200, 1438696800, 1438700400, 1438704000,
1438707600, 1438711200, 1438714800, 1438718400, 1438722000, 1438725600,
1438729200, 1438732800, 1438736400, 1438740000, 1438743600, 1438747200,
1438750800, 1438754400, 1438758000, 1438761600, 1438765200, 1438768800,
1438772400, 1438776000, 1438779600, 1438783200, 1438786800, 1438790400,
1438794000, 1438797600, 1438801200, 1438804800, 1438808400, 1438812000,
1438815600, 1438819200, 1438822800, 1438826400, 1438830000, 1438833600,
1438837200, 1438840800, 1438844400, 1438848000, 1438851600, 1438855200,
1438858800, 1438862400, 1438866000, 1438869600, 1438873200, 1438876800,
1438880400, 1438884000, 1438887600, 1438891200, 1438894800, 1438898400,
1438902000, 1438905600, 1438909200, 1438912800, 1438916400, 1438920000,
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1438945200, 1438948800, 1438952400, 1438956000, 1438959600, 1438963200,
1438966800, 1438970400, 1438974000, 1438977600, 1438981200, 1438984800,
1438988400, 1438992000, 1438995600, 1438999200, 1439002800, 1439006400,
1439010000, 1439013600, 1439017200, 1439020800, 1439024400, 1439028000,
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1439118000, 1439121600, 1439125200, 1439128800, 1439132400, 1439136000,
1439139600, 1439143200, 1439146800, 1439150400, 1439154000, 1439157600,
1439161200, 1439164800, 1439168400, 1439172000, 1439175600, 1439179200,
1439182800, 1439186400, 1439190000, 1439193600, 1439197200, 1439200800,
1439204400, 1439208000, 1439211600, 1439215200, 1439218800, 1439222400,
1439226000, 1439229600, 1439233200, 1439236800, 1439240400, 1439244000,
1439247600, 1439251200, 1439254800, 1439258400, 1439262000, 1439265600,
1439269200, 1439272800, 1439276400, 1439280000, 1439283600, 1439287200,
1439290800, 1439294400, 1439298000, 1439301600, 1439305200, 1439308800,
1439312400, 1439316000, 1439319600, 1439323200, 1439326800, 1439330400,
1439334000, 1439337600, 1439341200, 1439344800, 1439348400, 1439352000,
1439355600, 1439359200, 1439362800, 1439366400, 1439370000, 1439373600,
1439377200, 1439380800, 1439384400, 1439388000, 1439391600, 1439395200,
1439398800, 1439402400, 1439406000, 1439409600, 1439413200, 1439416800,
1439420400, 1439424000, 1439427600, 1439431200, 1439434800, 1439438400,
1439442000, 1439445600, 1439449200, 1439452800, 1439456400, 1439460000,
1439463600, 1439467200, 1439470800, 1439474400, 1439478000, 1439481600,
1439485200, 1439488800, 1439492400, 1439496000, 1439499600, 1439503200,
1439506800, 1439510400, 1439514000, 1439517600, 1439521200, 1439524800,
1439528400, 1439532000, 1439535600, 1439539200, 1439542800, 1439546400,
1439550000, 1439553600, 1439557200, 1439560800, 1439564400, 1439568000,
1439571600, 1439575200, 1439578800, 1439582400, 1439586000, 1439589600,
1439593200, 1439596800, 1439600400, 1439604000, 1439607600, 1439611200,
1439614800, 1439618400, 1439622000, 1439625600, 1439629200, 1439632800,
1439636400, 1439640000, 1439643600, 1439647200, 1439650800, 1439654400,
1439658000, 1439661600, 1439665200, 1439668800, 1439672400), tzone = "UTC", tclass = c("chron",
"dates", "times")), class = c("xts", "zoo"), .indexCLASS = c("chron",
"dates", "times"), tclass = c("chron", "dates", "times"), .indexTZ = "UTC", tzone = "UTC", .Dim = c(360L,
1L), .Dimnames = list(NULL, "x"))
And my y-variable:
> dput(Xq$y)
structure(c(-0.274050833, -0.236638333, -0.1994325, -0.174091667,
-0.153273333, -0.136978333, -0.124748333, -0.117348333, -0.147061667,
-0.346170833, -0.517939167, -0.575585833, -0.595914167, -0.563639167,
-0.53403, -0.5500525, -0.505650833, -0.471713333, -0.472485833,
-0.547393333, -0.390174167, -0.321545, -0.29781, -0.26912, -0.345084167,
-0.367618333, -0.279933333, -0.256805, -0.2514675, -0.314349167,
-0.3594375, -0.33482, -0.369094167, -0.4801075, -0.554780833,
-0.600498333, -0.604796667, -0.544491667, -0.636653333, -0.568401667,
-0.485494167, -0.453199167, -0.417475, -0.38417, -0.341585833,
-0.2821625, -0.248325, -0.2230575, -0.2449075, -0.2385375, -0.231885,
-0.214125, -0.194190833, -0.178575833, -0.1677675, -0.1615725,
