thr3ads.net - R help - [R] Creating a mean line plot [Apr 2019]

If this information is useful, please help other people find it:
Share via:

r@i@1290 m@iii@g oii @im@com

2019-Apr-11 21:57 UTC

[R] Creating a mean line plot

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!
	[[alternative HTML version deleted]]

Eric Berger

2019-Apr-12 15:47 UTC

head link

[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 at
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!
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

r@i@1290 m@iii@g oii @im@com

2019-Apr-12 16:39 UTC

head link

[R] Creating a mean line plot

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":>dput(head(subset,5))??? 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, 
??? 7.79559868387878, 7.14818593114614, 7.42282171268016, 9.04718739911914, 
??? 12.0141573250294, 11.0411503817886, 11.7892528418452, 11.2668004352599, 
??? 10.5345542309806, 14.2355003859848, 12.4114783946425, 13.1144292186946, 
??? 14.3049817532301, 14.7282858844846, 9.90791183430701, 10.4058899218217, 
??? 12.0624131988734, 13.2521220948547, 13.9345653355122, 12.5256763771176, 
??? 12.3285478446633, 11.9927407242358, 11.6441268939525, 11.6448875516653, 
??? 30.5602320469916, 30.6964941322803, 27.3358505219221, 27.5474566966295, 
??? 24.3847575969994, 15.1250814087689, 15.0272130500525, 14.9795342702419, 
??? 14.2658210825175, 13.437497522682, 10.7001833617687, 10.0823557935655, 
??? 10.1298170629889, 9.99525294173509, 10.6919908896089, 9.04134479351342, 
??? 9.57930330187082, 9.58402880933136, 8.82056106347591, 9.06912200152874, 
??? 11.0435656271875, 12.827942892909, 14.6962288767099, 15.984565531835, 
??? 16.3673574104905, 17.7882182411849, 17.1887206379324, 16.4347139652818, 
??? 15.4833788517863, 14.3649869598448, 10.0324214436114, 10.9937381464988, 
??? 10.7803415972739, 10.64134365879, 10.3700830601156, 10.7242427766323, 
??? 10.1225153775886, 9.59254063200206, 9.67734202276915, 9.9705743137747, 
??? 6.15209711249918, 7.6417050557211, 9.55170588567853, 12.123644258827, 
??? 14.6793850231916, 13.8236853294075, 14.3564789090306, 13.6828002054244, 
??? 13.0476749036461, 12.3909330926836, 12.5938401091844, 12.5098232645541, 
??? 12.4792913440615, 10.5595408938825, 10.0890464382246, 9.20089432038367, 
??? 8.92592284362763, 8.59467086847872, 9.42603517323732, 10.0353622343391, 
??? 11.7311725392938, 12.4379832297564, 12.9343897104263, 12.9055073484778, 
??? 10.8944955747575, 13.6480727232993, 13.5285727679729, 13.1794585380703, 
??? 12.8222310449928, 12.3997843824327, 12.7413347829133, 14.3273916095495, 
??? 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, 
??? 13.1087802350521, 12.6286820042878, 12.2278920840472, 11.8682594038546, 
??? 10.9492189250886, 12.2341319918633, 12.9464382771403, 12.5120461452752, 
??? 12.5263502821326, 12.6686599105597, 12.7322974149138, 12.1948833111674, 
??? 12.1215357910842, 11.9392029941082, 15.2677292469889, 16.3731585256755, 
??? 17.8960581310093, 18.6334447469562, 19.5818214677274, 8.80653981585056, 
??? 9.830889897421, 9.35642933472991, 8.49255602806807, 9.19627505354583, 
??? 9.56638909410685, 10.4608207242563, 11.0053240321577, 12.0839668437839, 
