[R] Problems trying to place a global map with Ncdf data plot
Hello there,
I am trying to overlay a global map with ncdf data of precipitation for a
particular location (using specific coordinates). The file is in ncdf format
(commonly used to store away climate data), and I am currently attempting to
place a global map on plotted precipitation values. However, I am having
difficulty placing a global map on this plot and am encountering errors. I
will show you what I have done:
#To create a plot of precipitation data using the following ncdf file - the
following works fine and provides the distributions global precipitation
values (Land+Water values):
library(ncdf4)
Can<-"MaxPrecCCCMACanESM2rcp45.nc"
>Model<-nc_open(Can)
>print(Model)
>attributes(Model$var)
>$names
>dat<-ncvar_get(Model, "onedaymax")
>dat[128,50,1] #View onedaymax for selected latitude, longitude and Year
>nc_lat<-ncvar_get(Model,attributes(Model$dim)$names[2]) #Retrieve latitude
>nc_lon<-ncvar_get(Model,attributes(Model$dim)$names[3]) #Retrieve longitude
>print(paste(dim(nc_lat), "latitudes and", dim(nc_lon), "longitudes"))
>library(maptools)
>map<-dat[,,5] #Precipitation for all longitudes, latitudes, and Year 5
>grid<-expand.grid(nc_lon=nc_lon, nc_lat=nc_lat)
>image(nc_lon,nc_lat,map, ylab="Latitude", xlab="Longitude", main="One-day
maximum precipitation")
>levelplot(map~nc_lon*nc_lat, data=grid, at=cutpoints, cuts=11,
ylab="Latitude", xlab="Longitude", >main="Year 5 one-day maximum
precipitation (mm/day) for CanESM2 under RCP4.5", pretty=T,
col.regions=(rev(brewer.pal(10, "Spectral"
#To place a global map on the map that map that returns using the above
code. *This is where errors begin:
>ggplot()+geom_point(aes(x=nc_lon,y=nc_lat,color="onedaymax"),
size=0.8)+borders("world",
colour="black")+scale_color_viridis(name="onedaymax")+theme_void()+coord_quickmap()
*Error: Aesthetics must be either length 1 or the same as the data (128): x,
y, colour*
Why doesn't this work? Could it be that I am not including the "time"
dimension in the ggplot function? The problem, though, is when I try to
obtain the "time" dimension, like I did for latitude and longitude, I
receive the following error:
t<-ncvar_get(Model,"time")
*Error in nc$dim[[idobj$list_index]] :
attempt to select more than one element*
If it helps, this is what the variables and dimensions look like in the ncdf
file:
/File MaxPrecCCCMACanESM2rcp45.nc (NC_FORMAT_NETCDF4):
3 variables (excluding dimension variables):
double onedaymax[lon,lat,time] (Contiguous storage)
units: mm/day
double fivedaymax[lon,lat,time] (Contiguous storage)
units: mm/day
short Year[time] (Contiguous storage)
3 dimensions:
time Size:95
lat Size:64
units: degree North
lon Size:128
units: degree East/
Any help would be greatly appreciated
Thanks,
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[R] Problems trying to make a time series using NetCDF file
Hi there, I am using climate model data in attempt to create a time series for a specific location. Getting longitude and latitude is fine. However, I am receiving the following error when using the "ncvar_get" function in trying to derive the "time" dimension: Error in nc$dim[[idobj$list_index]] : attempt to select more than one element in get1index What could be causing this? -This is what I have done so far: #CanESM2 plotting for specified yearncfname<-"MaxPrecCCCMACanESM2rcp45.nc" Prec<-raster(ncfname) print(Prec) Model<-nc_open(ncfname)longitude<-ncvar_get(Model, "lon") #Works fine latitude<-ncvar_get(Model, "lat") #Works fine time<-ncvar_get(Model, "time") #Error in nc$dim[[idobj$list_index]] : attempt to select more than one element in get1index Here is the structure of the file after using "nc_open": File MaxPrecCCCMACanESM2rcp45.nc (NC_FORMAT_NETCDF4): 3 variables (excluding dimension variables): double onedaymax[lon,lat,time] (Contiguous storage) units: mm/day double fivedaymax[lon,lat,time] (Contiguous storage) units: mm/day short Year[time] (Contiguous storage) 3 dimensions: time Size:95 lat Size:64 units: degree North lon Size:128 units: degree East What could be causing this error? Thanks in advance, and I look forward to your response! [[alternative HTML version deleted]] __ R-help@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.
