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I have a data frame called stats. I'd like to group by month_name and item and generate a random variable drawn from a normal distribution in a new column called rv.
This is the code I tried but it repeats the generation of 1 random variable in the rv column:
stats %>%
group_by(month_name, item) %>%
mutate(rv = rnorm(1, mean = mean, sd = sd))
The goal is to eventually replicate the rv output 10,000 times. How can I modify my code to generate the random variable for every row once and 10,000 times?
This is my data:
structure(list(month_name = structure(c(1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L), .Label = c("January",
"February", "March", "April", "May", "June", "July", "August",
"September", "October", "November", "December"), class = c("ordered",
"factor")), item = structure(c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L), .Label = c("a", "b", "e", "d", "c"), class = "factor"),
min = c(39853.3676768762, 11963.7336771844, 28475.0829411315,
36241.9007031999, 35743.7750504859, 16092.552892924, 12528.9369640133,
28363.8516762228, 29557.1911907891, 20577.9268503088, 26012.6643332399,
43743.1386573406, 33757.0104608081, 24012.3644652027, 29987.8232384625,
26663.1671529956, 50724.1357441692, 33156.7203254077, 36604.0975795671,
32448.5497811945, 47818.2983698804, 25173.5205474241, 29648.7882981325,
39034.0329768052, 15083.5548610647, 41560.8896893507, 40563.2944650284,
48794.4526055819, 35895.1783353774, 30085.4271923688, 39675.7305889162,
33628.9557047603, 36950.5993766457, 30593.5091646214, 28957.5398056329,
37080.7178800747, 45454.3924797489, 28755.6280571895, 34733.1340290652,
37227.9361452194, 21493.809533368, 33292.9944106622, 28137.6372068055,
25582.8046285949, 23073.0637573296, 28846.9082264882, 19454.182866794,
33869.2858697563, 19016.7538627489, 30647.6876387916, 35123.8965500988,
34146.2780735908, 40593.6508043686, 26908.3734089905, 47178.2458120079,
24665.5899193578, 22701.4906439165, 50735.1331088719, 36108.7624278488,
21415.5715318508), lower = c(54524.7101912146, 26928.6804993352,
25119.8847919585, 45942.5372327181, 52100.762800828, 23399.2712234262,
14178.7907654734, 71366.6268933559, 49209.2124037853, 54643.7588467776,
48369.7944054794, 29515.3335011807, 41577.635577101, 25357.3837384686,
43253.4733925982, 43401.4748829102, 37741.3586860236, 52294.4029786582,
58136.6122795486, 43617.5523486807, 46648.1777348884, 47822.6060157009,
37122.0182632065, 65447.4620274838, 29544.1919272749, 54822.3562275875,
64814.4174753617, 65538.2587526896, 39975.4034746898, 59117.6049731313,
49024.4324422717, 25273.7368374795, 56946.7596272533, 50660.5745923196,
37221.8185672126, 30508.2772838287, 47172.6674212663, 52956.1465111511,
45488.8349086128, 52660.1832157037, 37406.8854102724, 25601.012749268,
41414.610113642, 41145.7009104373, 26879.9690641376, 69323.7347440924,
59453.3099916568, 19260.9187209561, 14090.2250971317, 41778.9038974128,
35013.9160392596, 39672.0871995261, 57517.2881078087, 52765.3573599843,
57267.2271717807, 54869.720268229, 58525.9231470629, 44610.285805162,
47317.3995094377, 17599.590085043), mean = c(58549.8098049081,
56374.4327553941, 39864.1715264267, 85333.1530921059, 64454.2358008729,
63343.4098283811, 69838.6859070403, 41935.3881398536, 40239.4399412696,
70073.2291007902, 57535.295477502, 76197.4454180647, 60836.2074195693,
64601.7379215889, 51599.3556004457, 49092.0124309883, 47319.767991988,
63121.0872241636, 43048.0322965586, 77405.4987695189, 64320.8901918307,
53059.7915920758, 63712.4934804165, 37248.933469329, 48285.12302248,
60352.1030623367, 67648.010113929, 52282.8579266665, 63868.4373429784,
71370.1455147326, 59275.2217698193, 74524.7831867724, 62464.1935824186,
50255.8945012446, 31094.1686136834, 75833.6439248775, 32190.7391406323,
77010.5148506178, 69635.0888164364, 65885.8987213858, 54022.7135642953,
35801.3865465657, 60637.9983665307, 90783.7721781328, 57264.0603250172,
59977.2976696403, 71712.656969139, 76705.4011709067, 89462.5059367925,
76714.0458753254, 56859.5782454854, 66820.0053236744, 58243.7435076688,
52843.8704599132, 77247.3384533588, 55515.7748808548, 75004.3165800858,