-0.1739675, -0.2432125, -0.402414167, -0.448185833, -0.563599167,
-0.5855025, -0.55586, -0.516350833, -0.47892, -0.4603325, -0.434146667,
-0.37602, -0.320976667, -0.267863333, -0.250915, -0.241764167,
-0.271475, -0.2170225, -0.206605, -0.2088, -0.214511667, -0.2086825,
-0.203060833, -0.192895, -0.212629167, -0.319143333, -0.42647,
-0.4623275, -0.467844167, -0.506395833, -0.507088333, -0.496953333,
-0.511156667, -0.487846667, -0.4455525, -0.398383333, -0.565926667,
-0.429720833, -0.314555, -0.418586667, -0.578256667, -0.414858333,
-0.345411667, -0.3088925, -0.304373333, -0.334221667, -0.305029167,
-0.273269167, -0.315901667, -0.409731667, -0.500245833, -0.505959167,
-0.54742, -0.574725, -0.548458333, -0.5560675, -0.50246, -0.411618333,
-0.35965, -0.331884167, -0.312573333, -0.298478333, -0.289144167,
-0.274429167, -0.2218225, -0.198025833, -0.191955833, -0.1780825,
-0.157910833, -0.135935, -0.116965833, -0.099886667, -0.0864975,
-0.188904167, -0.325656667, -0.444044167, -0.5050425, -0.54236,
-0.547500833, -0.5399775, -0.521490833, -0.444911667, -0.388053333,
-0.327650833, -0.2478, -0.2026775, -0.1693425, -0.140848333,
-0.130440833, -0.103501667, -0.088843333, -0.09344, -0.126231667,
-0.158463333, -0.181145, -0.185095, -0.11657, -0.0349225, -0.223260833,
-0.431800833, -0.516, -0.487566667, -0.49941, -0.541773333, -0.511953333,
-0.4404425, -0.4021125, -0.380635, -0.3301275, -0.302468333,
-0.311290833, -0.2774125, -0.223738333, -0.192925, -0.176034167,
-0.158151667, -0.136234167, -0.117050833, -0.101201667, -0.08872,
-0.0855675, -0.257761667, -0.424794167, -0.528980833, -0.56834,
-0.591263333, -0.56574, -0.53178, -0.5410825, -0.552130833, -0.501294167,
-0.44576, -0.402855833, -0.316163333, -0.2515275, -0.213383333,
-0.186723333, -0.174315833, -0.170100833, -0.154446667, -0.147075833,
-0.169245, -0.188474167, -0.187344167, -0.2586975, -0.440815,
-0.534344167, -0.598391667, -0.613878333, -0.624658333, -0.583091667,
-0.508740833, -0.518310833, -0.51102, -0.4203825, -0.364895833,
-0.31302, -0.27689, -0.254130833, -0.232273333, -0.218198333,
-0.22501, -0.235655833, -0.242728333, -0.260448333, -0.263113333,
-0.243530833, -0.222301667, -0.274098333, -0.3974225, -0.484443333,
-0.506165, -0.6136375, -0.631805, -0.596274167, -0.539795, -0.446769167,
-0.398489167, -0.3986925, -0.4098625, -0.484515833, -0.375961667,
-0.291685, -0.273963333, -0.2621025, -0.2054525, -0.177625833,
-0.1564625, -0.141883333, -0.130685833, -0.121943333, -0.112834167,
-0.136975, -0.3204725, -0.458004167, -0.5262175, -0.527530833,
-0.520574167, -0.5800325, -0.561841667, -0.4909225, -0.4641625,
-0.443126667, -0.405705833, -0.3098625, -0.286379167, -0.30477,
-0.395341667, -0.38505, -0.356219167, -0.263050833, -0.219625833,
-0.197383333, -0.2019775, -0.226838333, -0.2333075, -0.2890475,
-0.3341175, -0.43458, -0.520441667, -0.577875, -0.568123333,
-0.551936667, -0.463691667, -0.468790833, -0.4747725, -0.4125925,
-0.394731667, -0.380213333, -0.308688333, -0.279549167, -0.249766667,
-0.233964167, -0.229904167, -0.244835833, -0.232436667, -0.215466667,
-0.198559167, -0.184533333, -0.185376667, -0.246823333, -0.395918333,
-0.4956775, -0.540474167, -0.5402375, -0.577863333, -0.561466667,
-0.503130833, -0.455221667, -0.4401875, -0.4187675, -0.389215,
-0.345275, -0.3378175, -0.348759167, -0.325149167, -0.29995,
-0.289409167, -0.291635, -0.301183333, -0.274799167, -0.2443375,
-0.2254225, -0.21272, -0.245265, -0.295081667, -0.356848333,
-0.4258325, -0.4329175, -0.487074167, -0.595525, -0.59333, -0.564645,
-0.464548333, -0.4294775, -0.425609167, -0.403769167, -0.353311667,
-0.282888333, -0.2464575, -0.231771667, -0.2275425, -0.224230833,
-0.218419167, -0.1902275, -0.161319167, -0.143495833, -0.133691667,
-0.181291667, -0.36056, -0.46681, -0.5194575, -0.532989167, -0.4899375,
-0.533224167, -0.4976575, -0.428966667, -0.412929167, -0.416463333,
-0.366666667, -0.316356667, -0.3023825, -0.282655, -0.267275833
), index = structure(c(1438380000, 1438383600, 1438387200, 1438390800,