[R] Weighted spatial averages across grid cells in NetCDF file
Hi there, I am currently working on a project that involves climate model data stored in a NetCDF file. I am currently trying to calculate "weighted" spatial annual "global" averages for precipitation. I need to do this for each of the 95 years of global precipitation data that I have. The idea would be to somehow apply weights to each grid cell by using the cosine of its latitude (which means latitude grid cells at the equator would have a weight of 1 (i.e. the cosine of 0 degrees is 1), and the poles would have a value of 1 (as the cosine of 90 is 1)). Then, I would be in a position to calculate annual weighted averages based on averaging each grid cell. I have an idea how to do this conceptually, but I am not sure where to begin writing a script in R to apply the weights across all grid cells and then average these for each of the 95 years. I would greatly appreciate any help with this, or any resources that may be helpful!!! At the very least, I have opened the .nc file and read-in the NetCDF variables, as shown below: ncfname<-"MaxPrecCCCMACanESM2rcp45.nc" Prec<-raster(ncfname) print(Prec) Model<-nc_open(ncfname) get<-ncvar_get(Model,"onedaymax")longitude<-ncvar_get(Model, "lon") latitude<-ncvar_get(Model, "lat") Year<-ncvar_get(Model, "Year") Also, if it helps, here is what the .nc file contains: 3 variables (excluding dimension variables): double onedaymax[lon,lat,time] (Contiguous storage) units: mm/day double fivedaymax[lon,lat,time] (Contiguous storage) units: mm/day short Year[time] (Contiguous storage) 3 dimensions: time Size:95 lat Size:64 units: degree North lon Size:128 units: degree East Again, any assistance would be extremely valuable with this! I look forward to your response! [[alternative HTML version deleted]] __ R-help@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.
[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!
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Re: [R] Creating a mean line plot
2, 14.7103695664555,
19.504395313561, 22.4196153692901, 22.2453631460667, 8.23867111466825,
8.1761412084, 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
To: rain1290
Cc: r-sig-geo ; R mailing list
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
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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Re: [R] Creating a mean line plot
Hi Bill, For the x-axis variable, in this case, indeed, I used rowMeans(cbind(get2.teratons, get5.teratons)). This averaged each value between these two 1-dimensional variables (i.e. value#1 of "get2.teratons" was averaged with value#1 of "get5.teratons" - this was done for all 90 values). To obtain the means for the values of the y-axis variables, which are 3-dimensional, I simply took each variable and divided by 2, in this case. Thus: (variableA+variableB)/2. This took the mean of the variable for each grid cell for each layer (90 layers). So, for grid cell #1, doing this averaged all 90 values corresponding to the 90 layers between the two variables. For example, the values of layer 1 of variableA and layer 1 of variableB were averaged (and then layer 2 with layer 2, and then layer 3 with layer 3.all the way to layer 90 with layer 90. This method simultaneously did this for all 8192 grid cells (128 lines of longitude and 64 lines of latitude). At the end, I obtained 90 averages for each grid cell. :) ~Trav.~ -Original Message- From: William Michels To: rain1290 Cc: r-help ; r-sig-geo Sent: Sun, Apr 14, 2019 4:46 am Subject: Re: [R] Creating a mean line plot So you're saying rowMeans(cbind(matrix_a, matrix_b)) worked to obtain your X-axis values? 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 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": > >dput(head(subset,5)) > structure(c(11.5447145886719, 11.2479725852609, 10.0223480723798, > 11.4909216295928, 12.5930442474
[R] Creating a median curve among multiple curvesin ggplot
Hi there,
Using ggplot, I have successfully created a plot that contains 37 separate line
plots that each represent a trend in precipitation over 140 years. However,
what I would like to do is show the curve that is the "median" of these lines.
Is this possible to do?
Here is what I have so far in an attempt to do this, but I am not certain if it
is the correct approach to create the desired median line - the first variable
shows x-variable objects (each 1-dimensional) and the second variable shows the
y-variable objects (3-dimensional):
RCP1pctCO2Median <- median(cbind(get, IPSL, IPSLMR, IPSL5, MIROC, HadGEM, MPI,
MPI5, GFDL, GFDL5))
RCP1pctCO2ModelMedian <- median(cbind(Model2, Model10, Model18, Model26,
subset14, Model42, subset20, subset24, Model60, Model68))
The first variable appears to work, but it only returns one value, and I want
all of the 140 values corresponding to whichever curve was returned. Why is it
that only one value returns? Here is what the variable contains:
>RCP1pctCO2Median[1] 1.189044
When I run the second variable the same way, I receive the following error:
Error in sort.int(x, na.last = na.last, decreasing = decreasing, ...) :
'x' must be atomic
Why does this appear? I tried "unlist" and "as.vector", but the same error
appears.
