[R-sig-Geo] [R] Plotting more than one regression line in ggplot

r@i@1290 m@iii@g oii @im@com r@i@1290 m@iii@g oii @im@com
Wed Jun 5 23:37:13 CEST 2019


Hi Rui (and everyone),

Thank you so much for your response! Much appreciated!
What if I wanted I create several regression lines and scatter plots in the same ggplot using a "melted" dataset? I would like to create a scatter plot and regression line for both the objects of "onepctCO2MEDIAN" and "RCP8.5MEDIANThis melted dataset looks like this:

>NewestdataUltra
 x variable       value              L1
1   0.000000000        y  0.00000000 onepctCO2MEDIAN
2   0.006794447        y  4.90024902 onepctCO2MEDIAN
3   0.014288058        y  0.16079999 onepctCO2MEDIAN
4   0.022087920        y  6.63491326 onepctCO2MEDIAN
5   0.030797357        y -1.24295056 onepctCO2MEDIAN
6   0.038451072        y  1.56433744 onepctCO2MEDIAN
7   0.048087904        y -2.26590352 onepctCO2MEDIAN
8   0.058677729        y  2.20700446 onepctCO2MEDIAN
9   0.069261406        y -2.36770013 onepctCO2MEDIAN
10  0.080524530        y -1.09135062 onepctCO2MEDIAN
11  0.092760246        y  0.40999399 onepctCO2MEDIAN
12  0.103789609        y -0.12597268 onepctCO2MEDIAN
13  0.116953168        y -2.41382534 onepctCO2MEDIAN
14  0.129253298        y  7.08902570 onepctCO2MEDIAN
15  0.141710050        y -0.75935388 onepctCO2MEDIAN
16  0.156002052        y  0.04544160 onepctCO2MEDIAN
17  0.170648172        y -1.53496826 onepctCO2MEDIAN
18  0.185318425        y  6.55242014 onepctCO2MEDIAN
19  0.199463055        y -0.83125628 onepctCO2MEDIAN
20  0.213513337        y -2.50991826 onepctCO2MEDIAN
21  0.228839271        y  0.13659682 onepctCO2MEDIAN
22  0.246981293        y -1.37198445 onepctCO2MEDIAN
23  0.263012767        y -0.87129883 onepctCO2MEDIAN
24  0.278505564        y  0.66325836 onepctCO2MEDIAN
25  0.293658361        y  0.79380363 onepctCO2MEDIAN
26  0.310747266        y  3.48806374 onepctCO2MEDIAN
27  0.325990349        y -4.46122081 onepctCO2MEDIAN
28  0.342517540        y  0.08717340 onepctCO2MEDIAN
29  0.362751633        y -1.41715777 onepctCO2MEDIAN
30  0.380199537        y -0.99565082 onepctCO2MEDIAN
31  0.394992948        y  0.32155262 onepctCO2MEDIAN
32  0.414373398        y  3.14038657 onepctCO2MEDIAN
33  0.430690214        y -0.73760988 onepctCO2MEDIAN
34  0.449738145        y -2.48605407 onepctCO2MEDIAN
35  0.470167458        y -3.42358584 onepctCO2MEDIAN
36  0.489019871        y  0.48247475 onepctCO2MEDIAN
37  0.507242471        y -0.97853863 onepctCO2MEDIAN
38  0.524314284        y  8.53596838 onepctCO2MEDIAN
39  0.543750525        y  5.48447420 onepctCO2MEDIAN
40  0.564234197        y  3.21493666 onepctCO2MEDIAN
41  0.583679616        y  3.91689160 onepctCO2MEDIAN
42  0.601459444        y  4.49070196 onepctCO2MEDIAN
43  0.619924664        y  6.54104103 onepctCO2MEDIAN
44  0.639932007        y  4.80686500 onepctCO2MEDIAN
45  0.661347181        y  8.15101701 onepctCO2MEDIAN
46  0.684117317        y  0.26974132 onepctCO2MEDIAN
47  0.704829752        y -0.18075007 onepctCO2MEDIAN
48  0.725045770        y  9.71812491 onepctCO2MEDIAN
49  0.745165825        y  1.54064657 onepctCO2MEDIAN
50  0.765016139        y -1.64760409 onepctCO2MEDIAN
51  0.783461511        y  4.80246029 onepctCO2MEDIAN
52  0.806382924        y  4.04215160 onepctCO2MEDIAN
53  0.829241335        y  9.37565122 onepctCO2MEDIAN
54  0.849924415        y  5.33050497 onepctCO2MEDIAN
55  0.871352434        y  7.54458026 onepctCO2MEDIAN
