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biomeutils's Introduction

R-CMD-check

RIVM Toolbox for Microbiome data analyses

In the last years at the RIVM much research has been done on data management and on the development of statistical methods for “data wrangling” (such as cleaning, transforming, merging data…), as well as for visualization of complex multivariable associations. The toolbox will implement these already available developments in combination with the new requirements in a user-friendly format, using the R language, and making them available to the scientific community within the RIVM as well as (inter)nationally.

biomeUtils

biomeUtils focus is towards utility tools for microbiota data formatting, handling, and exploration. The formatting and subsetting of microbiota data will be achieved with tools such as dplyr, tidyr, readr, tibble, stringr and forcats. Data visualization will be done mostly using the ggplot2 and related family of R packages, e.g. ggforce, patchwork and ggfortify.

Related tools

  • biomeStats
  • biomeViz

biomeutils's People

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biomeutils's Issues

collapse samples

Merge technical replicate of same samples into one.

  • Average counts
  • Keep sample with the higher number of reads

split phyloseq

Split one phyloseq into many

  • Based on taxonomic levels
  • Based on sample data

summarize sample_data

Get an overview of no. of samples based on a grouping variable

metadf.so <- meta(x) %>% 
   group_by(participant_id) %>% arrange(desc(day_of_sample)) %>% 
                    summarise(samples_for = paste(unique(day_of_sample), collapse = ","))


# OR 
gender_dist <- demo_data %>% 
  group_by(disease, gender ) %>% 
  tally() %>% 
  mutate(rel.freq = round(100 * n/sum(n), 1),
         rel.freq.pct = paste0(round(100 * n/sum(n), 0), "%"))

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