If it helps, to show what the 1-dimensional objects of the first variable look
like, here is an example of one (the object "get"):
>get
[1] 0. 0.00644777 0.01250601 0.01746194 0.02656210 0.03360072
0.04399755 0.05404601 0.06154399 0.07105423
[11] 0.08315538 0.08925389 0.10060635 0.10833009 0.12196157 0.13730928
0.14890850 0.16087072 0.17361501 0.18310140
[21] 0.19810085 0.21316248 0.22423607 0.23529990 0.24695577 0.26119599
0.27643532 0.28517213 0.29978970 0.31870887
[31] 0.33418021 0.34585044 0.36071229 0.37379062 0.39324498 0.4102
0.42437476 0.43643925 0.44687960 0.45864752
[41] 0.48055932 0.49634662 0.50779009 0.52861303 0.54971600 0.56138206
0.57472491 0.59202033 0.60975420 0.63057494
[51] 0.64653736 0.66088217 0.68168259 0.69649595 0.71363837 0.72988760
0.75010443 0.76776582 0.78613955 0.81099886
[61] 0.82979667 0.84431553 0.86824787 0.88447672 0.89909577 0.92162514
0.94854647 0.96631038 0.98120815 1.00357985
[71] 1.02769291 1.04733002 1.06585240 1.09083056 1.11592567 1.13627303
1.15866983 1.17941856 1.19748724 1.22012913
[81] 1.24142718 1.26937973 1.29283488 1.31011736 1.33134723 1.36014366
1.38395822 1.40615380 1.42895305 1.45046842
[91] 1.47504234 1.49920797 1.52142668 1.54559219 1.57354927 1.6896
1.62449229 1.64506662 1.67100036 1.70185161
[101] 1.72964287 1.75235510 1.77455521 1.80183053 1.83121216 1.85619974
1.88188243 1.91002214 1.9006 1.96133912
[111] 1.98944890 2.01225924 2.03804183 2.06614304 2.09558916 2.12400699
2.14846969 2.18011498 2.20532560 2.23031592
[121] 2.26106715 2.28796983 2.31954741 2.35274339 2.37794161 2.40643740
2.43963051 2.46948838 2.49733877 2.53035021
[131] 2.56229091 2.59265327 2.61934137 2.64592481 2.67754769 2.70779777
2.73824191 2.76880598 2.80077982 2.83057237
For the 3-D objects within the second variable, here is what is contained in
the object "Model2", for example:
>Model2 <- brick("MaxPrecCCCMACanESM21pctCO2.nc", var = "onedaymax")
>Model2class : RasterBrick
dimensions : 64, 128, 8192, 140 (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
data source : C:/Users/Travis/Documents/Other documents/All netCDF
files/netcdffiles/MaxPrecCCCMACanESM21pctCO2.nc
names : X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15,
...
z-value : 1, 140 (min, max)
varname : onedaymax
In any case, I am not even certain if what I have done above is the correct
approach altogether to create a median line, but I would greatly appreciate any
assistance! Your feedback would be extremely valuable to me!
Thank you, and I very much look forward to hearing from you!
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and provide commented, minimal, self-contained, reproducible code.