??? 12.6748947892338, 10.9087632503361, 11.0474556684494, 9.86553691327572, 
??? 11.7183218244463, 12.5948534812778, 9.51134513597935, 7.67265690956265, 
??? 8.47005187533796, 8.948102616705, 9.48919930960983, 8.92916852608323, 
??? 9.19180226046592, 9.93818349670619, 10.3347131051123, 9.19244724791497, 
??? 16.0914938896894, 16.6821955237538, 17.9938221350312, 19.0754321403801, 
??? 19.048942392692, 8.59134346246719, 8.39548541698605, 8.17942153662443, 
??? 8.02843223791569, 8.9953287737444, 7.97593365423381, 7.71139136049896, 
??? 7.85907462704927, 8.38070099707693, 9.28482818417251, 11.3056178670377, 
??? 11.601750086993, 11.2711317837238, 10.8186058234423, 10.7581429649144, 
??? 15.6826636288315, 16.9076268095523, 15.4331855010241, 15.1698420289904, 
??? 14.4226460717618, 11.3487603608519, 10.932231741026, 10.3945284616202, 
??? 9.96728525497019, 9.48596934322268, 10.508708213456, 10.0394641282037, 
??? 10.5090778553858, 10.1252990076318, 9.86525025218725, 21.985590364784, 
??? 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, 
??? 11.1714780796319, 9.62660552468151, 13.1034480873495, 12.0462608523667, 
??? 12.1476030908525, 12.087664520368, 12.486698012799, 12.6554797869176, 
??? 12.9096878226846, 13.7426960282028, 15.2569429948926, 17.1046711038798, 
??? 17.0782153028995, 8.75586932525039, 8.82860643323511, 8.69223182089627, 
??? 9.15108947083354, 9.4462743261829, 8.55356580577791, 8.69411900639534, 
??? 8.9102350641042, 9.00506707839668, 8.75238287262619, 12.8364848904312, 
??? 14.6456281654537, 13.9498212374747, 14.5683591719717, 14.3893217202276, 
??? 15.1805742178112, 16.7262759525329, 17.7521643228829, 18.5243777465075, 
??? 18.8792126253247, 7.70680792629719, 7.47225251980126, 7.72799758706242, 
??? 7.68415729980916, 7.50800217501819, 9.68811193015426, 10.5253741610795, 
??? 10.922572016716, 10.9020531177521, 10.406608460471, 22.1927281469107, 
??? 21.7946967110038, 22.5350291468203, 22.0015277154744, 23.2784972526133, 
??? 25.1319196075201, 24.1645314730704, 23.0207713320851, 14.8746414575726, 
??? 12.5255933962762, 19.3960575386882, 19.3368871696293, 19.8454126249999, 
??? 19.8410699609667, 19.8172997217625, 12.1799279004335, 11.8857935070992, 
??? 11.4909932948649, 11.3612791523337, 10.8840802218765, 11.1973982769996, 
??? 11.6429010406137, 11.2867686431855, 11.5507948212326, 11.7122428491712, 
??? 13.8513946440071, 14.9497504346073, 14.425096521154, 13.2822252810001, 
??? 12.4311964027584, 18.864199379459, 17.5528808031231, 17.7616731729358, 
??? 17.1655979007483, 16.6251927148551, 29.3679255992174, 28.4771841019392, 
??? 27.9151875525713, 26.65377818048, 25.2528126351535, 10.6545137241483, 
??? 10.91169398278, 11.0310669522732, 11.1646522767842, 11.2674177624285, 
??? 13.7821182142943, 14.1553220339119, 15.0969068985432, 15.9642276819795, 
??? 16.6291657369584, 9.4556876225397, 9.84383365139365, 11.0380863770843, 
??? 10.6556000187993, 11.1149505246431, 8.38961955159903, 9.4479993218556, 
??? 10.1951210992411, 10.6412279885262, 10.8386783860624, 8.28430177643895, 
??? 8.50012865848839, 8.0173090333119, 8.15484160557389, 8.07647814508528, 
??? 10.3200965328142, 10.4913098970428, 10.3476996067911, 10.6061836704612, 
