This is probably going to sound like a really stupid question but my plot in R will not change color!
this is my line in R
plot(MasterModCfs$V3,MasterModCfs$V5,col="blue")
I have tried it with spaces and without, with different colors, reloading, everything I could think of. If it's important, MasterModCfs contains literally thousands of data values.
I did one of the examples just to check
cars <- c(1, 3, 6, 4, 9)
plot(cars)
plot(cars, col="blue")
and it's blue. So that works.
Why won't my plot change colors?
Googling the same problem, I came across this present page - but subsequently found the source of my own issue through trial and error so posting here in case it helps. My x axis values were set up as factors - when I re-converted them to a normal string using as.character(), I was able to re-apply my own colours finally.
Without knowing what your par() settings are and what the classes of your columns, it's impossible to say.
You might try adding 'pch=21, bg="red"' to your plot command and see if that changes anything. It might give you a hint.
I suggest using the aes command in ggplot2. You can try
ggplot(data = MasterModCfs) + geom_point (mapping =aes (x=V3, y=V5), color="blue")
That should work!
Using plot directly I am not sure how to do it. I try your simple example (with cars) but it also fails if you add another vector for the "y" axis (as you wish to do in the end). To be honest, I have no idea why it works using only "cars" and the color.
Related
Hi I am using ggplot2 to create a scatter graph. For some reason it keeps using the y-axis variables to order my scatter graph. I'm new to this so I barely know what I'm doing, so if someoen could help, would be much appreciated!
What I've noticed is that my Rstudio orders the numbers going in order like 1,10,11,12...2,21,22,23 instead of going the standard 1,2,3,4,5,6,7
Diesel_Prices_Jan19 <- ggplot(Prices_4, aes(V4, V1))
Diesel_Prices_Jan19 + geom_point()
As a tip its probably best to give a good reprex. Check the type of that column and maybe try turning it into a factor
I am trying to plot a graph of certain values against time using the plot function.
I am simply trying to change the representation of the dots, using the pch= function. However R is simply ignoring me! I have also tried removing the dots so that I can place labels instead, but when I type in type="n" it ignores that too!
I am using the exact same format of code that I have used for other plots but this time it just isn't cooperating.
If I specify other features such as the title or the x/y axis labels, it will add those in but it simply ignores the pch or type commands.
This is my basic code:
plot(Differences ~ Time, data=subsetH)
But if I run
plot(Differences ~ Time, type="n", data=subsetH)
or
plot(Differences ~ Time, pch=2, data=subsetH)
it keeps plotting the same thing.
Is there something obvious I have missed?
I just came across your question because I encountered the same thing - creating an empty plot did not work, as type='n' was always ignored (as well as other type specifications).
With the help of this entry: Plotting time-series with Date labels on x-axis
I realized that my date format needed to be assigned as "date" class (as.Date()).
I know your entry dates back a little bit already, but maybe it's still useful.
My head is getting sore from me banging it so much.
I have a time-series that I've converted into an xts object w/ 7 variables. Now I'm trying to plot 4 of them, all price indices, on the same graph. I used autoplot (from the ggfortify package) to initialize the graph, and this is where the trouble begins.
Autoplot doesn't seem to work unless I give it at least one variable to plot. That's fine, but the two customizations I want for the variable -- its color and line type -- seem to have no effect.
But once I create the plot this way, I have little trouble adding the other 3 variables by adding geom_lines. Here's sort of what the code looks like:
p <- autoplot(foo.xts,xlab="Year",
ylab="Price Index",
columns="Variable1",linetype=4) # the linetype accomplishes nothing
p <- p + geom_line(aes(y="Variable2", color="green", linetype="solid"
# etc. for the other 2 variables
p # The 3 added variables do get the selected colors & line types.
But how can I customize the line for the first variable?
Then there's another problem in that I can't get a legend to appear. Here's how I'm trying to do that:
p <- p + scale_color_discrete(
name="Price Indices",
breaks=c("Variable1", "Variable2", "Variable3", "Variable4"),
labels=c("Index 1", "Index 2", "Index 3", "Index 4"))
This seems to accomplish nothing.
One thing I'd add is that in my various experiments trying to get the legend to work, I've sometimes gotten two sets of keys: one for colors and one for line types. This is obviously not what I'm after.
If someone could help me with this, I'd be forever in your debt!
I spent yesterday away from the computer, and when I returned in the evening fixed the problems. Here's how:
Stopped using autoplot. It's a classic case of hand-holding that throws you over the cliff. In other words, it automatically formats the plot in ways that are difficult (impossible?) to customize. Instead, ggplot makes the initial plot.
Since I'm making a series of plots, moved all the shared features to a separate, preamble section. This section creates a base plot, sets the x-axis variable (the date of the observation), labels the x-axis, and formats its tick marks. It also sets up standardized colors, line styles, and shapes to be used by all the "production" plots.
To set up the standardized elements, it uses scale_color_manual, etc. Each one has to be identical in all respects except those that are unique to its specific aesthetic attribute. E.g., scale_color_manual uses values like "red" whereas scale_linetype_manual uses values like "solid." Each manual setting includes the following elements: legend.title*, values, labels, and guide = guide_legend()*. (Items marked with * must be identical, otherwise you'll get different legends for each one.) For each plot, the actual legend title is first stored in a variable, legend.title, and then used in all the manual scale setting. This way the manual settings can be moved to the common section, but each plot has is own unique title for its legend.