1438394400, 1438398000, 1438401600, 1438405200, 1438408800, 1438412400,
1438416000, 1438419600, 1438423200, 1438426800, 1438430400, 1438434000,
1438437600, 1438441200, 1438444800, 1438448400, 1438452000, 1438455600,
1438459200, 1438462800, 1438466400, 1438470000, 1438473600, 1438477200,
1438480800, 1438484400, 1438488000, 1438491600, 1438495200, 1438498800,
1438502400, 1438506000, 1438509600, 1438513200, 1438516800, 1438520400,
1438524000, 1438527600, 1438531200, 1438534800, 1438538400, 1438542000,
1438545600, 1438549200, 1438552800, 1438556400, 1438560000, 1438563600,
1438567200, 1438570800, 1438574400, 1438578000, 1438581600, 1438585200,
1438588800, 1438592400, 1438596000, 1438599600, 1438603200, 1438606800,
1438610400, 1438614000, 1438617600, 1438621200, 1438624800, 1438628400,
1438632000, 1438635600, 1438639200, 1438642800, 1438646400, 1438650000,
1438653600, 1438657200, 1438660800, 1438664400, 1438668000, 1438671600,
1438675200, 1438678800, 1438682400, 1438686000, 1438689600, 1438693200,
1438696800, 1438700400, 1438704000, 1438707600, 1438711200, 1438714800,
1438718400, 1438722000, 1438725600, 1438729200, 1438732800, 1438736400,
1438740000, 1438743600, 1438747200, 1438750800, 1438754400, 1438758000,
1438761600, 1438765200, 1438768800, 1438772400, 1438776000, 1438779600,
1438783200, 1438786800, 1438790400, 1438794000, 1438797600, 1438801200,
1438804800, 1438808400, 1438812000, 1438815600, 1438819200, 1438822800,
1438826400, 1438830000, 1438833600, 1438837200, 1438840800, 1438844400,
1438848000, 1438851600, 1438855200, 1438858800, 1438862400, 1438866000,
1438869600, 1438873200, 1438876800, 1438880400, 1438884000, 1438887600,
1438891200, 1438894800, 1438898400, 1438902000, 1438905600, 1438909200,
1438912800, 1438916400, 1438920000, 1438923600, 1438927200, 1438930800,
1438934400, 1438938000, 1438941600, 1438945200, 1438948800, 1438952400,
1438956000, 1438959600, 1438963200, 1438966800, 1438970400, 1438974000,
1438977600, 1438981200, 1438984800, 1438988400, 1438992000, 1438995600,
1438999200, 1439002800, 1439006400, 1439010000, 1439013600, 1439017200,
1439020800, 1439024400, 1439028000, 1439031600, 1439035200, 1439038800,
1439042400, 1439046000, 1439049600, 1439053200, 1439056800, 1439060400,
1439064000, 1439067600, 1439071200, 1439074800, 1439078400, 1439082000,
1439085600, 1439089200, 1439092800, 1439096400, 1439100000, 1439103600,
1439107200, 1439110800, 1439114400, 1439118000, 1439121600, 1439125200,
1439128800, 1439132400, 1439136000, 1439139600, 1439143200, 1439146800,
1439150400, 1439154000, 1439157600, 1439161200, 1439164800, 1439168400,
1439172000, 1439175600, 1439179200, 1439182800, 1439186400, 1439190000,
1439193600, 1439197200, 1439200800, 1439204400, 1439208000, 1439211600,
1439215200, 1439218800, 1439222400, 1439226000, 1439229600, 1439233200,
1439236800, 1439240400, 1439244000, 1439247600, 1439251200, 1439254800,
1439258400, 1439262000, 1439265600, 1439269200, 1439272800, 1439276400,
1439280000, 1439283600, 1439287200, 1439290800, 1439294400, 1439298000,
1439301600, 1439305200, 1439308800, 1439312400, 1439316000, 1439319600,
1439323200, 1439326800, 1439330400, 1439334000, 1439337600, 1439341200,
1439344800, 1439348400, 1439352000, 1439355600, 1439359200, 1439362800,
1439366400, 1439370000, 1439373600, 1439377200, 1439380800, 1439384400,
1439388000, 1439391600, 1439395200, 1439398800, 1439402400, 1439406000,
1439409600, 1439413200, 1439416800, 1439420400, 1439424000, 1439427600,
1439431200, 1439434800, 1439438400, 1439442000, 1439445600, 1439449200,
1439452800, 1439456400, 1439460000, 1439463600, 1439467200, 1439470800,
1439474400, 1439478000, 1439481600, 1439485200, 1439488800, 1439492400,
1439496000, 1439499600, 1439503200, 1439506800, 1439510400, 1439514000,
1439517600, 1439521200, 1439524800, 1439528400, 1439532000, 1439535600,
1439539200, 1439542800, 1439546400, 1439550000, 1439553600, 1439557200,
1439560800, 1439564400, 1439568000, 1439571600, 1439575200, 1439578800,
1439582400, 1439586000, 1439589600, 1439593200, 1439596800, 1439600400,
