# [R] Creating a mean line plot

William Michels wjm1 @end|ng |rom c@@@co|umb|@@edu
Sun Apr 14 10:45:26 CEST 2019

```So you're saying rowMeans(cbind(matrix_a, matrix_b)) worked to obtain

Wild guess here, are you simply looking for:
colMeans(rbind(matrix_a, matrix_b)) to obtain your Y-axis values?

[Above assuming matrix_a and matrix_b have identical dimensions (nrow, ncol)].

--Bill

William Michels, Ph.D.

On Fri, Apr 12, 2019 at 11:09 AM rain1290--- via R-help

<r-help using r-project.org> wrote:
>
> Hi Eric,
>
> Ah, I apologize, and thank you for your response!
> I just figured out a way to average my x-values, so at least that is solved. I will still include the data for the two variables (1-dimensional) of interest that I was trying to average, just to show what was done:
> get2.teratons #(90 values)
> get5.teratons #(90 values)
> Here is what get2.teratons looks like (same idea for get5.teratons):
>     >print(get2.teratons)
>     [1] 0.4558545 0.4651129 0.4747509 0.4848242 0.4950900 0.5056109 0.5159335
>     0.5262532 0.5372275 0.5481839 0.5586787 0.5694379 0.5802970
>     [14] 0.5909211 0.6015753 0.6124256 0.6237733 0.6353634 0.6467227 0.6582857
>     0.6702509 0.6817027 0.6935311 0.7060161 0.7182312 0.7301909
>     [27] 0.7422574 0.7544744 0.7665907 0.7786409 0.7907518 0.8032732 0.8158733
>     0.8284363 0.8413905 0.8545881 0.8674711 0.8797701 0.8927392
>     [40] 0.9059937 0.9189707 0.9317215 0.9438155 0.9558035 0.9673665 0.9784927
>     0.9900898 1.0020388 1.0132683 1.0240023 1.0347708 1.0456077
>     [53] 1.0570347 1.0682903 1.0793535 1.0901511 1.1001753 1.1101276 1.1199142
>     1.1293237 1.1384669 1.1470002 1.1547341 1.1622488 1.1697549
>     [66] 1.1777542 1.1857587 1.1930233 1.1999645 1.2067172 1.2132979 1.2199317
>     1.2265673 1.2328599 1.2390689 1.2446050 1.2495579 1.2546455
>     [79] 1.2599212 1.2648733 1.2700068 1.2753889 1.2807509 1.2856922 1.2905927
>     1.2953338 1.3000484 1.3045992 1.3091128 1.3144190
> The following worked in terms of averaging all of the elements of get2.teratons and get5.teratons:
> rowMeans(cbind(get2.teratons,get5.teratons))
> However, I am trying to do something similar for the values on my y-axis. So, for now, here are the two variables (3-dimensional) that I would like to average:
>     subset
>     subset5
> Using the print function for "subset" (same idea for subset5):    >print(subset)
>     class       : RasterStack
>     dimensions  : 64, 128, 8192, 90  (nrow, ncol, ncell, nlayers)
>     resolution  : 2.8125, 2.789327  (x, y)
>     extent      : -181.4062, 178.5938, -89.25846, 89.25846  (xmin, xmax, ymin,
>     ymax)
>     coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
>     names       : X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14,
>     X15, ...     >dim(subset)
>     [1]  64 128  90>dim(subset5)
>     [1]  64 128  90
> I tried `mean(subset,subset5)`, which works, BUT it combines the 90 layers into 1 layer. I want keep the number of layers at 90, but simply average each of the grid cell values of "subset" and "subset5" for each layer. So, for instance, I want to average the values of each grid cell of layer 1 of "subset" with the values of each grid cell of layer 1 of "subset5", and then average those values of layer 2 of "subset" with those values of layer 2 of "subset5"......all the way to layer 90. That way, I have 90 averages across all grid cells.
