search for: free_x

...t example: dataset <- data.frame(Main = c("A", "A", "B", "B"), Detail = c( "b", "c", "1", "2"), resp = runif(4, min = 0.5, max = 1)) ggplot(dataset, aes(x = Detail, y = resp)) + facet_grid(.~Main, scales = "free_x")+ geom_point(aes( size=6,shape = c(16,16,15,15) ),colour="blue")+geom_hline(aes(yintercept=0.25),colour='blue', size=2) with this code all the point are blue (like the line below) But if i try the following code, where my goal is to have the point on the left blue and t...

my dataset looked like this in the beginning: >Daten V1 V2 V3 1 Dosis Gewicht Geschlecht 2 0 6.62 m 3 0 6.65 m 4 0 5.78 m 5 0 5.63 m I need box plots for V2 with all combination of V1 and V3, so I deleted the first row, and tried this: boxplot(Daten$V2[Daten$V3=="m"]) but it does not work and I

I am trying to graph weight data for our colony. We a data frame with Animal, Weight and Date. I'd like to display this data a series of simple xyplots. We only need to display these plots together, we do not need to make comparisons between animals. Each animal has been weighted over a different time period, so we do not want the same range for each X axis. Using the following, I can

Hello All, I try to use the attached code to produce a cross over plot. There are 13 subjects, 7 of them in for/sal group, and 6 of them in sal/for group. But in xyplot, all the subjects are listed in both subgraphs. Could anyone help me figure out how to get rid of the empty levels? Thanks library(lattice) pef1 <- c(310,310,370,410,250,380,330,370,310,380,290,260,90) pef2 <-

...iable name eg ".ASU_1.Biofilm_C" Then I try the following, but I get an error message : > qp<- qplot(Par.Value, Var.Value, data = z[z$Var==v,], ylab=v, geom=c("point","smooth"), method="lm") > qp<- qp + facet_wrap( facets= Par~ ., scales = "free_x", ncol=length(vPar)) > qp Erreur dans `[.data.frame`(plot$data, , setdiff(cond, names(df)), drop = FALSE) : colonnes non d?finies s?lectionn?es I can have this working by modifying the facets arguments to "Par~Var", and it does what I want, But it is not satisfying, and I am...

...plot2) pdf("shared_specific.pdf", width = 14, height = 8) p.test<-ggplot(mydata, aes(coordinate.2)) + geom_line(aes(y = shared, colour = "shared")) + geom_line(aes(y = specific, colour = "specific")) + facet_grid(coordinate.1 ~., scales = "free_x") + scale_x_continuous("coordinate.2") + scale_y_continuous("shared and specific") p.test dev.off() [[alternative HTML version deleted]]

...ary(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)) + > ? ? ?facet_wrap(~var, scale="free_x") + > ? ? ?geom_smooth(method = "glm", method.args = list(family = > "quasipoisson")) > # > > library(MASS) > g1 <- glm.nb(Catch~xx1+xx2+xx3+offset(log(Effort)), data=edata) > g2 <- update(g1, . ~ . + log(Effort)) >...

Hello, I want to use the function barplot do display several group of bars. A standard example is given at this link http://onertipaday.blogspot.com/2007/05/make-many-barplot-into-one-plot.html But in their example the 4 groups of bars are all composed of 8 bars. I want to be able do display the same kind of graph but where the number of bars in each group are not the same. For example the

...ed for objects produced by the `multcomp` package. * `stat_summary2d` and `stat_summary_hex`. These are work like `stat_bin2d` and stat_binhex but allow any summarisation function (instead of just count). They are 2d analogs of `stat_summary` * `facet_grid`: The space argument now supports `free_x` and `free_y` next to `free` and `fixed, this allows the user to adjust the spatial scaling of the facets in either the x or y direction. This is especially useful when the scales are very different. In this case space = `free` could make some facets very small. (Thanks to Willem Ligtenberg...

...ed for objects produced by the `multcomp` package. * `stat_summary2d` and `stat_summary_hex`. These are work like `stat_bin2d` and stat_binhex but allow any summarisation function (instead of just count). They are 2d analogs of `stat_summary` * `facet_grid`: The space argument now supports `free_x` and `free_y` next to `free` and `fixed, this allows the user to adjust the spatial scaling of the facets in either the x or y direction. This is especially useful when the scales are very different. In this case space = `free` could make some facets very small. (Thanks to Willem Ligtenberg...