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rplotvisualizationjitter

Jitter points by different amounts based on condition

I have a dataset with discrete X-axis values and a large number of Y-values. I also have a separate vector with measures of uncertainty in the X-axis values; this uncertainty varies across the X axis. I would like to jitter my X-axis values by an amount proportional to this uncertainty measure. It's easy but cumbersome to do this with a loop; I am looking for an efficient solution to this.

Reproducible example: 

#Create data frame with discrete X-axis values (a)
dat <- data.frame(a = c(rep(5, 5), rep(15,5), rep(25,5)), 
                  b = c(runif(5, 1, 2), runif(5, 2, 3), runif(5, 3, 4)))

#Plot raw, unjittered data
plot(dat$b ~ dat$a, data = dat, col = as.factor(dat$a), pch = 20, cex = 2)

enter image description here

#vector of uncertainty estimates
wid_vec <- c(1,10,3)

#Ugly manual jittering, not feasible for large datasets but 
#produces the desired result
dat$a_jit <- c(jitter(rep(5, 5), amount = 1), 
                jitter(rep(15, 5), amount = 10), 
                jitter(rep(25, 5), amount = 3))

plot(dat$b ~ dat$a_jit, col = as.factor(dat$a), pch = 20, cex = 2)

enter image description here

#Ugly loop solution, also works

newdat <- data.frame()
a_s <- unique(dat$a)

for (i in 1:length(a_s)){
  subdat       <- dat[dat$a == a_s[i],]
  subdat$a_jit <- jitter(subdat$a, amount = wid_vec[i])
  newdat <- rbind(newdat, subdat)
}

plot(newdat$b ~ newdat$a_jit, col = as.factor(newdat$a), pch = 20, cex = 2)

#Trying to make a vectorized solution, but this of course does not work.

jitter_custom <- function(x, wid){
  j <- x + runif(length(x), -wid, wid)
  j
}

#runif() does not work this way, this is shown to indicate the direction 
#I've been attempting

Basically, I need to split up dat by condition, call the relevant entry in the wid_vec vector, then create a new column by modifying the dat entries based on the wild_vec value. It sounds like there ought to be an elegant dplyr solution for this, but it eludes me right now.

Appreciate all suggestions!

Solution

  • As an alternative to 

    set.seed(1)
dat$a_jit <- c(jitter(rep(5, 5), amount = 1), 
                jitter(rep(15, 5), amount = 10), 
                jitter(rep(25, 5), amount = 3))

    you could do

    set.seed(1)
x <- with(dat, jitter(a, amount=setNames(c(1,10,3), unique(a))[as.character(a)]))

    The result is the same: 

    identical(x, dat$a_jit)
# [1] TRUE

    If you want the warning to vanish, you could wrap suppressWarnings() around jitter(...), or use something like with(dat, mapply(jitter, x=a, amount=setNames(c(1,10,3), unique(a))[as.character(a)])).