> Here is what the data looks like for "subset":
>     structure(c(11.5447145886719, 11.2479725852609, 10.0223480723798,
>     11.4909216295928, 12.5930442474782, 15.0295264553279, 14.6107862703502,
>     13.3623332250863, 10.4473929153755, 13.262210553512, 13.3166334126145,
>     13.7211008928716, 10.594900790602, 11.7217378690839, 10.8397546224296,
>     14.2727348953485, 13.6185416020453, 12.7485566306859, 11.7246472276747,
>     10.6815265025944, 13.1605062168092, 12.9131189547479, 12.6493454910815,
>     11.6938022430986, 11.4522186107934, 8.84930260945112, 11.5785481408238,
>     12.9859233275056, 13.6702361516654, 11.863912967965, 11.6624090820551,
>     12.1465771459043, 12.9789240192622, 13.5916746687144, 15.0383287109435,
>     7.89674604311585, 8.14079332631081, 7.05628590658307, 6.99759456329048,
>     8.06435288395733, 8.00622920505702, 7.35754533670843, 6.57949370797724,
>     6.26998774241656, 6.10911303665489, 10.1576759945601, 9.83650996349752,
>     10.6277788057923, 10.3647025069222, 9.38627037685364, 28.411143925041,
>     27.3436004295945, 25.7670222781599, 24.1854049265385, 22.7183715440333,
>     10.8529561199248, 11.1584928352386, 11.4545458462089, 11.7570801638067,
>     11.6314635146409, 13.7268429156393, 12.4547378160059, 12.8433785866946,
>     10.282119596377, 9.66278391424567, 6.39572446234524, 8.4569685626775,
>     12.253624945879, 12.4784250743687, 13.6823802720755, 8.65540341474116,
>     8.34308553021401, 8.30261853989214, 7.9798299819231, 7.96007991302758,
>     13.3976918645203, 15.2056947816163, 15.3097502421588, 18.0296610575169,
>     17.918016621843, 14.121591579169, 14.3091559410095, 14.7470911033452,
>     15.414851764217, 15.8059203531593, 22.9126498103142, 21.5608592145145,
>     19.7303873486817, 17.5689237657934, 15.4688697773963, 10.2526041911915,
>     10.4463449679315, 9.85705149360001, 9.5394266070798, 9.17961853556335,
>     14.064371259883, 12.626935634762, 12.1540617663413, 10.9235350973904,
>     9.32216013316065, 12.3676003888249, 12.9718807060272, 14.5685050170869,
>     13.8497828040272, 14.0683455392718, 8.09576804749668, 8.54510050266981,
>     8.02388715092093, 8.6679536383599, 9.38348234631121, 11.6279292851686,
>     11.5998465567827, 11.6469369269907, 11.6286710835993, 10.8152111526579,
>     17.4072104506195, 18.9169261604548, 19.5168524980545, 19.0377978142351,
>     19.5594304706901, 9.74474258255213, 10.2144323755056, 10.9722976572812,
>     11.5369332488626, 12.0274581480771, 14.007618650794, 14.0536692459136,
>     14.4861201290041, 14.133819937706, 13.045089924708, 19.9330265633762,
>     20.3158976510167, 21.4452845044434, 19.9475897010416, 20.3566399868578,
>     15.703826257959, 14.8260951507837, 14.6203982178122, 14.0476305037737,
>     13.2086589932442, 6.5044054761529, 6.51829722337425, 6.59741191193461,
>     6.57343484926969, 7.07112564705312, 8.42645864468068, 9.15604883339256,
>     10.8542435802519, 8.57339131180197, 7.89698304142803, 10.6029914226383,
>     9.90388663485646, 8.46301421988755, 12.9162973724306, 9.06370310112834,
>     9.92726711556315, 11.5754703059793, 8.74886247329414, 8.99941809475422,
>     9.90840594749898, 11.1468604300171, 11.1322306562215, 10.49438144546,
>     9.50155213940889, 8.31737467087805, 5.76932597905397, 6.14411209244281,
>     7.39980584476143, 8.47632132936269, 8.00714262295514, 8.64454926922917,
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>     12.0141573250294, 11.0411503817886, 11.7892528418452, 11.2668004352599,
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>     12.3285478446633, 11.9927407242358, 11.6441268939525, 11.6448875516653,
>     30.5602320469916, 30.6964941322803, 27.3358505219221, 27.5474566966295,
>     24.3847575969994, 15.1250814087689, 15.0272130500525, 14.9795342702419,
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>     11.0435656271875, 12.827942892909, 14.6962288767099, 15.984565531835,
>     16.3673574104905, 17.7882182411849, 17.1887206379324, 16.4347139652818,
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>     17.3931313678622, 18.2263168506324, 18.5841742437333, 6.59096706658602,
