How to check 2DArray length on Lua? - multidimensional-array

I'm having problems porting a program of mine from C# to Lua,
at some point, I need to check the length of the 2D array. In C# , I was using this :
testMap.GetLength(0)
or
testMap.GetLength(1)
for check the other dimension length. I've found solutions that use something like
#testmap
or similar, but I couldn't make it work at all.
Just in case, I'm working with this array :
`local testMap = {
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
{ 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1 },
{ 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1 },
{ 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1 },
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1 },
{ 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
};`


As #moteus said,
#testMap
for checking one dimension of the array and
#testMap[1]
for the other.


Since #testMap is not giving you the result you want, I assume that you want to retrieve the total number of items of the inner arrays.
Here is how you can do this:
local testMap = {
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
{ 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1 },
{ 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1 },
{ 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1 },
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1 },
{ 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1 },
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }
};
-- Either like this:
print(#testMap*#testMap[1])
-- Or like this:
local total = 0
for i=1, #testMap do
total = total + #testMap[i]
end
print(total)
You can try it here: https://repl.it/JBw1/0

Related

Combining multiple calibration curves in one plot

I would like some help in combining two or more calibration plots in one plot in R.
I am comparing the calibration of two models and I would like them in one plot.
I am using the calibration_plot function form the predtools package. Is this the correct package or are there more powerful packages for R?
calibration_plot(data = stackoverflow, obs = "event", pred = "model1", x_lim = c(0,1), y_lim = c(0,1),title = "Model1", points_col_list = NULL, data_summary = T)
calibration_plot(data = stackoverflow, obs = "event", pred = "model2", x_lim = c(0,1), y_lim = c(0,1),title = "Model2", points_col_list = NULL, data_summary = T)
dput of stackoverflow
structure(list(model1 = c(0.237760176222135, 0.71546420180643,
0.794432429369465, 0.656363881639676, 0.791708216360907, 0.687126456661465,
0.285599617509653, 0.184137148744874, 0.864448003819623, 0.68633722517368,
0.633141834438598, 0.342033236744753, 0.809527471856904, 0.44709310706345,
0.642309783414134, 0.478634921655348, 0.749205389344258, 0.218507206790561,
0.715674356193537, 0.722136223616077, 0.365559623908335, 0.633141834438598,
0.832424627307168, 0.530368910251955, 0.428880665771525, 0.775641696932919,
0.330128697609423, 0.783171338536037, 0.783793672057888, 0.468355345435376,
0.710245078226952, 0.81648327238482, 0.603693592753907, 0.592283374978545,
0.20507631783337, 0.485882139691015, 0.809455349796892, 0.754732165553727,
0.66377865123304, 0.438721686675472, 0.2709932360314, 0.176381161846607,
0.369232324737991, 0.654900775755287, 0.677447167734547, 0.180268404814802,
0.399419971681492, 0.73438881598655, 0.47646627399175, 0.853704813768205,
0.608974716778207, 0.634887771555211, 0.592291547214112, 0.663813688339367,
0.53943039194057, 0.7145666044468, 0.234543422020881, 0.202729809644918,
0.39672336107067, 0.158096774829406, 0.320329566236219, 0.740201212163183,
0.796777244675989, 0.308199837768449, 0.786598711217149, 0.667179376789805,
0.732390196193263, 0.117181865869223, 0.282712898098667, 0.813513774287869,
0.693399632523288, 0.493475284817971, 0.593983673049009, 0.738452518666474,
0.623275128184214, 0.855655675514564, 0.634887771555211, 0.69907205055215,
0.721513188211345, 0.622910777455488, 0.309031119328554, 0.268184852225134,
