rollapply classes each segment the same - r
I am trying to pass a function over zoo data with the following:
Behaviour <- rollapply(data = as.zoo(Out), width = 32, FUN = function(JRip){
JRip(Dist_Out, Speed_Out, Acceleration_Out, Absolute_Heading_Out, Relative_Heading_Out, Movement_Out, Changes_Out, AccPosNeg_Out, AccChange_Out)},
by = 32, by.column = FALSE, partial = TRUE, align = "center")
This function classes data segments of 32 rows with a behaviour and then goes down to the next 32 rows to predict the behaviour.
This seems to work fine when I only give it data of length 32 rows but when I give it anymore e.g. 64 it classes each Behaviour exactly the same rather than detecting differences. As I say as I feed it individual data of nrow 32 each it gets them all correct. Is there anything obviously wrong that I am missing here before I put up what is a large example?
Thanks.
Ok here is the first 33 lines of the data (or segment).
The function works through a set of if else statements and comes to a behavioural classification. When I run the function over this segment only with 'window = 32' size of 32 and 'by = 32' it correctly classifies the segment. But when I give it a whole dataset e.g. >32 rows it gives every segment the same classification. So if I gave it 66 rows to give 2 classes it will class them both the same when I know they should be different. This happens also when I give it greater datasets.
Out <- structure(list(Dist_Out = structure(c(223L, 224L, 195L, 195L,
195L, 235L, 299L, 64L, 336L, 28L, 191L, 129L, 63L, 303L, 249L,
194L, 222L, 177L, 199L, 309L, 165L, 276L, 298L, 308L, 21L, 297L,
175L, 253L, 316L, 268L, 281L, 251L, 60L), .Label = c("0", "0.110574578321468",
"0.110574578385818", "0.110574578646219", "0.110574578769975",
"0.110574578837889", "0.110574578901783", "0.110574578961973",
"0.110574579093701", "0.110574579157825", "0.11057457934999",
"0.110574579413902", "0.110574579478479", "0.11057457973394",
"0.110574579798528", "0.11057457999076", "0.110574580247396",
"0.11057458031112", "0.110574580503848", "0.110574580567801",
"0.110574580694844", "0.11057458095289", "0.110574581402704",
"0.110574583204168", "0.111304214830553", "0.111304253300095",
"0.111304307772237", "0.111304317308227", "0.111304330093688",
"0.11130434287376", "0.111304358897914", "0.111304361977123",
"0.11130436522592", "0.111304368417738", "0.11130437160922",
"0.111304371665862", "0.111304374743724", "0.111304374800365",
"0.111304374857007", "0.111304377934532", "0.111304384315138",
"0.11130438437178", "0.111304384428421", "0.111304387561577",
"0.111304390694397", "0.11130439080768", "0.111304393883521",
"0.111304393940163", "0.111304397072309", "0.11130439712895",
"0.111304397185592", "0.111304400317401", "0.111304400374043",
"0.111304403505515", "0.111304403562157", "0.111304413067836",
"0.111304413124478", "0.111304425756241", "0.15689285571989",
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"0.156892912024679", "0.156892934292016", "0.156892940915204",
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"0.156892954276312", "0.156892955820879", "0.156892955820883",
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"0.156892958714776", "0.156892958714863", "0.156892960893118",
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"1.59180424399744", "1.66256690191307", "1.7271568926302", "1.79454617273675",
"1.83497057428696", "3.07757525439021"), class = "factor"), Speed_Out = structure(c(2L,
8L, 8L, 4L, 15L, 15L, 6L, 15L, 17L, 8L, 8L, 10L, 23L, 25L, 17L,
11L, 8L, 8L, 9L, 8L, 9L, 11L, 8L, 10L, 14L, 24L, 26L, 26L, 23L,
18L, 22L, 13L, 15L), .Label = c("0", "0.03", "0.06", "0.09",
"0.095", "0.12", "0.125", "0.155", "0.185", "0.19", "0.215",
"0.22", "0.245", "0.25", "0.275", "0.28", "0.31", "0.34", "0.345",
"0.37", "0.375", "0.4", "0.405", "0.435", "0.465", "0.495", "0.5",
"0.525", "0.555", "0.62", "0.715", "0.745", "0.775", "0.965",
"1.085", "1.12"), class = "factor"), Acceleration_Out = structure(c(20L,
25L, 6L, 20L, 28L, 9L, 19L, 28L, 7L, 2L, 21L, 19L, 31L, 9L, 19L,
6L, 21L, 2L, 23L, 6L, 27L, 6L, 21L, 19L, 22L, 27L, 4L, 23L, 9L,
24L, 1L, 8L, 28L), .Label = c("-0.012", "-0.014", "-0.024", "-0.026",
"-0.036", "-0.038", "-0.048", "-0.05", "-0.062", "-0.074", "-0.076",
"-0.088", "-0.1", "-0.112", "-0.138", "-0.162", "-0.222", "-0.286",
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"0.05", "0.062", "0.074", "0.076", "0.086", "0.088", "0.1", "0.112",
"0.124", "0.162", "0.222", "0.286"), class = "factor"), Absolute_Heading_Out = structure(c(113L,
114L, 166L, 275L, 275L, 273L, 121L, 211L, 260L, 288L, 1L, 1L,
92L, 213L, 134L, 274L, 22L, 54L, 74L, 183L, 257L, 91L, 256L,
263L, 1L, 255L, 214L, 189L, 143L, 178L, 252L, 254L, 57L), .Label = c("0",
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"99.4635877243328", "99.4635933528321", "99.4635973787254"), class = "factor"),
Relative_Heading_Out = structure(c(84L, 3L, 120L, 273L, 213L,
403L, 38L, 180L, 297L, 73L, 409L, 213L, 74L, 210L, 326L,
166L, 121L, 10L, 162L, 212L, 295L, 44L, 256L, 5L, 395L, 163L,
88L, 263L, 342L, 55L, 288L, 128L, 181L), .Label = c("-1.08941367216175e-06",
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"-110.383710239579", "-111.798690769161", "-116.561876778859",
"-116.568179982698", "-119.048575511772", "-12.5275134113761",
"-125.54290253755", "-126.873674566913", "-134.996083788201",
"-134.99608874744", "-135.003907962816", "-135.003910222254",
"-135.003910440718", "-135.003912920006", "-135.003916226374",