56  0.893632233        y  6.46795471 onepctCO2MEDIAN
57  0.916052133        y  2.80960651 onepctCO2MEDIAN
58  0.938579470        y  5.39216613 onepctCO2MEDIAN
59  0.959907651        y  7.20436888 onepctCO2MEDIAN
60  0.981643587        y  3.33508065 onepctCO2MEDIAN
61  1.004116774        y  8.86907070 onepctCO2MEDIAN
62  1.028363466        y  1.78612989 onepctCO2MEDIAN
63  1.054009140        y  6.25550382 onepctCO2MEDIAN
64  1.072440803        y  7.60792365 onepctCO2MEDIAN
65  1.094457805        y  7.68714831 onepctCO2MEDIAN
66  1.123176277        y  4.77877639 onepctCO2MEDIAN
67  1.149430871        y 12.71105018 onepctCO2MEDIAN
68  1.170912921        y -0.71562844 onepctCO2MEDIAN
69  1.196743071        y  1.64908992 onepctCO2MEDIAN
70  1.218625903        y  3.03630241 onepctCO2MEDIAN
71  1.241868377        y  4.29747688 onepctCO2MEDIAN
72  1.267941594        y  1.95437781 onepctCO2MEDIAN
73  1.290708780        y  3.99869637 onepctCO2MEDIAN
74  1.313222289        y  4.51794725 onepctCO2MEDIAN
75  1.339045882        y  0.93379048 onepctCO2MEDIAN
76  1.362803459        y  3.30507700 onepctCO2MEDIAN
77  1.384450197        y  3.54229702 onepctCO2MEDIAN
78  1.409720302        y  5.99736597 onepctCO2MEDIAN
79  1.435851157        y  0.50818686 onepctCO2MEDIAN
80  1.455592215        y  7.96616301 onepctCO2MEDIAN
81  1.479495347        y  9.94604963 onepctCO2MEDIAN
82  1.506051958        y  3.79083717 onepctCO2MEDIAN
83  1.525728464        y  2.57358469 onepctCO2MEDIAN
84  1.549362063        y 10.14049742 onepctCO2MEDIAN
85  1.573440671        y 13.74083036 onepctCO2MEDIAN
86  1.600278735        y  0.93357712 onepctCO2MEDIAN
87  1.623879492        y  9.75887417 onepctCO2MEDIAN
88  1.650029302        y  1.27693947 onepctCO2MEDIAN
89  1.672362328        y 13.49709060 onepctCO2MEDIAN
90  1.700221121        y 10.20875018 onepctCO2MEDIAN
91  1.724793375        y  1.68112753 onepctCO2MEDIAN
92  1.751070559        y  6.11789915 onepctCO2MEDIAN
93  1.778022110        y -0.15676262 onepctCO2MEDIAN
94  1.803022087        y  3.82374792 onepctCO2MEDIAN
95  1.830668867        y  4.43314679 onepctCO2MEDIAN
96  1.855736911        y  5.97907067 onepctCO2MEDIAN
97  1.882615030        y 11.31043325 onepctCO2MEDIAN
98  1.909218490        y  8.21426074 onepctCO2MEDIAN
99  1.938130021        y 15.32096736 onepctCO2MEDIAN
100 1.963727593        y  5.81782169 onepctCO2MEDIAN
101 1.993271947        y  9.60049074 onepctCO2MEDIAN
102 2.022548139        y  3.40636456 onepctCO2MEDIAN
103 2.050679922        y  4.73750104 onepctCO2MEDIAN
104 2.078064442        y  3.01330195 onepctCO2MEDIAN
105 2.104113460        y  5.56595225 onepctCO2MEDIAN
106 2.133597612        y 12.03463325 onepctCO2MEDIAN
107 2.164026260        y -0.40283200 onepctCO2MEDIAN
108 2.194852829        y 10.59967795 onepctCO2MEDIAN
109 2.224257946        y  5.44795837 onepctCO2MEDIAN
110 2.252194643        y  4.70523736 onepctCO2MEDIAN
111 2.277335048        y 14.09620189 onepctCO2MEDIAN
112 2.304058313        y  5.71490162 onepctCO2MEDIAN
113 2.330930233        y  3.77800721 onepctCO2MEDIAN
114 2.357022762        y  4.41206201 onepctCO2MEDIAN
115 2.386489272        y  4.18660848 onepctCO2MEDIAN
116 2.417503953        y  6.90788020 onepctCO2MEDIAN
117 2.448524356        y  2.78257393 onepctCO2MEDIAN
118 2.478698969        y  7.61717857 onepctCO2MEDIAN
119 2.510175705        y 10.24106026 onepctCO2MEDIAN
120 2.539697886        y  8.18207107 onepctCO2MEDIAN