0.623816023317224, 0.620180359930797, 0.65607957849868, 0.318748098514039,
0.786138246273698, 0.770147379877078, 0.368364517958795, 0.889787669491316,
0.564862773577324, 0.644936236318243, 0.859568428574226, 0.825393586455367,
0.443267542815971, 0.662158972085537, 0.833393716075399, 0.710245078226952,
0.706450321722432, 0.83195359999601, 0.384551972953433, 0.669445207069002,
0.814471247707942, 0.809263905314706, 0.778049639424727, 0.208123706702556,
0.858117029177706, 0.816715622153779, 0.260468350284386, 0.601943372497283,
0.732426863335112, 0.570001496800709, 0.909610698898771, 0.789637001870557,
0.341839397973598, 0.571752088809212, 0.287188981135642, 0.736473504179658,
0.656363881639676, 0.300022596959034, 0.883593082700905, 0.515709672432742,
0.806949851044393, 0.597577448403585, 0.787642720305032, 0.283398218900544,
0.869505288499405, 0.225857887615281, 0.612365595338034, 0.834203334098187,
0.65572943010048, 0.38504423454054, 0.828390320481501, 0.875166731695581,
0.35145027474754, 0.534808195965384, 0.822411007103645, 0.455010761704292,
0.856585071099577, 0.757934255761915, 0.363235512112777, 0.298988516351143,
0.62357099020229, 0.821536981782977, 0.743740947803668, 0.268184852225134,
0.542172476801765, 0.789637001870557, 0.77057687698505, 0.688781883558654,
0.731975036767798, 0.58841221075051, 0.614992060475678, 0.772927795822135,
0.571844722455543, 0.786598711217149, 0.814236699887853, 0.24632201788091,
0.461305611875743, 0.357877442912028, 0.629777768176522, 0.340708895525214,
0.858328679154275, 0.896374497790043, 0.508117603191322, 0.868395985194952,
0.325391234150186, 0.217666716175776, 0.738651896306012, 0.44896380642455,
0.788773121465437, 0.855280657199334, 0.703134160788728, 0.243986928768584,
0.676029521174355, 0.578073026049121, 0.665893397554917, 0.923993035769077,
0.604501259273048, 0.300669521764063, 0.829237758054682, 0.660669727162949,
0.382885828324256, 0.357034833076844, 0.564862773577324, 0.800651230924735,
0.924025873813848, 0.547345708551748, 0.897415894457121, 0.378769589693012,
0.787320407651021, 0.835350498525985, 0.806949851044393, 0.498678748045157,
0.807549787634665, 0.758667475339789, 0.7145666044468, 0.713417011226846,
0.287188981135642, 0.390819912242695, 0.807549787634665, 0.648880360799679,
0.294317907149489, 0.634353464782934, 0.738303504906273, 0.703614919895444,
0.56018558222595, 0.365559623908335, 0.373744408366516, 0.422142971154104,
0.780393752718648, 0.420192692166099, 0.537551762931482, 0.58841221075051,
0.795977982509655, 0.750581504918475, 0.74077785828739, 0.357391060095084,
0.90335324857881, 0.88512447866342, 0.573593424038461, 0.376636890712417,
0.814471247707942, 0.516728126564283, 0.511769167592521, 0.753160027303678,
0.379974608419567, 0.247151253298088, 0.789834529579229, 0.27119953909381,
0.578282022920176, 0.289890319811112, 0.88870041654423, 0.605378010737711,
0.351803978568917, 0.852843747717878, 0.742703232488038, 0.770147379877078,
0.784056463649842, 0.678270092659001, 0.59309862179546, 0.399457633245555,
0.852843747717878, 0.794010119798579, 0.667825589460395, 0.825212537892541,
0.792966972627726, 0.770106127346002, 0.706404124524996, 0.744885306690795,
0.719123857349234, 0.53519415315944, 0.787668394777512, 0.506672792915601,
0.712945665228562, 0.660440139900567, 0.944906901834028, 0.430863999447529,
0.725700303601332, 0.783434677550229, 0.401698737182868, 0.662322756723675,
0.510386937625764, 0.767961401284267, 0.384551972953433, 0.785396724092888,
0.653104308767684, 0.687730601503667, 0.791984688175846, 0.885437716111375,
0.867068735171316, 0.213182756545354, 0.628989953572443, 0.381845536523953,
0.456595194489821, 0.206497805251196, 0.29389687736639, 0.56562528089224,
0.22423014383975, 0.876624588279104, 0.62948388586649, 0.341057473957848,
0.836726008192051, 0.959077640833203, 0.700380060001825, 0.550565054363783,
0.857986332751831, 0.73606239655555, 0.626958047968868, 0.801665344467059,