88370.1869726297, 68628.9281194796, 53895.0496305422), median = c(42352.1610450345,
57330.3183802072, 55273.2047201131, 82351.3852530883, 46370.4898234873,
52386.0432388715, 47943.0683307536, 53897.781347776, 67858.0064600009,
73013.024717384, 83116.7356352266, 44401.5903576421, 69025.6068023045,
81625.3403276092, 43344.4404418446, 49701.9746204065, 44889.5603216509,
86449.7649043697, 52150.9769065634, 58675.8138647348, 55665.7047792249,
44566.4888204713, 50517.7492643733, 73778.9515308994, 60652.1631558926,
87345.0069311662, 68268.9807235179, 41356.3226356087, 41585.1763113502,
75144.8373297139, 81967.7788670882, 66041.6207332688, 55103.8870449834,
77301.4195253735, 54130.4774678618, 65176.7990367632, 46834.9652749994,
65134.3889325556, 76621.5018669346, 89066.7483257445, 79344.8597627239,
50867.4889878177, 51326.3717332736, 74843.6262595514, 66235.6184875188,
98300.5112442494, 51378.9240605971, 61277.8214283028, 48915.1245226839,
52765.9194941648, 47028.8412992194, 74841.2039136489, 70896.5761749783,
67414.0877191645, 60655.1682545525, 42707.2850070942, 51244.6187187212,
70889.9732948709, 82834.1260629236, 56029.4540887989), upper = c(96808.9361470916,
72722.9262056796, 89079.513341868, 84709.1878768955, 87694.368834914,
87860.8548839792, 80996.3827453218, 84247.9259137302, 95585.6388675179,
57338.746606262, 88681.3926853573, 87957.989278465, 87360.6574510974,
92664.4254709955, 73493.0826366849, 84230.5990186054, 81442.2517006442,
87801.9592453634, 107883.319372054, 101919.939543795, 78090.4252899963,
70239.1417329303, 100675.767786787, 99806.9236049608, 71452.5071326737,
73879.3479602876, 106131.22309752, 125238.035074805, 76731.6350473027,
105563.285669622, 98604.105083167, 88657.8428176833, 81133.2031578456,
92495.2957986084, 104836.803460225, 102419.6178137, 86160.3548401189,
87287.9179449312, 72987.3973022452, 73185.0732579627, 90916.179982239,
111282.33982277, 142168.512194455, 100479.774695548, 118375.00968986,
116099.107730658, 105747.461541425, 106715.198136428, 128585.197217447,
87996.5319472346, 67831.1501517932, 109713.080164634, 78535.3157822644,
128602.704986898, 82213.8086826659, 118591.773718681, 66518.2467960131,
91250.5061727746, 117072.914540123, 114524.034290364), max = c(137612.711045413,
142519.370905613, 137456.124250483, 149209.014602568, 158745.717583772,
144886.189765236, 168837.723206789, 148308.890270968, 158590.65413993,
152288.303209753, 154042.306686713, 143922.848061827, 147477.579594905,
147438.066965268, 141502.628117831, 150285.096748915, 148713.594899874,
156656.255445038, 151517.357942321, 146177.731181398, 130056.291991729,
150991.849546995, 150476.190905448, 140149.802748207, 162573.574139209,
124218.878401843, 140313.610415297, 156852.359228369, 147676.550419975,
139922.178103581, 131822.195549853, 143008.968758112, 142237.425864494,
148756.818388612, 123905.560034301, 157126.60664862, 132868.19652461,
137884.902850549, 142164.212835827, 144616.429331364, 154277.663061656,
156870.781144851, 170948.478868233, 154970.297432983, 144661.430142095,
151193.528913062, 136056.623739965, 132695.069145067, 144366.408646971,
154456.483407293, 143518.023088591, 145811.265404348, 139900.024678788,
127547.709882734, 149995.24047052, 145400.958382574, 159524.480570906,
118905.663549293, 161631.72583606, 147524.546274058), sd = c(9989.37951375166,
9906.50689980405, 9903.6852849217, 10008.3321579478, 10075.4653993515,
10063.7122293343, 10053.0016932606, 9826.1129055558, 9855.88655389009,
10028.7176055065, 10070.3833732403, 9941.07465801432, 10094.2667749602,
9910.53181242413, 10104.5889493016, 9851.70104229335, 9972.91821342281,
10080.4485086333, 10044.5102818099, 10037.3707232711, 10025.1107006076,
10022.3659427419, 9941.51637265177, 9873.12826319285, 10027.9036424549,
10033.6518983864, 9970.47127759776, 9937.3319252128, 10013.3439414305,
10030.3125017708, 10168.5115559098, 10213.3568382367, 9990.24289183087,
9968.82189362707, 10048.7504375345, 10015.8411633632, 10037.6851291425,
9925.92765463682, 9835.81447415085, 9782.6505066721, 10033.5360418173,
9991.76186224687, 9924.86818104305, 9970.41809893224, 9980.55197551292,
9886.97032019385, 9925.73912143071, 9971.01687402101, 9858.19281102242,
