Averaging time-stamped data hourly in R - r
I've average distance travelled data meters for several days and times of the year. Here's how the dataset I'm working with looks like:
> print(datanet)
Date & Time [Local] meters
1: 2017-06-01 00:00:14 2.333355
2: 2017-06-01 01:00:13 6.952414
3: 2017-06-01 02:00:30 61.727543
4: 2017-06-01 03:00:15 235.873883
5: 2017-06-01 04:00:15 138.136375
---
1428: 2017-07-30 19:00:21 40.602983
1429: 2017-07-30 20:00:47 34.292888
1430: 2017-07-30 21:00:20 303.478297
1431: 2017-07-30 22:00:18 5.741059
Now, I would like to transform this table so that it provides the average distance travelled for each hour of the day from 0 to 23, based on data from multiple days. Here's the code I've been using for that purpose (additionally includes an sd column):
data_travel<-datanet %>%
mutate(
date = ymd_hms(`Date & Time [Local]`),
hour = hour(date)
) %>%
group_by(hour) %>%
summarise(
avg_meters = mean(meters),
sd_meters = sd(meters)
)
This works, but sadly the last hour of the day 23 always shows NA values:
> head(data_travel[19:24,])
# A tibble: 6 x 3
hour avg_meters sd_meters
<int> <dbl> <dbl>
1 18 57.4 109.
2 19 96.5 177.
3 20 121. 248.
4 21 141. 299.
5 22 76.4 86.4
6 23 NA NaN
Does somebody have an idea of how I could modify this code so that I also get the average distance travelled avg_meters and sd for hour 23? Any input is appreciated!
> dput(datanet[1:700,])
structure(list(`Date & Time [Local]` = structure(c(1464732013,
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1.88226712179479, 18.0388366074334, 48.3907627589146, 1.49068315465064,
9.44594654212787, 730.702774263893)), row.names = c(NA, -700L
), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000000002641ef0>)
```
Your meters field might have NA values.
Hence adding na.rm = T to mean and sd functions will solve this.
Related
ARDL model in R
I have this data frame on which I want to apply the ARDL model but every time I run it, it gives me an error. If anyone could please help me or point out what I am doing wrong would be highly appreciated. Error:'list' object cannot be coerced to type 'double' If I remove the prediction part of the code in the loop out then it runs otherwise no.
Data:
structure(list(Industrialproduction = c(1.65801981343852, 1.79541527049647,
-0.0326429293424051, 0.104752527715549, -0.992082392187777, -2.26823002723453,
-2.33809212404366, -3.02972688245404, -2.14713572609871, -1.29947561814794,
0.104752527715549, 0.228175565411677, 0.305023871901719, 0.218860619170459,
0.216531882610155, 0.139683576120113, 0.25146293101472, 0.249134194454415,
0.626389517223712, 1.13405408737005, 0.58214352257793, -0.0629165046263609,
-0.619484542539089, -0.652086854383349, 0.591458468819148, 2.0259601899666,
1.73021064680795, 0.561184893535192, 0.207216936368938, -0.489075295162048
), Householdconsumption = c(-1.5532531908672, -1.52804903107083,
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-1.21051793593124, -1.1953334626113, -1.17581056834279, -1.15804370160108
), Investmentgrowth = c(1.47348593810751, 2.17792802452104, 2.57620375293532,
3.11977876989162, 2.03003410582649, 1.238671909303, 0.670447905897604,
0.0127091622297187, -0.222104866574793, -0.1974558801257, -0.215618291193452,
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0.619852617923151, 0.675637166202676, 1.31910544403161, 1.23348264899792,
0.702880782804304, 1.61748791157326, 0.308496999618827, 0.395417109728784,
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-1.19380246339312, -1.19380246339312, -1.18183982294381, -1.16816823385889,
-1.14595190159589), Employment = c(-1.02390309005026, -0.997493370038682,
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-1.20499831298681, -1.19745267869778, -1.18990704440876, -1.17481577583072
), Unemploymentrate = c(-0.815370914670033, -0.815370914670033,
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0.0795302532996298, 0.463059325286628, 0.803974055941737, 1.05966010393307,
1.18750312792874, 1.3153461519244, 1.40057483458818, 1.44318917592007,
1.57103219991573, 1.6988752239114, 1.74148956524329, 1.82671824790707,
1.86933258923895, 1.91194693057084, 1.99717561323462, 1.95456127190273,
1.91194693057084, 1.86933258923895, 1.78410390657518, 1.74148956524329,
1.74148956524329, 1.74148956524329, 1.74148956524329, 1.78410390657518
), Stockmarketindex = c(-1.66493184730628, -1.66463355820282,
-1.6694061838583, -1.6887949755837, -1.67417880951378, -1.66493184730628,
-1.64285845364968, -1.63748924978726, -1.63152346771791, -1.61601243433759,
-1.62615426385549, -1.62078505999307, -1.60616889392316, -1.58976299323243,
-1.56082895019607, -1.53875555653946, -1.51548900646899, -1.47790457943206,
-1.48088747046674, -1.47074564094884, -1.41615873501425, -1.43345950301538,
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-1.3189164872838, -1.2792440365226, -1.17722916313666, -1.1805103432748
), Economicgrowth = c(-0.419475860074214, 1.77103185704958, -1.24091625399564,
0.219422224086891, -0.693289324714688, -1.14964509911548, -0.282569127753977,
-0.738924902154767, -0.282569127753977, -0.14566239543374, 0.26505780152697,
-0.191297972873819, -0.14566239543374, 0.26505780152697, -0.14566239543374,
0.0825154917666544, 0.630142421047602, 0.128151069206733, 0.26505780152697,
0.173786646646812, 0.0825154917666544, -0.647653747274609, 0.0368799143265754,
0.675777998487681, 0.128151069206733, 0.72141357592776, -0.191297972873819,
-0.14566239543374, 0.26505780152697, 0.0825154917666544), Consumptiongrowth = c(1.49948934326176,
2.80290969177971, 2.43863500922448, 3.0742184397245, 3.29809558837824,
1.78028441106475, 1.98708618397371, 1.30407115418265, 1.06311862978413,
1.41221520056623, 1.19213257985578, 1.30976294609757, 0.943590999570694,
0.717816586945313, 0.803193465669196, 0.856316856875168, 0.915132039996066,
0.932207415740842, 0.877186760563229, 0.550857357440829, 0.359233696305002,
0.484453118433365, 0.0879249483602165, 0.410459823539332, 0.437021519142318,
0.419946143397541, 0.898056664251289, 0.717816586945313, 0.896159400279647,
0.962563639287112), Governmentexpenditure = c(-1.40005492084802,
-1.38413625639177, -1.36971935273328, -1.35410104043658, -1.33277603710839,
-1.31355349889707, -1.28802356533516, -1.2715041965598, -1.26880102712384,
-1.2558858842631, -1.24627461515744, -1.24417215004058, -1.22765278126522,
-1.21503799056404, -1.2159390470427, -1.20692848225614, -1.18259995733244,
-1.17989678789647, -1.18139854869423, -1.17058587095036, -1.17509115334364,
-1.14956121978173, -1.13814783771876, -1.13093938588951, -1.13274149884682,
-1.12853656861309, -1.1210277646243, -1.11622213007147, -1.09820100049835,
-1.07867811012748), Longtermgovernmentbondyield = c(1.40229182288022,
1.52084996657255, 1.52084996657255, 2.1284604529957, 2.10623080105339,
1.8221852484572, 1.74561644732258, 1.8221852484572, 1.79254571253412,
2.04695172920723, 2.32358739782265, 2.3705166630342, 1.87899435897644,
1.81477536447643, 1.26150402724559, 1.27879375653405, 1.22939452999558,
1.13306603824557, 1.19728503274558, 1.07872688905325, 1.0515573144571,
1.28126371786097, 1.19234511009173, 1.10342650232249, 1.21704472336097,
1.14294588355326, 1.05649723711094, 1.08119685038018, 0.811971065745526,
0.752691993899364), BankRate = c(1.46586697149636, 1.35154387389459,
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1.3619748134568, 1.79713275011286, 1.62283175002842, 1.46140252936374,
1.11755703763521, 0.767494705927617, 1.00504892351727, 0.836192873884299,
0.743461821176297, 0.639548801257612, 0.614285065637952, 0.59615609267884,
1.0233656533885, 0.831874464905546, 1.44483819733896, 1.35112663631211,
1.16530987895099, 1.12197975600959, 1.27587783831036, 0.9050787987531
), ConsumerConfidenceIndex = c(0.846829650502804, 1.60472118016078,
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-1.59673510529459, -1.59602090595954, -0.999706472923775, 0.0694026684451194,
0.236084189726601, 0.627318384290896, 0.856229774103751, 1.00626414911988,
1.18224076468833, 1.53172580694677, 1.23795881575313, 0.203420073079754,
0.031697144727374, 0.197538431497049, -0.4751060554715, -0.723547646218374,
-0.38459179268613, -0.144363494292252, -0.345373846847121, -0.204466519215588
), RealPersonalDisposableIncome = c(-1.61847984374121, -1.5861635599299,
-1.57225712600034, -1.49848239094777, -1.53946529757862, -1.54497109045523,
-1.52492940490883, -1.54175692919285, -1.53951841454553, -1.57492545682787,
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), PersonalDisposableIncome = c(-1.63374935499688, -1.61912533368493,
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), SPPricechange = c(1.26871538917159, 0.851034944363406, -0.128615553459435,
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1.96915478475302, 1.07942932760234, 0.407164701459157, 0.581133366265157,
1.28073592544928, 0.633323965706957, -0.424156989649512, -0.316047890805783,
1.23848639256783, 0.96137916219827, 0.337577235536758, -0.0811901933176841,
1.76039238698582), HouseCompleted = c(0.727121448038834, 1.51512215827386,
1.27770235093872, 4.62342810215839, 1.63310378032383, 1.60397251562013,
1.87343671412934, 2.03511523323486, 1.11893695830357, 0.288695914248185,
0.362980639242614, 0.395025030416681, -0.395888806288709, -0.695940832736795,
-0.378410047466491, 0.467853192175925, -0.349278782762793, 0.483875387762959,
0.46930975541111, 1.47288182445349, 0.598943883342565, 0.362980639242614,
0.829080874501776, 1.38985772004796, 0.143039590729697, 0.281413098072261,
0.138669901024142, 0.973280634785079, -0.238579976888742, 0.502810709820363
), TermSpread = c(-0.982486215892947, -1.35440528815526, -1.77852352845439,
-2.1243430166983, -2.91385543325514, -2.47668740094681, -1.8046231124728,
-0.891137671828519, 0.83143487338641, 1.14462988160731, -0.499643911552399,
-0.127724839290084, 0.43994111311029, 0.727036537312779, 0.916258521446237,
0.629163097243749, 0.37469215306427, 0.857534457404817, 0.968457689483053,
0.968457689483053, 1.18377925763492, 1.444775097819, 0.335542777036658,
0.981507481492256, -1.19780778404481, -0.89766256783312, -0.656241415662847,
-0.538793287580011, -1.4718534162381, -0.1799240073269), BuildingPermits = c(-0.757294212433685,
1.98548228249596, 1.83296803797682, 1.68913346590998, -0.608499827536959,
0.0796742026103982, -0.690336739230158, -1.5744233761582, -1.21607689919859,
-0.263792835859544, -0.220394473597999, -0.966846304496574, -0.0195220539874191,
0.905483038787227, 0.704610619176647, 0.0697545769506165, 1.12867461613232,
1.95820331193156, 1.0703968153811, 0.399582130138359, 0.573175579184539,
1.27126923499168, 0.625253613898393, -0.429946565660888, -0.322070636610762,