"-135.003917053256", "-135.003920346791", "-14.0343965682479",
"-14.0344008293472", "-14.0381027887613", "-14.9333752469922",
"-15.2563802581807", "-151.923815028848", "-153.431797335435",
"-153.431814787652", "-153.438076376928", "-153.438078534744",
"-153.438081405064", "-155.224175835841", "-16.5895970517398",
"-161.564269704982", "-161.564270216045", "-163.070873427349",
"-168.688559655604", "-173.087896236407", "-178.210035361013",
"-18.4357312990837", "-18.4357320431515", "-18.4357376166744",
"-18.4372944963587", "-18.4373234043367", "-18.4374360606442",
"-180", "-180.00000165239", "-180.000001652846", "-180.000001653188",
"-180.00000165359", "-180.000001653757", "-189.462544556087",
"-19.440191483388", "-191.310236746892", "-198.432598104786",
"-198.435737329084", "-201.804133233895", "-203.197372900173",
"-206.561923646396", "-213.686445206476", "-213.693730144167",
"-216.866121212474", "-22.6226642752191", "-224.996065562981",
"-224.996082946744", "-224.996084848199", "-224.996086776282",
"-224.996090081518", "-225.003911859839", "-225.003915399271",
"-225.003918706729", "-23.2014262201339", "-235.306909073552",
"-236.306305752159", "-24.2299694795691", "-243.438082243148",
"-251.562697978643", "-251.572156808192", "-26.561920485388",
"-26.5619245326763", "-26.5682154538194", "-270.000000825739",
"-270.000000826081", "-270.000000826195", "-270.000000826423",
"-270.00000082722", "-270.00000165051", "-270.000001652902",
"-270.000001663556", "-270.000005782192", "-270.007837978621",
"-278.137789746516", "-296.568183955338", "-296.568186601703",
"-30.9658311909307", "-30.9672302310985", "-315.003904193081",
"-315.003911033758", "-315.003911845393", "-315.003912005172",
"-315.003913746145", "-315.003914572848", "-315.003916515341",
"-315.003917865386", "-315.00391928621", "-322.128861928285",
"-322.129645823558", "-326.313549702075", "-33.6864487776922",
"-33.6936950910497", "-33.6937058033612", "-33.6937221353792",
"-333.438075455662", "-341.567396605941", "-36.8661267834578",
"-36.8698974613882", "-38.6636779175493", "-4.39902960567105",
"-4.63602950075966", "-40.6024036695531", "-44.99604368852",
"-44.9960796387537", "-44.9960812782547", "-44.9960812928333",
"-44.9960821346143", "-44.996083165653", "-44.9960831802316",
"-44.9960837736256", "-44.9960862684335", "-44.9960870799939",
"-44.9960870802999", "-44.9960920371838", "-44.9960945292073",
"-45.0039062965318", "-45.0039099113321", "-45.0039104261803",
"-45.0039137610075", "-45.0039159931442", "-45.0039170385072",
"-45.0039170385603", "-45.0039184592101", "-45.0039195195613",
"-45.0039195342731", "-45.0039244841527", "-45.0039501982616",
"-49.2730539449748", "-50.1905434337506", "-50.5335956897864",
"-56.3063089215069", "-56.3135357050287", "-6.55934059068386e-07",
"-63.4286491352498", "-63.4317895372392", "-63.438071894588",
"-63.4380797638232", "-63.4381002876273", "-63.4381102417655",
"-66.7985762616031", "-69.0799574007877", "-69.2279887677529",
"-69.782098428514", "-7.01324449892127e-06", "-7.76602933812626",
"-71.5579915325294", "-71.5580050437492", "-71.5626782590484",
"-71.5674001952552", "-71.5674035490282", "-71.5720922472722",
"-71.5721163197041", "-71.5721653034134", "-78.6825596495758",
"-8.12994870632622", "-8.13108046273612", "-8.26243820029049e-07",
"-8.26479720217321e-07", "-8.26631662675936e-07", "-8.26992447855446e-07",
"-8.54759605317668e-07", "-81.8643827331808", "-82.8740180768513",
"-89.9921636459231", "-89.9921690352491", "-89.9921952742039",
"-89.999998346186", "-89.9999991719821", "-89.9999991731215",
"-89.9999991732354", "-89.9999991735772", "-89.9999991736912",
"-89.9999991738051", "-89.9999991739191", "-90.000000825967",
"-90.0000008260809", "-90.0000008264228", "-90.0000008267646",
"-90.0000008268785", "-90.0000008280179", "-90.0000033110747",
"-90.0078754520491", "-90.0079289020773", "-94.7688606836447",
"0", "1.21831646993087e-06", "1.6576310599703e-06", "1.66235020060412e-06",
"101.302350452585", "106.496883952078", "108.427537580527",
"108.44197890194", "11.3096332494953", "11.3102394284159",
"11.3113335227112", "11.3114583072521", "116.561916857852",
"116.56192105113", "116.568187269301", "117.402527859642",
"12.2476780128984", "120.957965917544", "125.532495222411",
"132.268196345264", "134.996085645806", "134.996087906417",
"134.996090907599", "135.00390464519", "135.003907948238",
"135.003910222254", "135.003911252625", "135.003911281782",
"135.003911680336", "135.003912905428", "135.003912920006",
"135.003912934585", "135.00391516638", "135.00391928621",
"135.003919548889", "14.0344008293472", "143.130106780739",
"15.2567559249757", "153.433391310084", "153.438081405064",
"153.438085623885", "161.562685384209", "161.564271367631",
"161.564271627437", "161.565836751711", "18.4341646234914",
"18.4341666601178", "18.4341668582031", "18.4341672821188",
"18.4357306931675", "18.4357346476826", "18.4357360052061",
"18.435740353848", "18.4372930648732", "18.8010696166384",
"180", "180.000001651478", "180.00000165239", "180.000001652497",
"180.000001652846", "180.000004968184", "187.12599956293",
"187.695688180681", "19.5660942200254", "191.309633889126",
"194.038104488152", "198.432585234004", "2.20765036829107e-06",
"201.804138766508", "206.561920224708", "206.561923646396",
"206.566618439315", "206.568189420149", "21.1640959090685",
"213.693732308918", "222.278699473017", "224.99608235309",
"224.996086790861", "224.996089777746", "224.996097816515",
"225.00391951929", "225.003920954773", "229.769652363221",
