How to customize titles, axis labels, etc. Expanded time series chart
I'm fairly familiar with the usual ways of modifying the plot by writing my own X-axis labels or main title, but I was unable to customize the output when plotting the time series decomposition results.
For example,
library(TTR)
t <- ts(co2, frequency=12, start=1, deltat=1/12)
td <- decompose(t)
plot(td)
plot(td, main="Title Doesn't Work") # gets you an error message
gives you a nice basic graph of the observed time series, trend, etc. With my own data (depth changes below the surface of the water), however, I would like to be able to toggle the y orientation (eg ylim = c (40,0) for "observables" or ylim = c (18,12) for "trend" ), changing from "seasonal" to "tidal" includes the units for the x-axis ("Time" (days) ') and provide a more detailed title for the figure.
My impression is that the time series analysis I am doing is pretty straightforward and in the end I might be better off using a different package, perhaps with better graphical control, but I would like to use ts () and decompose () if I can at the moment (yes, cake and consumption). Assuming it's not too bad.
Is there a way to do this?
Thank! Pete
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You can change the function plot.decomposed.ts
(the plot
" method " that is dispatched at startup plot
for the class object decomposed.ts
(which is the class td
).
getAnywhere(plot.decomposed.ts)
function (x, ...)
{
xx <- x$x
if (is.null(xx))
xx <- with(x, if (type == "additive")
random + trend + seasonal
else random * trend * seasonal)
plot(cbind(observed = xx, trend = x$trend, seasonal = x$seasonal, random = x$random),
main = paste("Decomposition of", x$type, "time series"), ...)
}
Note that in the code above, the function will hard-code the header. So let's change it so we can choose our own title:
my_plot.decomposed.ts = function(x, title="", ...) {
xx <- x$x
if (is.null(xx))
xx <- with(x, if (type == "additive")
random + trend + seasonal
else random * trend * seasonal)
plot(cbind(observed = xx, trend = x$trend, seasonal = x$seasonal, random = x$random),
main=title, ...)
}
my_plot.decomposed.ts(td, "My Title")
Here's an example of a ggplot plot. ggplot requires a dataframe, so the first step is to get the decomposed time series in the form of a dataframe and then print it.
library(tidyverse) # Includes the packages ggplot2 and tidyr, which we use below
# Get the time values for the time series
Time = attributes(co2)[[1]]
Time = seq(Time[1],Time[2], length.out=(Time[2]-Time[1])*Time[3])
# Convert td to data frame
dat = cbind(Time, with(td, data.frame(Observed=x, Trend=trend, Seasonal=seasonal, Random=random)))
ggplot(gather(dat, component, value, -Time), aes(Time, value)) +
facet_grid(component ~ ., scales="free_y") +
geom_line() +
theme_bw() +
labs(y=expression(CO[2]~(ppm)), x="Year") +
ggtitle(expression(Decomposed~CO[2]~Time~Series)) +
theme(plot.title=element_text(hjust=0.5))
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