1.22911082593761, 0.95260126067119, 0.330144750519887, -0.0877194803984184,
1.74989117307615), Date = structure(c(289094400, 297043200, 304992000,
312854400, 320716800, 328665600, 336614400, 344476800, 352252800,
360201600, 368150400, 376012800, 383788800, 391737600, 399686400,
407548800, 415324800, 423273600, 431222400, 439084800, 446947200,
454896000, 462844800, 470707200, 478483200, 486432000, 494380800,
502243200, 510019200, 517968000), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), NORTH = c(4.06976744186047, 5.51675977653633,
7.2799470549305, 4.75015422578655, 4.59363957597172, 3.15315315315317,
1.2008733624454, -0.377562028047452, -0.108283703302655, 0.650406504065032,
0.969305331179318, 0.106666666666688, 3.09003729355352, 2.11886304909562,
2.32793522267207, 5.68743818001977, -1.46934955545156, 3.95611702127658,
5.19438987619354, -0.0912012507600199, 2.81677896109541, 3.97412590369087,
1.30118326353028, 3.31553807249226, 1.32872294960955, 2.93700394923507,
0.908853875665812, 1.81241002546971, -1.3414545718222, 4.81772747317361
), YORKSANDTHEHUMBER = c(4.0121120363361, 5.45851528384282, 9.52380952380951,
6.04914933837431, 3.03030303030299, 5.42099192618225, 2.78993435448577,
-0.53219797764768, 1.97966827180309, 1.15424973767052, 0.466804979253115,
-1.96179659266907, 2.42232754081095, 0.719794344473031, -0.306278713629415,
3.37941628264209, 2.74393263992076, 3.91920555341303, 1.91585099967527,
0.892125625853447, 2.91888477848958, 3.78293078507868, 0.109815847271484,
6.83486625601216, 0.722691730511011, 3.56008625759656, -0.227160867754524,
2.69419041475355, -1.17134094520194, 2.78546324684064), NORTHWEST = c(6.57894736842105,
6.95256660168939, 6.50060753341436, 5.5904164289789, 4.59211237169096,
4.70041322314051, 2.96003946719288, -1.38955438428365, 0.242954324586984,
2.18128938439167, -0.853889943073994, -2.15311004784691, 0.929095354523226,
2.51937984496125, 0.189035916824195, 2.21698113207546, 2.51499769266268,
3.5066396578888, 1.77437592415414, 0.948636868643719, 4.60125296308836,
3.95775160859537, -0.237455720347246, 4.218042765725, 2.79306600771276,
2.22545984338008, 0.709042970141798, 0.258269945161875, 0.663420142564747,
2.23655612423752), EASTMIDS = c(4.98489425981872, 8.20143884892085,
6.91489361702127, 5.22388059701494, 5.61465721040189, 4.64465584778958,
2.03208556149733, 0.314465408805028, 2.82131661442007, 0, 2.79471544715448,
-0.939199209095414, -1.14770459081835, 2.97829379101462, -0.68627450980392,
3.40572556762095, 3.42243436754175, 4.89223242719342, 0.730408764905171,
2.10107893242476, 2.31025926242835, 5.01798109893785, 0.382256908497274,
4.64894882982943, 3.04374194526571, 2.25491999264298, 0.651125980286367,
1.40105078809108, 2.87265165133409, 3.59418899472349), WESTMIDS = c(4.65838509316771,
4.74777448071216, 8.66855524079319, 6.56934306569344, 3.22896281800389,
3.17535545023698, 0.643086816720257, -1.36923779096303, 1.61962054604351,
2.00364298724953, -0.491071428571428, -2.78151637505608, 0, 2.39963082602676,
0.540784136998647, 1.83774092335275, 4.66989436619718, 1.82498633362771,
2.51909973157134, 0.644511581067457, 3.9503702221333, 3.15724626520867,
0.548671245147809, 4.19837410445824, 3.20983256145349, 1.12526319422872,
1.4028740144042, 0.434226470984247, -0.194389516372279, 2.32714328889485
), EASTANGLIA = c(6.74525212835624, 8.58895705521476, 8.47457627118643,
10.7291666666667, 4.8447789275635, 4.84522207267835, -0.299529311082601,
1.45922746781116, 0.88832487309645, 0.29350104821803, -0.877926421404701,
1.64487557992411, -2.69709543568468, 3.49680170575694, 3.25504738360115,
2.39425379090184, 2.98519095869059, 4.36691137516082, 3.57868020304568,
1.66275772744776, 3.79450451070863, 4.52162951167727, 2.28203256419209,
4.17054552224914, 3.2439678284182, 4.76643873164257, 0.955633279171614,
2.91614381581101, 0.848198902642676, 5.02010671012167), OUTERSEAST = c(6.7110371602884,
7.53638253638255, 9.47317544707589, 8.56512141280351, 3.82269215128102,
2.11515863689776, 1.64940544687381, -1.73584905660378, 1.34408602150539,
1.78097764304659, 0.446760982874161, -1.26019273535953, 0.150150150150159,
3.11094452773611, 1.4176663031625, 2.54480286738352, 5.56448794127927,
4.89371564797033, 3.88257575757575, 1.85961713764815, 5.54859495256845,
4.29879599796508, 2.00525702517411, 3.63679834232127, 3.44509381728699,
3.46664684309643, 1.93988743863012, 2.50440502760482, 2.96578121060713,
4.47634947134114), OUTERMET = c(4.54545454545458, 6.58505698607005,
7.36633663366336, 7.08225746956843, 4.3747847054771, 1.68316831683168,
1.00616682895164, -1.28534704370181, 2.01822916666665, 0.797702616464613,
0.949667616334271, -0.940733772342415, 1.10794555238999, 2.19160926737633,
2.84926470588237, 2.62138814417631, 5.02467343976781, 5.65213786241397,
3.22555328833776, 3.73552294786995, 5.05948745510956, 4.28797321179426,
2.86300392436674, 2.60339894216597, 4.28031183318191, 3.43199821714381,
3.34554286721641, 3.04770569170409, 1.65167650683293, 4.62120252591965
), LONDON = c(8.11719500480309, 10.3065304309196, 6.32299637535239,
7.65151515151515, 1.30190007037299, 2.1535255296978, -0.204012240734436,
-0.306643952299836, 0.786056049213951, 1.18684299762631, 1.00536193029493,
-2.85335102853352, 2.76639344262296, 2.06048521103356, 1.23738196027352,
2.70183338694115, 3.30410272471031, 5.76322570865546, 4.73255747291176,
1.98428989791171, 6.03563952552197, 4.88977753030802, 2.12581135535556,
4.43247330120026, 5.42986425339366, 3.96781115879828, 3.43247538648888,
4.0668901660281, 4.09587727708534, 4.81707991010573), SOUTHWEST = c(6.17577197149644,
7.71812080536912, 7.63239875389407, 9.45489628557649, 2.46804759806079,
2.19354838709679, 1.72558922558922, 0.248241621845247, 1.48576145274456,
2.03334688897925, -0.677560781187733, -2.3274478330658, 1.80772391125718,
2.42130750605327, 1.85185185185186, 0.928433268858785, 5.95247221157533,
4.38447346525341, 3.30272049904696, 2.25107353730542, 3.86823714688802,
2.04371722787289, 3.04596811639065, 4.19057346270538, 2.45646407565451,
2.17525889239081, 2.83400809716597, 1.58015962290428, 2.77894958869438,
4.08650146221331), WALES = c(6.09418282548476, 8.35509138381203,
7.40963855421687, 7.01065619742007, 1.15303983228513, 3.47150259067357,
-0.150225338007013, 0.852557673019058, 0.944803580308295, -1.13300492610835,
0.946686596910786, -2.17176702862782, 3.98587285570131, 0.485201358563789,
3.62143891839691, 1.63094128611373, 1.61852361302152, 4.32251951450617,
1.28887158859911, 0.68747598104105, 3.71925360474978, 4.66941979801284,
1.44927536231884, 1.05121293800539, 1.67663757954501, 2.9419480568152,
-0.422309596621509, 2.67987715706347, 0.0249243368346056, 2.03260714794249
), SCOTLAND = c(5.15222482435597, 4.12026726057908, 5.40106951871658,
8.67579908675796, -0.280112044817908, 2.94943820224719, 1.04592996816735,
1.21512151215122, 1.33392618941751, 3.59806932865292, 0.974163490046604,
0.125838926174496, 1.46627565982404, 3.42691990090835, -0.838323353293421,
1.97262479871176, 3.40702724042636, 4.30649410147751, 2.44866586142527,
1.93997856377279, 2.09581887638873, 4.22573890357352, 0.833278440155458,
4.15155969296095, 2.01655899140689, 1.93980755633434, 0.325693606755129,
0.796561260069754, -0.381713535919834, 2.90974405029185), NIRELAND = c(4.54545454545454,
4.94752623688156, 4.42857142857145, 2.96397628818967, 6.06731620903454,
0.0835073068893502, -1.66875260742594, -2.96987696224015, -1.18058592041975,
-0.884955752212393, -1.74107142857143, -0.545206724216265, 1.96436729100047,
-0.224014336917564, -1.84104176021554, 1.6010978956999, 1.42278253039172,
1.97993429814437, 1.29287828660979, 1.61158623060724, 2.28387751649466,
1.84005954349984, 1.79057208981284, 2.22177901874749, 2.88757950598978,
-0.731975575530031, 3.07939176281808, -0.0593031875463392, -1.05696484201158,
3.40717418194087), UK = c(5.76890543055322, 7.20302836425676,
7.39543442582184, 7.22885986848197, 3.23472252213347, 2.95766398929048,
1.20271423347285, -0.554061107319231, 0.98913965036942, 1.55113136643479,
0.373986300291293, -1.61195434757029, 1.59052858167903, 2.07573082205217,
1.17628969016684, 2.44680851063832, 2.84453345201007, 4.10010457610617,
2.88208396840793, 1.58922558922557, 3.67559326527908, 3.90013106997858,
1.36611181194425, 4.12505691303686, 2.02017257462689, 2.93167985827357,
1.54068234183715, 2.12149379408387, 0.594313861969269, 3.83755588673622
)), row.names = c(NA, 30L), class = "data.frame")
Code:
library(tidyverse)
library(GGally)
library(Amelia)
library(inspectdf)
library(ggcorrplot)
library(ggplot2)
library(reshape2)
library(tseries)
library(dplyr)
library(caret)
library(tidyverse)
library(ARDL)
library(dLagM)
library(forecast)
in_sampleARDL <- data %>%
dplyr::filter(Date < '2020-03-01')
out_sampleARDL <-data %>%
dplyr::filter(Date >= '2020-03-01')
# Model Building
# Create the formulas
indep_vars <- expression(Industrialproduction, Householdconsumption, Investmentgrowth, ConsumerPriceIndex, Employment, Unemploymentrate,
Stockmarketindex, Economicgrowth, Consumptiongrowth, Governmentexpenditure, Longtermgovernmentbondyield,
BankRate, ConsumerConfidenceIndex, RealPersonalDisposableIncome, PersonalDisposableIncome, SPPricechange,
HouseStarts, HouseCompleted, TermSpread, BuildingPermits)
dep_vars <- expression(NORTH, YORKSANDTHEHUMBER, NORTHWEST, EASTMIDS, WESTMIDS, EASTANGLIA, OUTERSEAST, OUTERMET, LONDON,
SOUTHWEST, WALES, SCOTLAND, NIRELAND, UK)
# Formulae with diff()
formulae <- unlist(lapply(dep_vars, \(x) lapply(indep_vars, \(y) bquote(.(x)~diff(.(y))))))
length(formulae)
# Without diff()
formulae2 <- unlist(lapply(dep_vars, \(x) lapply(indep_vars, \(y) bquote(.(x)~.(y)))))
length(formulae2)
result <- vector('list', length = length(formulae))
names(result) <- formulae2
# Loop for H = 4
for (i in seq_along(formulae)){
# auto_ardl
result[[i]][[1]] <- auto_ardl(formula(formulae2[[i]]),
data = in_sampleARDL, max_order = 4, selection = 'BIC')
# prediction
result[[i]][[2]] <- forecast(ardlDlm(formula = formula(formulae[[i]]), data = in_sampleARDL, p = 3),
x = out_sampleARDL |> select(sub("\\s~.*", "", formula(formulae[[i]]))) |> pull(), h = 4)
# error
result[[i]][[3]] <- mean((out_sampleARDL |> select(sub("\\s~.*", "", formula(formulae[[i]]))) |> pull() |> (\(x) x[1:4])() - result[[i]][[2]][["forecasts"]])^2)
# set names
names(result[[i]]) <- c('auto_ardl','forecast','error')
}
print(result[[i]])
traceback() shows that this error is coming from the forecast call, and that it occurs with i==1, so look at the first parameter to forecast :: ardlDlm(formula = formula(formulae[[i]]), data = in_sampleARDL, p = 3) and realize that it is not something that forecast is designed to work with. forecast was expecting an atomic numeric vector.
