search for: scale_x_log10

Hi, Does anyone know how to do a coord_trans() in which the y-axis is tranformed into (for example) -1000/y? Thanks, _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall McCormick Road Charlottesville, VA 22903 Office: B011

Dear All, I would like to be able to plot histograms/densities on a semi-log or log-log scale. I found several suggestions online http://tolstoy.newcastle.edu.au/R/help/05/09/12044.html https://stat.ethz.ch/pipermail/r-help/2002-June/022295.html http://www.harding.edu/fmccown/R/#histograms Now, consider the code snippet taken from http://www.harding.edu/fmccown/R/#histograms # Get a random

Hi, Thanks for the quick answer. I looked into the link that you provided. But I am still not able to implement a solution. Here is my attempt : library(ggplot2) x<-1:10 y<-x^2 df<-data.frame(x=x,y=y) p1<-ggplot(df,aes(x,y))+geom_point()+ scale_x_log10( breaks = scales::trans_breaks("log10", function(x) 10^x), labels = scales::trans_format("log10", scales::math_format(10^.x)) ) + scale_y_log10( breaks = scales::trans_breaks("log10", function(x) 10^x), labels = scales::trans_format("log10&quot...

Dear all, I would like to generate a filled.contour plot with log x and y axis, however using: filled.contour(as.line,log="xy") results in a warning message. Does anybody knos what to do? Thanks Thomas

My question is related to plot( ) in the mgcv package. Before modelling the data, a few predictors were transformed to normalize them. Therefore, the x-axes in the plots show transformed predictor values. How do I back-transform the predictors so that the plots are easier to interpret? Thanks in advance, Suzan -- Suzan Pool Oregon State University Cooperative Institute for Marine

...; Hi, > Thanks for the quick answer. I looked into the link that you provided. But > I am still not able to implement a solution. Here is my attempt : > > library(ggplot2) > x<-1:10 > y<-x^2 > df<-data.frame(x=x,y=y) > p1<-ggplot(df,aes(x,y))+geom_point()+ > scale_x_log10( > breaks = scales::trans_breaks("log10", function(x) 10^x), > labels = scales::trans_format("log10", scales::math_format(10^.x)) > ) + > scale_y_log10( > breaks = scales::trans_breaks("log10", function(x) 10^x), > labels = scales::trans...

Hello, I am trying to figure out how to make a bode plot (a.k.a. bode diagram) in ggplot2. An example of such a diagram can be found here: http://meweb.ecn.purdue.edu/~me475/ctm/freq/bode2.GIF The example above shows the gain and phase characteristic of a linear system. In my case, I would like to compare visually several systems on the same diagram. The data in the data frame is arranged

...ale_discrete_position: fixed bug when limits were set * scale_discrete_position: more aesthetically pleasing expansion for a wider ranges of plots (picks expansion based on whether or not geom with width used, e.g. bars) * scale_gradient*: axes are correctly labelled when a transformation is used * scale_x_log10, scale_y_sqrt etc now correctly transform output from statistics as well as raw data * scale_z_* now removed because no longer used by anything * stat_bin: correctly returns 0 when no observations in a bin (was previously returning NA) * stat_quantreg: deal with yet another new output format from q...

...ale_discrete_position: fixed bug when limits were set * scale_discrete_position: more aesthetically pleasing expansion for a wider ranges of plots (picks expansion based on whether or not geom with width used, e.g. bars) * scale_gradient*: axes are correctly labelled when a transformation is used * scale_x_log10, scale_y_sqrt etc now correctly transform output from statistics as well as raw data * scale_z_* now removed because no longer used by anything * stat_bin: correctly returns 0 when no observations in a bin (was previously returning NA) * stat_quantreg: deal with yet another new output format from q...

Hi, I was taken off guard by the following behavior in a lattice plot. I frequently want to add a predicted curve defined at more points than in the formula expression of xyplot. There have been numerous examples of how to do this on r-help, but I still often struggle to make this work. I just realized that specifying one of the axes on a log scale does not guarantee that the added data for a

...A` values in the data. Colour/fill scales default to grey, which should stand out as different from non-missing values. * The new `oob` (out of bounds) parameter controls how scales deals with values outside the limits. The default action is `censor` - see `clip` for another option. * Only `scale_x_log10`, `scale_x_sqrt` and `scale_x_reverse` provided as convenience functions for x and y scales. Use e.g. `scale_x_continuous(trans = "log")` to access others * `set_default_scale` has been removed. If you want to change the default scale for an aesthetic, just create a function called...

...A` values in the data. Colour/fill scales default to grey, which should stand out as different from non-missing values. * The new `oob` (out of bounds) parameter controls how scales deals with values outside the limits. The default action is `censor` - see `clip` for another option. * Only `scale_x_log10`, `scale_x_sqrt` and `scale_x_reverse` provided as convenience functions for x and y scales. Use e.g. `scale_x_continuous(trans = "log")` to access others * `set_default_scale` has been removed. If you want to change the default scale for an aesthetic, just create a function called...

Hi see in line > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Vivek > Sutradhara > Sent: Friday, August 11, 2017 11:02 AM > To: r-help <r-help at r-project.org> > Subject: [R] Annotation Ticks on the axis > > Hi all, > > I would like to have help in getting annotation ticks (corresponding to the > minor

Hi all, I would like to have help in getting annotation ticks (corresponding to the minor grid) on my plot. Here is my toy example : par(mar=c(5, 6, 5, 5) + 0.1) x<-1:10 y<-x^2 plot(x,y,log="xy",xlab="log(x)",ylab="log(y)") par(new=T)