Pretty ticks for log normal scale using ggplot2 (dynamic not manual)

Question

I am trying to use ggplot2 to create a performance chart with a log normal y scale. Unfortunately I\'m not able to produce nice ticks as for the base plot function.

Answer 1

This function allows to specify both the desired number of major and minor ticks. It must be specified twice for that effect:

#' log scale
#'
#' Creates a function which returns ticks for a given data range. It uses some
#' code from scales::log_breaks, but in contrast to that function it not only
#' the exponentials of the base b, but log minor ticks (f*b^i, where f and i are 
#' integers), too.
#'
#' @param n Approximate number of ticks to produce
#' @param base Logarithm base
#'
#' @return
#'
#' A function which expects one parameter:
#'
#' * **x**: (numeric vector) The data for which to create a set of ticks.
#'
#' @export
logTicks <- function(n = 5, base = 10){
  # Divisors of the logarithm base. E.g. for base 10: 1, 2, 5, 10.
  divisors <- which((base / seq_len(base)) %% 1 == 0)
  mkTcks <- function(min, max, base, divisor){
    f <- seq(divisor, base, by = divisor)
    return(unique(c(base^min, as.vector(outer(f, base^(min:max), `*`)))))
  }

  function(x) {
    rng <- range(x, na.rm = TRUE)
    lrng <- log(rng, base = base)
    min <- floor(lrng[1])
    max <- ceiling(lrng[2])

    tck <- function(divisor){
      t <- mkTcks(min, max, base, divisor)
      t[t >= rng[1] & t <= rng[2]]
    }
    # For all possible divisors, produce a set of ticks and count how many ticks
    # result
    tcks <- lapply(divisors, function(d) tck(d))
    l <- vapply(tcks, length, numeric(1))

    # Take the set of ticks which is nearest to the desired number of ticks
    i <- which.min(abs(n - l))
    if(l[i] < 2){
      # The data range is too small to show more than 1 logarithm tick, fall
      # back to linear interpolation
      ticks <- pretty(x, n = n, min.n = 2)
    }else{
      ticks <- tcks[[i]]
    }
    return(ticks)
  }
}

Your example:

library(ggplot2)
library(scales)

# fix RNG
set.seed(seed=1)

# simulate returns
y=rnorm(999,0.02,0.2)

# M$Y are the cummulative returns (like an index)
M=data.frame(X=1:1000,Y=100)

for (i in 2:1000)
  M[i,"Y"]=M[i-1,"Y"]*(1+y[i-1])

ggplot(M,aes(x=X,y=Y))+geom_line()+
  scale_y_log10(breaks = logTicks(n = 4), minor_breaks = logTicks(n = 40))