Looking at the output of ardlDlm(formula = formula(formulae[[1]]), data = in_sampleARDL, p = 3), it appears that you really want numeric vectors contained in the $data leaf of that much longer list and in particular probably want only the i-th column, so try this:
for (i in seq_along(formulae)){
# auto_ardl
result[[i]][[1]] <- auto_ardl(formula(formulae2[[i]]),
data = in_sampleARDL,
max_order = 4, selection = 'BIC')
# prediction
#
result[[i]][[2]] <- forecast(ardlDlm(formula = formula(formulae[[i]]),
#---------------extract one col------------------\/-\/-\/-
data = in_sampleARDL, p = 3)$data[[i]],
x = out_sampleARDL |>
select(sub("\\s~.*", "", formula(formulae[[i]]))) |>
pull(), h = 4)
# error
result[[i]][[3]] <- mean((out_sampleARDL |> select(sub("\\s~.*",
"", formula(formulae[[i]]))) |>
pull() |> (\(x) x[1:4])() -
result[[i]][[2]][["forecasts"]])^2)
# set names
names(result[[i]]) <- c('auto_ardl','forecast','error')
}
Note that you only printed the last value in the much longer result object. The last such value looks like:
print(result[[i]])
$auto_ardl
$auto_ardl$best_model
Time series regression with "ts" data:
Start = 5, End = 30
Call:
dynlm::dynlm(formula = full_formula, data = data, start = start,
end = end)
Coefficients:
(Intercept) L(UK, 1) BuildingPermits L(BuildingPermits, 1) L(BuildingPermits, 2)
1.59718 -0.04719 0.90441 -0.04269 0.19583
L(BuildingPermits, 3) L(BuildingPermits, 4)
0.63773 0.02544
$auto_ardl$best_order
[1] 1 4
$auto_ardl$top_orders
UK BuildingPermits BIC
1 1 4 90.18992
2 2 4 93.11884
3 3 4 96.02905
4 1 3 98.36867
5 4 4 99.15721
6 3 3 100.20359
7 2 3 100.53056
8 2 2 104.78506
9 1 2 104.85999
10 1 1 106.10666
$forecast
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
$error
[1] NaN
Plotting Conditionally Summed Data (base R or ggplot)
I started with a dataframe containing info on West Nile cases in Canada from 2012-2015. 600 observations of 10 variables in total.
> head(mosquitoes)
Years Weeks Province Avg.Temp Avg..Precepitation Wind Number.of.cases Number.of.Dead.Birds Mosquito.Pools.Tested Google.Trend.Searches
1 2015 17 Alberta 48 0.01 8 0 0 0 1
2 2015 18 Alberta 46 0.03 10 0 0 0 2
3 2015 19 Alberta 44 0.07 8 0 0 0 2
4 2015 20 Alberta 51 0.00 9 0 0 0 2
5 2015 21 Alberta 56 0.01 9 0 0 0 4
6 2015 22 Alberta 58 0.10 7 0 0 0 1
Here is the entire data set....sorry it's large.
Years,Weeks,Province,Avg Temp ,Avg. Precepitation,Wind,Number of cases,Number of Dead Birds,Mosquito Pools Tested,Google Trend Searches
2015,17,Alberta,48,0.01,8,0,0,0,1
2015,18,Alberta,46,0.03,10,0,0,0,2
2015,19,Alberta,44,0.07,8,0,0,0,2
2015,20,Alberta,51,0,9,0,0,0,2
2015,21,Alberta,56,0.01,9,0,0,0,4
2015,22,Alberta,58,0.1,7,0,0,0,1
2015,23,Alberta,61,0.05,8,0,0,0,1
2015,24,Alberta,55,0.08,9,0,0,0,1
2015,25,Alberta,63,0.02,6,0,0,0,4
2015,26,Alberta,67,0.16,8,0,0,0,5
2015,27,Alberta,65,0.02,8,0,0,0,3
2015,28,Alberta,62,0.09,10,0,0,0,7
2015,29,Alberta,66,0.01,8,0,0,0,2
2015,30,Alberta,62,0.02,7,0,0,0,3
2015,31,Alberta,64,0.21,7,0,0,0,6
2015,32,Alberta,66,0.07,7,0,0,0,4
2015,33,Alberta,55,0.13,8,0,0,0,4
2015,34,Alberta,63,0,6,0,0,0,1
2015,35,Alberta,52,0.11,9,0,0,0,4
2015,36,Alberta,54,0.02,7,0,0,0,2
2015,37,Alberta,48,0.06,8,0,0,0,2
2015,38,Alberta,52,0.03,9,0,0,0,3
2015,39,Alberta,49,0.03,9,0,0,0,3
2015,40,Alberta,51,0,8,0,0,0,2
2015,41,Alberta,48,0,8,0,0,0,2
2014,17,Alberta,43,0.05,8,0,0,0,1
2014,18,Alberta,44,0.06,9,0,0,0,3
2014,19,Alberta,37,0.03,9,0,0,0,3
2014,20,Alberta,48,0.01,8,0,0,0,1
2014,21,Alberta,57,0.01,10,0,0,0,2
2014,22,Alberta,53,0.06,8,0,0,0,4
2014,23,Alberta,53,0.04,10,0,0,0,6
2014,24,Alberta,53,0.04,10,0,0,0,6
2014,25,Alberta,54,0.24,9,0,0,0,4
2014,26,Alberta,59,0.03,9,0,0,0,7
2014,27,Alberta,64,0.02,11,0,0,0,19
2014,28,Alberta,65,0.03,10,0,0,0,33
2014,29,Alberta,67,0.01,9,0,0,0,18
2014,30,Alberta,62,0.08,10,0,0,0,14
2014,31,Alberta,68,0,10,0,0,0,10
2014,32,Alberta,63,0.16,8,0,0,0,11
2014,33,Alberta,66,0.01,7,0,0,0,19
2014,34,Alberta,58,0.05,8,0,0,0,17
2014,35,Alberta,58,0.04,7,0,0,0,8
2014,36,Alberta,54,0.01,7,0,0,0,12
2014,37,Alberta,41,0.15,8,0,0,0,3
2014,38,Alberta,58,0,5,0,0,0,3
2014,39,Alberta,60,0.02,6,0,0,0,4
2014,40,Alberta,48,0.03,11,0,0,0,5
2014,41,Alberta,51,0,6,0,0,0,3
2013,17,Alberta,42,0,12,0,0,0,3
2013,18,Alberta,42,0.01,11,0,0,0,2
2013,19,Alberta,57,0,11,0,0,0,2
2013,20,Alberta,55,0.01,10,0,0,0,9
2013,21,Alberta,50,0.23,11,0,0,0,7
2013,22,Alberta,52,0.08,6,0,0,0,8
2013,23,Alberta,55,0.15,10,0,0,0,10
2013,24,Alberta,53,0.08,10,0,0,0,4
2013,25,Alberta,57,0.3,11,0,0,0,9
2013,26,Alberta,61,0.01,9,0,0,0,17
2013,27,Alberta,65,0.08,10,0,0,0,27
2013,28,Alberta,59,0.07,8,0,0,0,19
2013,29,Alberta,62,0.01,10,0,0,0,21
2013,30,Alberta,62,0.06,10,0,0,0,18
2013,31,Alberta,57,0.03,7,0,0,0,13
2013,32,Alberta,60,0.07,8,0,0,0,10
2013,33,Alberta,67,0,8,3,0,0,2
2013,34,Alberta,63,0,8,5,0,0,12
2013,35,Alberta,64,0.03,10,4,0,0,20
2013,36,Alberta,64,0.13,8,2,1,0,15
2013,37,Alberta,63,0,9,5,0,0,9
2013,38,Alberta,57,0.06,11,2,0,0,11
2013,39,Alberta,47,0,10,0,0,0,4
2013,40,Alberta,44,0,11,0,0,0,5
2013,41,Alberta,45,0.06,8,0,0,0,5
2012,17,Alberta,49,0.06,7,0,0,0,2
2012,18,Alberta,42,0.13,9,0,0,0,2
2012,19,Alberta,48,0,9,0,0,0,6
2012,20,Alberta,53,0.01,10,0,0,0,2
2012,21,Alberta,49,0.08,8,0,0,0,2
2012,22,Alberta,52,0,9,0,0,0,2
2012,23,Alberta,54,0.28,9,0,0,0,4
2012,24,Alberta,56,0.21,12,0,0,0,7
2012,25,Alberta,56,0.05,8,0,0,0,5
2012,26,Alberta,59,0.14,8,0,0,0,3
2012,27,Alberta,61,0.21,9,0,0,0,22
2012,28,Alberta,69,0,8,0,0,0,32
2012,29,Alberta,65,0.09,10,0,0,0,16
2012,30,Alberta,64,0.02,10,0,0,0,15
2012,31,Alberta,63,0.03,10,0,0,0,20
2012,32,Alberta,68,0,10,0,0,0,25
2012,33,Alberta,62,0.07,10,4,0,0,36
2012,34,Alberta,62,0.05,10,2,0,0,100
2012,35,Alberta,61,0.01,10,0,0,0,76
2012,36,Alberta,57,0,12,1,0,0,29
2012,37,Alberta,57,0,12,2,0,0,30
2012,38,Alberta,59,0,9,0,0,0,14
2012,39,Alberta,58,0.01,9,0,0,0,11
2012,40,Alberta,43,0.07,12,0,0,0,10
2012,41,Alberta,43,0.02,13,0,0,0,7
2015,17,British Columbia,53,0.03,10,0,0,0,5
2015,18,British Columbia,53,0.01,6,0,0,0,5
2015,19,British Columbia,58,0.01,7,0,0,0,5
2015,20,British Columbia,60,0,7,0,0,0,4
2015,21,British Columbia,62,0,7,0,0,0,6
2015,22,British Columbia,60,0.03,7,0,0,0,9
2015,23,British Columbia,62,0,13,0,0,0,9
2015,24,British Columbia,62,0.02,8,0,0,0,10
2015,25,British Columbia,66,0,9,0,0,0,7
2015,26,British Columbia,70,0,12,0,0,0,5
2015,27,British Columbia,67,0.01,9,0,0,0,11
2015,28,British Columbia,66,0,10,0,0,0,9
2015,29,British Columbia,65,0.04,9,0,0,0,14
2015,30,British Columbia,65,0.04,6,0,0,0,7
2015,31,British Columbia,65,0.02,9,0,0,0,7
2015,32,British Columbia,66,0.04,9,0,0,0,9
2015,33,British Columbia,65,0,9,0,0,0,11
2015,34,British Columbia,64,0.1,7,0,0,0,6
2015,35,British Columbia,57,0.12,10,0,0,0,4
2015,36,British Columbia,61,0.02,9,0,0,0,9
2015,37,British Columbia,58,0.09,9,0,0,0,9
2015,38,British Columbia,55,0.04,9,0,0,0,3
2015,39,British Columbia,52,0,6,0,0,0,3
2015,40,British Columbia,56,0.08,6,0,0,0,3
2015,41,British Columbia,51,0.04,7,0,0,0,7
2014,17,British Columbia,49,0.07,10,0,0,0,3
2014,18,British Columbia,54,0.03,8,0,0,0,4
