I'm trying to plot 2 series, a training set and a testing set with plots:
xp = append!(XTrain.humidity, data.humidity)
yp = append!(YTrain, predictions)
println(length(xp))
println(length(yp))
Plots.plot(x=xp, y=yp,seriestype=:scatter)
this outputs
382
382
which is the expected result, with both xp and yp being a vector of floats
xp = [82.00274423, 80.31964408, 82.3207629, 83.37011772, 82.63941394, 82.894086...]
yp = [49.027397, 49.027397, 49.027397, 49.027397 ...]
But the plot in the output looks like this:
You're getting this plot because there's nothing to plot. Plots.plot is supposed to be called like this, with the data to plot being passed as positional arguments:
Plots.plot(xp, yp, seriestype=:scatter)
AFAIK, keyword arguments are for customizing the plot (like setting series type), not for passing data to plot.
Related
I have the following data:
head(spca,3)
Axis 1 Axis 2 Axis 3 Axis 4 Axis 5
P2 0.41785706 -2.215135138 1.1873094 -1.90061886 1.42734543
P92 -3.10662325 2.062759336 2.9738782 0.34503630 -2.4026353
P144 -4.68879566 2.090462774 3.7421616 0.03259024 1.046545319
head(coord,3)
Longitude Latitude
P2 35.13092 32.51011
P92 35.34025 32.55186
P144 35.47386 32.85336
I create colours for the rows using the colorplot command from the adegenet package. The command uses the spca table and translates it into the RGB system.
plot(c(34.6,35.9),c(31.2,33.5),cex=.01, ylab="",xlab="")
colorplot(coord, spca1, axes=1:5, transp=TRUE, add=TRUE,cex=2.5)
But I actually want to plot this on a ggplot where colorplot does not work and I need colour gradients that represent the relationship of the rows as described by the 5 axis. So I wonder how I can create the colours like colorplot myself to use it in ggplot's geom_point?
Any ideas?
Thanks
In lack of dataset for testing, this might still be possible to answer.
In order to find an answer to these types of question initially try ?colorplot or help(colorplot). Look under the 'value section', which describes what the function returns for further use. Here we are told the function will return the colours used during the plotting.
plot(c(34.6,35.9),c(31.2,33.5),cex=.01, ylab="",xlab="")
colours <- colorplot(coord, spca1, axes=1:5, transp=TRUE, add=TRUE,cex=2.5)
ggplot(data = coords)+geom_point(aes(x = <column 1 name>, y = <column 1 name>),
col = colours, size = 2.5)
should give you the same (or a similar) answer (change the coordinates in the aes(...) argument to match your data, and change the size to your liking).
I have a dataframe which I have constructed by interpolating a series of origin destination points (they relate to a cycle share scheme that used to run in Seattle).
I've called the dataframe interpolated_flows:
line_id long lat seg_num count
1 1 -122.3170 47.61855 1 155
2 1 -122.3170 47.61911 2 155
3 1 -122.3170 47.61967 3 155
4 1 -122.3170 47.62023 4 155
5 1 -122.3169 47.62079 5 155
6 1 -122.3169 47.62135 6 155
What I would like to do (and I think is relatively simple if you know ggplot) is to plot these flows (lines) with the width of a line determined by the count and the gradient determined by the seg_num.
This is my attempt so far:
#Create variables to store relevant data for simplicity of code
X <- interpolated_flows$long
Y <- interpolated_flows$lat
sgn <- interpolated_flows$seg_num
ct <- interpolated_flows$count
#Create a map from flow data and include the bounded box as a base
g <- ggplot(interpolated_flows,aes(x=X, y=Y),group=interpolated_flows$line_id,color=sgn)
map <- ggmap(seattle_map,base_layer = g)
map <- map + geom_path(size=as.numeric(ct)/100,alpha=0.4)+
scale_alpha_continuous(range = c(0.03, 0.3))+coord_fixed(ratio=1.3)+
scale_colour_gradient(high="red",low="blue")
png(filename='Seattle_flows_gradient.png')
print(map)
dev.off()
And I end up with the image attached. I have spent a long time playing around with various parameters in the plotting part of the code but without success so would really appreciate if someone could point me in the right direction.
Edit:
base <- ggplot(interpolated_flows,aes(x=X, y=Y))
map <- ggmap(seattle_map,base_layer = g)
map <- map+geom_path(aes(color=seg_num,size=as.numeric(count)))+
scale_size_continuous(name="Journey Count",range=c(0.05,0.4))+
scale_color_gradient(name="Journey Path",high="white",low="blue",breaks=c(1,10), labels=c('Origin','Destination'))+
coord_fixed(ratio=1.3)+scale_x_continuous("", breaks=NULL)+
scale_y_continuous("", breaks=NULL)
png(filename='Seattle_flows_gradient.png')
print(map)
dev.off()
This is the plot I have now got to which looks like this. I have only two questions - 1) does anyone know a way to improve the resolution of the background map? I tried changing the zoom parameter in the get_map function but it didn't seem to help. 2) The lines I have plotted seem very 'white' heavy. It doesn't look to me like the gradient is evenly distributed. Anyone have any ideas why this would be and how to fix?