>     6.43405092414469, 6.25825286842883, 6.41100551001728, 6.47397979628295,
>     10.5375754879788, 11.7441980168223, 12.6210678834468, 13.6038213036954,
>     14.3639346119016, 14.6688716020435, 14.1826340463012, 15.2044224087149,
>     15.5630568042397, 15.0458208750933, 10.0154311163351, 9.7418615128845,
>     11.8866622913629, 10.4000290855765, 9.74880487192422, 12.071524746716,
>     11.5644979756325, 11.0723461490124, 10.6282578315586, 10.2157085202634,
>     14.5142644643784, 12.1188929770142, 12.3748247511685, 12.4087903182954,
>     11.9534945581108, 9.04913682024926, 10.3765605948865, 11.6044067312032,
>     11.8693192955106, 11.4852412138134, 9.60276927798986, 8.47671863157302,
>     6.53922976925969, 6.61022553686053, 6.93009907845408, 13.2296028546989,
>     13.0423339549452, 13.0597360432148, 12.6910961698741, 12.4157820828259,
>     10.1926731644198, 8.71818219311535, 7.08254557102919, 8.77621911931783,
>     10.0059285527095, 12.931788386777, 12.2630294412374, 11.4822425879538,
>     10.4378029704094, 9.7940765786916, 13.0133786704391, 11.9061049539596,
>     12.0638377033174, 12.3013137839735, 12.9490484017879, 13.2149957120419,
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>     16.0914938896894, 16.6821955237538, 17.9938221350312, 19.0754321403801,
>     19.048942392692, 8.59134346246719, 8.39548541698605, 8.17942153662443,
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>     22.3454732447863, 22.693102620542, 22.8635905310512, 23.2176823541522,
>     18.6908649746329, 16.1407203879207, 14.8633007425815, 13.0084274802357,
>     10.3990704054013, 6.98735397309065, 6.87530469149351, 8.9313744334504,
>     7.93048026971519, 8.05362006649375, 7.19595712143928, 6.09859018586576,
>     7.31170470826328, 8.58990701381117, 8.4448722191155, 10.6643167790025,
>     10.839969618246, 10.5106293456629, 10.4457534151152, 11.2185546196997,
>     12.6707960385829, 12.9902018699795, 12.9533659201115, 12.501154281199,
>     12.3501065187156, 25.9615670889616, 28.099115844816, 30.2258117124438,
>     32.2391155175865, 34.1092220507562, 13.0570391658694, 14.2825467512012,
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>     9.15108947083354, 9.4462743261829, 8.55356580577791, 8.69411900639534,
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>     14.6456281654537, 13.9498212374747, 14.5683591719717, 14.3893217202276,
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>     10.922572016716, 10.9020531177521, 10.406608460471, 22.1927281469107,
>     21.7946967110038, 22.5350291468203, 22.0015277154744, 23.2784972526133,
>     25.1319196075201, 24.1645314730704, 23.0207713320851, 14.8746414575726,
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>     16.011631116271, 16.6771751828492, 14.9888406973332, 14.0024574939162,
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>     16.033780714497, 17.3399481922388, 16.4341507013887, 15.3515323530883,
>     14.7840439807624, 18.8009101431817, 19.3318882025778, 20.5749990418553,
>     21.8101386912167, 21.9960610382259, 18.0659588892013, 17.8131891880184,
>     17.4943805672228, 17.3403216060251, 16.8955769855529, 12.620489532128,
>     12.2214950155467, 11.8860110174865, 11.3811555784196, 10.8314753975719,
>     13.4036011062562, 11.5633060690016, 11.6371187847108, 12.5311543699354,
>     13.4179203305393, 8.22134572081268, 7.50831649638712, 7.27005901280791,
>     7.60287002194673, 7.99200239125639, 7.90263516828418, 8.68863912764937,
>     10.4649641085416, 14.8291767574847, 13.2854715920985, 14.6683146245778,
>     15.3950218576938, 16.1753460299224, 18.3709637727588, 18.7799926847219,
>     9.85975402873009, 11.3263857085258, 14.0980262774974, 14.9891349021345,
>     15.565140126273, 17.7682626061141, 17.6397152245045, 18.1632375810295,
>     18.5020068660378, 18.6178280040622, 13.9469483401626, 13.3572864811867,
>     13.7237298768014, 15.0745737366378, 13.0753238685429, 7.80682750046253,
>     8.02811540197581, 8.54396957438439, 8.93615526147187, 9.23284823074937,
>     11.9208830874413, 11.34336409159, 9.64633170515299, 9.77506830822676,
>     9.60444209631532, 13.3866403251886, 13.6259520426393, 11.5198655985296,