121 2.567915559        y  4.82754944 onepctCO2MEDIAN
122 2.597463250        y 19.16248829 onepctCO2MEDIAN
123 2.627518773        y 16.06771094 onepctCO2MEDIAN
124 2.658759236        y 12.58970807 onepctCO2MEDIAN
125 2.692401528        y  9.29079880 onepctCO2MEDIAN
126 2.721903205        y  7.42625020 onepctCO2MEDIAN
127 2.753021359        y  9.39025180 onepctCO2MEDIAN
128 2.786313415        y 12.61935503 onepctCO2MEDIAN
129 2.819564104        y 11.11210397 onepctCO2MEDIAN
130 2.850823164        y 15.79070997 onepctCO2MEDIAN
131 2.880394101        y 10.74252868 onepctCO2MEDIAN
132 2.911391258        y  7.79714300 onepctCO2MEDIAN
133 2.942965150        y  8.80608578 onepctCO2MEDIAN
134 2.974468350        y 17.56062663 onepctCO2MEDIAN
135 3.008983612        y 17.30886049 onepctCO2MEDIAN
136 3.040015221        y 13.45005435 onepctCO2MEDIAN
137 3.072668672        y 14.63778842 onepctCO2MEDIAN
138 3.105982423        y  8.07985518 onepctCO2MEDIAN
139 0.467429527        y -1.55704023    RCP4.5MEDIAN
140 0.478266196        y -3.19367515    RCP4.5MEDIAN
141 0.489205229        y -2.44452679    RCP4.5MEDIAN
142 0.500039143        y  0.87504367    RCP4.5MEDIAN
143 0.511021115        y -0.39185002    RCP4.5MEDIAN
144 0.519874968        y -4.18935168    RCP4.5MEDIAN
145 0.528508358        y -3.64179524    RCP4.5MEDIAN
146 0.537377594        y -2.58167128    RCP4.5MEDIAN
147 0.546194211        y  2.20583694    RCP4.5MEDIAN
148 0.554720591        y -8.57764597    RCP4.5MEDIAN
149 0.563289814        y  2.88442536    RCP4.5MEDIAN
150 0.572032790        y -3.90829882    RCP4.5MEDIAN
151 0.580939066        y -3.39269048    RCP4.5MEDIAN
152 0.590921065        y -4.60849867    RCP4.5MEDIAN
153 0.601575326        y -1.62572657    RCP4.5MEDIAN
154 0.612425555        y  1.14198465    RCP4.5MEDIAN
155 0.623773319        y -3.38454122    RCP4.5MEDIAN
156 0.635363359        y  2.43414265    RCP4.5MEDIAN
157 0.646722666        y  3.30007615    RCP4.5MEDIAN
158 0.658285673        y -0.79555442    RCP4.5MEDIAN
159 0.670250852        y -2.05220500    RCP4.5MEDIAN
160 0.681702690        y -5.56808946    RCP4.5MEDIAN
161 0.693531145        y  2.24168605    RCP4.5MEDIAN
162 0.706016061        y -4.83673351    RCP4.5MEDIAN
163 0.718231249        y  0.40086819    RCP4.5MEDIAN
164 0.730190911        y -1.98026992    RCP4.5MEDIAN
165 0.741269845        y  0.39963115    RCP4.5MEDIAN
166 0.751000321        y -0.83241777    RCP4.5MEDIAN
167 0.760886972        y -1.66101404    RCP4.5MEDIAN
168 0.771137164        y -1.05452982    RCP4.5MEDIAN
169 0.781856383        y -1.18338156    RCP4.5MEDIAN
170 0.792607542        y  0.22722653    RCP4.5MEDIAN
171 0.803724128        y -1.90642564    RCP4.5MEDIAN
172 0.815066246        y  0.75010550    RCP4.5MEDIAN
173 0.826027437        y -1.31108646    RCP4.5MEDIAN
174 0.836766732        y  1.05961515    RCP4.5MEDIAN
175 0.847553312        y -2.06588010    RCP4.5MEDIAN
176 0.858331452        y  8.53403315    RCP4.5MEDIAN
177 0.869154422        y  0.09979751    RCP4.5MEDIAN
178 0.879572539        y -2.50854353    RCP4.5MEDIAN
179 0.889426601        y  5.29550783    RCP4.5MEDIAN
180 0.899009805        y  2.02909481    RCP4.5MEDIAN
181 0.908289566        y  2.66922982    RCP4.5MEDIAN
182 0.917284978        y -4.17757196    RCP4.5MEDIAN
183 0.926128960        y  3.40202916    RCP4.5MEDIAN
184 0.934752874        y -1.92292218    RCP4.5MEDIAN
185 0.943010943        y  6.36969150    RCP4.5MEDIAN