See if this suits you. I have create a new dataset so as to see diffencies. Once the data.frame is created you can use it as your first ggplot argument and reference columns by their names as Mako212 say.
long<-seq(-122,-123,length.out = 6)
lat<-seq(47,48,length.out = 6)
seg_num<-seq(1,6,1)
count<-seq(155,165,length.out = 6)
interpolated_flows<-data.frame(long,lat,seg_num,count,stringsAsFactors = false)
base_plot<-ggplot(interpolated_flows,aes(x=long, y=lat))
base_plot+
geom_path(aes(color=seg_num,size=as.numeric(count/100),alpha=lat))+
#notice that size, color and alpha are into aethetic
scale_size_continuous(name="Count")+
scale_alpha_continuous(name="Latitude",range = c(0.03, 0.3))+ #you won't need it if you don't want variable transparency
#just put the desired value into the aethteic
scale_color_gradient(name="Seg_num",high="red",low="blue")+
coord_fixed(ratio=1.3)
Hope it helps
I'm trying to visualize a high-dim point set x (here of dim (6 x 42)) in a series of 2D scatter plots (x[1] vs x[2] etc.) using bokeh. [edit2] See this nice example from scikit-opt as a reference. When x[1] occurs in two plots it should interact with the same range and the plots should rescale simultaneously. I have accomplished this, but I don't get it to scale correctly. Here's a minimal example: [edit2]
import bokeh
import bokeh.io
import numpy as np
import bokeh.plotting
bokeh.io.output_notebook()
# That's my fictional dataset
x = np.random.randn(6, 42)
x[2] *= 10
# Build the pairwise scatter plots
kw = dict(plot_width=165, plot_height=165)
# `ranges` stores the range in each dimension,
# used as both, x- and y-range depending on
# where the variable is.
figs, ranges = {}, {}
for r, row in enumerate(x):
for c, col in enumerate(x):
if r is not c:
fig = bokeh.plotting.figure(
x_range=ranges.get(c, None), y_range=ranges.get(r, None),
**kw)
fig.scatter(x=col, y=row)
fig.xaxis.axis_label = f'Dim {c}'
fig.yaxis.axis_label = f'Dim {r}'
if c not in ranges:
ranges[c] = fig.x_range
if r not in ranges:
ranges[r] = fig.y_range
figs[f'{r}_{c}'] = fig
else:
break
# Setup the plotting layout
plots = [[]]
for r, row in enumerate(x):
for c, col in enumerate(x):
if r is not c:
plots[-1].append(figs[f'{r}_{c}'])
else:
plots.append([])
break
staircase = bokeh.layouts.gridplot(plots, **kw)
bokeh.plotting.show(staircase)
.. into an ipython notebook (>=py3.6), bokeh sets the scale for dim 1, and 2 correctly. Then, it starts to set the scale for the following dimensions as in dim 2. Notice that I scaled dim 2 10-fold to make this point.
Interactively, I can rescale the plot back to optimal settings. However, I'd like to do that by default. What options do I have inside bokeh to rescale? I played a bit with fig.xaxis.bounds, but unsuccessfully. Thanks for your help!
Epilogue:
Following #bigreddot's answer, I added the lines:
for i, X in enumerate(x):
ranges[i].start = X.min()
ranges[i].end = X.max()
to fix the starting ranges. I still think that the behaviour is a bug.
From your code and description I still can't quite tell what you are hoping to accomplish. [1] But I will state that the default DataRange1d ranges that plot's use automatically make space for all renderers, across all plots they are shared by. In this sense, I see exactly what I would expect when I run your code. If you want something different, there are two things you could control:
DataRange1d has a .renderers property. If you only want the "auto" ranging to be over a subset of the renderers, then you can explicitly set this property to the list you want. Renderers are returned by the glyph functions, e.g. fig.scatter
Don't use the "auto" ranges. You can also set the x_range and y_range yourself to be Range1d objects. These have start and end properties that you can set, and these will be the definite bounds of the range, e.g. x-range=Range1d(0, 10)
[1] The ranges are linked in what I would consider an odd way, and I can't tell if that is intended. But that is a result of your looping/python code and not Bokeh.
