I'm plotting nice plots with the R language plotmo library, but he's truncating the main labels.
How can I avoid this?
The real name of the variable is tempMaxima_Maxima not tmpMxm_M.
plotmo(mars1, pt.col = 'steelblue1', do.par = F, smooth.col = 2, trace = -1)
Plotmo version 3.5.7 has a new argument to take care of this issue. Use prednames.abbreviate=FALSE for full (non-abbreviated) names in the graphs.
(Plotmo version 3.5.7 will be released in mid-April 2020.)
Related
I'm over-plotting three densities onto my data histogram, using denscomp in the fitdistrplus package in R. The code below is working perfectly, but I don't know how to make the lines thicker.
denscomp(list(TryWeibull, TryGamma, TryLognormal), legendtext = plot.legend,
fitcol = c("indianred3","gray38", "darkblue"), fitlty = c("dashed", "longdash", "dotdash"),
xlab = "Age", ylab = "Proportion", main="")
fitcol is giving me the correct colours, fitly is giving me the correct line types, but I can't work out the command to make the lines thicker. I have two distribution densities that are close together and I have been unsuccessful in clearly identifying them using colour/line type differences. .
I am trying to de-emphasize the Weibull and emphasise the gamma and lognormal. The proportions are estimates, so I am trying to fit the general shape, not the exact values.
I can't see an option in the denscomp function to specify line widths. I would rather not use the ggplot option, but can shift to that if required. I was hoping there was a function option I'm overlooking.
Edited to add: I raised this as a feature request on GitHub and it has been implemented into the package.
Although the author of this package allows you to specify multiple line types (fitlty) and line colours (fitcol), they didn't allow you to specify multiple line widths. But since R is open-source, you are free to modify the function in any way.
Type the following at the R console:
fix(denscomp)
Then add a new argument to the function after fitcol, called fitlwd.
..., fitcol, fitlwd, addlegend = TRUE, ...
Then after line 30 add the following:
if (missing(fitlwd))
fitlwd <- 1
Then after line 34 add the following:
fitlwd <- rep(fitlwd, length.out = nft)
Then modify line 136 as follows:
col = fitcol[i], lwd=fitlwd[i], ...)
Finally, modify line 142:
col = fitcol, lwd=fitlwd,
Save and call the new function as before but now specifying the fitlwd argument:
denscomp(..., fitlwd=c(1,3,3))
I had the same question and followed Edward's solution, which was great and I learnt a lot, but it turned out you can just use ggplot to do that.
denscomp(..., plotstyle = "ggplot") + geom_line(linetype = "dashed",size = 1))
I need to repeat the thing done in:
tiny pie charts to represent each point in an scatterplot using ggplot2 but I stumbled into the problem that the package ggsubplot is not available for 3.3.1 R version.
Essentially I need a histogram or a pie chart in predefined points on the scatterplot. Here is the same code that is used in the cited post:
foo <- data.frame(X=runif(30),Y=runif(30),A=runif(30),B=runif(30),C=runif(30))
foo.m <- melt(foo, id.vars=c("X","Y"))
ggplot(foo.m, aes(X,Y))+geom_point()
ggplot(foo.m) +
geom_subplot2d(aes(x = X, y = Y, subplot = geom_bar(aes(variable,
value, fill = variable), stat = "identity")), width = rel(.5), ref = NULL)
The code used libraries reshape2, ggplot2 and ggsubplot.
The image that I want to see is in the post cited above
UPD: I downloaded the older versions of R (3.0.2 and 3.0.3) and checkpoint package, and used:
checkpoint("2014-09-18")
as was described in the comment bellow. But I get an error:
Using binwidth 0.0946
Using binwidth 0.0554
Error in layout_base(data, vars, drop = drop) :
At least one layer must contain all variables used for facetting
Which I can't get around, because when I try to include facet, the following error comes up:
Error: ggsubplots do not support facetting
It doesn't look like ggsubplot is going to fix itself any time soon. One option would be to use the checkpoint package, and essentially "reset" your copy of R to a time when the package was compatible. This post suggests using a time point of 2014-09-18.
Utilizing the chartSeries function in the quantmod package, I want to modify the RSI oscillator. Given an xts object containing OHLC price data, here is the call that I am using:
chartSeries(plot_report[, 1:4],
name = substr(ticker, 1, nchar(ticker) - 4),
theme = chartTheme('white.mono', grid.col = NA),
TA = c(addRSI(n = 14, maType = "SMA")),
type = "line",
bar.type = 'ohlc',
major.ticks = 'months',
show.grid = FALSE,
log.scale = TRUE)
Generating this chart:
I have four questions:
How can I change the default color of blue to something else? I have tried: c(addRSI(n = 14, maType = "SMA", col = "black")). However, I get the "unused argument" error.