1439604000, 1439607600, 1439611200, 1439614800, 1439618400, 1439622000,
1439625600, 1439629200, 1439632800, 1439636400, 1439640000, 1439643600,
1439647200, 1439650800, 1439654400, 1439658000, 1439661600, 1439665200,
1439668800, 1439672400), tzone = "UTC", tclass = c("chron", "dates",
"times")), class = c("xts", "zoo"), .indexCLASS = c("chron",
"dates", "times"), tclass = c("chron", "dates", "times"), .indexTZ = "UTC", tzone = "UTC", .Dim = c(360L,
1L), .Dimnames = list(NULL, "y"))
My code looks like this:
library(zoo)
mat_slope=matrix(nrow=i)
mat_intercept=matrix(nrow=i)
for(i in seq(from=24, to=240, by=24)){
mov.reg<- rollapplyr(Xq,
width= i,
by=24,
FUN = function(y,x)
{
coefficients(lm(formula=y~x, data = Xq))
},
by.column=FALSE)
mat_slope[i] <- coefficients(mov.reg)[2]
mat_intercept[i] <-coefficients(mov.reg)[1]
mat_intercept=rbind(i,mat_intercept)
mat_slope[n,i]=rbind(i, mat_slope)
}
I either get an empty matrix as result, or the following error message:
Error in merge.xts(res, xts(, idx, attr(data, "frequency"))) :
(list) object cannot be coerced to type 'double'
If anyone have some inputs or ideas it would be very appreciated! Thank's a lot!

mov.reg is not an lm objct, so it does not make sense to take coefficients of it. Also there is a reference to chron in the dput output so we need to load the chron package.
Xq is not specified but rather two separate xts objects are shown so we have assumed that those two are called x and y. Then create a single zoo object z from them. Now define a Coef function which takes a matrix with x and y columns and performs the indicated regression returning the coefficients. Also define roll which takes the width as input and runs rollapplyr with the desired arguments. Now use lapply to run roll with each width producing a list of zoo objects. This list has one component for each element in widths consisting of a zoo object with intercept and slope columns. Finally extract the intercepts from each component in the list and form a zoo object and do the same for the slopes. Note that if L is a list of zoo objects then do.call("merge", L) will produce a single zoo object from it.
library(xts) # this also loads zoo
library(chron)
# inputs are xts objects x and y
z <- cbind(x = as.zoo(x)[, 1], y = as.zoo(y)[, 1])
Coef <- function(m) coef(lm(y ~ x, as.data.frame(m)))
roll <- function(w) rollapplyr(z, w, Coef, by = 24, by.column = FALSE)
widths <- seq(24, 240, 24)
names(widths) <- widths # lapply will use these names for its output
L <- lapply(widths, roll)
intercepts <- do.call("merge", lapply(L, "[", TRUE, 1)) # extract 1st columns
slopes <- do.call("merge", lapply(L, "[", TRUE, 2)) # 2nd columns
Alternately do the rollapplyr twice replacing the last 3 lines with:
intercepts <- do.call("merge", lapply(widths, function(w) roll(w)[, 1]))
slopes <- do.call("merge", lapply(widths, function(w) roll(w)[, 2]))

Error when using cluster package to compute euclidean distances

I have been working on a text mining project. I have performed some LDA topic modelling and now I have my topic probabilities. I would like to use the cluster package so that I can get the euclidean distances between documents so I can create a network graph, but I keep on getting an error. Any recommendation for good visualisation techniques would also be warmly welcomed :)
library(cluster)
FundDist <- as.matrix(daisy(EUTopicNetworks, metric = "euclidean", stand = TRUE))
Error in daisy(EUTopicNetworks, metric = "euclidean", stand = TRUE) : invalid type character for column numbers 1
In addition: Warning messages:
1: In data.matrix(x) : NAs introduced by coercion
2: In daisy(EUTopicNetworks, metric = "euclidean", stand = TRUE) :
with mixed variables, metric "gower" is used automatically
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
I have never uploaded reproducible data on this website using the dput() function before. So I hope I have done this correct. I have copied and pasted the output below. Thank you for taking the time to read my problem.
