density-plot

问题 How can I group a density plot and have the density of each group sum to one, when using weighted data? The ggplot2 help for geom_density() suggests a hack for using weighted data: dividing by the sum of the weights. But when grouped, this means that the combined density of the groups totals one. I would like the density of each group to total one. I have found two clumsy ways to do this. The first is to treat each group as a separate dataset: m <- ggplot() m + geom_density(data = movies

问题 My data file is a set of (x,y,z) points located around the axis origin. They represent points where a kind of measure has failed. These points are in this link. Gnuplot can plot them, set encoding iso_8859_1 set term postscript eps enhanced color size 4.7in,4in set xlabel "X" set ylabel "Y" set zlabel "Z" set output "test_gp.eps" set style line 1 lc rgb '#0060ad' pt 7 ps 0.5 lt 1 lw 0.5 # --- blue set style fill transparent solid 0.15 noborder splot "data.dat" u 1:2:3 w p ls 1 title "P_{error

问题 In psychology, it's common to display histograms with an overlaying normal curve. Also showing the density of the observed values with geom_line would facilitate comparison to the normal curve, so I wrote another histogram function that does this ( powerHist in the userfriendlyscience package). However, it performs very slowly for large vectors (currently working with 16.7 million datapoints), so I'm trying to make it faster. I used to use density to manually compute the density estimates,

问题 In psychology, it's common to display histograms with an overlaying normal curve. Also showing the density of the observed values with geom_line would facilitate comparison to the normal curve, so I wrote another histogram function that does this ( powerHist in the userfriendlyscience package). However, it performs very slowly for large vectors (currently working with 16.7 million datapoints), so I'm trying to make it faster. I used to use density to manually compute the density estimates,

问题 I'm trying to combine multiple density plots with overlay. ggplot and geom_density do the job, but the densities are stacked on top of each other. ggplot(all.complete, aes(x=humid_temp)) + geom_density(aes(group=height, colour=height, fill=height.f, alpha=0.1)) + guides(fill = guide_legend(override.aes = list(colour = NULL))) + labs(main="Temperature by Height", x="Temperature", y="Density") Something similar to this is what I'm trying to achieve: In my case, the years would be substituted by

问题 How can I get the area under overlapping density curves? How can I solve the problem with R? (There is a solution for python here: Calculate overlap area of two functions ) set.seed(1234) df <- data.frame( sex=factor(rep(c("F", "M"), each=200)), weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5))) ) (Source: http://www.sthda.com/english/wiki/ggplot2-density-plot-quick-start-guide-r-software-and-data-visualization ) ggplot(df, aes(x=weight, color=sex, fill=sex)) + geom_density

问题 I would like to plot the two dimensional densities of two different datasets using the smoothScatter() function: smoothScatter(confdiffCovCorrect$meth.diff, confdiffCovCorrect$qvalue) smoothScatter(logdiffCovCorrect$meth.diff, logdiffCovCorrect$qvalue)` But, in order to compare the two datasets using the plots, I want the color code to be identical. That is, I want a given value Xi of the density function to correspond to the same color in both plots. Is this possible? 来源： https:/

问题 I am trying to have 2 "shadows" on the background of the below plot. These shadows should represent the density of the orange and blue points separately. Does it make sense? Here is the ggplot to improve: Here is the code and the data (matrix df ) I used to create this plot: PC1 PC2 aa A_akallopisos 0.043272525 0.0151023307 2 A_akindynos -0.020707141 -0.0158198405 1 A_allardi -0.020277664 -0.0221016281 2 A_barberi -0.023165596 0.0389906701 2 A_bicinctus -0.025354572 -0.0059122384 2 A

问题 I am trying to do a density plot for a data containing two columns with different ranges. The RMSD column is [0-2] and Angle is [0-200] ranges. My data in the file is like this: 0.0225370 37.088 0.1049553 35.309 0.0710002 33.993 0.0866880 34.708 0.0912664 33.011 0.0932054 33.191 0.1083590 37.276 0.1104145 34.882 0.1027977 34.341 0.0896688 35.991 0.1047578 36.457 0.1215936 38.914 0.1105484 35.051 0.0974138 35.533 0.1390955 33.601 0.1333878 32.133 0.0933365 35.714 0.1200465 33.038 0.1155794 33

问题 I've explored similar questions asked about this topic but I am having some trouble producing a nice curve on my histogram. I understand that some people may see this as a duplicate but I haven't found anything currently to help solve my problem. Although the data isn't visible here, here is some variables I am using just so you can see what they represent in the code below. Differences <- subset(Score_Differences, select = Difference, drop = T) m = mean(Differences) std = sqrt(var