Can I draw horizontal lines in the oscillator panel? Traditional RSI's have a horizontal red line at a y-axis value of 70 and a horizontal green line at a y-axis value of 30 to indicate overbought/oversold levels.
Is it possible to plot another calculation as an oscillator line below the chart? I have some proprietary oscillators that I want to visualize instead of the RSI or any of the indicators in the TTR package.
How can I get involved in improving the quantmod charting functionality; is this project being actively maintained?
You can't. You would need to add ... to the arguments for addRSI and modify the body of the function to use ... appropriately. A work-around is to calculate RSI manually, then call addTA as done in the answer to Change line colors of technical indicators made by R quantmod TTR?.
Use addLines:
getSymbols("SPY"); chartSeries(SPY, TA="addRSI();addLines(h=c(30,70), on=2)")
Use addTA with the object containing your proprietary data.
See quantmod's GitHub page. Yes, it's actively maintained. The last update was pushed to CRAN a couple months ago.
I am having trouble adjusting the colors of a multiple time series plot using xtsExtra.
This is the code of a minimal example:
require("xtsExtra")
n <- 50
data <- replicate(2, rnorm(n))
my.ts <- as.xts(ts(data, start=Sys.Date()-n, end=Sys.Date()))
plot.zoo(my.ts, col = c('blue', 'green'))
plot.xts(my.ts, col = c('blue', 'green'))
The plot.zoo commands yields
,
whereas the plot command from the xtsExtra package results in
.
In the second plot, the two time series are nicely overlaid, but seem insensitive to the col option.
I'm using the latest version 0.0-1 of the xtsExtra package (rev. 862).
It is my understanding that the xts and xtsExtra packages are designed as extensions of zoo and should work with the same arguments (plus many additional ones). Even though I can get the same overlay behavior in plot.zoo using the screens option, I cannot really resort to using it because the call to plot.xts that causes my problems is within the quantstrat package (functions chart.forward.training and chart.forward.testing for example) which I'd loathe to modify. (Incidentally, the dev.new() in these functions is causing me trouble as well.)
Question: Why does plot from the xtsExtra package seem not to respond to the col= option and what can be done about it, if modifying
the call to the function is not a real option?
Q1. If you take time to read the help text for plot.xts, you see that the function does not have a col argument. Together with the fact that partial matching of argument names doesn't seem to be allowed in the function, it explains why plot.xts it does not respond col =.
Compare with a case where partial matching works:
plot(x = 1:2, y = 1:2, type = "b"); plot(x = 1:2, y = 1:2, ty = "b"); "ty" matches "type".
See here: "If the name of the supplied argument matches exactly with the first part of a formal argument then the two arguments are considered to be matched".
Q2. Instead you may use the colorset argument:
"color palette to use, set by default to rational choices" (colorset = 1:12).
plot.xts(my.ts, colorset = c('blue', 'green'))
I'm having trouble adding some text to an plot of time series data in R using xts. I've produced a simple example of the problem.
My text() command seems to do nothing, whereas I can add a points to the plot. I've tried to keep the code simple by using defaults where possible
require(quantmod)
# fetch the data and plot it using default options
getSymbols('MKS.L')
plot(MKS.L$MKS.L.Close)
# try to add text - doesn't appear
text(as.Date('2012-01-01'),y=500,"wobble", cex=4)
# add a point - this does appear
testPos <- xts(600, as.Date('2012-01-01'))
points( testPos, pch = 3, cex = 4, col = "red" )
Any help appreciated - I'm pretty new to R and I've spent hours on this!
Not a direct answer, but the plot.xts function that comes with the xts package is not fully developed.
You're much better off using plot.zoo or plot.xts from the xtsExtra package (which was written as a Google Summer of Code project with the intention being to roll it into the xts package)
Either of these will work:
plot(as.zoo(MKS.L$MKS.L.Close))
text(as.Date('2012-01-01'),y=500,"wobble", cex=4)
#install.packages("xtsExtra", repos="http://r-forge.r-project.org")
xtsExtra::plot.xts(MKS.L$MKS.L.Close)
text(as.Date('2012-01-01'),y=500,"wobble", cex=4)