2014,19,British Columbia,53,0.18,9,0,0,0,4
2014,20,British Columbia,60,0,8,0,0,0,6
2014,21,British Columbia,59,0.06,7,0,0,0,6
2014,22,British Columbia,56,0.09,7,0,0,0,6
2014,23,British Columbia,59,0,8,0,0,0,8
2014,24,British Columbia,60,0.03,10,0,0,0,7
2014,25,British Columbia,58,0.09,9,0,0,0,8
2014,26,British Columbia,62,0.05,7,0,0,0,10
2014,27,British Columbia,64,0.01,8,0,0,0,7
2014,28,British Columbia,66,0.01,8,0,0,0,19
2014,29,British Columbia,68,0,9,0,0,0,13
2014,30,British Columbia,63,0.06,8,0,0,0,12
2014,31,British Columbia,67,0,6,0,0,0,16
2014,32,British Columbia,66,0,7,0,0,0,25
2014,33,British Columbia,67,0.08,7,0,0,0,17
2014,34,British Columbia,65,0,6,0,0,0,13
2014,35,British Columbia,66,0,7,0,0,0,30
2014,36,British Columbia,61,0.05,7,0,0,0,9
2014,37,British Columbia,60,0,6,0,0,0,11
2014,38,British Columbia,61,0.02,6,0,0,0,3
2014,39,British Columbia,62,0.12,9,0,0,0,8
2014,40,British Columbia,56,0.04,6,0,0,0,9
2014,41,British Columbia,58,0.03,5,0,0,0,7
2013,17,British Columbia,50,0.03,7,0,0,0,14
2013,18,British Columbia,50,0,12,0,0,0,8
2013,19,British Columbia,59,0.03,6,0,0,0,5
2013,20,British Columbia,56,0.07,8,0,0,0,7
2013,21,British Columbia,54,0.04,8,0,0,0,4
2013,22,British Columbia,55,0.09,7,0,0,0,8
2013,23,British Columbia,60,0.01,9,0,0,0,14
2013,24,British Columbia,58,0.01,7,0,0,0,16
2013,25,British Columbia,62,0.04,8,0,0,0,10
2013,26,British Columbia,63,0.1,7,0,0,0,17
2013,27,British Columbia,67,0,8,0,0,0,29
2013,28,British Columbia,63,0,8,0,0,0,30
2013,29,British Columbia,66,0,9,0,0,0,20
2013,30,British Columbia,64,0,8,0,0,0,34
2013,31,British Columbia,64,0.02,8,0,0,0,11
2013,32,British Columbia,66,0,6,0,0,1,13
2013,33,British Columbia,66,0.02,8,0,0,1,16
2013,34,British Columbia,63,0.01,8,0,0,1,16
2013,35,British Columbia,65,0.17,7,0,1,1,12
2013,36,British Columbia,64,0.06,6,0,0,1,8
2013,37,British Columbia,63,0,6,0,0,1,14
2013,38,British Columbia,60,0.19,6,0,0,1,6
2013,39,British Columbia,54,0.23,10,0,0,1,6
2013,40,British Columbia,51,0.15,9,0,0,1,6
2013,41,British Columbia,51,0.01,8,0,0,1,8
2012,17,British Columbia,53,0.05,8,0,0,0,5
2012,18,British Columbia,50,0.11,7,0,0,0,6
2012,19,British Columbia,52,0,9,0,0,0,7
2012,20,British Columbia,54,0,10,0,0,0,8
2012,21,British Columbia,55,0.06,8,0,0,0,9
2012,22,British Columbia,57,0.07,7,0,0,0,8
2012,23,British Columbia,53,0.07,8,0,0,0,4
2012,24,British Columbia,57,0.04,8,0,0,0,4
2012,25,British Columbia,58,0.13,8,0,0,0,7
2012,26,British Columbia,60,0.04,8,0,0,0,8
2012,27,British Columbia,59,0.03,7,0,0,0,22
2012,28,British Columbia,66,0,6,0,0,0,30
2012,29,British Columbia,66,0.05,8,0,0,0,30
2012,30,British Columbia,63,0.03,8,0,0,0,38
2012,31,British Columbia,65,0,8,0,0,0,60
2012,32,British Columbia,67,0.01,8,0,0,0,34
2012,33,British Columbia,69,0,7,0,0,0,63
2012,34,British Columbia,63,0,8,0,0,0,100
2012,35,British Columbia,62,0,7,0,0,0,51
2012,36,British Columbia,62,0,7,0,0,0,32
2012,37,British Columbia,58,0.01,8,0,0,0,24
2012,38,British Columbia,60,0,6,0,0,0,13
2012,39,British Columbia,57,0,6,0,0,0,13
2012,40,British Columbia,53,0,8,0,0,0,6
2012,41,British Columbia,52,0.09,5,0,0,0,8
2015,17,Manitoba,56,0,10,0,0,0,4
2015,18,Manitoba,48,0,13,0,0,0,4
2015,19,Manitoba,46,0,10,0,0,0,4
2015,20,Manitoba,52,0,14,0,0,0,4
2015,21,Manitoba,57,0,10,0,0,12,4
2015,22,Manitoba,60,0,12,0,0,4,8
2015,23,Manitoba,67,0,9,0,0,87,8
2015,24,Manitoba,59,0,9,0,0,82,8
2015,25,Manitoba,66,0,7,0,0,44,8
2015,26,Manitoba,68,0,7,0,0,75,11
2015,27,Manitoba,66,0,10,0,0,73,17
2015,28,Manitoba,70,0,7,0,0,132,8
2015,29,Manitoba,69,0,9,0,0,139,17
2015,30,Manitoba,70,0,11,0,0,204,4
2015,31,Manitoba,63,0,9,0,0,275,13
2015,32,Manitoba,73,0,9,0,0,195,23
2015,33,Manitoba,62,0,10,0,0,228,13
2015,34,Manitoba,62,0,11,0,0,69,12
2015,35,Manitoba,73,0,11,1,0,92,10
2015,36,Manitoba,57,0,10,1,0,113,8
2015,37,Manitoba,60,0,11,2,0,34,4
2015,38,Manitoba,61,0,13,1,0,0,4
2015,39,Manitoba,53,0,13,0,0,0,6
2015,40,Manitoba,48,0,11,0,0,0,6
2015,41,Manitoba,44,0,11,0,0,0,6
2014,17,Manitoba,42,0,11,0,0,0,4
2014,18,Manitoba,42,0,14,0,0,0,0
2014,19,Manitoba,46,0,9,0,0,0,0
2014,20,Manitoba,45,0,10,0,0,0,0
2014,21,Manitoba,57,0,12,0,0,0,0
2014,22,Manitoba,66,0,8,0,0,0,0
2014,23,Manitoba,62,0,10,0,0,0,5
2014,24,Manitoba,60,0,11,0,0,0,13
2014,25,Manitoba,62,0,12,0,0,0,9
2014,26,Manitoba,66,0,10,0,0,0,7
2014,27,Manitoba,65,0,15,0,0,0,9
2014,28,Manitoba,67,0,11,0,0,0,36
2014,29,Manitoba,63,0,11,0,0,0,24
2014,30,Manitoba,68,0,9,0,0,0,53
2014,31,Manitoba,65,0,8,0,0,7,41
2014,32,Manitoba,71,0,8,0,0,7,48
2014,33,Manitoba,68,0,8,1,0,14,14
2014,34,Manitoba,67,0,8,2,0,19,18
2014,35,Manitoba,61,0,11,2,0,22,9
2014,36,Manitoba,60,0,8,0,0,24,4
2014,37,Manitoba,50,0,11,0,0,24,11
2014,38,Manitoba,52,0,10,0,0,24,4
2014,39,Manitoba,65,0,13,0,0,24,15
2014,40,Manitoba,47,0,16,0,0,24,4
2014,41,Manitoba,39,0,13,0,0,24,4
2013,17,Manitoba,36,0.01,12,0,0,0,4
2013,18,Manitoba,38,0.11,9,0,0,0,4
2013,19,Manitoba,49,0.02,12,0,0,0,4
2013,20,Manitoba,56,0.02,10,0,0,0,5
2013,21,Manitoba,55,0.05,14,0,0,0,4
2013,22,Manitoba,58,0.16,15,0,0,0,4
2013,23,Manitoba,57,0.01,9,0,0,0,9
2013,24,Manitoba,63,0.03,10,0,0,0,16
2013,25,Manitoba,66,0.1,9,0,0,0,23
2013,26,Manitoba,69,0.24,10,0,0,0,14
2013,27,Manitoba,72,0,6,0,0,0,23
2013,28,Manitoba,70,0.06,10,0,0,1,19
2013,29,Manitoba,66,0.1,9,0,0,1,45
2013,30,Manitoba,60,0.19,8,0,1,7,35
2013,31,Manitoba,61,0.03,7,0,0,10,31
2013,32,Manitoba,59,0.04,7,0,0,16,22
2013,33,Manitoba,64,0.02,8,1,0,16,24
2013,34,Manitoba,71,0.17,10,0,0,16,49
2013,35,Manitoba,76,0.01,7,0,0,17,14
2013,36,Manitoba,64,0,10,1,0,17,11
2013,37,Manitoba,63,0.01,8,0,0,19,9
2013,38,Manitoba,54,0,11,0,0,19,6
2013,39,Manitoba,60,0.1,12,0,0,19,13
2013,40,Manitoba,50,0.03,11,0,0,19,8
2013,41,Manitoba,52,0,10,0,1,19,4
2012,17,Manitoba,46,0.01,12,0,0,0,0
2012,18,Manitoba,51,0.05,11,0,0,0,0
2012,19,Manitoba,56,0.06,13,0,0,0,5
2012,20,Manitoba,58,0.16,12,0,0,0,6
2012,21,Manitoba,53,0.02,11,0,0,0,5
2012,22,Manitoba,53,0.13,9,0,0,0,5
2012,23,Manitoba,67,0.08,8,0,0,0,8
2012,24,Manitoba,62,0.17,11,0,0,0,10
2012,25,Manitoba,60,0.04,8,0,0,0,11
2012,26,Manitoba,68,0,10,0,0,0,11
2012,27,Manitoba,73,0.03,7,0,0,0,15
2012,28,Manitoba,73,0,7,0,0,0,17
2012,29,Manitoba,69,0.05,8,1,0,2,21
2012,30,Manitoba,71,0,8,1,0,20,36
2012,31,Manitoba,71,0.2,9,4,0,48,100
2012,32,Manitoba,67,0,9,7,0,62,47
2012,33,Manitoba,62,0.04,8,7,0,98,31
2012,34,Manitoba,69,0.01,7,6,0,108,84
2012,35,Manitoba,70,0.01,11,7,0,111,75
2012,36,Manitoba,63,0.01,11,1,0,116,22
2012,37,Manitoba,59,0.01,11,3,0,116,23
2012,38,Manitoba,47,0.01,12,2,0,116,13
2012,39,Manitoba,50,0,8,0,0,116,5
2012,40,Manitoba,46,0.02,15,0,0,116,7
2012,41,Manitoba,37,0.02,10,0,0,116,5
2015,17,Quebec,53,0,8,0,0,0,8
2015,18,Quebec,65,0.06,8,0,0,0,8
2015,19,Quebec,58,0.09,10,0,0,0,8
2015,20,Quebec,59,0.05,11,0,0,0,8
2015,21,Quebec,69,0.11,11,0,0,0,8
2015,22,Quebec,56,0.07,9,0,0,0,8
2015,23,Quebec,65,0.16,9,0,0,0,8
2015,24,Quebec,64,0.16,7,0,0,0,16
2015,25,Quebec,67,0.18,8,0,0,0,8
2015,26,Quebec,64,0.07,9,0,0,120,19
2015,27,Quebec,71,0.01,8,0,0,127,24
2015,28,Quebec,70,0.05,9,0,1,132,24
