search for: theme_set

...3 = rnorm(36), Val4 = rnorm(36)) You can plot the data nicely with x=Time and y=Val1 by grouping PID and facetting for Dset. p <- ggplot(df) + geom_line(aes(x=Time,y=Val1,group=PID)) + geom_point(aes(x=Time,y=Val1,colour=PID)) + facet_grid(. ~ Ecc) theme_set(theme_bw()) p I would now like to normalize these data to the mean of the two values at Time = 0 for each subject (so having plots in % of the mean Time=0 value rather than absolute values). Any suggestions are welcome! Herbert

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Dear Russ, I had the same problem in my vignettes and setting both message and warning to FALSE seems to remove all unwanted output: ```{r message=FALSE, warning=FALSE} library("afex") library("ggplot2") library("cowplot") theme_set(theme_grey()) ``` Result: https://cran.r-project.org/web/packages/afex/vignettes/afex_plot_introduction.html Best, Henrik Am Di., 23. Juli 2019 um 14:52 Uhr schrieb Ben Bolker <bbolker at gmail.com>: > > Does setting message=FALSE in the chunk options of the vignette help? > &...

...me_update( axis.text.x = theme_blank(), axis.ticks = theme_blank(), axis.title.x = theme_blank(), axis.title.y = theme_blank(), legend.position = "none" ) p <- p + theme_set(theme) print(p) -- View this message in context: http://r.789695.n4.nabble.com/hclust-and-ggplot2-tp4193353p4193353.html Sent from the R help mailing list archive at Nabble.com.

...le_datetime: improved breaks calculation * scale_gradient: better default colour scheme from red to blue (thanks to Charlotte Wickham for providing the Munsell colours) * scale_size and scale_area: increased default size range * stats: all give useful error message if missing a required aesthetic * theme_set warns if missing needed theme elements * theme_text: now possible to right and left align text with hjust=0 and hjust=1 respectively Bug fixes * be explicit about plyr::is.discrete to avoid clash with Hmisc * facet_wrap: work around R bug so no longer crashers when ncol = 1 * geom_errorbar now wo...

...le_datetime: improved breaks calculation * scale_gradient: better default colour scheme from red to blue (thanks to Charlotte Wickham for providing the Munsell colours) * scale_size and scale_area: increased default size range * stats: all give useful error message if missing a required aesthetic * theme_set warns if missing needed theme elements * theme_text: now possible to right and left align text with hjust=0 and hjust=1 respectively Bug fixes * be explicit about plyr::is.discrete to avoid clash with Hmisc * facet_wrap: work around R bug so no longer crashers when ncol = 1 * geom_errorbar now wo...

...on* of the data, you should check > overdispersion of a fitted (e.g. Poisson) model, as below. > > ? ?cheers > ? ? Ben Bolker > > > edata <- data.frame(Catch, Effort, xx1, xx2, xx3) > > ## graphical exploration > > library(ggplot2); theme_set(theme_bw()) > library(tidyr) > edata_long <- edata |> pivot_longer(names_to="var", cols =-c("Catch", > "Effort")) > ggplot(edata_long, aes(value, Catch)) + > ? ? ?geom_point(alpha = 0.2, aes(size = Effort)) + > ? ? ?fac...

Hi Listers, Is it possible to produce an ordination plot in 2d, where bubbles represent the location of sites (this part is easy enough) and the size of the bubbles is proportional to the sites location in 3d space (I am stuck on this option). So sites that are very near the 2d plane of the xy axes would be larger while sites that are actually further away in 3 d space would be proportionally

Lionel, Thanks for your response. I understand that method overriding can be a serious issue, but as you say, this is not something that the user can act upon. Yet the message lands at the user?s feet. In my case, the messages are cluttering my package vignettes, and may or may not represent what users see if they themselves run the vignette code, depending on what version of ggplot2, etc. they

Hello there, I’m computing the total value of an order from the price of the order items using a “for” loop and the “ifelse” function. I do this on a large dataframe (close to 1m lines). The computation of this function is painfully slow: in 1min only about 90 rows are calculated. The computation time taken for a given number of rows increases with the size of the dataset, see the example with