I have these two df.
x;
experiment expression
1 HC 50
2 LC 4
3 HR 10
4 LR 2
y;
HC_conf_lo HC_conf_hi LC_conf_lo LC_conf_hi HR_conf_lo HR_conf_hi LR_conf_lo LR_conf_hi
1 63.3293 109.925 2.33971 5.26642 8.8504 16.7707 0.124013 0.434046
I want to use df:y to plot low and high conf. points. Output should be a barplot with errorbars. Can someone show me using lines in the basic package how to do this?
So don't know if your data is valid. Assuming the confidence intervals are valid.
Here's what you can do to get error bars in your data
#First reading in your data
x<-read.table("x.txt", header=T)
y<=read.table("y.txt", header =T)
#reshaping y to merge it with x
y.wide <-data.frame(matrix(t(y),ncol=2,byrow=T)) #Transpose Y,
#matrix with 2 cols, byrow,
#so we get the lo and hi values in one row
names(y.wide)<-c("lo","hi") #name the columns in y.wide
#Make a data.frame of x and y.wide
xy.df <-data.frame(x,y.wide) # this will be used for plotting the error bars
#make a matrix for using with barplot (barplot takes only matrix or table)
xy<-as.matrix(cbind(expression=x$expression,y.wide))
rownames(xy)<-x$experiment #rownames, so barplot can label the bars
#Get ylimts for barplot
ylimits <-range(range(xy$expression), range(xy$lo), range(xy$hi))
barx <-barplot(xy[,1],ylim=c(0,ylimits[2])) #get the x co-ords of the bars
barplot(xy[,1],ylim=c(0,ylimits[2]),main = "barplot of Expression with ? bars")
# ? as don't know if it's C.I, or what
with(xy.df, arrows(barx,expression,barx,lo,angle=90, code=1,length=0.1))
with(xy.df, arrows(barx,expression,barx,hi,angle=90, code=1,length=0.1))
Resultant Plot
But it doesn't look right, This is because your expression values don't fall between the lo and hi values.
With the hack below,
barplot(xy[,1],ylim=c(0,ylimits[2]),main = "barplot of Expression with ? bars")
with(xy.df, arrows(barx,lo,barx,hi,angle=90, code=2,length=0.1))
with(xy.df, arrows(barx,hi,barx,lo,angle=90, code=2,length=0.1))
The resultant plot
So look at the both arrows call carefully, and you will see how I achieved it.
I would recommend double checking your calculations though.
And this is far easier with ggplot2. Look at this page for examples and code
http://docs.ggplot2.org/0.9.3.1/geom_errorbar.html
I want to plot a wireframe in R using the lattice package. However, I get the following error message "error using packet 1 negative length vectors are not allowed". The data looks like the following:
> result_mean
experiment alpha beta packet
1 0 1.0 1 3.000000
2 0 1.1 1 2.571429
The command to create the data is the following
png(file=paste("foobar.png"),width=1280, height=1280);
plot <- wireframe(result_mean$packet ~ result_mean$alpha * result_mean$beta,
data=result_mean, scales = list(arrows=FALSE, cex= .45, col = "black", font = 3),
drape = TRUE, colorkey = TRUE, main = "Foo",
col.regions = terrain.colors(100),
screen = list(z = -60, x = -60),
xlab="alpha", ylab="beta", zlab="mean \npackets");
print(plot);
dev.off();
I'm wondering what this error message means and if there is a good way to debug this?
Thanks in advance!
Debugging lattice graphics is a bit difficult because (a) the code is complex and multi-layered and (b) the errors get trapped in a way that makes them hard to intercept. However, you can at least get some way in diagnosing the problem.
First create a minimal example. I suspected that your problem was that your data fall on a single line, so I created data that looked like that:
d <- data.frame(x=c(1,1.1),
y=c(1,1),
z=c(2,3))
library(lattice)
wireframe(z~y*x,data=d)
Now confirm that fully three-dimensional data (data that define a plane) work just fine:
d2 <- data.frame(expand.grid(x=c(1,1.1),
y=c(1,1.1)),
z=1:4)
wireframe(z~y*x,data=d2)
So the question is really -- did you intend to draw a wireframe of two points lying on a line? If so, what did you want to have appear in the plot? You could hack things a little bit to set the y values to differ by a tiny bit -- I tried it, though, and got no wireframe appearing (but no error either).
edit: I did a bit more tracing, with various debug() incantations (and searching the source code of the lattice package and R itself for "negative length") to deduce the following: within a function called lattice:::panel.3dwire, there is a call to a C function wireframePanelCalculations, which you can see at https://r-forge.r-project.org/scm/viewvc.php/pkg/src/threeDplot.c?view=markup&root=lattice
Within this function:
nh = (nx-1) * (ny-1) * ng; /* number of quadrilaterals */
sHeights = PROTECT(allocVector(REALSXP, nh));
In this case nx is zero, so this code is asking R to allocate a negative-length vector, which is where the error comes from.
In this case, though, I think the diagnosis is more useful than the explicit debugging.