3A. Actually, I was wrong about this. I was thinking LaTeX, where most things are evaluated where they appear at execution time. So a scale_color_manual statement at the start could change later on just by changing the value of legend.title. But in R, things are evaluated sequentially, and changing legend.title after the scale_color_manual statement is executed will have no effect. I worked around this by defining several variables in the preamble (e.g., one with the colors I'm using) and then using these variables in the various source_x_manual statements. This way, the only thing that change is the legend title.
Then each production plot starts by copying the base plot, labeling the y-axis, and then adds the geometric objects that it needs.
This approach has several advantages. 1) It modularizes the plotting so that problems are easier to isolate and solve, and most solved problems in the preamble section are solved for all plots. 2) It standardizes the plots, ensuring that their common features are formatted identically. 3) It reduces each production plot to a few statements; since this is the unique part for each plot, creating a new style of plot becomes relatively easy. 4) The value added by autoplot becomes minimal because this approach, separating shared elements in a preamble, compensates by isolating reusable code and the preamble, once debugged, allows much more fine-grain customization.
If you have any questions, please feel free to ask.
How do you set the shape attribute for points when building a scatterChart with nplot from rCharts? Point size can be set by providing a column in the input dataframe named "size" but if there's a corresponding "shape" column consisting of strings such as "square" or "cross" the resulting graph still has the default circle points. New to R and NVD3 so I apologize for my lack of vocabulary.
It appears the newest version of nvd3 no longer works the same way as the old version. See for example. The screenshot shows shapes, and the data has shape:, but only circles are rendered in the actual chart. Also, the tests do not produce anything other than circles. I glanced at the source, and I could not find where or how to set shape. If you know how to do with nvd3, I could easily translate into a rCharts example.
I don't have a reputation of 50, but I'd like to comment.
Line 18 in this NVD3 example(Novus.github) shows how it's currently done. Likewise, all you need to do with the live code(nvd3.org) is uncomment the 'size' line in the data tab.
I attempted making a column in my df named 'shape', and using n1 <- nPlot(x~y, data=df, shape='shape', type='scatterChart'); n1$chart(onlyCircles=FALSE); and a number of other combinations. I've only spent the last two days working with rCharts but have made some exciting progress. I'm giving up on this but found it curious that these two examples weren't mentioned here, so I thought I'd mention them.
I know this question is a bit "ancient" but I faced the same problem and it took me a while to find out how to change the shapes.
I followed the approach in this example for changing the size:
nvd3 scatterPlot with rCharts in R: Vary size of points?
Here my solution:
library(rCharts)
df=data.frame(x=rep(0:2,3),y=c(rep(1,3),rep(2,3),rep(3,3)),
group=c(rep("a",3),rep("b",3),rep("c",3)),shape=rep("square",9))
p <- nPlot(y~x , group = 'group',data = df, type = 'scatterChart')
#In order to make it more pleasant to look at
p$chart(xDomain=c(-0.5,2.5))
p$chart(yDomain=c(0,4))
p$chart(sizeRange = c(200,200))
#here the magic
p$chart(scatter.onlyCircles = FALSE)
p$chart(shape = '#! function(d){return d.shape} !#')
p
I don't know if you have seen some unwanted bold-face font like picture below:
As you see the third line is bold-faced, while the others are not. This happens to me when I try to use ggplot() with lapply() or specially mclapply(), to make the same chart template based on different data, and put all the results as different charts in a single PDF file.
One solution is to avoid using lapply(x, f) when f() is a function that returns a ggplot() plot, but I have to do so for combining charts (i.e. as input for grid.arrange()) in some situation.
Sorry not able to provide you reproducible example, I tried really hard but was not successful because the size of code and data is too big with several nested functions and when I reduced complexity to make a reproducible example, the problem did not happen.
I asked the question because I guessed maybe someone has faced the same experience and know how to solve it.
My intuition is that it's not actually being printed in bold, but rather double-printed for some reason, which then looks bold. This would explain why it doesn't come up with a simpler example. Especially given your mention of nested functions and probably other complicated structures where it's easy to get an off-by-one or similar error, I would try doing something where you can see exactly what's being plotted -- perhaps by examining the length() of the return value from apply().
Changing the order of elements of the vector, so that the order of the elements in the key is different, may also help. If you consistently get the bold-face on the last element, that also tells you a little bit more about where something is going wrong.
As #Dinre also mentioned, it could also be related to your plotting device. You can try out changing your plotting device. I have my doubts about this though, seeing as it's not a consistent problem. You could also try changing the position of the key, which depending on your plotting device and settings, may move you in or out of a compression block, thus changing which artifacts crop up.
Reproducible example and a solution may be as follows:
library(ggplot2)
d <- data.frame(x=1:10, y=1:10)
ggplot(data = d, aes(x=x, y=y)) +
geom_point() +
geom_text(aes(3,7,label = 'some text 10 times')) +
geom_text(data = data.frame(x=1,y=1),
aes(7,3, label = 'some text one time'))
When we try to add a label by geom_text() manually inserting x and y do not shorten the data. Then same label happen to be printed as many times as the number of rows our data has. Data length may be forced to 1 by replacing data within geom_text().