EUTopicNetworks <- structure(list(Filename = c("AT_Burenland_2007.txt", "AT21_Kaernten_07.txt",
"AT12_LowerAustria_07_13.txt", "AT_Nat_2007.txt", "AT34_Salzburg_07.txt",
"AT22_Steiermark_07.txt", "AT36_Tirol_07.txt", "UpperAustria2007.txt",
"AT13_Vienna_07.txt", "vorarlberg2007.txt", "AT_Austria_1.txt",
"AT11_Burgenland_1", "lowe austria 2014.txt", "AT13_Vienna2_14.txt",
"AT21_Kaernten_14.txt", "AT22_Steiermark_14.txt", "AT31_UpperAustria_14.txt",
"AT35_Salzburg_14.txt", "AT36_Tirol_14.txt", "AT37_Vorarlberg_14.txt",
"abbruzzo2007-2013.txt", "calabria2007-2013.txt", "campania2007-2013.txt",
"emiliaromagna2007-2013.txt", "sicily2007.txtt", "friuli2007-2013.txt",
"lazio2007-2013.txt", "liguria2007.txt", "lombardy2007-2013.txt",
"piemonte2007-2013.txt", "puglia2007-2013.txt", "sardinia2007-2013.txt",
"Bolzano_07.txt", "umbria 2007-2013.txt", "valledaosta 2007-2013.txt",
"tuscany2007.txt", "VENETO2007-2013.txt", "abruzzo2014-2020.txt",
"basilicata2014-2020.txt", "calabria2014-2020.txt", "campania2014-2020.txt",
"emiliaromagna2014-2020.txt", "sicily2014.txt", "friuli2014-2020.txt",
"lazio2014.txt", "liguria2014.txt", "lombardia2014-2020.txt",
"piemonte2014-2020.txt", "puglia_14.txt", "sardinia2014.txt",
"Bolzano_14.txt", "umbria2014.txt", "valledaosta 2014-2020.txt",
"tuscany2014.txt", "molise_14.txt", "molise_07.txt", "trento2007.txt",
"trento2014.txt", "ITALIANSTRATEGICPLAN2007-2013.txt", "italyinnovationstrategy2014-2020.txt",
"veneto2014-2020.txt", "aquitanie2014-2020.txt", "aquitanie2007.txt",
"auvergne2014-2020.txt", "auvergne_07.txt", "bretagne2014-2020.txt",
"bretagne_07.txt", "centre2014-2020.txt", "centre2007.txt", "champagne-ardenne 2007.txt",
"champagne-ardenne 2014.txt", "PICARDIE2007.txt", "picardie2014.txt",
"bassenormandie 2007.txt", "bassenormandie 2014.txt", "bourgogne2014.txt",
"bourgogne_07.txt", "midi-pyrenees2007.txt", "midipyrennes14.txt",
"franche-comte2014-2020.txt", "franche-comte_2007.txt", "hautenormandie2007.txt",
"hautenormandie2014-2020.txt", "limousine2014-2020.txt", "limousine2007.txt",
"loire2007.txt", "loire2014-2020.txt", "lorraine2014-2020.txt",
"lorraine2007.txt", "nordpasdecalais2007.txt", "nordpasdecalais2014-2020.txt",
"rhonealpes2014-2020.txt", "rhone-alpes2007.txt", "poitou-charenter2007.txt",
"poituou-charentes2014.txt", "corse2007.txt", "corsica.txt",
"bretagne_07.txt", "bretagne2014-2020.txt", "Baden-Wu_07.txt",
"Baden-wu14.txt", "bavaria2007.txt", "BAVARIA_14.txt", "BERLIN2014-2020.txt",
"Berlin_07.txt", "bradenburgh2014.txt", "Bradenburgh2007.txt",
"bremen2007.txt", "bremen2014.txt", "hamburg_07.txt", "HAMBURGO2014-2020.txt",
"Hessen_07.txt", "Hessian1.txt", "LowerSaxony2_07.txt", "LOWERSAXONY2014-2020.txt",
"Mecklenburg_07.txt", "MECKPOMM2014-2020.txt", "rheinland2014-2020.txt",
"RhinelanPlatz_07.txt", "saarland2014-2020.txt", "saarland_07.txt",
"sachsen-anhalt2014-2020.txt", "sachsen-anhelt2007.txt", "saxony_07.txt",
"saxony_14.txt", "Schleswig-Holstein2020.txt", "Schleswig-Holstein_07.txt",
"thuringia2007.txt", "THURINGIA2014-2020.txt", "Andalucia_2007-2013.txt",
"Andalusia_14.txt", "Aragon_14.txt", "Aragon_2007.txt", "Asturias_2007.txt",
"ES12_Asturias.txt", "Baleares_2007.txt", "Balears_14.txt", "Canarias_07.txt",
"Canaries_14.txt", "Cantabria_2007.txt", "ES13_Cantabria_14.txt",
"Castillala_Mancha_2007.txt", "ES42_Castilla-la_mancha.txt",
"CastillayLeon_dic_2007.txt", "ES41_Castilla-Leon.txt", "ES51_Catalonia_14.txt",
"catalonia2007.txt", "Madrid_2007-13.txt", "Madrid_14.txt", "Murcia_14.txt",
