# Create a bar plot with error bars (mean +/- sd) bp2 <- ggbarplot( df, x = "dose", y = "len", add = "mean_sd", color = "supp", palette = c("#00AFBB", "#E7B800"), position = position_stack() ) # Add p-values onto the bar plots # Specify the p-value...
> axis.POSIXct(1, at = seq(r[1], r[2], by =”year”), format = “%b %Y”) # label the x axis bymonth-years > > plotCI( + x=depmCount$mo.yr, + y = depmCount$count, + uiw=depmCount$se, # error bar length (default is toput this much above and below point) +...
Key argument: stat = "identity" to plot the data as it is. Use the functions scale_color_manual() and scale_fill_manual() to set manually the bars border line colors and area fill colors. # Stacked bar plots of y = counts by x = cut, # colored by the variable color ...
library(ggpubr) library(rstatix) # Transform `dose` into factor variable df <- ToothGrowth df$dose <- as.factor(df$dose) head(df, 3) # Create a bar plot with error bars (mean +/- sd) bp2 <- ggbarplot( df, x = "dose", y = "len", add = "mean_sd", color = "supp",...
Before beginning with the estimation, we can activate the progress bar visualization. This allows us to gauge estimation progress along the way. tidyfit uses the progressr-package internally to generate a progress bar — run progressr::handlers(global=TRUE) to activate progress bars in your envir...