186 0.950999217        y  1.86490308    RCP4.5MEDIAN
187 0.958795701        y  8.32126161    RCP4.5MEDIAN
188 0.966310396        y 10.15048356    RCP4.5MEDIAN
189 0.973635493        y  6.68925964    RCP4.5MEDIAN
190 0.980834088        y -1.01615369    RCP4.5MEDIAN
191 0.987694790        y  0.20892853    RCP4.5MEDIAN
192 0.994548581        y -1.52787222    RCP4.5MEDIAN
193 1.001274595        y -0.72374597    RCP4.5MEDIAN
194 1.007810612        y  2.26062309    RCP4.5MEDIAN
195 1.014270389        y -2.40270340    RCP4.5MEDIAN
196 1.022719711        y -1.94548262    RCP4.5MEDIAN
197 1.032070810        y -1.13053235    RCP4.5MEDIAN
198 1.041118812        y  0.56107969    RCP4.5MEDIAN
199 1.050189571        y  3.27941835    RCP4.5MEDIAN
200 1.059380475        y  3.01333588    RCP4.5MEDIAN
201 1.067877585        y  4.87457336    RCP4.5MEDIAN
202 1.076078766        y  1.02457895    RCP4.5MEDIAN
203 1.084707357        y  4.49174869    RCP4.5MEDIAN
204 1.093223180        y  8.24629303    RCP4.5MEDIAN
205 1.101414382        y -0.03364132    RCP4.5MEDIAN
206 1.108886304        y  9.12509848    RCP4.5MEDIAN
207 1.115482896        y  1.74254621    RCP4.5MEDIAN
208 1.121856558        y  2.27004536    RCP4.5MEDIAN
209 1.127809421        y -0.65627179    RCP4.5MEDIAN
210 1.133265961        y 12.02566969    RCP4.5MEDIAN
211 1.138549712        y -1.04260843    RCP4.5MEDIAN
212 1.143910237        y -6.47611327    RCP4.5MEDIAN
213 1.149437787        y  8.88410567    RCP4.5MEDIAN
214 1.154488347        y -4.24916247    RCP4.5MEDIAN
215 1.159872903        y  7.90741918    RCP4.5MEDIAN
216 1.165477487        y -3.91386711    RCP4.5MEDIAN
217 1.171103424        y  1.02370701    RCP4.5MEDIAN
218 1.177498256        y -3.71206616    RCP4.5MEDIAN
219 1.184003888        y -1.05694182    RCP4.5MEDIAN
220 1.190395856        y  1.10501459    RCP4.5MEDIAN
221 1.197284280        y  2.67668639    RCP4.5MEDIAN
222 1.204590551        y  2.21693031    RCP4.5MEDIAN
223 1.210807614        y  2.90252830    RCP4.5MEDIAN
224 1.216470664        y  2.75093766    RCP4.5MEDIAN
225 1.221914148        y -0.73815245    RCP4.5MEDIAN
226 1.227580480        y  3.58554626    RCP4.5MEDIAN
227 1.233317788        y 10.89961658    RCP4.5MEDIAN
228 1.238093406        y  3.23374387    RCP4.5MEDIAN
229 0.466622908        y -1.92366466    RCP8.5MEDIAN
230 0.474211509        y  4.09292949    RCP8.5MEDIAN
231 0.480383051        y -0.84736312    RCP8.5MEDIAN
232 0.486304903        y -0.80597889    RCP8.5MEDIAN
233 0.492151615        y -0.50244413    RCP8.5MEDIAN
234 0.499312643        y  3.07785701    RCP8.5MEDIAN
235 0.508859905        y -6.15175322    RCP8.5MEDIAN
236 0.518758845        y -0.51590144    RCP8.5MEDIAN
237 0.528675758        y  3.33135956    RCP8.5MEDIAN
238 0.538928423        y  2.62280891    RCP8.5MEDIAN
239 0.549621221        y -6.90096009    RCP8.5MEDIAN
240 0.560062840        y -3.45706029    RCP8.5MEDIAN
241 0.570860791        y  1.36192518    RCP8.5MEDIAN
242 0.581923368        y  0.34822359    RCP8.5MEDIAN
243 0.592628298        y  3.06882935    RCP8.5MEDIAN
244 0.604230648        y -3.56142825    RCP8.5MEDIAN
245 0.615975167        y 10.35932554    RCP8.5MEDIAN
246 0.627448279        y 10.21751629    RCP8.5MEDIAN
247 0.639401050        y  3.31040335    RCP8.5MEDIAN
248 0.651949591        y -0.53558775    RCP8.5MEDIAN
249 0.664634427        y  2.66081860    RCP8.5MEDIAN
250 0.677343552        y  3.21379656    RCP8.5MEDIAN

