graphpad prism 6 two sets of symbols one graph

Labeller = labeller (sex = c (male = '♂', female = '♀' ) ) ) + geom_hline (yintercept = 0, linetype = 2, size = 0.3 ) + stat_summary (geom = 'crossbar', aes (fill = genotype ),fun = mean, Ref.group = 'Tps1MIC/+' ) %>% rstatix :: add_x_position (x = 'measure', dodge = 0.9 ) %>% # dodge must match points mutate (label = c ( '***', '*', 'P = 0.26', '***', '***', 'P = 0.65' ) ) ggplot ( wings, aes (x = measure, y = percent.change ) ) + ggbeeswarm :: geom_beeswarm ( aes (fill = genotype ), dodge.width = 0.9, shape = 21 ) + facet_wrap ( ~ sex, scales = 'free', Wings_pvals % group_by ( sex, measure ) %>% rstatix :: t_test ( percent.change ~ genotype, With this p-value table, we can now add p-value brackets. # perform t-tests to compare the means of the genotypes for each measure # and correct the p-values for multiple testing wings_pvals % group_by ( sex, measure ) %>% rstatix :: t_test ( percent.change ~ genotype, Lastly, we manually change the bracket labels to appear as we would like them to. We also use the add_x_position() function from rstatix to make sure the bracket ends have the correct x axis positions (specifically the xmin and xmax columns). In this case we group the wings data by sex and wing measure, and then test whether the percentage change in wing size/cell size/cell number is explained by the fly genotype. The easiest way to generate the p-value table is with the rstatix (you could also do it manually but using rstatix ensures the table is of the correct format).

To do this we need to generate a table of p-values with the correct format, and then use this table with the add_pvalue() function from ggprism. The last data-related element to be added are the p-value brackets. # plot solid red lines at the mean of each group p <- p + stat_summary (

Graphpad prism 6 two sets of symbols one graph