0.437818514822793, 0.84172513914277, 0.249938361820059, 0.844698016882311,
0.698481177252194, 0.425091126036881, 0.270744340397641, 0.911317329972866,
0.628317598316906, 0.798858730229755, 0.536048796135506, 0.558330930540637,
0.769744711534908, 0.835724887768629, 0.716896341598601, 0.277571045573504,
0.767928060731326, 0.171725546041467, 0.62357099020229, 0.372575487235624,
0.704787562421115, 0.279904670683375, 0.320055251165285, 0.470719150191045,
0.634353464782934, 0.260468350284386, 0.165380669830666, 0.633502115957914,
0.270438131899591, 0.298663446487892, 0.160383482822362, 0.153892088510337,
0.201934325478114, 0.248088137480339, 0.581594778507714, 0.185982855759208,
0.442026886814123, 0.180184819010326, 0.294941540461375, 0.27916583091498,
0.179726924309564, 0.176381161846607, 0.169591266926947, 0.220985714153048,
0.218292082577411, 0.256088777375909, 0.200434356927467, 0.294697652744116,
0.315059393256192, 0.309483384308751, 0.731559469346124, 0.153818437822602,
0.117181865869223, 0.230597455066238, 0.213538098408414, 0.171703216612226,
0.117342457219404, 0.268154040879572, 0.249203995151824, 0.289135705273671,
0.260767305152473, 0.243986928768584, 0.191193730214216, 0.343771287624594,
0.270744340397641, 0.63744932909826, 0.523368837567113, 0.154394806405882,
0.410858823948176, 0.363235512112777, 0.173279305917588, 0.134889443422701,
0.261718547032863, 0.357391060095084, 0.672111652745064, 0.314724695162023,
0.109741058161593, 0.268184852225134, 0.346565137119793, 0.604872114491051,
0.230805654103775, 0.251721750397117, 0.182834120641036, 0.322972096586922,
0.322746359746767, 0.229914079265902, 0.645775875936576, 0.201934325478114,
0.553545512735033, 0.187303180765189, 0.198028579423478, 0.290364891461295,
0.229265182965244, 0.710032536799647, 0.735760882566064, 0.182834120641036,
0.401191274559514, 0.297035951335436, 0.441177176238355, 0.298988516351143,
0.553928890511863, 0.24317228518234, 0.229914079265902, 0.289890319811112,
0.0624502711689494, 0.162722362310802, 0.169591266926947, 0.642273712916542,
0.169075242884674, 0.25265127289933, 0.165887669057233, 0.235150322065362,
0.589857972739157, 0.338141490381861, 0.125767093771288, 0.373381352261149,
0.362344177990451, 0.25212074667222, 0.256751801775933, 0.187067135355391,
0.216054935520473, 0.238425678298765, 0.141675251821082, 0.392138013572663,
0.267385232516098, 0.255390506596019, 0.186538897906035, 0.310084569356542,
0.214186337707389, 0.154094207815253, 0.209949367371578, 0.260468350284386,
0.340233482715629, 0.205675021254086, 0.212922641652452, 0.169293313540589,
0.237148695480572, 0.252848455583522, 0.359702689379285, 0.092827863257841,
0.554763268477136, 0.136704135686601, 0.162722362310802, 0.350614534717934,
0.269714920758443, 0.208123706702556, 0.309815028457651, 0.171703216612226,
0.487788581673596, 0.187817836035314, 0.320516166012631, 0.205675021254086,
0.310307964614977, 0.16055325992601, 0.338141490381861, 0.261718547032863,
0.277571045573504, 0.570001496800709, 0.638837851776372, 0.273858461205031
), model2 = c(0.287514786741101, 0.750187800029493, 0.988418181061398,
0.838173140408305, 0.645239560308121, 0.677315834518225, 0.158242529420476,
0.125115299019299, 0.730655708202056, 0.650323967533871, 0.46668206957655,
0.477695763474193, 0.517548581264567, 0.777222315964033, 0.710407521182698,
0.265575276954178, 0.712813831748586, 0.536061703578936, 0.799982280979083,
0.82465248790953, 0.304409261779039, 0.532580860681508, 0.663549547438732,
0.525529652068901, 0.438036097303733, 0.500581402303234, 0.340790071460301,
0.6585150480268, 0.764755402974609, 0.495348039833107, 0.936052199799697,
0.86884447627363, 0.64779296673596, 0.729130744413302, 0.212308846717215,
0.921575063542082, 0.896725345760408, 0.908630767579495, 0.552735032621137,
0.333089198076349, 0.411240252256137, 0.210089558407819, 0.73811428745976,
0.598277326712665, 0.812100882633133, 0.553780145134378, 0.285594535525249,
0.976265415980042, 0.479482389182052, 0.884418579477935, 0.675232647472854,