"243.431808769598", "247.626592898806", "251.567401848443",
"251.572173725974", "255.961909409804", "26.561909979624",
"26.5619143761148", "26.5619152872187", "26.561918159163",
"26.561920485388", "26.5619245326763", "26.5634850427263",
"26.5656849738252", "26.5666170276959", "26.5674578635275",
"26.5681908992693", "270.000000825739", "270.000000825967",
"270.000000826081", "270.000000826195", "270.000000826423",
"270.000000826651", "270.000000826765", "270.000000826879",
"270.00000082722", "270.000000827562", "270.00000165051",
"270.000001657631", "270.007821474476", "270.007864984721",
"288.442043790965", "3.5553682664613e-07", "3.96653496181898e-06",
"30.9602599760489", "30.9658300606604", "30.9658510086751",
"30.967256419496", "315.003906325462", "315.003911033758",
"315.003913746145", "315.003916515341", "315.003917865386",
"315.003917879964", "33.6864487776922", "33.6936728303563",
"33.6937173620532", "33.6941930107691", "333.438072719985",
"333.438075455662", "333.438081416726", "348.691571595965",
"350.538959809096", "38.0930036092936", "38.6628898865961",
"4.26803802337022", "40.6024008285175", "44.9960829613228",
"44.9960831802316", "44.9960848481989", "44.9960862684335",
"44.9960870799939", "44.9960870802999", "44.9960870948785",
"44.9960879211313", "44.9960906183937", "44.9960920517623",
"44.9960945292073", "45.0039102076872", "45.0039106205144",
"45.0039107371338", "45.0039110191976", "45.003911048449",
"45.0039137464255", "45.0039137464256", "45.0039165156846",
"45.0039195196753", "45.0039249122307", "45.0039253261187",
"49.579530827573", "58.6778036558414", "6.34009731534894",
"6.34105728056107", "63.4380772030091", "63.4380789368118",
"63.4380805900751", "63.438083494651", "63.4381110097399",
"63.4412765398765", "63.4412905471348", "64.6593257332515",
"66.0346118387025", "68.2013072611666", "7.12550172992948",
"7.76603379364138", "7.96445306150417e-07", "7.97902600879752e-07",
"71.5626800707728", "71.5627008196788", "71.5720922326936",
"74.052516819087", "74.7389307588194", "78.682521535368",
"78.6885588270165", "78.6915931880158", "8.12994364547711",
"8.12994419615723", "8.26137920739711e-07", "8.28036945677013e-07",
"83.0050894051913", "85.2304874382689", "87.5169609528689",
"89.9921898840171", "89.999998346186", "89.9999991716402",
"89.9999991731215", "89.9999991732354", "89.9999991735772",
"89.9999991736912", "89.9999991738051", "89.9999991739191",
"89.999999174033", "89.9999991742609", "9.46359418141975",
"90.0000008260809", "90.0000008261949", "90.0000008263088",
"90.0000008264227", "90.0000008264228", "90.0000008267646",
"90.0000008268785", "90.0000016529025", "93.1724357049647"
), class = "factor"), Movement_Out = structure(c(2L, 1L,
1L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 3L, 3L, 3L, 2L, 4L, 2L, 1L,
3L, 3L, 2L, 3L, 2L, 4L, 1L, 3L, 2L, 2L, 1L, 1L, 1L, 4L, 1L,
2L), .Label = c("forward", "left", "non-moving", "right"), class = "factor"),
Changes_Out = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L), .Label = c("0",
"1"), class = "factor"), AccPosNeg_Out = structure(c(2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 2L), .Label = c("0", "1"), class = "factor"), AccChange_Out = structure(c(2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L), .Label = c("0", "1"), class = "factor")), .Names = c("Dist_Out",
"Speed_Out", "Acceleration_Out", "Absolute_Heading_Out", "Relative_Heading_Out",
"Movement_Out", "Changes_Out", "AccPosNeg_Out", "AccChange_Out"
), row.names = c(NA, 33L), class = "data.frame")
This is the function in rollapply:
function (Dist_Out, Speed_Out, Acceleration_Out, Absolute_Heading_Out,
Relative_Heading_Out, Movement_Out, Changes_Out, AccPosNeg_Out,
AccChange_Out)
{
if (max(Speed_Out) >= 2.45) {
Behaviour <- "walking"
}
else if (mean(Acceleration_Out[Acceleration_Out > 0]) >=
0.03 & (max(Dist_Out[Movement_Out != "non-moving"]) <=
2.17 & (mean(Speed_Out) >= 0.23 & (sum(Dist_Out[Movement_Out !=
"non-moving"]) >= 11.92)))) {
Behaviour <- "grazing"
} else Behaviour <- "resting"
return (Behaviour)
}
This is a shortened version of the function. Would appreciate any help with this. Thanks.
The first argument of rollapply must have columns all of the same type, e.g. all numeric so the data frame of the question which has columns of different types cannot be used. Also even if that were fixed and a data.framne with numeric columns only were used, the rollapply function in the question would attempt to pass a matrix to it but the function then uses the matrix as if it were a function.
The example in the question is really too large for SO (examples should be cut down to make them minimal yet still illustrate the problem) so here is a smaller example using the built in data frame BOD to illustrate rollapplying a function divide over a data.frame all of whose columns are numeric.
library(zoo)
divide <- function(demand, Time) sum(demand) / sum(Time)
rollapply(BOD, 3, function(m) divide(m[, "demand"], m[, "Time"]), by.column = FALSE)
## [1] 6.266667 5.033333 4.216667 3.212500
The above the same as:
c(divide(BOD[1:3, "demand"], BOD[1:3, "Time"]),
divide(BOD[2:4, "demand"], BOD[2:4, "Time"]),
divide(BOD[3:5, "demand"], BOD[3:5, "Time"]),
divide(BOD[4:6, "demand"], BOD[4:6, "Time"]))
The rollapply command could alternately be written like this using with
rollapply(BOD, 3, function(m) with(as.data.frame(m), divide(demand, Time)),
by.column = FALSE)
Related
How to make scatterplot with colors based on a column and add a mean line through stats_summary with grouping based on another column?
