100)plot.df <- reshape2::melt(plot.df, id.vars = "t")# reorder in case# plot.df <- plot.df[order(plot.df$variable,plot.df$t),]# adding a new column to make progressive the reveal of the dataplot.df$transition_value <- 1:nrow(plot.df)# your ggplot plotp <- ggplot(plot....
100)plot.df <- reshape2::melt(plot.df, id.vars = "t")# reorder in case# plot.df <- plot.df[order(plot.df$variable,plot.df$t),]# adding a new column to make progressive the reveal of the dataplot.df$transition_value <- 1:nrow(plot.df)# your ggplot plotp <- ggplot(plot....
What happens if you erroneously plot the residuals versus the true outcome, instead of the predictions? Let’s try this with the model for the linear process (which we know is a well-fit model): # the wrong residual graphggplot(df, aes(x=y, y=residual)) + geom_point(alpha=0.5) + ...
参考:https://jonathanth.github.io/mixOmics_examples.html; https://github.com/crlero/vir2asth/blob/main/6-classification.Rmd # 进行AUC值箱线图绘制的函数# Plot PLS-DA model components AUC distributionauc_components_plot = function(plsmodel) { plsmodel$pred %>% separate(Resample, c("Fold", ...
Using a ggplot2 syntax for using a grammar of plot-layout operations. Extends the amazing ggplot2 package. How patchwork works Here’s the simplified patchwork syntax. That’s all we need to create a two-column layout with the right-column containing two rows is in the simple syntax. Let’...
1)ggplot()函数 2)geom操作 3)scale操作 4)facet操作 5)labs操作 6)theme操作 当我们熟悉ggplot2包这些知识后,我们就可以使用它设计和实现一系列有用的图形,以帮助我们获取数据洞见和增强沟通效果。 学习资料: https://rkabacoff.github.io/datavis/IntroGGPLOT.html ...
1)ggplot函数 2)geom操作 3)scale操作 4)facet操作 5)labs操作 6)theme操作 当我们熟悉ggplot2包这些知识后,我们就可以使用它设计和实现一系列有用的图形,以帮助我们获取数据洞见和增强沟通效果。 学习资料: https://rkabacoff.github.io/datavis/IntroGGPLOT.html ...
Finally, I’ve been experimenting a bit with using the input in a formula interface, more similar to the way ggplot in R allows you to do this. So this is a new function,plot_form, and here is an example Poisson linear model:
Hello, I performed trajectory analysis on a dataset containing human monocytes and its progenitor cells. When I tried to plot genes that changed expression over pseudotime I get a plot that looks like this: I checked counts for this gene...
ggplot(df2, aes(x = dose, y = len, fill = supp)) + geom_bar(stat = "identity", position = "dodge") + ggtitle("Error Bar 95% confidence interval") + ylab("len") + geom_errorbar(aes(ymin = len - 1.96 * sd, ymax = len + 1.96 * sd), width = 0.2, position = position...