0.549910482547915, 0.503157835462091, 0.665670569303347, 0.468546618084848,
0.568520882937806, 0.270816408384732, 0.221250962981007, 0.326798623488733,
0.731113083904769, 0.264546800699518, 0.637416563146408, 0.612254123798448,
0.137589225529098, 0.973856314310539, 0.57355849354993, 0.795724887855882,
0.207887346031912, 0.315264931197944, 0.570327096970961, 0.821667790125253,
0.34700932633174, 0.700348491487529, 0.917036300698461, 0.499629857729573,
0.945677859174471, 0.734707685742723, 0.793812332967952, 0.770460729642163,
0.527396810927778, 0.288295112916966, 0.425445404428372, 0.483612884217641,
0.675259561878702, 0.592376322434429, 0.179112926596139, 0.91845435860965,
0.559221364493348, 0.2420893235427, 0.987070707204451, 0.47041204527438,
0.561066676064396, 0.979709257022779, 0.97459617254262, 0.493734807579198,
0.633862083931177, 0.984516332934101, 0.740187112417555, 0.766781488979252,
0.97975192431102, 0.33055072207903, 0.533054583463754, 0.790202073936537,
0.680287263575367, 0.660197257135086, 0.265210247355631, 0.635683810747643,
0.929441105581883, 0.200564850844362, 0.624207163066426, 0.677315834518225,
0.576813581489832, 0.870011812596891, 0.784528876996448, 0.349112814763239,
0.713692748225076, 0.246612007341578, 0.851081180574875, 0.687137281881956,
0.341479975901666, 0.877294727463993, 0.345744536252705, 0.813818470175183,
0.559690409480896, 0.621134908742507, 0.278731001691605, 0.982206757909454,
0.202689509120882, 0.607302163137454, 0.851508613763468, 0.649527821417898,
0.296858688986366, 0.637473293434683, 0.861231144697065, 0.754589517375169,
0.963050340651459, 0.796382681438869, 0.321017842587893, 0.548527383260705,
0.440018622160444, 0.40853771801107, 0.26933993698801, 0.520368464701376,
0.7280745989701, 0.471834600908167, 0.204830907885538, 0.506228982219536,
0.731428209461483, 0.776728970181845, 0.722816963684959, 0.877294727463993,
0.479842448557783, 0.576319001528652, 0.74198934756818, 0.712480680225209,
0.704672118556432, 0.98052035986783, 0.217382974876404, 0.367373216386543,
0.307185123289256, 0.512795273384737, 0.260517614803284, 0.968697596752212,
0.749162451797087, 0.68041909739219, 0.79699918505845, 0.238291353978693,
0.219963202913491, 0.618951001840467, 0.580254548293966, 0.818616105457586,
0.942296461590003, 0.666912717900493, 0.332605259941962, 0.651690837935606,
0.50857664168015, 0.90399764992811, 0.846846428061044, 0.799716912284323,
0.181061805447383, 0.767483350924048, 0.384190617749709, 0.381014832615475,
0.286346662970687, 0.415112136598318, 0.871297702675277, 0.904491992447163,
0.691290885733912, 0.883663383425187, 0.218031207659998, 0.845471915435013,
0.851225057418906, 0.974737142979397, 0.304384913954126, 0.809217536951246,
0.673169505573611, 0.587832517321454, 0.690755125022416, 0.22291607285211,
0.179953907381919, 0.970837663453446, 0.738032658514138, 0.164877770046258,
0.559221364493348, 0.737961463367539, 0.574811799071391, 0.823334805691295,
0.397181521409539, 0.450228391674526, 0.388746691319082, 0.610474606608326,
0.614247050968821, 0.233533077961654, 0.513485424702372, 0.645035767493623,
0.764733321673292, 0.635712234455418, 0.321432791509016, 0.980875944753058,
0.930793776634344, 0.515141614894262, 0.267122769086816, 0.742852208475956,
0.353042840393565, 0.747623556575278, 0.568520882937806, 0.336815676156739,
0.294901862001193, 0.795145496446215, 0.249762053766042, 0.780927060658826,
0.799538641669496, 0.736927326640062, 0.560628178989406, 0.277966452045218,
0.955942719184481, 0.99446721952086, 0.806262158705558, 0.622506360194255,
0.599162290212476, 0.911568883259971, 0.919636749455923, 0.755663660003546,
0.786091771573376, 0.663124547545389, 0.728451212871414, 0.915017637389918,
0.540976155120713, 0.966571939655214, 0.944017655309776, 0.769111705432106,
0.730655708202056, 0.793731921435595, 0.672331547859415, 0.954188301697073,
0.771490498213196, 0.759323050904125, 0.371749915918801, 0.896029675337791,