2015,29,Quebec,70,0.3,8,0,1,131,16
2015,30,Quebec,75,0.07,9,1,2,129,16
2015,31,Quebec,67,0.02,9,1,3,126,8
2015,32,Quebec,69,0.31,7,0,0,133,8
2015,33,Quebec,76,0.11,9,1,1,125,16
2015,34,Quebec,68,0.01,8,2,1,123,11
2015,35,Quebec,70,0,8,1,3,131,31
2015,36,Quebec,72,0.15,8,2,4,128,15
2015,37,Quebec,69,0.21,9,6,0,123,7
2015,38,Quebec,58,0,7,5,0,108,7
2015,39,Quebec,55,0.17,11,2,2,107,11
2015,40,Quebec,49,0.03,7,5,0,0,7
2015,41,Quebec,51,0.11,11,8,0,0,15
2014,17,Quebec,46,0.05,9,0,0,0,0
2014,18,Quebec,49,0.18,12,0,0,0,0
2014,19,Quebec,53,0.09,10,0,0,0,0
2014,20,Quebec,62,0.17,13,0,0,0,0
2014,21,Quebec,59,0.01,9,0,0,0,13
2014,22,Quebec,59,0.08,9,0,0,0,13
2014,23,Quebec,66,0.13,8,0,0,0,40
2014,24,Quebec,66,0.28,11,0,0,0,18
2014,25,Quebec,65,0.14,8,0,0,0,27
2014,26,Quebec,69,0.14,6,0,0,0,33
2014,27,Quebec,75,0.02,9,0,0,0,23
2014,28,Quebec,70,0.08,12,0,0,0,40
2014,29,Quebec,69,0.05,9,0,0,1,27
2014,30,Quebec,72,0.06,10,0,0,4,28
2014,31,Quebec,66,0.18,8,0,0,9,54
2014,32,Quebec,70,0.04,6,0,0,10,24
2014,33,Quebec,67,0.2,10,1,2,19,34
2014,34,Quebec,66,0,7,1,0,19,9
2014,35,Quebec,70,0,8,1,1,39,17
2014,36,Quebec,72,0.11,10,1,0,70,8
2014,37,Quebec,60,0.12,9,0,3,99,12
2014,38,Quebec,52,0.02,9,1,2,112,13
2014,39,Quebec,61,0.02,9,0,0,119,15
2014,40,Quebec,58,0.06,11,0,1,119,16
2014,41,Quebec,51,0.1,13,1,0,119,16
2013,17,Quebec,46,0.03,11,1,0,0,9
2013,18,Quebec,60,0.01,7,0,0,0,9
2013,19,Quebec,65,0.08,8,0,0,0,9
2013,20,Quebec,51,0.01,11,0,0,0,18
2013,21,Quebec,64,0.19,10,0,0,0,17
2013,22,Quebec,64,0.18,9,0,0,0,9
2013,23,Quebec,59,0.11,10,0,0,0,21
2013,24,Quebec,64,0.11,9,0,0,0,18
2013,25,Quebec,62,0.09,8,0,0,0,9
2013,26,Quebec,69,0.14,9,0,0,0,37
2013,27,Quebec,72,0.02,9,0,0,0,9
2013,28,Quebec,73,0.06,8,0,0,0,45
2013,29,Quebec,79,0.28,9,0,0,2,49
2013,30,Quebec,66,0.06,7,0,0,3,73
2013,31,Quebec,70,0.12,9,1,3,5,40
2013,32,Quebec,68,0.04,9,3,2,11,74
2013,33,Quebec,66,0.08,9,8,4,23,56
2013,34,Quebec,69,0.02,10,3,5,36,64
2013,35,Quebec,70,0.06,7,4,9,36,29
2013,36,Quebec,63,0.06,10,2,6,40,32
2013,37,Quebec,62,0.18,8,3,4,47,20
2013,38,Quebec,58,0.12,9,1,2,59,8
2013,39,Quebec,54,0.03,6,1,0,60,16
2013,40,Quebec,61,0,6,1,0,60,24
2013,41,Quebec,55,0.11,10,0,0,60,20
2012,17,Quebec,40,0.17,13,0,0,0,0
2012,18,Quebec,50,0.03,7,0,0,0,10
2012,19,Quebec,55,0.07,8,0,0,0,10
2012,20,Quebec,61,0.02,7,0,0,0,10
2012,21,Quebec,69,0.1,7,0,0,0,11
2012,22,Quebec,62,0.16,8,0,0,0,10
2012,23,Quebec,61,0.02,8,0,0,0,10
2012,24,Quebec,68,0.08,7,0,0,0,11
2012,25,Quebec,76,0.01,9,0,0,0,11
2012,26,Quebec,69,0.13,9,0,0,0,26
2012,27,Quebec,73,0.12,6,0,0,0,40
2012,28,Quebec,72,0,8,0,2,0,24
2012,29,Quebec,71,0.21,6,1,0,0,11
2012,30,Quebec,71,0.1,7,1,0,0,11
2012,31,Quebec,76,0.01,7,0,1,5,78
2012,32,Quebec,72,0.17,10,2,5,8,31
2012,33,Quebec,70,0.02,7,6,2,19,94
2012,34,Quebec,70,0,6,10,5,19,100
2012,35,Quebec,71,0.01,11,9,8,19,76
2012,36,Quebec,71,0.11,6,14,1,19,70
2012,37,Quebec,63,0.07,8,23,6,19,43
2012,38,Quebec,58,0.12,10,16,0,19,34
2012,39,Quebec,54,0.01,9,27,0,19,38
2012,40,Quebec,57,0.16,8,11,0,19,14
2012,41,Quebec,45,0.06,10,8,0,19,19
2015,17,Ontario,53,0,9,0,0,0,2
2015,18,Ontario,61,0.04,5,0,0,0,2
2015,19,Ontario,58,0.07,7,0,0,0,4
2015,20,Ontario,58,0,8,0,0,0,5
2015,21,Ontario,70,0.11,8,0,0,0,8
2015,22,Ontario,57,0.14,7,0,0,180,8
2015,23,Ontario,65,0.18,6,0,0,356,5
2015,24,Ontario,65,0.08,5,0,1,852,5
2015,25,Ontario,67,0.33,7,0,0,886,13
2015,26,Ontario,63,0.02,7,0,0,954,15
2015,27,Ontario,68,0.04,5,0,0,1152,13
2015,28,Ontario,67,0.03,6,1,0,1216,21
2015,29,Ontario,72,0.01,7,1,4,1219,16
2015,30,Ontario,76,0.03,6,1,1,1222,22
2015,31,Ontario,68,0.06,6,0,8,1176,24
2015,32,Ontario,69,0.21,6,0,0,1168,15
2015,33,Ontario,73,0.09,5,1,0,1168,24
2015,34,Ontario,64,0.01,5,5,1,987,12
2015,35,Ontario,75,0,5,2,1,881,18
2015,36,Ontario,70,0.11,5,5,0,802,9
2015,37,Ontario,65,0.07,6,1,2,712,6
2015,38,Ontario,60,0,5,5,4,526,4
2015,39,Ontario,55,0.04,9,2,2,396,6
2015,40,Ontario,53,0.14,6,3,0,65,5
2015,41,Ontario,52,0.04,8,3,4,0,2
2014,17,Ontario,46,0.05,8,0,0,0,3
2014,18,Ontario,47,0.14,9,0,0,0,2
2014,19,Ontario,53,0,9,0,0,0,2
2014,20,Ontario,56,0.13,6,0,0,0,3
2014,21,Ontario,57,0.09,5,0,0,0,4
2014,22,Ontario,65,0.02,6,0,0,0,7
2014,23,Ontario,63,0.04,6,0,0,0,10
2014,24,Ontario,65,0.19,6,0,0,0,16
2014,25,Ontario,66,0.16,5,0,0,0,13
2014,26,Ontario,69,0.06,4,0,0,0,7
2014,27,Ontario,72,0.09,7,0,0,0,20
2014,28,Ontario,68,0.12,6,0,0,0,17
2014,29,Ontario,66,0.21,5,1,0,0,13
2014,30,Ontario,68,0.03,5,0,0,2,14
2014,31,Ontario,67,0.35,5,0,0,5,35
2014,32,Ontario,68,0.21,4,0,0,9,22
2014,33,Ontario,65,0.12,7,2,0,11,30
2014,34,Ontario,67,0.02,4,0,2,13,11
2014,35,Ontario,67,0,6,2,3,30,18
2014,36,Ontario,71,0.39,5,5,0,43,13
2014,37,Ontario,60,0.15,6,1,0,52,10
2014,38,Ontario,53,0.02,4,0,1,56,7
2014,39,Ontario,60,0.08,4,0,0,56,3
2014,40,Ontario,61,0.06,4,0,0,56,6
2014,41,Ontario,50,0.06,6,0,0,56,4
2013,17,Ontario,43,0.05,6,0,0,0,2
2013,18,Ontario,57,0.05,6,0,0,0,3
2013,19,Ontario,59,0.04,5,0,0,0,4
2013,20,Ontario,51,0.02,8,0,0,0,3
2013,21,Ontario,60,0.17,8,0,0,0,7
2013,22,Ontario,64,0.16,6,1,0,0,9
2013,23,Ontario,58,0.05,7,1,0,0,9
2013,24,Ontario,64,0.29,6,0,0,0,12
2013,25,Ontario,64,0.11,5,0,0,0,12
2013,26,Ontario,73,0.06,4,0,1,2,12
2013,27,Ontario,71,0.05,5,1,0,2,20
2013,28,Ontario,72,0.13,6,2,0,4,15
2013,29,Ontario,80,0.05,5,1,2,12,20
2013,30,Ontario,65,0.12,6,5,0,22,56
2013,31,Ontario,66,0.26,5,4,8,41,43
2013,32,Ontario,67,0.04,6,5,6,65,32
2013,33,Ontario,63,0,5,5,2,89,24
2013,34,Ontario,70,0,5,2,0,131,30
2013,35,Ontario,72,0.2,3,2,8,155,22
2013,36,Ontario,63,0.12,6,7,2,179,12
2013,37,Ontario,64,0.04,6,3,2,190,15
2013,38,Ontario,57,0.17,4,5,2,194,9
2013,39,Ontario,55,0,4,0,1,196,5
2013,40,Ontario,61,0.04,4,5,0,198,9
2013,41,Ontario,56,0.04,4,1,0,198,4
2012,17,Ontario,40,0.06,11,0,0,0,4
2012,18,Ontario,50,0.12,6,0,0,0,3
2012,19,Ontario,56,0.07,6,0,0,0,3
2012,20,Ontario,58,0.02,4,0,0,0,3
2012,21,Ontario,69,0.01,6,0,0,0,5
2012,22,Ontario,64,0.09,8,0,0,0,3
2012,23,Ontario,63,0.03,6,1,0,0,6
2012,24,Ontario,67,0.08,6,0,0,0,4
2012,25,Ontario,76,0.17,6,0,0,2,7
2012,26,Ontario,70,0.04,7,0,0,6,10
2012,27,Ontario,75,0.04,5,3,1,10,39
2012,28,Ontario,73,0.02,5,5,3,19,24
2012,29,Ontario,75,0.06,6,9,1,30,19
2012,30,Ontario,72,0.38,6,14,2,89,17
2012,31,Ontario,73,0.16,4,23,1,162,77
2012,32,Ontario,70,0.14,6,44,1,249,46
2012,33,Ontario,68,0.05,4,44,8,312,64
2012,34,Ontario,67,0,4,38,4,375,83
2012,35,Ontario,70,0.15,6,26,0,409,100
2012,36,Ontario,69,0.56,4,25,0,434,79
2012,37,Ontario,61,0.03,5,17,2,454,37
2012,38,Ontario,57,0.16,5,3,4,462,23
2012,39,Ontario,53,0,6,2,6,462,24
2012,40,Ontario,57,0.03,5,3,0,464,18
2012,41,Ontario,42,0.04,5,1,0,464,10
2015,17,Saskatchewan,50,0,10,0,0,0,6
2015,18,Saskatchewan,46,0,11,0,0,0,12
2015,19,Saskatchewan,46,0,9,0,0,0,6
2015,20,Saskatchewan,53,0,8,0,0,0,6
2015,21,Saskatchewan,56,0,8,0,0,2,9
2015,22,Saskatchewan,60,0,10,0,0,0,9
2015,23,Saskatchewan,64,0,10,0,0,3,9
2015,24,Saskatchewan,57,0,8,0,0,3,12
2015,25,Saskatchewan,65,0,7,0,0,10,31
2015,26,Saskatchewan,70,0,6,0,0,13,15
2015,27,Saskatchewan,66,0,9,0,0,16,13
2015,28,Saskatchewan,67,0,8,0,0,40,15
2015,29,Saskatchewan,68,0,10,0,0,47,16