"murcia2007.txt", "Valencia_14.txt", "Valenciana_2007.txt", "laRioja2007.txt",
"CombiEngland_07.txt", "EastWales_07.txt", "NorthernIreland_07.txt",
"Scotland_07.txt", "WestWales_07.txt", "EastWales_14.txt", "England_14.txt",
"Northern_Ireland14.txt", "Scotland14.txt", "Westwales_14.txt",
"malta2007-2013.2.txt", "malta2014-2020.txt2.txt"), Funds = c(0.028649302,
0.036198106, 0.041060412, 0.036543709, 0.047044295, 0.01659907,
0.019221094, 0.056763265, 0.052615278, 0.045216842, 0.048176521,
0.038976137, 0.027341846, 0.037721688, 0.049252945, 0.05918185,
0.05440539, 0.017412537, 0.029307636, 0.022385126, 0.019737738,
0.027626844, 0.0334503, 0.043976555, 0.042856083, 0.021046234,
0.018061427, 0.014983543, 0.067145641, 0.019741648, 0.019018285,
0.030614714, 0.019666862, 0.028158874, 0.026009936, 0.019330949,
0.023088856, 0.044273539, 0.021168401, 0.017627883, 0.030486684,
0.017509486, 0.034035728, 0.034106673, 0.043486846, 0.029087254,
0.050564915, 0.047219925, 0.051437475, 0.029694445, 0.008588781,
0.045469371, 0.060967658, 0.049260664, 0.015106536, 0.026186649,
0.023254401, 0.053579943, 0.031056644, 0.045125396, 0.057680642,
0.01125217, 0.042532521, 0.041545015, 0.047940862, 0.036641552,
0.072252939, 0.035679102, 0.067488953, 0.008492444, 0.021052205,
0.020152732, 0.040564092, 0.02921307, 0.018565646, 0.022775302,
0.011711217, 0.019967731, 0.00877454, 0.022250866, 0.003696986,
0.011277284, 0.007740289, 0.02790784, 0.008134596, 0.014931457,
0.03269353, 0.041386999, 0.066164327, 0.011440048, 0.006215758,
0.010688796, 0.003811851, 0.003303556, 0.023094521, 0.010550119,
0.018023822, 0.022757839, 0.017667203, 0.02073341, 0.013537221,
0.011950717, 0.009010298, 0.019796088, 0.011314152, 0.01098032,
0.008832217, 0.040330019, 0.005822583, 0.006599734, 0.016338338,
0.013906508, 0.010973094, 0.010448791, 0.003723683, 0.013769165,
0.007583811, 0.009724543, 0.00237987, 0.005005899, 0.005048481,
0.013000829, 0.012671508, 0.003054379, 0.03508621, 0.012981055,
0.021982606, 0.009448894, 0.014883524, 0.018772709, 0.006068872,
0.018122102, 0.020449118, 0.015102835, 0.005449833, 0.011014679,
0.016602374, 0.006482356, 0.009969209, 0.002646448, 0.01205523,
0.04659564, 0.010866707, 0.0144986, 0.046946229, 0.028629168,
0.034634807, 0.059078927, 0.002919951, 0.016168915, 0.024403654,
0.09171777, 0.009978063, 0.015196456, 0.015174811, 0.047399696,
0.015303701, 0.011753077, 0.014862118, 0.01487099, 0.011742448,
0.018346786, 0.010785336, 0.010421162, 0.013791872, 0.026389358
), Biotech = c(0.024814541, 0.005668351, 0.017716491, 0.00853945,
0.015916015, 0.03888657, 0.001333459, 0.017368849, 0.023781704,
0.051278428, 0.005484117, 0.021759003, 0.027973849, 0.002774256,
0.005744201, 0.004244159, 0.00468969, 0.000581776, 0.022734494,
0.03445351, 0.000800523, 0.000362683, 0.026945766, 0.006823146,
0.005847249, 0.000630851, 0.020794353, 0.035979974, 0.006165474,
0.027793267, 0.00504312, 0.018927097, 0.000760576, 0.012289583,
0.002109001, 0.000442817, 0.000594334, 0.00037428, 0.06596126,
0.027988907, 0.019067461, 0.024872467, 0.015379713, 0.015295277,
9.36e-05, 0.000117979, 4e-05, 0.031220784, 0.001357913, 0.040951957,
0.000438858, 0.038880733, 0.00115553, 0.041152387, 0.042576251,
0.002254845, 0.022345729, 0.002596388, 0.022562024, 0.000243528,
0.000885187, 0.013339204, 0.001418329, 0.028089687, 0.002057198,
0.000244579, 0.000140129, 0.051721762, 0.014989271, 0.001673642,
0.04500578, 0.001615416, 0.00010688, 8.18e-05, 0.000526549, 0.024849247,
0.032961749, 0.033875354, 0.032145136, 0.012619383, 0.003522134,
0.012225185, 0.043464039, 0.077400519, 0.056308327, 0.020638077,