>     10.6700826901942, 9.85463059041649, 16.529045579955, 14.2629016656429,
>     12.7639583777636, 13.6573225725442, 15.0617569684982, 9.50025964993984,
>     9.68771148473024, 9.27095026709139, 9.30016769561917, 9.69172285404056,
>     7.99956496339291, 7.4167326791212, 7.22712711431086, 8.56165643781424,
>     9.04990502167493, 16.1096038296819, 15.6424694694579, 16.1224633455276,
>     15.2468092739582, 15.2601830195636, 14.6924834232777, 15.2172856964171,
>     15.6576700508595, 15.8558295574039, 15.6930990982801, 10.0672576809302,
>     10.4989007581025, 10.7346505858004, 10.9321122989058, 10.1002658251673,
>     7.57602006196976, 8.28179977834225, 9.00425424333662, 8.75011347234249,
>     9.78429929818958, 8.22318575810641, 7.62580542359501, 7.52632019575685,
>     7.3945076437667, 8.00606575794518, 9.82791453134269, 10.3108039358631,
>     10.8194808941334, 11.0586643684655, 12.7866649534553, 16.4375944063067,
>     16.122004436329, 15.8343450631946, 15.183718688786, 14.59901179187,
>     13.086870778352, 13.8396339956671, 13.0286106839776, 12.6303931698203,
>     11.8594408035278, 12.4039673712105, 9.90002802573144, 9.60356576833874,
>     11.081666406244, 11.0487984493375, 15.9987502265722, 14.9749074596912,
>     13.8462209142745, 12.3910789377987, 11.7417626548558, 10.7962236274034,
>     11.77659323439, 11.0980827827007, 10.4603781597689, 10.4605271480978,
>     12.797769298777, 11.2864379771054, 9.58062659483403, 9.57864196971059,
>     9.7400170750916, 15.1035780552775, 15.3101249132305, 15.6179285142571,
>     14.4825984723866, 11.6881796624511, 11.791490809992, 11.2104086671025,
>     8.8539243908599, 8.34417999722064, 8.39954141993076, 9.41099112387747,
>     8.93235134426504, 9.60718737915158, 9.41101815551519, 9.83936337288469,
>     13.6638214811683, 14.4527215976268, 14.7365185897797, 13.2517122197896,
>     11.0009524505585, 9.60110148880631, 8.54964307509363, 8.75000974629074,
>     8.88564947526902, 7.84255138132721, 11.6202082950622, 12.075385870412,
>     12.8382677212358, 14.9491381365806, 20.0978868640959, 8.93126882147044,
>     9.09663643687963, 9.05409744009376, 8.98246862925589, 8.80278556142002,
>     8.68155935313553, 8.91096869017929, 7.71334832534194, 9.87222944386303,
>     11.2759735900909, 17.2249065712094, 17.9082475136966, 17.6210721954703,
>     16.7172310408205, 16.2506423424929, 12.9267014097422, 14.7103695664555,
>     19.504395313561, 22.4196153692901, 22.2453631460667, 8.23867111466825,
>     8.10000761412084, 7.8771845670417, 7.56322089582682, 7.14911003597081,
>     9.50618146453053, 8.6958515457809, 7.36113237217069, 6.79777669720352,
>     6.69330381788313), .Dim = c(10L, 90L), .Dimnames = list(NULL,
>     c("X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10",
>     "X11", "X12", "X13", "X14", "X15", "X16", "X17", "X18", "X19",
>     "X20", "X21", "X22", "X23", "X24", "X25", "X26", "X27", "X28",
>     "X29", "X30", "X31", "X32", "X33", "X34", "X35", "X36", "X37",
>     "X38", "X39", "X40", "X41", "X42", "X43", "X44", "X45", "X46",
>     "X47", "X48", "X49", "X50", "X51", "X52", "X53", "X54", "X55",
>     "X56", "X57", "X58", "X59", "X60", "X61", "X62", "X63", "X64",
>     "X65", "X66", "X67", "X68", "X69", "X70", "X71", "X72", "X73",
>     "X74", "X75", "X76", "X77", "X78", "X79", "X80", "X81", "X82",
>     "X83", "X84", "X85", "X86", "X87", "X88", "X89", "X90")))
>
> Is there any way to compute the means in this way? I just tried this, but I received the following error:
> result <- rowMeans(cbind(c(subset), c(subset5)));dim(result) <- dim(subset);colnames(result) <- colnames(subset)
>
> Error in rowMeans(cbind(c(subset), c(subset5))) : 'x' must be numeric
>
> Thanks,
> -----Original Message-----
> From: Eric Berger <ericjberger using gmail.com>
> To: rain1290 <rain1290 using aim.com>
> Cc: r-sig-geo <r-sig-geo using r-project.org>; R mailing list <r-help using r-project.org>
> Sent: Fri, Apr 12, 2019 11:47 am
> Subject: Re: [R] Creating a mean line plot
>
> I don't have your data. Are the x-values the same in both plots?Does this example cover the situation?
