[ACCEPTED]-Reshaping time series data from wide to tall format (for plotting)-time-series
you can also use melt() from the 'reshape' library 9 (I think it's easier to use than reshape() itself) - that'll 8 save you the extra step of having to add 7 the time column back in...
> library(reshape) > m <- melt(dat,id="t",variable_name="symbol") > names(m) <- sub("value","price",names(m)) > head(m) t symbol price 1 2009-01-01 X -1.14945096 2 2009-01-02 X -0.07619870 3 2009-01-03 X 0.01547395 4 2009-01-04 X -0.31493143 5 2009-01-05 X 1.26985167 6 2009-01-06 X 1.31492397 > class(m$t)  "Date" > library(lattice) > xyplot( price ~ t | symbol, data=m ,type ="l", layout = c(1,3) )
For this particular 6 task, however, I would consider using the 5 'zoo' library, which would not require you 4 to reshape the data frame:
> library(zoo) > zobj <- zoo(dat[,-1],dat[,1]) > plot(zobj,col=rainbow(ncol(zobj)))
R developers/contributors 3 (Gabor and Hadley in this case) have blessed 2 us with many great choices. (and can't forget 1 Deepayan for the lattice package)
From tidyr gather help page:
library(tidyr) library(dplyr) # From http://stackoverflow.com/questions/1181060 stocks <- data.frame( time = as.Date('2009-01-01') + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4) ) gather(stocks, stock, price, -time) stocks %>% gather(stock, price, -time)
If it is a multivariate time series, consider 13 storing it as a zoo object by using the 12 package of the same name. This makes indexing, merging, subseting 11 a lot easier --- see the zoo vignettes.
But 10 as you asked about lattice plots -- and 9 this can also be done. In this example, we 8 construct a simple 'long' data.frame with 7 a date column, as well as a value column 6 'val' and a variable id column 'var':
> set.seed(42) > D <- data.frame(date=rep(seq(as.Date("2009-01-01"),Sys.Date(),by="week"),2),\ val=c(cumsum(rnorm(30)), cumsum(rnorm(30))), \ var=c(rep("x1",30), rep("x2",30)))
Given 5 that dataset, plotting per your description 4 is done by xyplot from the lattice package 3 by asking for a plot of 'value given data 2 grouped by variable' where we turn on lines 1 in each panel:
> library(lattice) > xyplot(val ~ date | var, data=D, panel=panel.lines)
For a dataframe 'temp' with the date in 2 the first column and values in each of the 1 other columns:
> par(mfrow=c(3,4)) # 3x4 grid of plots > mapply(plot,temp[,-1],main=names(temp)[-1],MoreArgs=list(x=temp[,1],xlab="Date",type="l",ylab="Value") )
Many thanks for the answers folks - Dirk's 8 answer was on mark.
The missing step turned 7 out to be using "stack()" function to convert 6 the data frame from a wide to a long format. I'm 5 aware there may be an easier way to do this 4 with the reshape() function, happy to see 3 an example if someone wants to post it.
So 2 here's what I ended up doing, using the 1 'dat' dataframe mentioned in the question:
## use stack() to reshape the data frame to a long format ## <time> <stock> <price> stackdat <- stack(dat,select=-t) names(stackdat) <- c('price','symbol') ## create a column of date & bind to the new data frame nsymbol <- length(levels(stackdat$symbol)) date <- rep(dat$t, nsymbol) newdat <- cbind(date,stackdat) ## plot it with lattice library(lattice) xyplot(price ~ date | symbol, ## model conditions on 'symbol' to lattice data=newdat, ## data source type='l', ## line layout=c(nsymbol,1)) ## put it on a single line ## or plot it with ggplot2 library(ggplot2) qplot(date, price, data = newdat, geom="line") + facet_grid(. ~ symbol)
More Related questions