Maybe something like this?

lusher<-ggplot(NewestdataULTRA) + geom_jitter(aes(x,value,onepctCO2MEDIAN=L1), colour="green") + geom_smooth(aes(x, value, onepctCO2MEDIAN=L1), method=lm) + geom_jitter(aes(x, value, RCP8.5MEDIAN=L1), colour="red")

I receive this warning, however:
Warning: Ignoring unknown aesthetics: onepctCO2MEDIAN
Warning: Ignoring unknown aesthetics: onepctCO2MEDIAN
Perhaps I am not assigning the columns properly? Essentially, I just want create two scatter plots and two regression lines for these two objects. 
Once again, any assistance would be greatly appreciated!
-----Original Message-----
From: Rui Barradas <ruipbarradas using sapo.pt>
To: rain1290 <rain1290 using aim.com>; r-help <r-help using R-project.org>; r-sig-geo <r-sig-geo using r-project.org>
Sent: Wed, Jun 5, 2019 10:52 am
Subject: Re: [R] Plotting more than one regression line in ggplot

Hello,

This is pretty basic ggplot.


lm1 <- ggplot(onepctCO2MEDIAN, aes(x, y)) +
  geom_point(colour = 'blue') +
  geom_smooth(method = 'lm')

lm1


If you want to combine several datasets, you will have to have a 
variable telling which dataset is which. In the example below, this is 
column 'id'.


onepctCO2MEDIAN2 <- onepctCO2MEDIAN
onepctCO2MEDIAN2$y <- jitter(onepctCO2MEDIAN2$y) + 2
onepctCO2MEDIAN$id <- 1
onepctCO2MEDIAN2$id <- 2
df2 <- rbind(onepctCO2MEDIAN, onepctCO2MEDIAN2)

ggplot(df2, aes(x, y, group = id, colour = factor(id))) +
  geom_point() +
  geom_smooth(method = 'lm')