[R] Plotting more than one regression line in ggplot
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 0.0 0.000 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 1.31389 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
Re: [R] Plotting more than one regression line in ggplot
Hi Jeff (and everyone), Thank you for your response and feedback. Yes, I know what you mean - it was a blind and quick choice to use "lm" as my object name. Unfortunately, changing the object name to something else does not eliminate that error/warning message. As a result, the same error/warning appears when running it. Oddly enough, the scatter plot is just fine - it's the regression line that struggles to appear. Could there be another reason for that? Thanks, again, -Original Message- From: Jeff Newmiller To: rain1290 ; rain1290--- via R-help ; r-help ; r-sig-geo Sent: Wed, Jun 5, 2019 10:49 am Subject: Re: [R] Plotting more than one regression line in ggplot Please read the Posting Guide... posting HTML on a plain text mailing list really interferes with clear communication. If you had spent even a small amount of time working with R tutorials then you would know that "lm" is the name of a very basic, very important R function. However, you are defining your own object called "lm" that is very different indeed than the usual "lm" function. I would guess that in a clean new R workspace where you had not already run your ggplot function and assigned the result to your own "lm" object then this code might run. However, once you have run it once and try to run it again, your "method" argument gives the wrong version of "lm" to geom_smooth and you confuse it. As the doctor said to the man pounding his own head against the wall, "If it hurts, don't do that." Avoid re-using important object names in R... some common names I see abused this way are df, data, c, t, T, and F. Your choice was unusual, but quite effective at illustrating the problem. On June 5, 2019 7:21:57 AM PDT, rain1290--- via R-help wrote: >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 0.0 0.000 > 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
Re: [R] Plotting more than one regression line in ggplot
Hi David (and everyone), Thank you for your response. I changed the column names to x and y, but the error/warning persists: Warning message: Computation failed in `stat_smooth()`: 'what' must be a function or character string It is quite baffling as to why this is happening. Why would it work for the scatter plot and not the regression line? -Original Message- From: David Winsemius To: r-help Sent: Wed, Jun 5, 2019 12:00 pm Subject: Re: [R] Plotting more than one regression line in ggplot On 6/5/19 8:04 AM, rain1290--- via R-help wrote: > Hi Jeff (and everyone), > > Thank you for your response and feedback. Yes, I know what you mean - it was > a blind and quick choice to use "lm" as my object name. Unfortunately, > changing the object name to something else does not eliminate that > error/warning message. As a result, the same error/warning appears when > running it. Oddly enough, the scatter plot is just fine - it's the regression > line that struggles to appear. Could there be another reason for that? > Thanks, again, TRhe error came because you did not reference the column names correctly. This succeeds with the data you offered: ggplot(onepctCO2MEDIAN) + geom_jitter(aes(x,y), colour="blue") + geom_smooth(aes(x,y), method=lm) # At some point you changed the column names from (RCP1pctCO2cumulativeMedian, departurea) to (x,y) , but didn't adjust your code. Best; David. > > -----Original Message- > From: Jeff Newmiller > To: rain1290 ; rain1290--- via R-help > ; r-help ; r-sig-geo > > Sent: Wed, Jun 5, 2019 10:49 am > Subject: Re: [R] Plotting more than one regression line in ggplot > > Please read the Posting Guide... posting HTML on a plain text mailing list > really interferes with clear communication. > > If you had spent even a small amount of time working with R tutorials then > you would know that "lm" is the name of a very basic, very important R > function. However, you are defining your own object called "lm" that is very > different indeed than the usual "lm" function. I would guess that in a clean > new R workspace where you had not already run your ggplot function and > assigned the result to your own "lm" object then this code might run. > However, once you have run it once and try to run it again, your "method" > argument gives the wrong version of "lm" to geom_smooth and you confuse it. > > As the doctor said to the man pounding his own head against the wall, "If it > hurts, don't do that." Avoid re-using important object names in R... some > common names I see abused this way are df, data, c, t, T, and F. Your choice > was unusual, but quite effective at illustrating the problem. > > On June 5, 2019 7:21:57 AM PDT, rain1290--- via R-help > wrote: >> 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 0.0 0.000 >> 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 >> l
Re: [R] Plotting more than one regression line in ggplot
O2MEDIAN, 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 0.0 0.000 > 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 1.31389 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 3.0
Re: [R] Plotting more than one regression line in ggplot
ctCO2MEDIAN 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 > To: rain1290 ; r-help ; > r-sig-geo > 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 0.0 0.000 > > 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
Re: [R] Plotting more than one regression line in ggplot
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 mailto:ruipbarra...@sapo.pt>> > > To: rain1290 mailto:rain1...@aim.com>>; r-help > mailto:r-help@R-project.org>>; > > r-sig-geo mailto:r-sig-...@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 0.0 0.000 > > > 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 > > >
Re: [R] Plotting more than one regression line in ggplot
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 <mailto:ruipbarra...@sapo.pt> <mailto:ruipbarra...@sapo.pt > <mailto:ruipbarra...@sapo.pt>>> > > > To: rain1290 mailto:rain1...@aim.com> > <mailto:rain1...@aim.com <mailto:rain1...@aim.com>>>; r-help > > mailto:r-help@R-project.org> > <mailto:r-help@R-project.org <mailto:r-help@R-project.org>>>; > > > r-sig-geo <mailto:r-sig-...@r-project.org> <mailto:r-sig-...@r-project.org > <mailto:r-sig-...@r-project.org>>> > > > Sent: Wed, Jun 5, 2019 10:52 am > > > Subject: Re: [R] Plotting more than one regression line in ggplot > > >
Re: [R] Plotting more than one regression line in ggplot
6722666 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 RCP8.5MED