> f1 <- function(x) { x^3 - 2 }f2 <- function(x) { 2 - x^2 }
> xV <- seq(from=0,to=2,length=50)y1 <- f1(xV)y2 <- f2(xV)y3 <- .5*(y1+y2)plot(x=xV,y=y1,col="blue",lwd=2,type='l',xlab="x",ylab="y")lines(x=xV,y=y2,col="green",lwd=2)lines(x=xV,y=y3,col="red",lwd=2)legend("topleft",legend=c("y1","y2","mean"),col=c("blue","green","red"),lwd=rep(2,3))
>
>
> On Fri, Apr 12, 2019 at 5:34 PM rain1290--- via R-help <r-help using r-project.org> wrote:
>
> Hi there,
> I am trying to create a mean line plot that shows the mean of a series of separate line plots that correspond to two climate models. Let's first try getting the mean of two line plots. To create the separate line plots, here is what I did to set up the x and y axis variables:
>
> ####Getting cumulative emissions data for x-axis: 1-dimensional ####
>
> #For CanESM model#
>
> ncfname <- "cumulative_emissions_1pctCO2.nc"
> Model1 <- nc_open(ncfname)
> get <- ncvar_get(Model1, "cum_co2_emi-CanESM2")     #units of terratones of carbon (TtC) for x-axis (140 values)
> #For IPSL LR Model#
> #Getting cumulative emissions data for x-axis IPSL LR 1pctCO2 IPSL <- ncvar_get(Model1, "cum_co2_emi-IPSL-CM5A-LR")     #units of terratones of carbon (TtC) for x-axis (140 values)
>
> ############################################################################################################
>
> #####Getting precipitation data for y-axis - these are 3-dimensional####
>
> #For CanESM2 model#
> Model2 <- brick("MaxPrecCCCMACanESM21pctCO2.nc", var="onedaymax")
>
>
> #For IPSL LR Model#
> Model10 <- brick("MaxPrecIPSLIPSL-CM5A-LR1pctCO2.nc", var="onedaymax")
> #############################################################################################################
> To create plots for a specific location:
> lonlat <- cbind(103,3)          #specifies a specific longitude and latitude
> Hope2 <- extract(Model2,lonlat)      #CanESM2
> Hope6 <- extract(Model10,lonlat)   #start IPSL CM5A LR
> plot(get,Hope2, type="l",col="green", lwd="3", xlab="Cumulative CO2 emissions (TtC)", ylab="One-day maximum precipitation (mm/day)", main="One-day maximum precipitation for random location for 1pctCO2 scenario")
> lines(IPSL, Hope6, type="l", lwd="3", col="green")
> #############################################################################################################
> So, the idea would be to create a plot that shows the mean of these two plots. Given what I showed above, how should I go about creating the mean of these two green line plots? Would you have to get the mean of the x-values, and then obtain the mean of the y-values, and then plot these?
> Thanks, and any help would be greatly appreciated!
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>
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> and provide commented, minimal, self-contained, reproducible code.
>
>
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>
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