0.049992043, 0.038864222, 0.039459316, 0.034937031, 0.037406742,
0.029987413, 0.002413193, 0.000584526, 0.004584848, 0.012491496,
0.031710331, 0.017858395, 0.030812232, 0.003435739, 0.02648106,
0.006927007, 0.030785802, 0.044329986, 0.009838859, 0.002951219,
0.030722621, 0.020511401, 0.013623405, 0.081263322, 0.029623712,
0.003790876, 0.00335598, 0.018842609, 0.008430911, 0.032611226,
0.057455638, 0.004304486, 0.015733474, 0.043981231, 7.95e-05,
0.004054158, 0.045173701, 0.016378658, 0.015906368, 2.92e-05,
0.00057313, 0.00079682, 0.013209159, 0.039911915, 0.000237856,
0.022373161, 0.015821272, 0.026750309, 0.048698356, 0.041430357,
0.00287091, 0.007965338, 0.034481633, 0.001543219, 0.022152119,
0.041801127, 0.017463336, 0.038010604, 0.050393079, 0.045031199,
0.043613378, 0.037411148, 0.00186188, 0.018962051, 0.043254408,
0.018666636, 0.027696462, 0.024293257, 0.062711642, 0.000519461,
0.001056595, 0.031300324, 0.024742217, 0.024718682, 0.000780182,
0.01862668, 0.000973041, 0.000542227, 0.001011475, 0.011077226
), Transfers = c(0.00473547, 0.00038783, 0.000424567, 0.000695775,
0.000135175, 0.010334213, 0.000106781, 0.003008423, 0.000608193,
0.010326284, 0.000934925, 0.031277279, 0.00572826, 0.000260722,
0.001021529, 0.000154104, 0.000220061, 4.32e-05, 0.018335222,
0.013011634, 2.49e-05, 4.83e-05, 0.021935677, 0.000390414, 0.000130749,
3.77e-05, 0.009460382, 0.146681735, 7.44e-05, 0.082389135, 0.000592343,
0.000562132, 1.53e-05, 0.020403948, 1.31e-05, 2.46e-05, 5.51e-05,
0.000321357, 0.037377138, 0.006516009, 0.022055996, 0.041838049,
0.002549792, 0.00271147, 8.55e-05, 0.001550897, 0.001094715,
0.002059784, 2.73e-05, 0.012813067, 9.84e-06, 0.009924993, 8.74e-05,
0.004619721, 0.013069859, 2.14e-05, 0.053722696, 5.79e-05, 0.006753522,
1.18e-05, 0.005116721, 0.000108002, 2.73e-05, 0.003596542, 2.79e-05,
0.00438903, 8.31e-05, 0.026310482, 0.001005592, 0.000428282,
0.049529581, 1.93e-05, 8.57e-05, 0.001610554, 9.92e-06, 0.094923027,
0.031919217, 0.13955002, 0.083229087, 0.000284159, 0.000267466,
0.000349366, 0.056697448, 0.049064161, 0.075636951, 0.004204928,
0.006115066, 0.007264789, 0.002044115, 0.043477142, 0.046506897,
0.082070827, 0.00035585, 0.010126049, 0.000178782, 0.000133394,
0.019258021, 9.19e-05, 0.069771158, 0.164961859, 0.030302868,
0.008376654, 0.095394069, 0.069931231, 0.000553351, 0.000544636,
0.095332857, 0.001748097, 0.000288915, 0.049584358, 0.095331287,
0.000598831, 0.001574565, 0.124263691, 3.34e-05, 0.107925558,
0.087354139, 0.000618826, 0.000110399, 0.035831715, 5.52e-06,
0.003000538, 0.076722556, 0.001625612, 0.00057855, 2.15e-05,
6.78e-05, 0.000268523, 0.000567245, 0.04113056, 1.71e-05, 0.03401376,
0.001848523, 0.029357767, 0.078771496, 0.05552954, 0.068487283,
0.001617493, 0.045003856, 0.000170027, 0.102169304, 0.033286348,
0.000645582, 0.123061518, 0.024437451, 0.002628661, 0.013120533,
0.002000205, 0.000545963, 0.103891281, 0.01547252, 0.004918401,
0.032767954, 0.084638687, 0.093356166, 0.000156201, 0.000752217,
0.109659324, 0.208642497, 0.208474925, 0.000404265, 0.078084401,
0.000538784, 0.012066067, 0.018067282, 0.000205862), Collab = c(0.030001488,
0.036707564, 0.01458121, 0.026231048, 0.018525526, 0.011553297,
0.058634057, 0.001686141, 0.001348074, 0.006757227, 0.013508918,
0.003715637, 0.002921306, 0.009278328, 0.004626478, 0.002879119,
0.055770088, 0.095661212, 0.017193222, 0.004260887, 0.0994825,
0.094794299, 0.00236101, 0.05708391, 0.070789976, 0.093534164,
0.001109712, 0.009766358, 0.033402635, 0.011669702, 0.06682796,