I have a data.frame (see below) and I would like to build a scatterplot, where colours of dots is based on a factor column (replicate). I simultaneously want to add a line that represents the mean of y, for each x. The problem is that when I define the stat_summary it uses the colours I requested for groupingand hence I get three mean lines (for each color) instead of one. Trying to redefine groups either in ggplot() or stat_summary() function did not work.
if I disable colors I get what I want (a single mean line).
How do I have colors (plot # 1), yet still have a single mean line (plot # 2)?
structure(list(conc = c(10L, 10L, 10L, 25L, 25L, 25L, 50L, 50L,
50L, 75L, 75L, 75L, 100L, 100L, 100L, 200L, 200L, 200L, 300L,
300L, 300L, 400L, 400L, 400L, 500L, 500L, 500L, 750L, 750L, 750L,
1000L, 1000L, 1000L), citric_acid = c(484009.63, 409245.09, 303193.26,
426427.47, 332657.35, 330875.96, 447093.71, 344837.39, 302873.98,
435321.69, 359146.09, 341760.28, 378298.37, 342970.87, 323146.92,
362396.98, 361246.41, 290638.14, 417357.82, 351927.66, 323611.37,
416280.3, 359430.65, 327950.99, 431167.14, 361429.91, 291901.43,
340166.41, 353640.91, 341839.08, 393392.69, 311375.19, 342103.54
), MICIT = c(20771.28, 18041.97, 12924.35, 49814.13, 38683.32,
38384.72, 106812.16, 82143.12, 72342.43, 156535.39, 128672.12,
119397.14, 187208.46, 167814.92, 159418.62, 350813.47, 357227.48,
295948.31, 505553.77, 523282.46, 489652.3, 803544.84, 704431.61,
654753.29, 1030485.41, 895451.64, 717698.52, 1246839.19, 1309712.63,
1212111.53, 1930503.38, 1499838.89, 1642091.64), replicate = structure(c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L
), .Label = c("1", "2", "3"), class = "factor"), MICITNorm = c(0.0429150139016862,
0.0440859779160698, 0.0426274317575529, 0.116817357005636, 0.116285781751102,
0.116009395182412, 0.238903293897827, 0.238208275500519, 0.238853235263062,
0.359585551549246, 0.358272367659634, 0.34935932285636, 0.494869856298879,
0.489297881187402, 0.493331701877276, 0.968036405822146, 0.98887482369721,
1.01827072661558, 1.21131974956166, 1.48690347328766, 1.51308744189056,
1.93029754230503, 1.95985403582026, 1.99649737297637, 2.38999059622215,
2.47752500616233, 2.45870162403795, 3.6653801002868, 3.70350995307641,
3.54585417793659, 4.90731889298706, 4.81682207885606, 4.79998435561351
)), class = "data.frame", row.names = c(NA, -33L))
ggplot(xx, aes (conc, MICIT, colour = replicate)) + geom_point () +
stat_summary(geom = "line", fun = mean)
Use aes(group = 1): ggplot(xx, aes(conc, MICIT, colour = replicate)) + geom_point() + geom_line() + stat_summary(aes(group = 1), geom = "line", fun = mean)
ggpairs formatting for points only
I'm looking to increase the size of the points AND outline them in black while keeping the line weight the same across the remaining plots.
library(ggplot2)
library(GGally)
pp <- ggpairs(pp.sed, columns = c(1,2), aes(color=pond.id, alpha = 0.5)) +
theme_bw()
print(pp)
Which gives me the following figure:
Data for reproducibility, and TIA!
> dput(pp.sed)
structure(list(Fe.259.941 = c(905.2628883, 825.7883359, 6846.128702,
1032.932924, 997.8037721, 588.9599882, 6107.641947, 798.4493611,
1046.38376, 685.2485692, 6452.273486, 730.8656684, 902.8585447,
1039.886406, 7408.801001, 2512.089991, 911.2101809, 941.3712067,
659.1069185, 1070.090445, 1017.666402, 925.3221586, 645.0500668,
954.0009756, 1022.594904, 803.5865352, 7653.184537, 1082.714082,
1048.51115, 773.9070604, 6889.060748, 973.0971769, 1002.091143,
798.9670583, 5089.035978, 2361.713222, 970.8258109, 748.3574529,
3942.04816, 889.1760124), Mn.257.611 = c(17.24667962, 14.90488024,
14.39265671, 20.51133433, 19.92596564, 11.76690074, 19.76386229,
14.29779164, 20.23646264, 13.55374658, 16.8847698, 13.11784439,
15.91777975, 20.64068844, 16.78681661, 28.61732162, 15.88328987,
19.59750367, 13.09735943, 21.59458118, 17.680152, 19.87127449,
12.8082581, 20.12050221, 17.57143193, 18.72196029, 16.21525793,
22.0518966, 18.39642397, 18.32238508, 16.17696923, 20.69668404,
17.96018218, 18.71945309, 16.50162126, 30.60719123, 17.69058768,
14.99048753, 16.28302375, 18.32277507), pond.id = structure(c(6L,
5L, 2L, 1L, 3L, 5L, 2L, 1L, 3L, 5L, 2L, 1L, 6L, 3L, 2L, 4L, 6L,
3L, 4L, 4L, 6L, 3L, 4L, 1L, 6L, 3L, 2L, 1L, 6L, 3L, 2L, 1L, 6L,
3L, 2L, 1L, 6L, 5L, 2L, 1L), .Label = c("LIL", "RHM", "SCS",
"STN", "STS", "TS"), class = "factor")), class = "data.frame", row.names = c(11L,
12L, 13L, 15L, 26L, 27L, 28L, 30L, 36L, 37L, 38L, 40L, 101L,
102L, 103L, 105L, 127L, 128L, 129L, 131L, 142L, 143L, 144L, 146L,
157L, 158L, 159L, 161L, 172L, 173L, 174L, 176L, 184L, 185L, 186L,
188L, 199L, 200L, 201L, 203L))
The GGally package already offers a family of wrap_xxx functions which could be used to set parameters to override default behaviour, e.g. using wrap you could override the default size of points using wrap(ggally_points, size = 5).
To use the wrapped function instead of the default you have to call
ggpairs(..., lower = list(continuous = wrap(ggally_points, size = 5))).
Switching the outline is a bit more tricky. Using wrap we could switch the shape of the points to 21 and set the outline color to "black". However, doing so the points are no longer colored. Unfortunately I have found no way to override the mapping. While it is possible to add a global fill aes, a drawback of doing so is that we lose the black outline for the densities.