2015,30,Saskatchewan,63,0.02,9,0,0,69,43
2015,31,Saskatchewan,63,0,8,0,0,67,16
2015,32,Saskatchewan,70,0,8,0,0,80,28
2015,33,Saskatchewan,58,0,8,0,0,94,38
2015,34,Saskatchewan,62,0,8,0,0,42,21
2015,35,Saskatchewan,61,0,10,0,1,41,14
2015,36,Saskatchewan,53,0,8,0,0,0,9
2015,37,Saskatchewan,52,0,8,0,0,0,5
2015,38,Saskatchewan,54,0,10,0,0,0,5
2015,39,Saskatchewan,48,0,8,0,0,0,5
2015,40,Saskatchewan,48,0,9,0,0,0,8
2015,41,Saskatchewan,44,0,11,0,0,0,5
2014,17,Saskatchewan,40,0,12,0,0,0,6
2014,18,Saskatchewan,41,0,10,0,0,0,6
2014,19,Saskatchewan,41,0,9,0,0,0,6
2014,20,Saskatchewan,45,0,7,0,0,0,6
2014,21,Saskatchewan,59,0,10,0,0,0,13
2014,22,Saskatchewan,57,0,11,0,0,0,20
2014,23,Saskatchewan,55,0,8,0,0,0,17
2014,24,Saskatchewan,53,0,10,0,0,0,13
2014,25,Saskatchewan,57,0,10,0,0,0,7
2014,26,Saskatchewan,63,0,8,0,0,0,21
2014,27,Saskatchewan,66,0,11,0,0,0,26
2014,28,Saskatchewan,65,0,10,0,0,0,69
2014,29,Saskatchewan,64,0,9,0,0,0,65
2014,30,Saskatchewan,63,0,9,0,0,1,60
2014,31,Saskatchewan,67,0,6,0,0,1,36
2014,32,Saskatchewan,69,0,6,0,2,2,47
2014,33,Saskatchewan,67,0,7,0,0,9,67
2014,34,Saskatchewan,64,0,8,0,0,19,45
2014,35,Saskatchewan,58,0,9,0,0,20,34
2014,36,Saskatchewan,56,0,8,0,0,20,13
2014,37,Saskatchewan,46,0,9,0,0,20,19
2014,38,Saskatchewan,55,0,8,0,0,20,6
2014,39,Saskatchewan,61,0,9,0,0,20,16
2014,40,Saskatchewan,44,0,12,0,0,20,12
2014,41,Saskatchewan,45,0,9,0,0,20,6
2013,17,Saskatchewan,34,0,10,0,0,0,10
2013,18,Saskatchewan,40,0,12,0,0,0,14
2013,19,Saskatchewan,50,0,12,0,0,0,14
2013,20,Saskatchewan,59,0,9,0,0,0,7
2013,21,Saskatchewan,57,0,13,0,0,0,7
2013,22,Saskatchewan,60,0,9,0,0,0,14
2013,23,Saskatchewan,57,0,9,0,0,0,21
2013,24,Saskatchewan,57,0,10,0,0,0,20
2013,25,Saskatchewan,61,0,10,0,0,0,14
2013,26,Saskatchewan,64,0,7,0,0,0,41
2013,27,Saskatchewan,69,0,7,0,0,0,61
2013,28,Saskatchewan,65,0,8,0,0,1,65
2013,29,Saskatchewan,62,0,9,0,3,1,81
2013,30,Saskatchewan,60,0,9,0,1,3,75
2013,31,Saskatchewan,59,0,8,0,2,3,33
2013,32,Saskatchewan,60,0,6,0,1,18,44
2013,33,Saskatchewan,69,0,8,0,0,29,75
2013,34,Saskatchewan,66,0,8,1,1,29,60
2013,35,Saskatchewan,69,0,8,3,0,36,24
2013,36,Saskatchewan,67,0,7,1,0,40,21
2013,37,Saskatchewan,62,0,9,0,0,40,26
2013,38,Saskatchewan,57,0,10,1,2,40,32
2013,39,Saskatchewan,51,0,9,0,1,40,13
2013,40,Saskatchewan,45,0,11,0,0,40,29
2013,41,Saskatchewan,46,0,10,0,0,40,10
2012,17,Saskatchewan,44,0,13,0,0,0,24
2012,18,Saskatchewan,46,0,12,0,0,0,16
2012,19,Saskatchewan,51,0,13,0,0,0,16
2012,20,Saskatchewan,54,0,12,0,0,0,9
2012,21,Saskatchewan,48,0,11,0,0,0,17
2012,22,Saskatchewan,53,0,9,0,0,0,16
2012,23,Saskatchewan,61,0,13,0,0,0,8
2012,24,Saskatchewan,56,0,11,0,0,0,16
2012,25,Saskatchewan,58,0,7,0,0,0,25
2012,26,Saskatchewan,64,0,12,0,0,0,22
2012,27,Saskatchewan,65,0,9,0,0,0,23
2012,28,Saskatchewan,71,0,7,0,1,0,67
2012,29,Saskatchewan,67,0,10,0,0,0,34
2012,30,Saskatchewan,67,0,8,0,0,0,28
2012,31,Saskatchewan,64,0,8,0,0,0,59
2012,32,Saskatchewan,68,0,8,0,0,3,58
2012,33,Saskatchewan,59,0,8,2,0,4,34
2012,34,Saskatchewan,65,0,9,1,0,6,100
2012,35,Saskatchewan,64,0,9,0,0,6,49
2012,36,Saskatchewan,55,0,11,3,0,6,41
2012,37,Saskatchewan,58,0,13,0,0,6,16
2012,38,Saskatchewan,50,0,8,3,0,6,19
2012,39,Saskatchewan,55,0,6,0,0,6,15
2012,40,Saskatchewan,42,0,10,0,0,6,11
2012,41,Saskatchewan,36,0,8,0,0,6,7
First I produced this plot
But I did that in the most brute force way imaginable
#split out each year
cases2015 <- subset(mosquitoes, mosquitoes$Years==2015)
cases2014 <- subset(mosquitoes, mosquitoes$Years==2014)
cases2013 <- subset(mosquitoes, mosquitoes$Years==2013)
cases2012 <- subset(mosquitoes, mosquitoes$Years==2012)
#get the sums by week
aggregate2015 <- aggregate(cases2015$Number.of.cases, by=list(Weeks=cases2015$Weeks), FUN=sum)
aggregate2014 <- aggregate(cases2014$Number.of.cases, by=list(Weeks=cases2014$Weeks), FUN=sum)
aggregate2013 <- aggregate(cases2013$Number.of.cases, by=list(Weeks=cases2013$Weeks), FUN=sum)
aggregate2012 <- aggregate(cases2012$Number.of.cases, by=list(Weeks=cases2012$Weeks), FUN=sum)
#put the sums back together into a dataframe
aggregateSums <- aggregate2012
aggregateSums <- cbind(aggregateSums, aggregate2013[,2])
aggregateSums <- cbind(aggregateSums, aggregate2014[,2])
aggregateSums <- cbind(aggregateSums, aggregate2015[,2])
#give the columns useful names
colnames(aggregateSums) <- c("Weeks","Cases.2012","Cases.2013","Cases.2014","Cases.2015")
#base R plot
#plot the first set of points
plot(x=aggregateSums$Weeks,y=aggregateSums$Cases.2012,pch=16,col="Red",main="West Nile Cases",xlab="Week",ylab="Number of Cases")
#add additional years
points(x=aggregateSums$Weeks,y=aggregateSums$Cases.2013,pch=15,col="Blue")
points(x=aggregateSums$Weeks,y=aggregateSums$Cases.2014,pch=14,col="Orange")
points(x=aggregateSums$Weeks,y=aggregateSums$Cases.2015,pch=13,col="Brown")
#add the connecting lines
lines(x=aggregateSums$Weeks,y=aggregateSums$Cases.2012,col="Red")
lines(x=aggregateSums$Weeks,y=aggregateSums$Cases.2013,col="Blue")
lines(x=aggregateSums$Weeks,y=aggregateSums$Cases.2014,col="Orange")
lines(x=aggregateSums$Weeks,y=aggregateSums$Cases.2015,col="Brown")
#click to place legend
legend(locator(1),c("2012","2013","2014","2015"),pch=c(16,15,14,13), col=c("Red","Blue","Orange","Brown"))
So surely there has to be a more efficient way to get there.
My next step is to produce the same plot but for just one province at a time. I don't want to have to go through the above 6 times...
I'm opening to accomplishing this via ggplot. If possible, I'd like to do it without resorting to additional packages (like plyr) as I'm trying to learn the base functionality for manipulating data.
Just to close the loop after Biranjan's answer...
mosq2 <- mosquitoes %>%
select(Years,Weeks,Province,Number.of.cases) %>%
group_by(Years,Weeks,Province) %>%
summarise(sum_case=sum(Number.of.cases))
ggplot(data=mosq2, aes(x=as.factor(Weeks),y=sum_case,color=as.factor(Years))) +
geom_point(aes(shape=as.factor(Years))) +
geom_line(aes(group=as.factor(Years))) +
labs(title="West Nile Cases", x="weeks", y="Number of cases") +
theme(legend.title=element_blank()) +
facet_wrap(~Province,ncol=3) +
scale_x_discrete(breaks=c(17,30,41))
Turned out quite nicely
ggplot(data=data1, aes(x=as.factor(Weeks),y=sum_case,color=as.factor(Years)))+ geom_point(aes(shape=as.factor(Years)))+ geom_line(aes(group=as.factor(Years)))+ labs(title="West Nile cases",x="weeks",y="Number of cases")+ theme(legend.title=element_blank()) Update: I had too few points in my simulation so it rendered fine so that was the problem. I could't find a way to plot just using ggplot. The same code works if "dplyr" is used first and variable name edited accordingly. I know it is not what you are looking for, sorry to disappoint you. library(dplyr) data1 <- data %>% select(Years,Weeks,Number.of.cases) %>% group_by(Years,Weeks) %>% summarise(sum_case=sum(Number.of.cases))
questionnaires filled on the same day
I am working with a data set with multiple questionnaires which were supposed to be filled in on different timepoints i.e.