0.806875130512909, 0.428847795602538, 0.879100543795116, 0.683667479610655,
0.652853497164203, 0.322424456351873, 0.63223630618384, 0.739111324425798,
0.842480963836762, 0.656399746538259, 0.650393771706781, 0.811904090325055,
0.232195146241057, 0.475599646446869, 0.262723383404926, 0.375875902973021,
0.121452811688801, 0.267495498619919, 0.48738518650336, 0.145570737158047,
0.836295028297079, 0.866323236416769, 0.339370978784442, 0.950244949336958,
0.932430555794746, 0.553175128755609, 0.581965592354742, 0.894949559660516,
0.576843540774841, 0.87024074399061, 0.894254944829874, 0.439366370246273,
0.701519570518178, 0.283657063015796, 0.93318279804328, 0.805782242770265,
0.992222214238406, 0.394450834972146, 0.901654917225723, 0.933970824223853,
0.768817215838203, 0.651466105934146, 0.501057173441579, 0.892777201339042,
0.858754977433982, 0.695586052629751, 0.248716707595335, 0.856898191452172,
0.158303873916073, 0.619956780913837, 0.226391315408489, 0.558313224726938,
0.298026108292324, 0.234215057380327, 0.428383617689134, 0.549439406197447,
0.220289907919949, 0.286164787814256, 0.803010050278043, 0.170897787205529,
0.250118826376969, 0.169329123235089, 0.105169643729422, 0.230818986882482,
0.18870738933734, 0.643086595448451, 0.351739919456562, 0.346870221210139,
0.170151016814197, 0.293716098173589, 0.178851307909495, 0.164354353678441,
0.137152736940256, 0.162048818603576, 0.215166581519086, 0.187007449141651,
0.237439972839634, 0.14258798699763, 0.330156871723161, 0.237946098901768,
0.194887699919184, 0.890822417716102, 0.14467963257714, 0.158733788855624,
0.315702415553601, 0.205141048154358, 0.186429463355873, 0.157989199214296,
0.271192384689673, 0.176903209875729, 0.268965581092294, 0.235561057229909,
0.168828874843123, 0.212308846717215, 0.570545224925858, 0.218680833334588,
0.520812798555289, 0.570357173420229, 0.148201635635738, 0.252266585477661,
0.370890047542259, 0.14213825397019, 0.112490591448769, 0.214524529099719,
0.21837679510844, 0.6077559310819, 0.295668356350065, 0.193398997617611,
0.188167557710595, 0.236590688745572, 0.608692142201058, 0.136927677224389,
0.203613730295902, 0.182193295757089, 0.182761098473245, 0.212627281905761,
0.210109927239357, 0.629520107298525, 0.162566320963278, 0.465261364100146,
0.241020865363341, 0.187877013216933, 0.210961933060156, 0.320603176249504,
0.557843871658153, 0.590069216234008, 0.17636750730924, 0.359238412133482,
0.228170276870657, 0.476807428925973, 0.249072482719156, 0.480317465811968,
0.329288559790918, 0.183045514601816, 0.265264079554823, 0.1192496449915,
0.188458447253358, 0.14328740848045, 0.558313224726938, 0.179673238503788,
0.199063071112044, 0.217080239755128, 0.168828874843123, 0.62158265253597,
0.441681318415537, 0.0992888953767996, 0.2825232763026, 0.251191180467748,
0.197248787997729, 0.174714952234069, 0.138494990579275, 0.26635431665589,
0.297256081297999, 0.135360948278321, 0.291010824664818, 0.166192163105498,
0.436117818696803, 0.25871219982226, 0.296090406042039, 0.295297734909279,
0.108413551555087, 0.237256621439176, 0.190192131110892, 0.650196567547233,
0.188167557710595, 0.262699610356166, 0.182761098473245, 0.209773911216204,
0.188458447253358, 0.274945653887741, 0.125323762673128, 0.435395196228578,
0.0971197191841969, 0.194589266165696, 0.199082640326805, 0.200870163474221,
0.164894670499535, 0.276441228444482, 0.160008586958398, 0.747010603204249,
0.15649233284349, 0.374808964328024, 0.18476385704053, 0.248716707595335,
0.150361187732168, 0.224878905369068, 0.235218536432899, 0.231856035256552,
0.49592350798512, 0.761359026335127, 0.417676219153237), event = c(1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,
1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,
0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,
1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,
1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0)), row.names = c(NA, -450L), class = "data.frame")
Thank you in advance!