One option to fix that is to write a wrapper for ggally_points which adjusts the mapping so that the fill aes is used instead of color.
library(ggplot2)
library(GGally)
ggally_points_filled <- function(data, mapping, ...) {
names(mapping)[grepl("^colour", names(mapping))] <- "fill"
ggally_points(data, mapping, ..., shape = 21)
}
w_ggally_points_filled <- wrap(ggally_points_filled, size = 5, color = "black")
ggpairs(pp.sed, columns = c(1, 2), aes(color = pond.id, alpha = 0.5),
lower = list(continuous = w_ggally_points_filled)) +
theme_bw()
ddply dropping rows with zero sum
I am trying to sum my data per Meter, then average out the sumCover by Transect. My issue is that when I mean the transects, at the meter points where the cover data was taken if no native species were recorded, then that transect is effectively dropped from the dataframe after the ddply function. I have tried using the .drop function, but the issue is each site has unequal transect sampling because it was scaled to site size, so it effectively adds transects to every site. What I need to figure out to do is how to fill in within a list of numbers for missing Transect while taking into account each site varies from 3 to 16 transects - EDIT - the data preview seem to of got cut off and does not have sufficient rows so here is a file:
Here is a downloadable link of the data csv
read.csv()
require(ddply)
NativeNonnativeCoverperMeter <- ddply(RestoredGrasslandSurveys, c("Site","Transect","Locality","Meter"), summarise,
sumCover = sum(Cover))
NativeNonnativeCoverperTransect <- ddply(NativeNonnativeCoverperMeter, c("Site","Transect","Locality"), summarise,
avgCover = mean(sumCover), .drop = F)
dput(RestoredGrasslandSurveys[1:10, ])
structure(list(Site = structure(c(10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L), .Label = c("AzevedoNorth", "AzevedoSouth",
"Big.Banana", "BlohmRanch", "CypressGrove", "Diablo.Canyon",
"Dipsea.Moors", "Elkhorn.Nursery", "Elkhorn.Owl", "ElkhornHotwire",
"FacultyHousing", "Glass.Beach", "Hanson.ESHA", "Hanson.Uplands",
"Hawk.Hill", "LightHouse", "Modoc", "MooreCreek", "Morning.Sun",
"Noyo.Headlands", "Paradise.Ridge", "Prosper.Ridge", "RussianRidge",
"Stinson.Gulch", "Tennessee.Valley", "Watsonville.Uplands", "YoungerLagoon"
), class = "factor"), County = structure(c(4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L), .Label = c("Humboldt", "Marin", "Mendocino",
"Monterery", "Monterey", "San.Luis.Obispo", "SanMateo", "Santa.Barbara",
"SantaCruz", "Sonoma"), class = "factor"), Transect = c(3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), Meter = c(0L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L), Lifeform = structure(c(4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("AnnualForb", "AnnualGrass",
"Fern", "Groundcover", "Horsetail", "Nfixer", "PerennialForb",
"PerennialGrass", "PerrenialForb", "Rush", "Sedge", "Shrub",
"Tree"), class = "factor"), Locality = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Groundcover", "Native",
"Nonnative"), class = "factor"), Species = structure(c(265L,
265L, 265L, 265L, 265L, 265L, 265L, 265L, 265L, 265L), .Label = c("Achillea.millefolium",
"Acmispon.glaber", "Acmispon.maritimus", "Acmispon.parviflorus",
"Acmispon.strigosus", "Agropyron.cristatum", "Aira.caryophyllea",
"Aira.elegans", "Aira.praecox", "Amsinckia.menziesii", "Anaphalis.margaritacea",
"Angelica.hendersonii", "Anthoxanthum.odoratum", "Anthriscus.caucalis",
"Artemisia.californica", "Asclepias.fascicularis", "Atriplex.semibucatta",
"Avena.barbata", "Avena.Barbata", "Avena.fatua", "Baccharis. pilularis",
"Baccharis.pilularis", "Bareground", "Bellis.perennis", "Berberis.pinnata",
"Brachypodium.distachyon", "Brassica.nigra", "Brassica.rapa",
"Brassica.tournefortii", "Briza.maxima", "Briza.minor", "Bromus.carinatus",
"Bromus.catharticus", "Bromus.diandrus", "Bromus.hordeaceous",
"Bromus.madritensis", "Bromus.maritimus", "Bromus.tectorum",
"Calamagrostis.nutkaensis", "Calandrinia.menziesii", "Calendula.arvensis",
"Calystegia.collina", "Calystegia.purpurata", "Cardamine.oligiosperma",
"Carduus.pycnocephalus", "carex.athrostachya", "Carex.gynodynama",
"Carex.lasiocarpa", "Carex.Praegracilis", "Carex.spp", "Carex.suberecta",
"Carex.tomentosa", "Carex.tumulicola", "Carpobrotus.edulis",
"Castilleja.affinis", "Castilleja.densiflora", "Cerastium.fontanum",
"Cerastium.glomeratum", "Chlorogalum.pomeridianum", "Cirsium.brevistylum",
"Cirsium.vulgare", "Clarkia.purpurea", "Clarkia.spp", "Claytonia.perfoliata",
"Clinopodium.douglasii", "Conium.maculatum", "Convolvulus.arvensis",
"Corethrogyne.filaginifolia", "Cortaderia.jubata", "Cotula.coronopifolia",
"Crassula.connata", "Crepis.vesicaria", "Croton.setigerus", "Cynodon.dactylon",
"Cynosurus.echinatus", "Cyperus.eragrostis", "Danthonia.californica",
"Daucus.pusillus", "Deschampsia.cespitosa", "Dichelostemma.capitatum",
"Dichondra.donelliana", "Dichondra.Donelliana", "Dichondra.micrantha",
