Plotly: add_trace in a loop

Question

I\'m trying to add_trace ad each loop, but I get only one plot with multiplies lines on over each other.

mean <- -0.0007200342
sd   <- 0.3403711
N=10
T         


        

Answer 1

Nasty, but works:

mean <- -0.0007200342
sd   <- 0.3403711
N=10
T=1
Delta = T/N

W = c(0,cumsum( sqrt(Delta) * rnorm(N, mean=mean, sd=sd)))
t <- seq(0,T, length=N+1)


for(i in 1:5){

  W <- c(0,cumsum( sqrt(Delta) * rnorm(N, mean=mean, sd=sd)))

  assign(paste("W_",i,sep=""),W)
  assign(paste("Name_", i, sep=""), paste("Name",i,sep=""))
  if(i==1){
    pString<-"p<-plot_ly(x = t, y = W_1, name='W1')"
  } else {
    pString<-paste(pString, " %>% add_trace(x=t, y =",  eval(paste("W", i, sep="_")),", name=", eval(paste("Name", i, sep="_")), ")", sep="")
  }

}
eval(parse(text=pString))
print(p)

Answer 2

It is described here : http://www.r-graph-gallery.com/129-use-a-loop-to-add-trace-with-plotly/

save your plot in a variable, and then do add_trace :

p <- plotly(...)

p<- add_trace(p, ...)

Answer 3

Use evaluate = TRUE in add_trace.

Answer 4

The plot_ly and add_trace functions have an evaluation = FALSE option that you can change to TRUE, which should fix the scope issues.

Answer 5

I'd do this like this:

mean <- -0.0007200342
sd   <- 0.3403711
N=10
T=1
Delta = T/N

# a list with the trace Y values
Ws <- lapply(
  1:15,
  function(idx){
    c(0,cumsum( sqrt(Delta) * rnorm(N, mean=mean, sd=sd)))
  } 
)

# this could be a list with the trace X values, but is just a seq
t <- seq(0,T, length=N+1)

# a list with plotly compliant formatted objects
formattedW <- lapply(
  seq_along(Ws),
  function(idx, datasetY, datasetX){
    return(list( x = datasetX, y = datasetY[[idx]], type="scatter", mode = 'lines+markers'))
  },
  datasetX = t,
  datasetY = Ws
)

# Reduce the list of plotly compliant objs, starting with the plot_ly() value and adding the `add_trace` at the following iterations
Reduce(
  function(acc, curr){
    do.call(add_trace,c(list(p=acc),curr))
  },
  formattedW,
  init=plot_ly()
)