Reading in and plotting .mat matrix in R

I have a .mat file containing a matrix of 41 columns and 83 rows of binary values. I have imported it using library('R.matlab') such that ag <- readMat(file.choose(), header = TRUE). I am trying to plot this file like a raster/heatmap, but having no success. I have tried with ggplot2, plot.matrix, and raster.
ggplot(ag) +
+ geom_raster(x = ncol(ag), y = nrow(ag)
gives me
Error in `$<-.data.frame`(`*tmp*`, "xmin", value = numeric(0)) :
replacement has 0 rows, data has 83
I have also tried
ggplot(data.frame(ag)) +
+ geom_tile(x = ncol(ag), y = nrow(ag))
which gives me
Error in eval(substitute(list(...)), `_data`, parent.frame()) :
object 'x' not found
In addition: Warning messages:
1: In min(x, na.rm = na.rm) :
no non-missing arguments to min; returning Inf
2: In max(x, na.rm = na.rm) :
no non-missing arguments to max; returning -Inf
3: In min(diff(sort(x))) : no non-missing arguments to min; returning Inf
4: In min(x, na.rm = na.rm) :
no non-missing arguments to min; returning Inf
5: In max(x, na.rm = na.rm) :
no non-missing arguments to max; returning -Inf
6: In min(diff(sort(x))) : no non-missing arguments to min; returning Inf
with plot.matrix I have tried
ag = as.data.frame(ag)
plot(ag)
which shows
Error in plot.new() : figure margins too large
even when I type
par(mar=c(1,1,1,1))
Is there an error with how I am reading in the file? How can I get the data to behave for visualizations? I have worked with matrices in R before and not encountered issues like this, but have no experience with matlab. The data was provided by a professor. Here is a dump of the data
ag <- structure(list(mat.agr = structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), .Dim = c(83L, 41L))), header = list(
description = "MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Tue Aug 11 11:44:30 2020 ",
version = "5", endian = "little"))

The class of the ag object is list, with a single element (the matrix mat.agr).
library('R.matlab')
ag <- readMat("Matrix_Agriculture.mat", header = TRUE)
str(ag)
List of 1
$ mat.agr: num [1:83, 1:41] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "header")=List of 3
..$ description: chr "MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Tue Aug 11 11:44:30 2020 "
..$ version : chr "5"
..$ endian : chr "little"
Thus, is you want to plot your Matlab matrix, you can use the image command
mtx <- ag$mat.agr
image(mtx)
or, if you want to use the library ggplot2
library(tidyr)
# From wide to long format
ag_long <- mtx %>%
as.data.frame() %>%
mutate(x=1:n()) %>%
gather(y, z, V1:V41, factor_key=TRUE)
ggplot(data=ag_long, aes(x,y)) +
geom_raster(aes(fill=z)) +
theme_void()