"Distichlis.spicata", "Dudleya.cymosa", "Dudleya.farinosa", "Dysphania.ambrosioides",
"Ehrharta.erecta", "Elymus.condensatus", "Elymus.glaucus", "Elymus.triticoides",
"Elymus.vancouverensis", "Epilobium.brachycarpum", "Epilobium.cilatum",
"Equisetum.arvense", "Erigeron.canadensis", "Erigeron.glaucus",
"Erigeron.sumatrensis", "Eriogonum.latifolium", "Eriogonum.parvifolium",
"Eriophyllum.staechadifolium", "Erodium.botrys", "Erodium.cicutarium",
"Erodium.moscatum", "Eschscholzia.californica", "Eucalyptus.globulus",
"Festua.muyros", "Festuca.arundinacea", "Festuca.bromioides",
"Festuca.californica", "Festuca.idahoensis", "Festuca.microstachys",
"Festuca.muyros", "Festuca.perennis", "Festuca.pratensis", "Festuca.rubra",
"Foeniculum.vulgare", "Fragaria.vesca", "Frangula.californica",
"Fritillaria.affinis", "Galium.aparine", "Galium.divaricatum",
"Galium.porrigens", "Gamochaeta.ustulata", "Genista.monspessulana",
"Geranium.dissectum", "Geranium.molle", "Gilia.capitata", "Gnaphalium.palustre",
"Grindelia.latifolia", "Grindelia.stricta", "Helminthotheca.echioides",
"Hemiparasitic.ericaceae", "Heracleum.lanatum", "Heterotheca.grandiflora",
"Heterotheca.sessiliflora", "Hirschfieldia.incana", "Holcus.lanatus",
"Hordeum.brachyantherum", "Hordeum.marinum", "Hordeum.murinum",
"Horkelia.californica", "Hosackia.gracilis", "Hypochaeris.spp",
"Iris.douglasiana", "Iris.macrosiphon", "Juncus.bufonis", "Juncus.effusus",
"Juncus.mexicanus", "Juncus.occidentalis", "Juncus.patens", "Juncus.phaeocephalus",
"Koeleria.macrantha", "Lactuca.serriola", "Lasthenia.californica",
"Lathyrus.vestitus", "Leontodon.taraxacoides", "Lichen", "Linum.bienne",
"Logfia.gallica", "Lomatium.dasycarpum", "Lomatium.utriculatum",
"Lonicera.hispidula", "Lotus.corniculatus", "Lotus.micranthus",
"Lupinus.arboreus", "Lupinus.bicolor", "Lupinus.littoralis",
"Lupinus.nanus", "Lupinus.variicolor", "Luzula.comosa", "Luzula.subsessilis",
"Lysimachia.arvensis", "Lythrum.hyssopifolia", "Madia.exigua",
"Madia.gracilis", "Madia.madioides", "Madia.spp", "Malva.parviflora",
"Marah.fabaceus", "Matricaria.discoides", "Medicago.polymorpha",
"Melica.californica", "Melica.imperfecta", "Melica.torreyana",
"Melilotus.indicus", "Melilotus.officinalis", "Modiola.caroliniana",
"Moss", "Mulch", "Mushroom.cover", "Myosotis.discolor", "Oxalis.corniculata",
"Oxalis.pes-caprae", "Parentucellia.latifolia", "Parentucellia.viscosa",
"Paronychia.franciscana", "Pennisetum.clandestinum", "Perideridia.kelloggii",
"Phacelia.californica", "Phacelia.malvifolia", "Phalaris.aquatica",
"Pholistoma.auritum", "Plagiobothyrs.nothofulvus", "Plantago.coronopus",
"Plantago.erecta", "Plantago.lanceolata", "Poa.annua", "Poa.pratensis",
"Polygonum.arenastrum", "Polygonum.aviculare", "Polypodium.califomicum",
"Polypodium.californicum", "Polypogon.monspeliensis", "Polystichum.munitum",
"Prunella.vulgaris", "Pseudognaphalium.beneolens", "Pseudognaphalium.bioletti",
"Pseudognaphalium.californicum", "Pseudognaphalium.canescens",
"Pseudognaphalium.luteoalbum", "Pseudognaphalium.ramosissimum",
"Pseudotsuga.meziesii", "Pteridium.aquilinum", "Quercus.agrifolia",
"Ranunculus.californicus", "Ranunculus.occidentalis", "Raphanus.sativus",
"Raphanus.spp", "Rock", "Rubus.armeniacus", "Rubus.ursinus",
"Rumex.acetosella", "Rumex.crispus", "Rumex.Crispus", "Rumex.transitorius",
"Salix.lasiolepis", "Sanicula.arctopoides", "Sanicula.bipinnatifida",
"Sanicula.crassicaulis", "Scandix.peten-veneris", "Senecio.vulgare",
"Sherardia.arvensis", "Sidalcea.malviflora", "Silene.gallica",
"Sisyrinchium.bellum", "Solanum.americanum", "Solidago.velutina",
"Soliva.sessilis", "Sonchus.asper", "Sonchus.oleraceus", "Spergula.arvensis",
"Stachys.ajugoides", "Stachys.bullata", "Stellaria.media", "Stipa.cernua",
"Stipa.lepida", "Stipa.pulchra", "Stipa.purpurata", "Symphiotrichum.chilensis",
"Taraxia.ovata", "Tauschia.hartwegii", "Thatch.cover", "Thatch.Cover",
"Thatch.Depth", "Thysanocarpus.laciniatus", "Toxicodendron.diversilobum",
"Toxicoscordion.fremontii", "Tragopogon.porrifolius", "Tribulus.terrestris",
"Trifolium.angustifolium", "Trifolium.barbigerum", "Trifolium.bifidum",
"Trifolium.depauperatum", "Trifolium.dubium", "Trifolium.glomeratum",
"Trifolium.hirtum", "Trifolium.hybridum", "Trifolium.macraei",
"Trifolium.microcephalum", "Trifolium.repens", "Trifolium.subterraneum",
"Trifolium.variegatum", "Trifolium.willdenovii", "Triphysaria.pusilla",
"Triphysaria.versicolor", "Trisetum.canescens", "Vaccinium.ovatum",
"Veronica.persica", "Vicia.americana", "Vicia.benghalensis",
"Vicia.sativa", "Vicia.tetrasperma", "Vicia.villosa", "Viola.adunca",
"Viola.pedunculata", "Wyethia.angustifolia", "Wyethia.glabra"
), class = "factor"), Cover = c(1, 1, 0.5, 0.5, 0.5, 8, 2, 2,
5, 1)), row.names = c(NA, 10L), class = "data.frame")
Rotating y axis labels with mosaic plots WITHOUT overlap
This question is extremely similar to this one yet from another point of view which has not been responded.
