Closed DSWallach closed 2 years ago
This should be done if there are not enough samples in a particular group to produce the default statistics
https://github.com/jupyter/nbconvert/issues/1451 Related to broken summary functionality
https://github.com/t-makaro/nb_pdf_template Use this Also fix the formatting for the level value in the taxa summary
also add automatic prefixes to column names to allow them to start with numbers in Python
Taxa_bar_plot not appearing in file index of analyses
Skip continuous variables for beta group significance. Or automatically create bins
Cast all df column when running summary for alpha diversity files
as ` Stack all the different groups into a single dataframe
df = pd.concat(group_means, axis=0, sort=False) df.SamplingDepth = df.SamplingDepth.astype(float) df.Error = df.Error.astype(float) df.AverageValue = df.AverageValue.astype(float) df.Grouping = df.Grouping.astype(str) df.GroupID = df.GroupID.astype(str) df.GroupName = df.GroupName.astype(str) `
YlOrRed
and use the max_colors value when defining itload_config
allow nans in continuous variable columnsFor taxa tables in francesca's data the id column was labeled 'level_0' rather than 'index' because a different column was 'index'. Add checks to automatically catch this situatio
The colors are defined starting from color0 but are used starting from color1. This can cause problems
Closing, linking in #322
The default for upper is
'cor'
which calcluates the correlations between groups, but for some datasets this will cause errors if there are not enough data points for a certain group. Modify to use a safer default e.g.ggpairs(df[,c(1:3)], upper = list(continuous = "points", combo = "box_no_facet"), lower = list(continuous = "points", combo = "dot_no_facet"), aes(color = df$GroupID, label = rownames(df), alpha=0.5)) + theme_bw() + theme(legend.position = 'none', plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5)) + labs(title = 'PCA plot', subtitle = 'Colored by SpecimenTimepoint')