173 9/13/2013 10/29/2013 9/26/2014
174 10/21/2013 11/25/2013 11/3/2014
175 7/1/2014 7/3/2015 4/27/2016
176 1/15/2014 2/24/2014 6/10/2015
177 3/15/2014 4/1/2015
178 7/18/2014 9/18/2014 8/17/2015
179 6/30/2013 8/15/2013 7/15/2014
180 4/22/2013 6/24/2013 5/11/2014
181 12/7/2014 12/26/2015
182 4/2/2015 5/17/2015 4/20/2016
183 1/12/2015 2/26/2015 1/28/2016
184 7/18/2014 8/26/2014 8/14/2015
185 8/27/2013 10/19/2013 9/21/2014
186 10/29/2013 11/30/2013 11/6/2014
187 9/17/2014 11/18/2014 10/20/2015
188 5/10/2014 6/27/2014 6/1/2015
189 10/4/2013 10/5/2014
190 1/22/2013 4/11/2013
191 10/21/2014 10/21/2014
I would like to know how to see how many participants filled in all questionnaires on the same day, how many participants filled in at least 2 questionnaires on the same day. how many at least 3 on the same day etc.
Any help would be highly appreciated.
Reproducible data:
Label = c(
"1/25/2015", "1/25/2016", "1/26/2014", "1/26/2015", "1/27/2014",
"1/27/2015", "1/28/2014", "1/28/2015", "1/29/2015", "1/3/2014",
"1/3/2015", "1/3/2016", "1/30/2015", "1/31/2014", "1/4/2014",
"1/4/2015", "1/4/2016", "1/5/2014", "1/5/2015", "1/6/2014",
"1/6/2015", "1/7/2014", "1/7/2015", "1/8/2014", "1/8/2015",
"1/9/2014", "1/9/2015", "1/9/2016", "10/1/2012", "10/1/2013",
"10/1/2014", "10/1/2015", "10/10/2013", "10/10/2014", "10/11/2013",
"10/11/2014", "10/11/2015", "10/12/2013", "10/12/2014", "10/12/2015",
"10/13/2013", "10/13/2014", "10/13/2015", "10/14/2013", "10/14/2014",
"10/14/2015", "10/15/2014", "10/15/2015", "10/16/2013", "10/16/2014",
"10/16/2015", "10/17/2013", "10/17/2014", "10/17/2015", "10/18/2013",
"10/18/2014", "10/18/2015", "10/19/2013", "10/19/2014", "10/19/2015",
"10/2/2013", "10/2/2014", "10/20/2013", "10/20/2014", "10/20/2015",
"10/21/2013", "10/21/2014", "10/22/2013", "10/22/2014", "10/22/2015",
"10/23/2012", "10/23/2013", "10/23/2014", "10/23/2015", "10/24/2013",
"10/24/2014", "10/24/2015", "10/25/2013", "10/25/2014", "10/26/2013",
"10/26/2014", "10/26/2015", "10/27/2013", "10/27/2014", "10/27/2015",
"10/28/2013", "10/28/2014", "10/29/2013", "10/29/2014", "10/3/2014",
"10/3/2015", "10/30/2014", "10/31/2012", "10/31/2013", "10/31/2014",
"10/31/2015", "10/4/2013", "10/4/2014", "10/4/2015", "10/5/2014",
"10/5/2015", "10/6/2013", "10/6/2014", "10/6/2015", "10/7/2013",
"10/7/2014", "10/8/2012", "10/8/2014", "10/8/2015", "10/9/2013",
"10/9/2014", "10/9/2015", "11/1/2013", "11/1/2014", "11/1/2015",
class = "factor")
Label = c(
"4/6/2015", "4/7/2015", "4/9/2012", "5/12/2015", "5/13/2014",
"5/14/2015", "5/15/2014", "5/15/2015", "5/17/2014", "5/19/2014",
"5/20/2014", "5/25/2014", "5/27/2014", "5/29/2014", "5/30/2014",
"5/30/2015", "5/31/2015", "5/4/2014", "5/9/2015", "6/1/2015",
"6/10/2014", "6/11/2014", "6/11/2015", "6/12/2015", "6/16/2014",
"6/16/2015", "6/18/2014", "6/21/2014", "6/24/2015", "6/25/2014",
"6/25/2015", "6/26/2015", "6/27/2015", "6/29/2015", "6/5/2014",
"6/6/2015", "6/8/2014", "7/1/2014", "7/13/2014", "7/14/2015",
"7/16/2014", "7/2/2014", "7/21/2014", "7/25/2014", "7/27/2014",
"7/27/2015", "7/28/2014", "7/29/2014", "7/30/2014", "7/31/2014",
"7/31/2015", "7/4/2014", "7/4/2015", "8/1/2014", "8/11/2014",
"8/11/2015", "8/25/2014", "8/27/2015", "8/5/2014", "8/8/2014",
"8/9/2015", "9/1/2014", "9/10/2015", "9/15/2015", "9/22/2013",
"9/3/2012", "9/30/2014", "9/8/2014", "9/8/2015"), class = "factor")
Label = c(" ",
"1/16/2016", "1/26/2015", "10/11/2015", "10/14/2015", "10/16/2015",
"10/6/2014", "10/7/2013", "11/11/2015", "11/15/2015", "11/17/2013",
"11/18/2013", "11/2/2015", "11/20/2013", "11/29/2013", "2/17/2014",
"2/17/2015", "2/21/2015", "2/23/2014", "2/25/2014", "2/25/2015",
"3/11/2016", "3/2/2014", "3/22/2015", "3/4/2014", "3/4/2016",
"4/11/2014", "4/12/2013", "4/18/2016", "4/21/2015", "4/23/2015",
"4/29/2015", "4/3/2015", "4/5/2016", "5/23/2015", "5/26/2015",
"5/27/2015", "5/28/2015", "5/29/2014", "5/29/2015", "5/8/2015",
"6/16/2015", "6/22/2015", "6/28/2015", "7/24/2015", "7/27/2015",
"7/4/2014", "7/8/2015", "9/14/2015", "9/15/2015", "9/16/2014",
"9/17/2014", "9/22/2014", "9/23/2014", "9/24/2014", "9/24/2015",
"9/26/2014", "9/28/2015", "9/30/2015", "9/9/2015"), class = "factor")), .Names = c("1A_RespDate",
"1B_RespDate", "1C_1_RespDate", "1C_2_RespDate",
"1C_RespDate", "2A_1_RespDate", "2A_RespDate", "2B_RespDate",
"2C_RespDate"), row.names = c(NA, -4831L), class = "data.frame")
I'll call you dataframe df: sapply(apply(df,1,unique),length) will give you the number of unique dates for each individual as a vector. The highest value is 7 and the min 1 (all questionnaires answered on the same day). which(sapply(apply(df,1,unique),length)<7) Will give you the index of the individuals who filled at least 2 questionnaires on the same day. length(which(sapply(apply(df,1,unique),length)<7)) Will tell you how many individuals filled at least 2 questionnaires on the same day. Edit: This is inelegant (there must be a cleaner way) but it seems to work which(sapply(sapply(sapply(apply(df,1,table),function(x) x==Z),which),function(x) any(x>0))) Z is to be set to the number of questionnaires filled on the same day. Explaination: apply(df,1,table) gives a list with for each individual the unique dates and how many times they appear. sapply(apply(df,1,table),function(x) x==Z) will give you the same list with True/False on whether a date appears exactly Z times. sapply(sapply(apply(df,1,table),function(x) x==Z),which) will give either "interger(0)" or a positive integer which is the index of the date for the individual (it's not something we are interested in). sapply(sapply(sapply(apply(df,1,table),function(x) x==Z),which),function(x) any(x>0)) will give a vector of True/False corresponding to the index of the individual then next step with "which" is to get the index for the True. We therefore get the individuals for which a date appears exactly Z times.
How do I convert milliseconds to dd:hh:mm using moment.js?
I'm trying to get the days, hours and minutes of 9000000 milliseconds, but moment.js is returning 0 days. I'm using Format plugin for the Moment Duration object. https://github.com/jsmreese/moment-duration-format
moment.duration(9000000, "milliseconds").format("dd:hh:mm");
returns "02:30"
How did I get 9000000?
var ms = moment.duration({
days: 1,
hours: 2,
minutes: 30,
})
console.log(ms._milliseconds);
// 9000000
Sounds like humanizeduration is what you are looking for: humanizeDuration(97320000) // '1 day, 3 hours, 2 minutes' Here is the github link: https://github.com/EvanHahn/HumanizeDuration.js
1000 x 60 x 60 x 24 = 86'400'000 milliseconds. Of course 9 mil is 0 days. 9'000'000 / (1000 x 60 x 60) = 2.5h = 2 hours 30 min I hope I know how to use calculator Check this place
Divide data frame in three subframes based on values
I have a data frame like below. I would like to divide this into three submatrices bases on the average row.values. Basically I need to first calculate the average row.value and then divide the data frame based on the ID and the average row.value. Any ways to do this directly in one command?