Following the proposed code, I am able to generate mosaic plots and rotate the labels so that they are legible. The problem comes when (it seems) the mosaic() function from vcd package does not recognise the rotation and so it does not adapt the graph to fit the labels, yielding results like the following:
Is there any way to change the margins between the labels and the titles? I would be surprised if I am the first one that has encountered this issue. I am open to using other packages to get mosaic graphs if applicable as well.
Code
aux = structure(c(0L, 0L, 3L, 46L, 107L, 14L, 0L, 0L, 4L, 0L, 0L, 2L,
9L, 0L, 23L, 2L, 1L, 3L, 14L, 1L, 8L, 26L, 6L, 11L, 6L, 1L, 6L,
0L, 1L, 1L, 29L, 10L, 62L, 1L, 3L, 1L, 1L, 3L, 1L), .Dim = c(3L,
13L), .Dimnames = list(abcdefghi = c("Madrid", "Valencia", "Granada"
), jklmnopqr = c("roknbjftxcwl", "mfchldbxuyig", "gtyoxeduijpw",
"akbcefymvsiw", "ucbfxplietqk", "mzeykauprfdh", "piermgawyjht",
"chjvatqbylxo", "merhcogjflbd", "wiyrugvmhjlq", "glszdqmjhkov",
"giowaxrtsknm", "pxucytzvljqw")), class = "table")
library(vcd)
colours = c("brown","darkgreen","darkgrey","orange","darkred","gold","blue","red",
"white","pink","purple","navy","lightblue","green","peachpuff","violet","yellow","yellow4")
aux_names = names(attr(aux,"dimnames"))
mosaic(aux,main=paste(aux_names,collapse=" vs. "),
gp=gpar(fill=matrix(sample(colours,max(nrow(aux),ncol(aux))),1,max(nrow(aux),ncol(aux)))),
pop = FALSE,labeling = labeling_border(rot_labels=c(90,0,0,0),
just_labels=c("left","right")))
This code should do what i think you're after.
mosaic(aux,main=paste(aux_names,collapse=" vs. "),
gp=gpar(fill=matrix(sample(colours,max(nrow(aux),ncol(aux))),1,max(nrow(aux),ncol(aux)))),
pop = FALSE,labeling = labeling_border(rot_labels=c(90,0,0,0),
just_labels=c("left","right"),
offset_varnames = c(8,8,8,8)),
margins = c(10, 10, 10, 10))
For loop for function that works on segments of data
I have a sample dataset as follows (Out):
Out <- structure(list(Dist_Out = structure(c(223L, 224L, 195L, 195L,
195L, 235L, 299L, 64L, 336L, 28L, 191L, 129L, 63L, 303L, 249L,
194L, 222L, 177L, 199L, 309L), .Label = c("0", "0.110574578321468",
"0.110574578385818", "0.110574578646219", "0.110574578769975",
"0.110574578837889", "0.110574578901783", "0.110574578961973",
"0.110574579093701", "0.110574579157825", "0.11057457934999",
"0.110574579413902", "0.110574579478479", "0.11057457973394",
"0.110574579798528", "0.11057457999076", "0.110574580247396",
"0.11057458031112", "0.110574580503848", "0.110574580567801",
"0.110574580694844", "0.11057458095289", "0.110574581402704",
"0.110574583204168", "0.111304214830553", "0.111304253300095",
"0.111304307772237", "0.111304317308227", "0.111304330093688",
"0.11130434287376", "0.111304358897914", "0.111304361977123",
"0.11130436522592", "0.111304368417738", "0.11130437160922",
"0.111304371665862", "0.111304374743724", "0.111304374800365",
"0.111304374857007", "0.111304377934532", "0.111304384315138",
"0.11130438437178", "0.111304384428421", "0.111304387561577",
"0.111304390694397", "0.11130439080768", "0.111304393883521",
"0.111304393940163", "0.111304397072309", "0.11130439712895",
"0.111304397185592", "0.111304400317401", "0.111304400374043",
"0.111304403505515", "0.111304403562157", "0.111304413067836",
"0.111304413124478", "0.111304425756241", "0.15689285571989",
"0.156892869769221", "0.15689287418468", "0.156892889774207",
"0.156892912024679", "0.156892934292016", "0.156892940915204",
"0.156892943136249", "0.156892949836902", "0.156892951976477",
"0.156892954196025", "0.156892954236129", "0.156892954236208",
"0.156892954276312", "0.156892955820879", "0.156892955820883",
"0.156892955861062", "0.156892955861066", "0.156892958674593",
"0.156892958714776", "0.156892958714863", "0.156892960893118",
"0.156892960893133", "0.156892960933301", "0.156892960933317",
"0.156892962437304", "0.156892962437361", "0.156892962477487",
"0.156892962477544", "0.156892962517727", "0.156892969726529",
"0.156892969766712", "0.15689296976675", "0.156892969806895",
"0.156892971984017", "0.156892971984051", "0.1568929720242",
"0.156892972024234", "0.156892974161436", "0.156892974201513",
"0.156892974201619", "0.156892974241696", "0.156892974241802",
"0.156892975744282", "0.156892975824626", "0.156892978001093",
"0.156892978001154", "0.156892980852422", "0.156892980892616",
"0.156892983068896", "0.156892983109079", "0.156892989757201",
"0.156892991892444", "0.156892991892504", "0.15689299197287",
"0.156892994188015", "0.156892995093831", "0.15689299509388",
"0.156892998578116", "0.221149157095331", "0.221149157735701",
"0.221149158248122", "0.221149158251639", "0.221149158504029",
"0.221149158760615", "0.221149158892054", "0.221149159532802",
"0.221149160045349", "0.221149161071762", "0.221149161712213",
"0.22114916222707", "0.222608333407496", "0.22260837837114",
"0.222608384735182", "0.222608455374887", "0.222608493783746",
"0.222608596296507", "0.222608692192514", "0.222608730508481",
"0.222608736835477", "0.222608736892118", "0.222608768800201",
"0.222608794314542", "0.222608800691444", "0.222608857996604",
"0.247579423347402", "0.247579452035982", "0.247579496248119",
"0.247579500729161", "0.24757950684186", "0.247579510261327",
"0.247579510261392", "0.247579513391942", "0.247579513417395",
"0.247579513417407", "0.247579513442859", "0.247579516860718",
"0.247579518205451", "0.247579519185166", "0.247579519236095",
"0.247579525289911", "0.247579525315375", "0.24757952875664",
"0.247579529784064", "0.248558365959636", "0.248558423080156",
"0.248558474437371", "0.2485585599757", "0.248558577056858",
"0.248558611101923", "0.248558622479214", "0.24855862258067",
