rplotclickstream

Replace x-axis label from the plot of a MarkovChain object

Here is some code to generate a 0th order Markov Chain plot. I'd like to replace the plot's x-axis labels (c, d, h, i, o, p) with 45 degree rotated labels showing the first six months of the year. However, using xaxt="n" in the plot call doesn't seem to work. This code just writes over the existing labels, rather than replacing them. How can I replace the labels with the ones I want?

library(clickstream)
clickstreams <- c("User1,h,c,c,p,c,h,c,p,p,c,p,p,o",
                   "User2,i,c,i,c,c,c,d",
                   "User3,h,i,c,i,c,p,c,c,p,c,c,i,d",
                   "User4,c,c,p,c,d",
                   "User5,h,c,c,p,p,c,p,p,p,i,p,o",
                   "User6,i,h,c,c,p,p,c,p,c,d")
csf <- tempfile()
writeLines(clickstreams, csf)
cls <- readClickstreams(csf, header = TRUE)
mc <- fitMarkovChain(cls, order=0)
plot(mc, xaxt="n")
text(x=1:6, y=par()$usr[3], labels = month.name[1:6], srt=45, adj = c(1.1,1.1), xpd = TRUE, cex=.9)

clickstream plot with wrong x-axis labels

Solution

  • str(mc) reveals the structure of the S4 object. Passing into plot just the xy-coordinates rather than the whole "MarkovChain" object recovers the xaxt option functionality. Using with makes this most convenient.

    Moreover this allows us to use the implemented S4 plot method for signature 'MarkovChain' of the clickstream package.

    library(clickstream)
with(mc@transitions[[1]], plot(states, probability, xaxt="n"))
text(x=1:6, y=par()$usr[3], labels=month.name[1:6], srt=45, adj=rep(1.1, 2), xpd=TRUE, cex=.9)

    Result

    enter image description here

    Data

    library(clickstream)
mc <- new("MarkovChain", states = c("h", "c", "p", "o", "i", "d"), 
    order = 0, transitions = list(structure(list(states = structure(1:6, .Label = c("c", 
    "d", "h", "i", "o", "p"), class = "factor"), frequency = c(25L, 
    4L, 5L, 7L, 2L, 17L), probability = c(0.416666666666667, 
    0.0666666666666667, 0.0833333333333333, 0.116666666666667, 
    0.0333333333333333, 0.283333333333333)), class = "data.frame", row.names = c(NA, 
    -6L))), lambda = 0, logLikelihood = -88.4241188515082, observations = 60, 
    start = structure(c(c = 0.166666666666667, h = 0.5, i = 0.333333333333333
    ), class = "table", .Dim = 3L, .Dimnames = structure(list(
        c("c", "h", "i")), .Names = "")), end = structure(c(d = 0.666666666666667, 
    o = 0.333333333333333), class = "table", .Dim = 2L, .Dimnames = structure(list(
        c("d", "o")), .Names = "")), transientStates = c("c", 
    "h", "i", "p"), absorbingStates = c("d", "o"), absorbingProbabilities = structure(list(
        state = structure(1:4, .Label = c("c", "h", "i", "p"), class = "factor"), 
        d = c(0.792201957232916, 0.864224245314581, 0.903842865008182, 
        0.517925028202586), o = c(0.207798042767084, 0.13577575468542, 
        0.0961571349918185, 0.482074971797414)), class = "data.frame", row.names = c(c = 1L, 
    h = 3L, i = 4L, p = 6L)))