9969.19466304141, 9955.12658457894, 10139.5950943687, 9967.09479735319,
10168.1650679826, 10023.9501235604, 9821.41776472295, 10064.1149573067,
10134.8532916488, 9943.57024828908, 9833.93164357077)), row.names = c(NA,
-60L), groups = structure(list(month_name = structure(1:12, .Label = c("January",
"February", "March", "April", "May", "June", "July", "August",
"September", "October", "November", "December"), class = c("ordered",
"factor")), .rows = structure(list(1:5, 6:10, 11:15, 16:20, 21:25,
26:30, 31:35, 36:40, 41:45, 46:50, 51:55, 56:60), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, 12L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
you can try data.table:
library(data.table) # load data.table
setDT(stats) # convert stats to data.table
stats[, rv := rnorm(.N, mean = mean, sd = sd), by = .(month_name, item)]
In your example there's only one record of each combination month_name and item, but I can imagine your real data has more.
The := is an assignment operator. You'll assign the result of rnorm into a new column called rv.
The by = part serves for grouping, see ?data.table.
EDIT TO ADD:
If you want 10,000 random variables, then:
stats[, new_rv := .(list(rnorm(1e4, mean, sd))), by = .(month_name, item)]
You already know the := and the by = parts, so let's dive into the expression in the middle:
The .(list()) bit will assign the resulting list (vector of 10,000 random numbers, in our case) to the variable (because we are using the assignment operator :=).
The very interesting thing is that with this .(list()) "combo" you can store complex things in a variable (column) of a data.table. I use it often to store things such as forecasts, plots or linear models, etc. by group: it is very useful!
Now, if you want to operate on your new variable, please keep in mind that it is a list, so you need to subset it accordingly:
If you want to check that the standard deviation of new_rv of row 1 is close to what you expect, the following code will throw an error:
stats[1, sd(new_rv)]
> Error in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm = na.rm) :
> is.atomic(x) is not TRUE
It is complaining that you are attempting to calculate sd() on a list. The correct code is:
stats[1, sd(new_rv[[1]])]
[1] 9926.439
The [[1]]] part is correctly subsetting the first element of the list.
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))
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",
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"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), .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.
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",
"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",
"0.846808722035823", "0.866982968100186", "0.866983288894348",
"0.897273124467317", "0.897273302678894", "0.897273328167299",
"0.897273404335645", "0.917485187536906", "0.917485560660896",
"0.917485808994169", "0.955359158283399", "0.955359809834417",
"0.994234273564382", "1.0017384527634", "1.00782274966041", "1.0233303727427",
"1.09503832959811", "1.09503864342042", "1.09825019601709", "1.12793099939348",
"1.14021123855837", "1.24279165758788", "1.2441584622863", "1.25514264374951",
"1.2638862424017", "1.34543853012779", "1.44555679702924", "1.45117535398978",
"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",
"0", "0.012", "0.014", "0.024", "0.026", "0.036", "0.038", "0.048",
"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",
"104.038094380956", "104.038099175016", "104.038100287972", "104.038104254506",
"104.933371095486", "108.437291746436", "108.437292238906", "108.437292594443",
"108.437321740952", "111.804138048148", "113.201423738397", "113.965388161297",
"116.568179327572", "116.568179982698", "116.568185226911", "116.568202500823",
"116.568207149774", "123.693672004617", "123.693687366444", "123.693691078493",
"123.693703315054", "123.693711549325", "126.873674566913", "130.605207664786",
"135.003902542311", "135.00390464519", "135.003904674347", "135.003905470793",
"135.003907948238", "135.003907962816", "135.003908259113", "135.003908774132",
"135.003908968467", "135.003910222254", "135.003911252625", "135.003911281782",
