I have a plot of time series in ggplot2 package and I have performed the Moving average and I would like to add the result of moving average to the plot of time series.
Sample of Data-set (p31):
ambtemp dt
-1.14 2007-09-29 00:01:57
-1.12 2007-09-29 00:03:57
-1.33 2007-09-29 00:05:57
-1.44 2007-09-29 00:07:57
-1.54 2007-09-29 00:09:57
-1.29 2007-09-29 00:11:57
Applied code for time series presentation:
Require(ggplot2) library(scales) p29$dt=strptime(p31$dt, "%Y-%m-%d %H:%M:%S") ggplot(p29, aes(dt, ambtemp)) + geom_line() + scale_x_datetime(breaks = date_breaks("2 hour"),labels=date_format("%H:%M")) + xlab("Time 00.00 ~ 24:00 (2007-09-29)") + ylab("Tempreture")+ opts(title = ("Node 29"))
Sample of time series presentation
Sample of Moving average plot Sample of expected results
The challenge is that time series data ov=btained from data-set which includes timestamps and temperature but Moving average data include just the average column and not the timestamps and fitting these two can cause inconsistency.
Moving averages have the property to reduce the amount of variation present in the data. In the case of time series, this property is used to eliminate fluctuations, and the process is called smoothing of time series.
Calculating rolling averages To calculate a simple moving average (over 7 days), we can use the rollmean() function from the zoo package. This function takes a k , which is an 'integer width of the rolling window. The code below calculates a 3, 5, 7, 15, and 21-day rolling average for the deaths from COVID in the US.
Moving averages are a simple and common type of smoothing used in time series analysis and time series forecasting. Calculating a moving average involves creating a new series where the values are comprised of the average of raw observations in the original time series.
One solution is to use rollmean()
function from library zoo
to calculate moving average.
There is some confusion with data frame names in your question (p31 and p29), so I will use p 29.
p29$dt=strptime(p29$dt, "%Y-%m-%d %H:%M:%S") library(zoo) #Make zoo object of data temp.zoo<-zoo(p29$ambtemp,p29$dt) #Calculate moving average with window 3 and make first and last value as NA (to ensure identical length of vectors) m.av<-rollmean(temp.zoo, 3,fill = list(NA, NULL, NA)) #Add calculated moving averages to existing data frame p29$amb.av=coredata(m.av) #Add additional line for moving average in red ggplot(p29, aes(dt, ambtemp)) + geom_line() + geom_line(aes(dt,amb.av),color="red") + scale_x_datetime(breaks = date_breaks("5 min"),labels=date_format("%H:%M")) + xlab("Time 00.00 ~ 24:00 (2007-09-29)") + ylab("Tempreture")+ ggtitle("Node 29")
If line colors should appear in legend, then aes()
in ggplot()
and geom_line()
has to be modified and scale_colour_manual()
should be added.
ggplot(p29, aes(dt)) + geom_line(aes(y=ambtemp,colour="real")) + geom_line(aes(y=amb.av,colour="moving"))+ scale_x_datetime(breaks = date_breaks("5 min"),labels=date_format("%H:%M")) + xlab("Time 00.00 ~ 24:00 (2007-09-29)") + ylab("Tempreture")+ scale_colour_manual("Lines", values=c("real"="black", "moving"="red")) + ggtitle("Node 29")
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