tapply of two variables in a loop

My data :
TEST <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0,
0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2,
0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,
3, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 1, 0, 0, 1,
0, 0, 0), .Dim = c(22L, 20L), .Dimnames = list(NULL, c("month",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "")))
and :
month <- c(1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2)
I want to sum by row, so I used this code :
su_test <- list()
for (i in 1:ncol(TEST)){
su_test[[i]] <- tapply(TEST[,i], month, sum)
}
su_test <- do.call(cbind, su_test)
and to check the quantile :
su_test_obs <- apply(su_test,1,quantile,c(0.1,0.9))
This is an observation simulation per month. However I also have the detail by area. :
TEST2 <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 1, 0,
0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 3, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 1), .Dim = c(22L, 20L), .Dimnames = list(NULL, c("month",
"area", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "")))
area <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
I would like to have the same result of su_test_obs
but with the details of the areas in addition like list(month, area) but I don't understand the logic.
Would you have a solution please? Maybe there is a solution simpler with dplyr?
Thanks

It would be much simpler if you convert your matrix to dataframe. We can then use aggregate which can be applied to multiple groups easily
df <- data.frame(TEST2)
apply(aggregate(.~month + area, df, sum)[-c(1, 2)], 1, quantile, c(0.1,0.9))
# [,1] [,2]
#10% 2 1.7
#90% 6 5.3

venn diagram with categorical data

I have three categorical vectors that represent symptoms. And I would like plot a venn diagram that show how many people have one two or three of them.
I tryed do
library(gplots)
venn(list(sym1, sym2, sym3))
but didn't work
Thank you
sym1=c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0,
0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0,
1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0,
1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,
1, 0, 0, 1, 1, 0)
sym2=c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1,
1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 0, 1, 0, 1, 0)
sym3=c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,
0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1,
0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,
0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,
1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 1, 0)

Here is an example on how to achieve this with the library eulerr which provides much better looking (at least in my opinion) diagrams:
library(eulerr)
library(tidyverse)
set.seed(123) #for reproducible plot
data.frame(sym1, sym2, sym3)%>% #combine the vectors to a data frame
mutate_at(1:3, as.logical) %>% #convert to logical
euler(shape = "ellipse", input = "disjoint") %>% #calculate euler object, plot as ellipse
plot(quantities = T) plot it
with venn from gplots:
library(gplots)
data.frame(sym1, sym2, sym3)%>%
mutate_at(1:3, as.logical) %>%
venn()
From the help of venn:
Either a list list containing vectors of names or indices of group
intersections, or a data frame containing boolean indicators of group
intersectionship (see below)
In your case I trust the second options is desired.