??? 12.1657092589885, 10.3872286621481, 9.38602960668504, 9.82730537652969, 
??? 9.79454554617405, 9.12395850755274, 12.1763132046908, 12.7074157353491, 
??? 12.6221365761012, 13.4234247263521, 15.5103187076747, 9.88674920517951, 
??? 9.41792191006243, 8.58000149019063, 7.98727499786764, 7.34257609583437, 
??? 13.8378750532866, 14.5356948953122, 14.5302697084844, 14.6059796679765, 
??? 14.1489790286869, 14.9558734148741, 15.146628767252, 15.4630133416504, 
??? 15.5585858970881, 15.4571908526123, 11.8359496816993, 11.2020426895469, 
??? 11.4698356948793, 11.8119870778173, 13.0321650300175, 17.7426278125495, 
??? 18.6734465416521, 18.8405636698008, 18.8715255819261, 18.9619445241988, 
??? 8.8628712343052, 8.674994437024, 9.01558804325759, 9.04601749498397, 
??? 8.85597188025713, 7.58305897470564, 7.92995095252991, 8.35649385116994, 
??? 9.23873609863222, 9.14969765581191, 12.9726023878902, 12.2728526126593, 
??? 13.0261426325887, 12.6654123421758, 11.5908016450703, 13.0077322013676, 
??? 12.6599280629307, 11.9994106236845, 10.1917257998139, 9.89739338401705, 
??? 10.7914459425956, 11.8336362764239, 11.7934257723391, 11.2242249771953, 
??? 11.4056261256337, 7.95377462636679, 7.26088020019233, 7.43080170359462, 
??? 7.50569254159927, 7.62218066956848, 11.2671461887658, 10.8180299866945, 
??? 9.43983325269073, 9.29652785416692, 10.826626047492, 14.3595944624394, 
??? 13.2217460777611, 12.7365244086832, 12.05212357454, 12.3027219437063, 
??? 13.1963438820094, 12.8045422956347, 13.7076315935701, 14.145736489445, 
??? 14.4983648322523, 14.3930621445179, 13.7241447810084, 13.0053710192442, 
??? 12.2289746068418, 11.4307265728712, 22.3180065862834, 17.3237380106002, 
??? 12.7182623371482, 13.0704908631742, 15.2839343994856, 11.1243085004389, 
??? 10.2472041500732, 10.5197993572801, 11.790946405381, 10.6045705731958, 
??? 15.1506495662034, 17.2426456119865, 18.0581725202501, 17.5418430939317, 
??? 16.011631116271, 16.6771751828492, 14.9888406973332, 14.0024574939162, 
??? 12.2754199896008, 10.462130815722, 14.700809167698, 14.7662508767098, 
??? 14.6368321962655, 13.8920741155744, 13.6426123324782, 7.52487180288881, 
??? 6.8714844295755, 7.11258086375892, 7.18187426682562, 7.26737848017365, 
??? 8.01721725147218, 9.51534896157682, 9.49199174065143, 9.66430208645761, 
??? 9.95999739971012, 12.6632636412978, 12.3405989259481, 12.1739520225674, 
??? 11.8746338412166, 11.4930238109082, 17.375064175576, 16.5855303872377, 
??? 14.6908791270107, 12.4465051107109, 10.6631374452263, 9.17110545560718, 
??? 8.15483720507473, 8.49230268504471, 9.13922635372728, 9.57141006365418, 
??? 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 at gmail.com>
To: rain1290 <rain1290 at aim.com>
Cc: r-sig-geo <r-sig-geo at r-project.org>; R mailing list <r-help at
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 at
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!
? ? ? ? [[alternative HTML version deleted]]

______________________________________________
R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.


	[[alternative HTML version deleted]]

R help - Apr 2019 - Creating a mean line plot

[R] Creating a mean line plot

[R] Creating a mean line plot

[R] Creating a mean line plot