"135.003911680336", "135.003912078869", "135.003912905428", "135.003912920006",
"135.003912934585", "135.00391516638", "135.003915180958", "135.00391581295",
"135.003916226374", "135.003917038677", "135.003917053256", "135.003918707167",
"135.003919548889", "135.003920346791", "135.003922843585", "135.003923671108",
"135.003938050425", "135.003939340192", "135.003948537839", "135.003950183691",
"135.0039588283", "14.0343965682479", "14.0344008293472", "141.344008995685",
"146.313535791806", "146.313537999457", "153.438076376928", "153.438078534744",
"153.438079763631", "153.438085623885", "153.43812488595", "161.567394954315",
"161.567399368491", "180", "191.30841097092", "198.432585234004",
"198.432587185674", "198.432598104786", "201.798689939092", "206.561897223892",
"206.561914376115", "206.561918159163", "206.561920224708", "206.561923646396",
"210.960297150126", "213.686445206476", "213.686465120369", "216.866121212474",
"216.866126783458", "218.655948156729", "224.99604368852", "224.996057271547",
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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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"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)
My data set:
structure(list(Site = c(2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L), Average.worm.weight..g. = c(0.1934,
0.249, 0.263, 0.262, 0.4186, 0.204, 0.311, 0.481, 0.326, 0.657,
0.347, 0.311, 0.239, 0.4156, 0.31, 0.3136, 0.4033, 0.302, 0.277
), Average.total.immune.cell.count = structure(c(8L, 16L, 11L,
12L, 10L, 1L, 4L, 15L, 4L, 3L, 17L, 13L, 18L, 7L, 5L, 6L, 9L,
14L, 2L), .Label = c("0", "168750", "18650000", "200,000", "21,600,000",
"226666.6", "22683333.33", "2533333.33", "283333.333", "291666.6",
"335833.3", "435800", "474816666.7", "500000", "6450000", "729166.667",
"7433333.3", "9916667"), class = "factor"), Average.eleocyte.number = structure(c(2L,
5L, 14L, 10L, 1L, 1L, 6L, 1L, 6L, 7L, 1L, 9L, 15L, 8L, 12L, 3L,
11L, 13L, 4L), .Label = c("0", "1266666.67", "153333.3", "168740",
"17", "200,000", "2266666.667", "22683333.33", "23116666.67",
"264000", "283333.333", "442", "500000", "7.3", "9916667"), class = "factor")), .Names = c("Site",
"Average.worm.weight..g.", "Average.total.immune.cell.count",
"Average.eleocyte.number"), class = "data.frame", row.names = c(NA,
-19L))
This is my R script so far:
Plotting multiple data series on a graph
y1<-dframe1$"Average.total.immune.cell.count"
y2<-dframe1$"Average.eleocyte.number"
x<-dframe1$"Average.worm.weight..g."
plot.default(y1~x,type="p" )
points(y2~x)
I am trying to add to y series to the same scatterplot and I am struggling to do so, I want to have different symbols for the points so as to tell apart the two different data series. Also I would like the axes to meet on the bottom left hand side and would appreciate being informed as to how I can do that? I would also like the y axis to be in standard form, but do not know how to get R to do that.
Best regards.
K.
So this is an object lesson is getting your data in the correct format to begin with. Your numbers have commas, which R does not like. Hence the numbers get converted to character and imported as factors (which your structure(...) clearly shows. You need to fix that, or better yet get rid of the commas prior to exporting.
Something like this will work
colnames(dframe) <- c("Site","x","y1","y2")
dframe$y1 <- as.numeric(as.character(gsub(",","",dframe$y1,fixed=TRUE)))
dframe$y2 <- as.numeric(as.character(gsub(",","",dframe$y2,fixed=TRUE)))
plot(y1~x,dframe, col="red", pch=20)
points(y2~x,dframe, col="blue", pch=20)
But there are additional problems. One of the numbers (in row 12) is a factor of 10 larger than all the others, so the plot above is not very informative. It's hard to know if this is a data input error, or a genuine outlier in your data.
EDIT: Response to OP's comment
dframe <- dframe[-12,] # remove row 12
dframe <- dframe[order(dframe$x),] # order by increasing x
plot(y1~x,dframe, col="red", pch=20, type="b")
points(y2~x,dframe, col="blue", pch=20, type="b")
legend("topleft",legend=c("y1","y2"),col=c("red","blue"),pch=20)