Hope this helps,

Rui Barradas

Às 15:21 de 05/06/19, rain1290--- via R-help escreveu:
> I am trying to plot, using ggplot, a series of scatter plots with regression lines for several datasets. I started with the following dataset, "onepectCO2MEDIAN". The data for this dataset is as follows:
>      onepctCO2MEDIAN
>                      x          y
>      layer.1   0.000000000  0.0000000
>      layer.2   0.006794447  4.9002490
>      layer.3   0.014288058  0.1608000
>      layer.4   0.022087920  6.6349133
>      layer.5   0.030797357 -1.2429506
>      layer.6   0.038451072  1.5643374
>      layer.7   0.048087904 -2.2659035
>      layer.8   0.058677729  2.2070045
>      layer.9   0.069261406 -2.3677001
>      layer.10  0.080524530 -1.0913506
>      layer.11  0.092760246  0.4099940
>      layer.12  0.103789609 -0.1259727
>      layer.13  0.116953168 -2.4138253
>      layer.14  0.129253298  7.0890257
>      layer.15  0.141710050 -0.7593539
>      layer.16  0.156002052  0.0454416
>      layer.17  0.170648172 -1.5349683
>      layer.18  0.185318425  6.5524201
>      layer.19  0.199463055 -0.8312563
>      layer.20  0.213513337 -2.5099183
>      layer.21  0.228839271  0.1365968
>      layer.22  0.246981293 -1.3719845
>      layer.23  0.263012767 -0.8712988
>      layer.24  0.278505564  0.6632584
>      layer.25  0.293658361  0.7938036
>      layer.26  0.310747266  3.4880637
>      layer.27  0.325990349 -4.4612208
>      layer.28  0.342517540  0.0871734
>      layer.29  0.362751633 -1.4171578
>      layer.30  0.380199537 -0.9956508
>      layer.31  0.394992948  0.3215526
>      layer.32  0.414373398  3.1403866
>      layer.33  0.430690214 -0.7376099
>      layer.34  0.449738145 -2.4860541
>      layer.35  0.470167458 -3.4235858
>      layer.36  0.489019871  0.4824748
>      layer.37  0.507242471 -0.9785386
>      layer.38  0.524314284  8.5359684
>      layer.39  0.543750525  5.4844742
>      layer.40  0.564234197  3.2149367
>      layer.41  0.583679616  3.9168916
>      layer.42  0.601459444  4.4907020
>      layer.43  0.619924664  6.5410410
>      layer.44  0.639932007  4.8068650
>      layer.45  0.661347181  8.1510170
>      layer.46  0.684117317  0.2697413
>      layer.47  0.704829752 -0.1807501
>      layer.48  0.725045770  9.7181249
>      layer.49  0.745165825  1.5406466
>      layer.50  0.765016139 -1.6476041
>      layer.51  0.783461511  4.8024603
>      layer.52  0.806382924  4.0421516
>      layer.53  0.829241335  9.3756512
>      layer.54  0.849924415  5.3305050
>      layer.55  0.871352434  7.5445803
>      layer.56  0.893632233  6.4679547
>      layer.57  0.916052133  2.8096065
>      layer.58  0.938579470  5.3921661
>      layer.59  0.959907651  7.2043689
>      layer.60  0.981643587  3.3350806
>      layer.61  1.004116774  8.8690707
>      layer.62  1.028363466  1.7861299
>      layer.63  1.054009140  6.2555038
>      layer.64  1.072440803  7.6079236
>      layer.65  1.094457805  7.6871483
>      layer.66  1.123176277  4.7787764
>      layer.67  1.149430871 12.7110502
>      layer.68  1.170912921 -0.7156284
>      layer.69  1.196743071  1.6490899
>      layer.70  1.218625903  3.0363024
>      layer.71  1.241868377  4.2974769
>      layer.72  1.267941594  1.9543778
>      layer.73  1.290708780  3.9986964
>      layer.74  1.313222289  4.5179472
>      layer.75  1.339045882  0.9337905
>      layer.76  1.362803459  3.3050770
>      layer.77  1.384450197  3.5422970
>      layer.78  1.409720302  5.9973660
>      layer.79  1.435851157  0.5081869
>      layer.80  1.455592215  7.9661630
>      layer.81  1.479495347  9.9460496
>      layer.82  1.506051958  3.7908372
>      layer.83  1.525728464  2.5735847
>      layer.84  1.549362063 10.1404974
>      layer.85  1.573440671 13.7408304
>      layer.86  1.600278735  0.9335771
>      layer.87  1.623879492  9.7588742
>      layer.88  1.650029302  1.2769395
>      layer.89  1.672362328 13.4970906
>      layer.90  1.700221121 10.2087502
>      layer.91  1.724793375  1.6811275
>      layer.92  1.751070559  6.1178992
>      layer.93  1.778022110 -0.1567626
>      layer.94  1.803022087  3.8237479
>      layer.95  1.830668867  4.4331468
>      layer.96  1.855736911  5.9790707
>      layer.97  1.882615030 11.3104333
>      layer.98  1.909218490  8.2142607
>      layer.99  1.938130021 15.3209674
>      layer.100 1.963727593  5.8178217
>      layer.101 1.993271947  9.6004907
>      layer.102 2.022548139  3.4063646
>      layer.103 2.050679922  4.7375010
>      layer.104 2.078064442  3.0133019
>      layer.105 2.104113460  5.5659522
>      layer.106 2.133597612 12.0346333
>      layer.107 2.164026260 -0.4028320
>      layer.108 2.194852829 10.5996780
>      layer.109 2.224257946  5.4479584
>      layer.110 2.252194643  4.7052374