0.001608723, 0.076258585, 0.0177607, 0.081032098, 0.094412392,
0.105163053, 0.000130001, 0.000308904, 0.000673957, 0.000108183,
0.006185235, 0.001417778, 0.001392482, 0.001763266, 4.19e-05,
0.000316372, 0.000538187, 0.057255911, 0.000888558, 0.117687659,
0.002003037, 0.068194122, 0.000653657, 0.000152612, 0.089555908,
0.002829031, 0.032391752, 0.000114824, 0.001213285, 0.000386851,
0.015705495, 0.049863754, 0.000186015, 0.036288112, 0.000121075,
0.001514642, 0.00150885, 0.000594681, 0.139375952, 0.002323917,
0.075647519, 0.002870689, 3.77e-05, 0.077144908, 0.026437255,
0.000115174, 0.00227099, 0.004700389, 0.041492391, 0.122675327,
0.020817113, 6.89e-05, 0.000303617, 0.000137477, 0.001432608,
0.000184365, 0.001050974, 0.000709209, 0.000270104, 0.000303001,
0.018320147, 0.099247105, 0.082998488, 0.000888759, 0.016183068,
0.006294048, 0.002853816, 0.019514895, 0.038458183, 0.002923949,
0.106293548, 0.011739459, 0.000128574, 0.007004556, 0.114129525,
0.012154148, 0.00942754, 0.009594396, 1.79e-05, 0.003734627,
8.05e-06, 0.119908919, 0.018081544, 0.075305864, 0.008538072,
0.000172614, 0.011539718, 0.001156176, 2.3e-05, 0.06492041, 0.12754611,
0.00024379, 0.006267908, 0.00306844, 0.001193837, 0.013286424,
0.113241894, 0.00550093, 0.000513184, 0.164987722, 0.008430982,
0.01127053, 0.00073653, 0.000330426, 0.002238095, 0.104762755,
0.010050252, 0.000469937, 0.145991698, 0.016278919, 0.000640692,
0.005282822, 0.005445685, 0.00014593, 0.000589578, 0.003085291,
0.003763146, 0.118843056, 0.019891671, 0.007112815, 0.004553507,
0.014161345, 0.011043344, 1.65e-05, 0.05419503, 0.107074967,
0.01952576, 0.015831838, 0.015618949, 0.133629759, 0.016718132,
0.120940954, 0.072855599, 0.066799617, 0.006925232)), .Names = c("Filename",
"Funds", "Biotech", "Transfers", "Collab"), class = "data.frame", row.names = c(NA,
-166L))

As #Cath mentioned, your problem is with the column of text. Removing this works:
EUTopicNetworks2 <- EUTopicNetworks[,-1]
class(EUTopicNetworks2)
library(cluster)
FundDist <- as.matrix(daisy(EUTopicNetworks2, metric = "euclidean", stand = TRUE))

By running this code I was able to answer the question I posed in one of the comments
row.names(EUTopicNetworks) <- EUTopicNetworks[,1]
EUTopicNetworks <- EUTopicNetworks[,-1]
library(cluster)
FundDist <- as.matrix(daisy(EUTopicNetworks, metric = "euclidean", stand = TRUE))

Querying trades from IB using reqExecutions

Im trying to query trades from IB by using function reqExecutions:
library(IBrokers)
con <- twsConnect(clientId=1)
id <- reqIds(con)
Order <- twsOrder(orderId=id, action="BUY", totalQuantity = 1, orderType="LMT", lmtPrice = 600, tif="GTC")
placeOrder(con, twsSTK("AAPL", Order)
print(reqExecutions(con, reqId = as.character(.Last.orderId), ExecutionFilter = twsExecutionFilter(clientId="1")))
although trades are executed in IB, it always returns NULL.

Can't you just use reqOpenOrders?
Warning: The following will execute a trade. Make sure you are connected to a paper trading account before running this code!
library(IBrokers)
#con <- twsConnect(clientId=1)
con <- ibgConnect(clientId=1)
id <- reqIds(con)
Order <- twsOrder(orderId=id, action="BUY", totalQuantity = 1, orderType="LMT",
lmtPrice = 600, tif="GTC")
placeOrder(con, twsSTK("AAPL"), Order)
> reqOpenOrders(twsconn=con)
TWS Message: 2 -1 2100 New account data requested. API client has been unsubscribed from account data.
TWS Execution: orderId=1 time=2012-03-26 08:47:29 side=BOT shares=1 symbol=AAPL conId=265598 price=597.91

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