"0.248558628217488", "0.248558650962925", "0.248558656698174",
"0.248558662331659", "0.313785772234975", "0.313785790083671",
"0.313785798944933", "0.313785861163643", "0.313785884051598",
"0.313785919567943", "0.313785932204374", "0.313785932284703",
"0.31378593228474", "0.313785941750578", "0.313785949945705",
"0.313785953785589", "0.313785958853473", "0.313785958853573",
"0.331723736573009", "0.331723737017998", "0.331723741058604",
"0.331723747874274", "0.33172374819982", "0.333912827211437",
"0.333912980750125", "0.333913067001972", "0.333913133973521",
"0.349898963305821", "0.349898967518276", "0.349898985849967",
"0.349898989161391", "0.349898989161427", "0.349898997505614",
"0.349898997505615", "0.351744805744357", "0.351744887722199",
"0.351745133189943", "0.351745187980364", "0.3517453055621",
"0.35174531490497", "0.351745323909966", "0.399493757974483",
"0.399493788597454", "0.399493795359845", "0.399493832734277",
"0.399493843627049", "0.399493845554748", "0.400505368728602",
"0.400505423976401", "0.400505448284272", "0.400505527258931",
"0.400505598712409", "0.400505694328303", "0.400505709727528",
"0.400505725573516", "0.400505765671134", "0.40050589965877",
"0.400505923319168", "0.442298314835368", "0.442298317140206",
"0.442298324709909", "0.442298327797584", "0.445216846546918",
"0.445217192536371", "0.445217307591311", "0.445217345994231",
"0.445217512021335", "0.456088216475352", "0.456088223195701",
"0.458742575215647", "0.45874273685115", "0.458742885695134",
"0.458742910728267", "0.458743282342636", "0.470678474711803",
"0.47067880878535", "0.470678942002923", "0.495158931652482",
"0.495158944897039", "0.497116508401871", "0.49711679476651",
"0.49711679476653", "0.497116851569911", "0.497116897580156",
"0.554189163095155", "0.55418920215693", "0.554189262709751",
"0.554189324595007", "0.555210390817594", "0.555210746638391",
"0.556520945842888", "0.556521218492923", "0.556521458662341",
"0.563965519478444", "0.567399619528133", "0.567399713876817",
"0.567399933968277", "0.567399934023851", "0.567400184553882",
"0.596005878229009", "0.59600588239606", "0.598851042068279",
"0.598851279385347", "0.59885148700117", "0.627571384004223",
"0.627571616364075", "0.645883965371449", "0.645884171696778",
"0.64588421378803", "0.6478858784372", "0.647886220479167", "0.647886397912576",
"0.663447495427633", "0.667825269848697", "0.667825558298464",
"0.676917586632606", "0.676917795416644", "0.676918079344979",
"0.676918476303909", "0.703489456836576", "0.703489456890362",
"0.703490602009756", "0.709849619231796", "0.709850031391416",
"0.710875054488227", "0.745675302791038", "0.745675354954646",
"0.74567545723098", "0.745675662288711", "0.774022055439241",
"0.779129683432762", "0.779130802335089", "0.784464537327535",
"0.784464615110459", "0.786936919065818", "0.801010975018698",
"0.809907498350714", "0.809907713628001", "0.846808290999277",
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"90.0000008260809", "90.0000008261949", "90.0000008263088",
"90.0000008264227", "90.0000008264228", "90.0000008267646",
"90.0000008268785", "90.0000016529025", "93.1724357049647"
), class = "factor"), Movement_Out = structure(c(2L, 1L,
1L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 3L, 3L, 3L, 2L, 4L, 2L, 1L,
3L, 3L, 2L), .Label = c("forward", "left", "non-moving",
"right"), class = "factor"), Changes_Out = structure(c(1L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L), .Label = c("0", "1"), class = "factor"),
AccPosNeg_Out = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), AccChange_Out = structure(c(2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("0", "1"), class = "factor")), .Names = c("Dist_Out",
"Speed_Out", "Acceleration_Out", "Absolute_Heading_Out", "Relative_Heading_Out",
"Movement_Out", "Changes_Out", "AccPosNeg_Out", "AccChange_Out"
), row.names = c(NA, 20L), class = "data.frame")
and the following function:
JRip <- function (Dist_Out, Speed_Out, Acceleration_Out, Absolute_Heading_Out,
Relative_Heading_Out, Movement_Out, Changes_Out, AccPosNeg_Out,
AccChange_Out)
{
if (max(Speed_Out) >= 2.45) {
Behaviour <- "walking"
}
else if (mean(Acceleration_Out[Acceleration_Out > 0]) >=
0.03 & (max(Dist_Out[Movement_Out != "non-moving"]) <=
2.17 & (mean(Speed_Out) >= 0.23 & (sum(Dist_Out[Movement_Out !=
"non-moving"]) >= 11.92)))) {
Behaviour <- "grazing"
}
else Behaviour <- "resting"
return(Behaviour)
}
I want to analyse the dataset in segments of say 5 instances and then to the next 5 instances and apply the function here again. So for this dataset of 20 instances I would expect 4 predictions from the function. It seemed that the logical thing to do was pass a sliding window over the dataset - after much trial and finally realising that rollapply will not take mixed types, I was wondering if this can be done using a for loop? The function uses mean and max and although I have tried many for loops I cannot get it to work. Does anyone know how to code a for loop to achieve this?
First, you need to convert your data to numeric types, since they are stored as factors: Out[,-6] <- as.data.frame(sapply(Out[,-6], function(x) as.numeric(as.character(x)))) Now, you can use split-apply-combine to split up your data frame into sets of 5-row chunks, apply your function to each, and grab the result: sapply(split(Out, rep(1:4, each=5)), do.call, what=JRip) # 1 2 3 4 # "resting" "resting" "resting" "resting" For the provided data frame, it looks like each 5-row segment is in the resting state. This is easy to confirm, as max(Out$Speed_Out) is 0.465, meaning no segment is walking, and sum(Out$Dist_Out) is 8.94, meaning no segment is grazing.