weight data with R Part II

Given is the following data frame:
structure(list(UH6401 = c(1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
1, 0, 1, 1), UH6402 = c(1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0,
1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1), UH6403 = c(1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,
1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,
0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,
1, 1), UH6404 = c(0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1,
0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,
1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,
1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,
0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1,
1), UH6409 = c(1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,
1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0
), UH6410 = c(1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,
1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,
1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0
), UH6411 = c(0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1,
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,
1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1
), UH6412 = c(1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1
), UH6503 = c(1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1
), UH66 = c(1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
UH68 = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), UH6501a = c(1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), UH6405a = c(1,
0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,
0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,
1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
1, 0, 1, 1), UH6407a = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1), weight = c(405.002592353822,
479.360356183825, 526.548105855472, 810.005184707644, 312.321528531308,
930.961115757095, 567.383058387095, 475.323944260643, 1226.91439266118,
517.086839792615, 1200.2669656949, 810.005184707644, 656.723784884795,
605.370463928298, 668.467435759576, 558.112457492436, 793.751055244424,
479.360356183825, 1226.91439266118, 1606.54816212786, 1657.48609449633,
300.803580980276, 605.370463928298, 1140.55078447979, 669.102760422943,
810.005184707644, 1657.48609449633, 305.569853371963, 2994.30343152033,
762.922030382216, 479.360356183825, 1147.36030437824, 668.467435759576,
517.086839792615, 479.360356183825, 399.141865860217, 656.723784884795,
913.364738988386, 312.321528531308, 569.10576379231, 775.630259688922,
1207.22952429547, 1053.09621171094, 1140.55078447979, 314.857225320909,
668.467435759576, 2416.57081451012, 573.680152189121, 396.875527622212,
605.370463928298, 1036.3159447043, 3088.62283807823, 569.10576379231,
1140.55078447979, 2416.57081451012, 1147.36030437824, 762.922030382216,
702.064141140629, 351.032070570315, 629.714450641817, 517.086839792615,
1996.20228768022, 828.743047248167, 475.323944260643, 920.185794495882,
793.751055244424, 796.08788273764, 1197.42559758065, 405.002592353822,
418.584343119327, 300.803580980276, 654.76828203733, 2740.09421696516,
351.032070570315, 1069.6202614693, 2094.91447516374, 399.141865860217,
654.76828203733, 1003.65414063441, 573.680152189121, 851.074587580641,
913.364738988386, 762.922030382216, 1034.17367958523, 573.680152189121,
479.360356183825, 3208.8607844079, 654.76828203733, 908.055695892447,
328.361892442398, 1036.3159447043, 702.064141140629, 613.457196330588,
601.607161960551, 567.383058387095, 479.360356183825, 306.261087672466,
920.185794495882, 654.76828203733, 828.743047248167)), .Names = c("UH6401",
"UH6402", "UH6403", "UH6404", "UH6409", "UH6410", "UH6411", "UH6412",
"UH6503", "UH66", "UH68", "UH6501a", "UH6405a", "UH6407a", "weight"
), row.names = c(NA, 100L), class = "data.frame")
In social science we often have a weight variable to weight a case (row) by the factor of that variable to correct the sample to fit e.g. the population by age classes. If the weight variable of a row is "1.6" it means that this row need do be observed 1.6 times to fit the basis population.
In SPSS I would write
WEIGHT BY weight.
and all procedures after that command will weight the data accordingly.
In R I can do that with stabs with the command
xtabs(weight ~ UH6401, data=df)
But what if I want to do a SVD or PCA analysis? Here there is no function to weight data like it is in xtabs.
So the question is, is there a method to weight data in R like it is possible in SPSS?
The point with whole numbers would be easy, with the factor "2" we would just double the line, but what is with all the factors that are decimal?
UPDATE:
The SVD or PCA was just an example! Take any other statistical procedure.
In social science the samples are never perfect, but to do an statistical analysis with sample data, the sample needs to represent the basic population, but a sample mostly doesn't. So we try to fix that deficit with weights, so the sample represent the basic population!

First of all, doing PCA on this data doesn't make sense. Second, SPSS does not perform PCA but factor analysis, which is something else. I know they call it PCA, but it isn't.
The WEIGHT BY in SPSS is nothing more than a replication weight, and is exactly the same as doing your analysis by repeating your cases using rep(): complete madness. To link to your example: In SPSS, FACTOR (which is used for the socalled PCA) does not take fractional weights.
If you want to perform weighted procedures, the only sensible way of doing that is using the correct method/function/package for that. In statistics, there is no one-size-fits-all weight procedure, contrary to what SPSS likes to make you believe.
In your example : weighted PCA in R is contained in FactoMineR and aroma.light. But I strongly suggest you take also a look at the vegan package, as that contains a lot more useful ordination methods for the data you're describing.

You probably need to get acquainted with the search engines for R. Baron's RSiteSearch and Rseek:
This is one of the first hits on "weighted PCA" at Baron's site:
http://finzi.psych.upenn.edu/R/library/aroma.light/html/wpca.matrix.html
With the clarification in the comment to Joris Meys response, the answer is often that one needs to be clear that one is desires sample weights versus other types of weighting. Regression weighting is done with the survey package. Lumley's book on survey methods distinguishes among three types of weights. (The "weights" in the lm function are variance weights, NOT sample weights.)
Note: Both PCA and factor analysis (experimental) are included in the survey package. So maybe Dominick's question requestiong a unified approach to weighting in regression methods has a single "answer".

I am not sure if this would suite you. See the R package weights.

I have just found a Post in R-Bloggers which introduces a svydesign() function. As far as I know, this function from the 'survey' package is like SPSS function, allowing you to create a weighted data to use in further analysis. I find it more useful than using different functions from several packages in order to do multivariable analysis.
Note to #djhurio: The answer would have been better with code. It does seem a bit duplicative of my answer which pointed to the survey package that contains 'svydesign'. The cited webpage is still there 4 years later, but that might not always be the case.

Resources