dput(head(matrix_cpm_spike_norm_mature,6))
structure(c(3.60338983255681, 2.60614455986074, 7.38026015357423,
0.485930325361538, 10.0728954273074, 11.2022336797709, 2.3081852592818,
1.92925594571705, 4.42105830784866, 1.97655358070322, 11.4414272084792,
10.5245987429194, 3.79928941804137, 1.43988964742944, 2.43182718729225,
1.85054706660746, 10.6157635199152, 9.93246596282924, 1.76499400158053,
0.867000595584972, 4.17937486393757, 1.62313934970233, 10.9760540633484,
11.4384402909535, 3.12565733850373, 1.02306009758869, 6.15822532096255,
1.45585403170949, 11.1021517437179, 10.9107307780349, 1.84965418527454,
1.57953239648466, 3.92960542264649, 0.968010692772174, 11.0236055961936,
10.5913003928287, 4.16812971357324, 2.58284099400479, 5.77874217548982,
2.71612416959378, 11.6945755132658, 12.2125038706251, 2.28044423317132,
2.28044423317132, 4.20181017967427, 1.93489752637374, 12.0201280590592,
11.4757936894966, 3.29573463740003, 3.1840621322311, 5.6701373082146,
1.96630490538038, 10.7374381660113, 13.2995125381539, 2.91175170429137,
2.1569190504634, 5.20328658455966, 2.1569190504634, 10.2217040585341,
9.83559070490264, 3.93309437957696, 2.51818736936141, 7.40221631369804,
0.335828139281744, 10.56613413102, 10.6619866025163, 2.27796568023663,
1.66056419670379, 5.29496497910422, 0.699488415484219, 10.310084339381,
9.75424847574386, 3.64268206121968, 2.37908914926602, 8.24035280411754,
1.86474527822428, 8.96352397286089, 10.2227828989983, 2.33835251965787,
2.06871044842623, 4.5954355631791, 1.01279126170234, 12.3601973455172,
11.4656151558222, 2.44441563566059, 2.40421119777587, 6.02973594460351,
1.52508878726709, 10.4309455175175, 9.04837239279591, 2.19268157249804,
1.24045869709133, 3.09706859211401, 1.13115919913948, 10.7933496311904,
9.9695518430138, 2.92171136565383, 0.408245399247249, 2.04506253637125,
1.21781706285458, 11.799265629373, 10.0589147547578, 2.01873582708745,
1.46069392909814, 3.53893363843314, 1.196243674246, 11.337876323565,
11.2124904638672, 2.52480547770426, 1.46864391283124, 4.93028527141748,
0.732496016009805, 10.1639221283662, 9.76884595465318, 1.44174154410475,
1.64024858387856, 5.63721432238654, 1.72991175679785, 11.1211504054224,
10.6024617121637, 3.22964454373919, 1.02206068958127, 6.97852650980799,
1.30645573502801, 12.8991256257565, 13.0000814142899, 2.826661275181,
1.2548377150201, 4.36474368941959, 2.12408033247534, 12.4705231651432,
11.9618071621679, 2.69847153335308, 2.27631143253466, 4.20951126623905,
-0.0664422382974684, 8.98849795367716, 9.41468863016541, 2.43225940352207,
2.09595624532069, 4.63715429950104, 1.25491429244567, 10.9872971587847,
10.4925408800726, 3.45374293794281, 3.13429367766325, 7.39158804595222,
1.54024232057877, 10.8542522087055, 10.6155963202093, 2.69223465587794,
0.809912072570472, 4.64506635251942, 1.6137442296552, 11.418571423468,
12.2726701702859, 2.77267844825925, 1.78512859921307, 9.14121538410264,
-4.43688074939158, 9.73718972990295, 10.2957742347039, 1.49118640848005,
1.5813150312641, 4.0667005226673, 0.59204571815906, 10.0375004920899,
9.54533950930523, 2.16345862609174, 3.05436004610877, 8.07790690254631,
1.79368307261622, 12.3560914214054, 13.0051227140041, 2.58124788178095,
1.88617681546547, 2.73360657353678, 1.77592657048602, 10.9528780864007,
10.4348473225329, 3.00497812997592, 1.7631510316129, 5.90700548290542,
0.877605937609275, 9.87041660889873, 9.7614993662457, 2.05885671172016,
1.76410360006183, 6.3570619407495, 1.76410360006183, 11.685705297838,
11.3602560106241, 2.82800833596723, 2.03247959283134, 6.95870308054991,
1.27136597104121, 10.8372565515699, 9.81173432822107, 2.98238612294936,
1.83072752194121, 5.68689117080157, 1.16721273421709, 12.1340028664354,
11.3680995516162, 3.39653560487816, 2.69687166650826, 6.48412064518055,
1.29454179328719, 10.0028066556529, 10.512440462176, 1.4596941835747,
0.727974609981551, 3.88239656448561, 0.800353161112983, 10.6363990565411,
10.1243371999547, 3.3438733844774, 0.697114163823588, 8.47892011973568,
0.697114163823588, 10.6855866660417, 10.6118837404499, 2.13674190430653,
1.1527414454512, 4.18464270261989, 1.27853746250223, 10.7320904309318,
9.89189546554829, 3.823237482187, 0.874031743370891, 1.95508453030069,
1.87846554046761, 9.88713195004868, 9.86183499157279, 2.53517059781643,
0.728446508864785, 3.50839578521561, 1.9965292874828, 10.9487017925789,
10.9382935538901, 2.62649833974985, 1.92098108152647, 3.36108007152541,
0.488100205530239, 10.4894419819966, 10.2226664777547, 1.33967056255352,
0.0917001628908314, 3.81741552255672, 2.03856822618478, 12.2392733147047,
11.4938478602584, 3.25046552747648, 1.48275456354609, 4.81128734606174,
1.96154924623549, 12.1358994156854, 11.4729501435715, 2.22982247734829,
1.68173260626794, 5.2904025048379, 1.68173260626794, 11.5137150099059,
11.010850144881, 3.91979174751635, 2.17257846100131, 3.53963311590271,
1.6107186758943, 11.4951784786694, 10.1260992493434, 2.35254220581856,
1.26444532526964, 4.6646553023283, 1.17529994013973, 10.9857870198993,
10.5814974795421, 3.75054671795939, 3.03029499770489, 9.00153489042535,
3.27015511904915, 11.5616951026666, 10.6545660172833, 2.18325656314713,
1.50344897782345, 3.97329240591904, 1.50344897782345, 11.3467527232018,
10.707768513033, 2.7932432629389, 1.70473241476131, 5.05340701550577,
0.547092257741687, 11.7624630621943, 9.91053139355945, 2.80574376605516,
1.63874222935751, 3.92143952780207, 1.834040048536, 11.4458009229089,
10.9155922874574, 2.81245930319929, 1.47154823046698, 4.17913709197299,
0.836392585913434, 12.5796307717185, 11.6787505410903, 1.7257545501894,
0.949499417203444, 5.34316953950321, 1.63528985628147, 12.2668256759477,
11.696888238442, 2.43827755616949, 1.81088892324984, 5.13790770293989,
1.62918292819459, 10.2635519811317, 10.3028832599596, 2.07789375455372,
1.08266801489579, 3.36924723755809, 0.361290751749976, 9.978530962904,
9.62993954501451, 3.46479867592192, 2.12156177109216, 8.78601850725548,
1.28447344131806, 11.5362902737332, 11.569001443705, 1.88432186226604,
0.738852102905189, 3.93512805821853, 2.28505136817535, 12.2366325701317,
11.5419247012961, 2.72781971767877, 0.981047395388437, 2.42660975369917,
1.39151946304238, 9.47444971798845, 10.1389766220257, 1.9620421090479,
1.31100308652463, 3.9625997590393, 1.31100308652463, 11.0538785110822,
10.5756552873608, 2.24522309004463, 1.98668802513104, 4.91591774317019,
1.41382278476808, 10.9307830177181, 9.75061291019652, 2.89312477978526,
1.41138577836645, 5.3114727278592, 1.18511963455519, 12.8070122897496,
11.9900344090709, 3.27111851368705, 0.108008163568448, 3.27111851368705,
1.04663775582004, 10.5015959502624, 10.9439135408891, 1.96611160593682,
1.75218878170152, 1.96611160593682, -0.675585494894112, 11.1733364353258,
10.5537870233171, 2.76365773789404, 2.24622484994411, 4.82500775257594,
0.859189165848259, 11.3729184708685, 10.4792280890542, 2.2746810191026,
1.26862765095299, 3.1182053649793, 1.35619562745757, 11.541422194603,
11.0876007441211, 2.93955281780494, 2.41792430951958, 7.43488775671718,
1.59455665002896, 10.8522501620988, 10.7066940648954, 2.56175708272954,
1.63789352792862, 5.62616733093608, 1.77321879142546, 11.5341769241796,
11.3614738869516, 3.52512139810446, 2.04455677275519, 9.07892025450834,
1.80422229584867, 12.2658423589039, 12.7955854152128, 2.41662572851679,
0.739356302189606, 6.58079778253096, 2.2056509250739, 11.4453206203817,
11.2577755286648, 4.43488006430324, 2.47577649074622, 7.97026648668841,
2.47577649074622, 10.5347452823169, 10.9336199647057, 0.253851439446271,
1.33349021364545, 1.90668843852765, 1.38668924767966, 11.529988569981,
9.68152140791945, 2.69321031115829, 1.69780604669796, 9.9826122018356,
-0.0693106968443571, 11.2474341893491, 10.2586104182553, 2.30251678501683,
1.67135129150304, 5.1970813744178, 1.88064524158918, 11.4072019068968,
10.9762764532365, 2.73053313404777, 2.92486117981077, 9.751901699517,
2.31573050368619, 11.0745724079847, 11.5397308498818, 1.85114298699175,
1.74446232598571, 4.67405729933025, 1.36771765795199, 11.1449736215866,
10.6467965079355, 3.12408501589049, 2.00482310148083, 4.90897925267742,
0.753058050023766, 10.3589506698824, 10.3513173985724, 2.72147165969401,
1.46186080167698, 3.9354821726169, 1.23699756995357, 11.1881701541595,
10.931341142772, 2.99260441125988, 2.23826963603629, 3.87458928218141,
0.65507014009726, 9.74342426391136, 10.8918928191143, 2.78046014914365,
0.505490227190171, 4.43327034269568, 1.9329054288394, 11.6078716913683,
10.9011106771378, 2.92822672503857, 1.76286882344236, 6.82012516033837,
1.49833750295022, 9.57121023826284, 8.39324323219324, 2.81052780333747,
1.40588646539131, 3.18875457138522, 1.18481091851206, 11.462231229116,
10.5770583597432, 3.30911666206261, 1.33469191841737, 4.96029091396055,
2.17413398672633, 10.5583178628059, 10.0329059364534, 1.88186145438922,
0.989773711428552, 5.0108103947152, 0.769365496730378, 11.7867744116038,
11.2871021676365, 2.88127273360004, 1.87947399714945, 2.88127273360004,
1.92593791384916, 9.46125879965226, 9.29462968277333, 2.36391085778722,
1.74079268896779, 3.93604270788606, 1.34641095936402, 10.2068762533305,
10.0118036485061, 2.57361661875718, 2.03528106162882, 4.33420919282757,
1.1624557079541, 11.0754175076428, 11.0622083554009, 1.93155319127651,
1.52032894976282, 4.46324520367219, 1.52032894976282, 11.3476141375944,
10.4138620742203, 3.85081675794147, 2.74371623100224, 1.11770920823725,
1.98482146279348, 12.566434472332, 11.9424300470872, 2.53925962537711,
1.48382075377967, 4.87249055114745, 1.07284442662947, 11.7111768375923,
11.5795125663534, 2.15535566170556, 1.6234973786264, 7.72868868952004,
0.562556076894618, 9.85480133708155, 9.92934562704339, 2.54728578873058,
1.8265104903494, 3.76801234500258, 1.2821466213423, 11.2568527058396,
10.8343412926481, 3.05211979795212, 0.152264838074353, 2.54493306112919,
1.77792835724608, 11.5338955198532, 9.97573017264385, 2.42059823105591,
0.60936602148068, 3.12812928480832, 1.14355342605761, 11.5524961118039,
10.5285363661307), .Dim = c(6L, 92L), .Dimnames = list(c("hsa-miR-671-5p",
"hsa-miR-3909", "hsa-miR-1247-5p", "hsa-miR-628-3p", "hsa-miR-23b-3p",
"hsa-miR-127-3p"), c("100G", "100R", "106G", "106R", "122G",
"122R", "124G", "124R", "126G", "126R", "134G", "134R", "141G",
"141R", "167G", "167R", "185G", "185R", "192G", "192R", "235G",
"235R", "239G", "239R", "243G", "243R", "246G", "246R", "261G",
"261R", "267G", "267R", "26G", "26R", "270G", "270R", "279G",
"279R", "299G", "299R", "301G", "301R", "305G", "305R", "342G",
"342R", "350G", "350R", "356G", "356R", "35G", "35R", "361G",
"361R", "366G", "366R", "367G", "367R", "377G", "377R", "379G",
"379R", "388G", "388R", "400G", "400R", "402G", "402R", "46G",
"46R", "48G", "48R", "55G", "55R", "57G", "57R", "60G", "60R",
"68G", "68R", "70G", "70R", "73G", "73R", "77G", "77R", "82G",
"82R", "93G", "93R", "94G", "94R")))
I guess we need cut to split up the rows of the matrix after getting the rowMeans
v1 <- rowMeans(m1)
v2 <- cut(v1, breaks=3)
v2
#[1] (5.52,8.48] (11.4,14.4] (5.52,8.48] (11.4,14.4] (5.52,8.48] (5.52,8.48]
#Levels: (5.52,8.48] (8.48,11.4] (11.4,14.4]
We split the sequence of rows of the matrix ('m1') by 'v2', subset the rows of 'm1' based on the row index to create a list of 3 matrices.
lst <- lapply(split(1:nrow(m1), v2), function(i) m1[i,])
So here is a different approach to grouping by row-means. This uses kmeans clustering on the row-means to divide based on the closeness of the values. I think this is more in line with your description "low", "med", "high". Your data set as provided is a matrix with 6 rows and 96 columns (the ID's are in the row.names. df <- data.frame(ID=rownames(df),df) library(data.table) setDT(df)[, rm:=rowMeans(.SD), .SDcols=2:ncol(df), by=ID] df[,grp:=kmeans(rm, centers=3)$cluster] df[,list(ID,rm,grp)] # ID rm grp # 1: hsa-let-7a-3p 5.819544 1 # 2: hsa-let-7a-5p 14.396980 3 # 3: hsa-let-7b-3p 5.526127 1 # 4: hsa-let-7b-5p 11.548722 3 # 5: hsa-let-7c-5p 7.881395 2 # 6: hsa-let-7d-3p 6.368912 1 result <- split(df,df$grp) # split into 3 data.tables based on group You can see that 5.5, 5.8, and 6.3 are in the first group, 7.9 is in the second group, and 11.5 and 14.4 are in the 3rd group.