>      layer.111 2.277335048 14.0962019
>      layer.112 2.304058313  5.7149016
>      layer.113 2.330930233  3.7780072
>      layer.114 2.357022762  4.4120620
>      layer.115 2.386489272  4.1866085
>      layer.116 2.417503953  6.9078802
>      layer.117 2.448524356  2.7825739
>      layer.118 2.478698969  7.6171786
>      layer.119 2.510175705 10.2410603
>      layer.120 2.539697886  8.1820711
>      layer.121 2.567915559  4.8275494
>      layer.122 2.597463250 19.1624883
>      layer.123 2.627518773 16.0677109
>      layer.124 2.658759236 12.5897081
>      layer.125 2.692401528  9.2907988
>      layer.126 2.721903205  7.4262502
>      layer.127 2.753021359  9.3902518
>      layer.128 2.786313415 12.6193550
>      layer.129 2.819564104 11.1121040
>      layer.130 2.850823164 15.7907100
>      layer.131 2.880394101 10.7425287
>      layer.132 2.911391258  7.7971430
>      layer.133 2.942965150  8.8060858
>      layer.134 2.974468350 17.5606266
>      layer.135 3.008983612 17.3088605
>      layer.136 3.040015221 13.4500543
>      layer.137 3.072668672 14.6377884
>      layer.138 3.105982423  8.0798552dput(onepctCO2MEDIAN)    dput(onepctCO2MEDIAN)
>      structure(list(x = c(0, 0.00679444684647024, 0.014288058038801,
>      0.0220879195258021, 0.0307973567396402,0.0384510718286037,0.0480879042297602,
>      0.0586777292191982, 0.0692614056169987, 0.080524530261755,0.0927602462470531,
>      0.103789608925581, 0.116953168064356, 0.129253298044205, 0.141710050404072,
>      0.156002052128315, 0.170648172497749, 0.185318425297737, 0.199463054537773,
>      0.21351333707571, 0.22883927077055, 0.246981292963028, 0.263012766838074,
>      0.278505563735962, 0.29365836083889, 0.310747265815735, 0.325990349054337,
>      0.342517539858818, 0.362751632928848, 0.380199536681175, 0.39499294757843,
>      0.414373397827148, 0.430690214037895, 0.449738144874573, 0.470167458057404,
>      0.489019870758057, 0.507242470979691, 0.524314284324646, 0.543750524520874,
>      0.56423419713974, 0.583679616451263, 0.601459443569183, 0.619924664497375,
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>      0.725045770406723, 0.745165824890137, 0.765016138553619, 0.783461511135101,
>      0.806382924318314, 0.829241335391998, 0.84992441534996, 0.871352434158325,
>      0.893632233142853, 0.916052132844925, 0.938579469919205, 0.959907650947571,
>      0.981643587350845, 1.00411677360535, 1.02836346626282, 1.05400913953781,
>      1.07244080305099, 1.09445780515671, 1.12317627668381, 1.14943087100983,
>      1.17091292142868, 1.19674307107925, 1.21862590312958, 1.24186837673187,
>      1.26794159412384, 1.2907087802887, 1.31322228908539, 1.33904588222504,
>      1.36280345916748, 1.38445019721985, 1.40972030162811, 1.43585115671158,
>      1.45559221506119, 1.47949534654617, 1.50605195760727, 1.52572846412659,
>      1.5493620634079, 1.5734406709671, 1.60027873516083, 1.62387949228287,
>      1.65002930164337, 1.67236232757568, 1.70022112131119, 1.72479337453842,
>      1.75107055902481, 1.77802211046219, 1.80302208662033, 1.83066886663437,
>      1.85573691129684, 1.88261502981186, 1.90921849012375, 1.93813002109528,
>      1.96372759342194, 1.99327194690704, 2.02254813909531, 2.05067992210388,
>      2.07806444168091, 2.1041134595871, 2.13359761238098, 2.16402626037598,
>      2.19485282897949, 2.2242579460144, 2.25219464302063, 2.27733504772186,
>      2.30405831336975, 2.33093023300171, 2.35702276229858, 2.38648927211761,
>      2.41750395298004, 2.44852435588837, 2.47869896888733, 2.51017570495605,
>      2.53969788551331, 2.567915558815, 2.59746325016022, 2.62751877307892,
>      2.65875923633575, 2.69240152835846, 2.72190320491791, 2.75302135944366,
>      2.78631341457367, 2.8195641040802, 2.85082316398621, 2.88039410114288,
>      2.91139125823975, 2.94296514987946, 2.97446835041046, 3.00898361206055,
>      3.04001522064209, 3.07266867160797, 3.10598242282867), y = c(0,
>      4.90024901723162, 0.160799993152722, 6.63491326258641, -1.24295055804536,
>      1.56433744259162, -2.26590352245208, 2.20700446463354, -2.36770012911069,
>      -1.09135061899174, 0.409993989292701, -0.125972681525582, -2.41382533818026,
>      7.08902570153028, -0.759353880417294, 0.0454415959640926, -1.53496826259972,
>      6.55242014096194, -0.831256280861552, -2.50991825629084, 0.136596820654013,
>      -1.37198445498419, -0.871298832596736, 0.663258363762466, 0.793803634291308,
>      3.48806373666998, -4.46122081238949, 0.0871733966938564, -1.41715777257774,
>      -0.995650815648318, 0.32155262317503, 3.14038657369241, -0.737609879885404,
>      -2.48605406511292, -3.423585843908, 0.482474753780281, -0.978538630093809,
>      8.53596837794201, 5.48447420320695, 3.21493665820644, 3.91689160157513,
>      4.49070195980797, 6.54104103157039, 4.80686500146557, 8.15101701282067,
>      0.26974132191657, -0.180750068063062, 9.71812491230244, 1.54064657400204,
>      -1.64760408795688, 4.80246028991894, 4.04215159914344, 9.37565121768513,
>      5.33050496938428, 7.54458026088508, 6.46795470819342, 2.80960651433971,
>      5.39216613235986, 7.20436888038562, 3.3350806460997, 8.86907069895943,
>      1.78612988613659, 6.25550382050395, 7.60792364896564, 7.68714830528144,
>      4.77877638957615, 12.7110501777314, -0.715628443181046, 1.64908991824022,
>      3.03630240714679, 4.29747688442346, 1.95437780501881, 3.99869636910933,
>      4.51794724689848, 0.933790484492299, 3.30507700050003, 3.5422970157433,
>      5.99736597322524, 0.508186860060022, 7.96616300581067, 9.94604963036295,
>      3.79083717222623, 2.57358468532258, 10.1404974171776, 13.7408303595752,
>      0.933577123801399, 9.75887417074129, 1.27693947132921, 13.4970905965787,
>      10.2087501765735, 1.68112753028756, 6.1178991508927, -0.156762622680077,
>      3.82374791691426, 4.43314678736265, 5.97907067167507, 11.3104332518482,
>      8.21426074201525, 15.320967360602, 5.81782169471483, 9.6004907412354,
>      3.40636455909704, 4.73750103921864, 3.0133019468806, 5.56595224859066,
>      12.0346332527215, -0.40283199827104, 10.5996779538754, 5.44795836991128,
>      4.70523736412729, 14.096201892183, 5.71490161813391, 3.77800720810782,
>      4.41206200639436, 4.18660847858423, 6.90788020044911, 2.78257393345915,
>      7.61717857379431, 10.2410602647684, 8.18207106836167, 4.82754943871433,
>      19.1624882857155, 16.0677109398509, 12.589708067017, 9.29079879799404,
>      7.42625019725314, 9.39025179806185, 12.6193550331438, 11.1121039747257,
>      15.7907099734986, 10.7425286789233, 7.79714300307344, 8.80608578166101,
>      17.5606266346039, 17.3088604929222, 13.4500543478523, 14.6377884248645,
>      8.07985518296064)), class = "data.frame", row.names = c("layer.1",
>      "layer.2", "layer.3", "layer.4", "layer.5", "layer.6", "layer.7",
>      "layer.8", "layer.9", "layer.10", "layer.11", "layer.12", "layer.13",
>      "layer.14", "layer.15", "layer.16", "layer.17", "layer.18", "layer.19",
>      "layer.20", "layer.21", "layer.22", "layer.23", "layer.24", "layer.25",
>      "layer.26", "layer.27", "layer.28", "layer.29", "layer.30", "layer.31",
>      "layer.32", "layer.33", "layer.34", "layer.35", "layer.36", "layer.37",
>      "layer.38", "layer.39", "layer.40", "layer.41", "layer.42", "layer.43",
>      "layer.44", "layer.45", "layer.46", "layer.47", "layer.48", "layer.49",
>      "layer.50", "layer.51", "layer.52", "layer.53", "layer.54", "layer.55",
>      "layer.56", "layer.57", "layer.58", "layer.59", "layer.60", "layer.61",
>      "layer.62", "layer.63", "layer.64", "layer.65", "layer.66", "layer.67",
>      "layer.68", "layer.69", "layer.70", "layer.71", "layer.72", "layer.73",
>      "layer.74", "layer.75", "layer.76", "layer.77", "layer.78", "layer.79",
>      "layer.80", "layer.81", "layer.82", "layer.83", "layer.84", "layer.85",
>      "layer.86", "layer.87", "layer.88", "layer.89", "layer.90", "layer.91",
>      "layer.92", "layer.93", "layer.94", "layer.95", "layer.96", "layer.97",
>      "layer.98", "layer.99", "layer.100", "layer.101", "layer.102",
>      "layer.103", "layer.104", "layer.105", "layer.106", "layer.107",
>      "layer.108", "layer.109", "layer.110", "layer.111", "layer.112",
>      "layer.113", "layer.114", "layer.115", "layer.116", "layer.117",
>      "layer.118", "layer.119", "layer.120", "layer.121", "layer.122",
>      "layer.123", "layer.124", "layer.125", "layer.126", "layer.127",
>      "layer.128", "layer.129", "layer.130", "layer.131", "layer.132",
>      "layer.133", "layer.134", "layer.135", "layer.136", "layer.137",
>      "layer.138"))
> I started with the following to generate the first regression line and scatter plot:    lm<-ggplot(onepctCO2MEDIAN) +
>      geom_jitter(aes(RCP1pctCO2cumulativeMedian[1:138], departurea),
>      colour="blue") + geom_smooth(aes(RCP1pctCO2cumulativeMedian[1:138],
>      departurea), method=lm)
> But I receive this error:    Warning message:
>      Computation failed in `stat_smooth()`:
>      'what' must be a function or character string
> A blue scatter plot is successfully generated, but the problem is that the regression line does not appear, presumably related to the above warning.
> Is there a reason for this? I would appreciate any assistance!
>     [[alternative HTML version deleted]]
> 
> ______________________________________________
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> 

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