In ying14/yingtools2: Ying's R Tools

knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE)
options(tibble.print_min=10,tibble.print_max=20,
        pillar.bold=TRUE,
        tibble.max_extra_cols=10)

Ying Taur's tools for analysis, with particular focus on microbiome data

yingtools2 is an R package containing many tools and functions for working with clinical and microbiome data.

Installation

Install this package from Github:

remotes::install_github("ying14/yingtools2")

Sample Microbiome Dataset

library(yingtools2)
library(tidyverse)
library(phyloseq)

describe <- function(obj) {
  obj <- get(obj)
  text <- ""
  if (is.data.frame(obj)) {
    text <- paste(pretty_number(nrow(obj)),"rows")
  } else if (is(obj,"phyloseq")) {
    text <- paste0("phyloseq object, ",pretty_number(sum(sample_sums(obj)))," seqs, ",pretty_number(nsamples(obj))," samples, ",pretty_number(ntaxa(obj))," OTUs")
  }
  return(text)
}

x <- data(package="yingtools2")[["results"]] %>% as_tibble() %>%
  mutate(desc=sapply(Item,describe)) %>%
  arrange(fct_relevel(Item,"cid.phy","cid.patients","cid.hosp","cid.meds","cid.bsi","cid.cdiff"))

n.otu <- ntaxa(cid.phy) %>% pretty_number()
n.samples <- nsamples(cid.phy) %>% pretty_number()
n.pts <- cid.phy %>% get.samp() %>% pull(Patient_ID) %>% n_distinct()
datasets <- paste0("- `",x$Item,"`: ",x$Title," (",x$desc,")",collapse="\n")

Included is a de-identified microbiome dataset of stool samples collected from a cohort of bone marrow transplant recipients at Memorial Sloan Kettering Cancer Center. This was previously published in Clinical Infectious Diseases (2012). The dataset includes sequence data for r n.samples samples (from r n.pts patients), as well as a variety of accompanying clinical metadata.

r datasets

Self Help and Code Examples

Below are various use coding examples using yingtools2.

Core Basics

For those starting to learn R for data science

• Coding Curriculum. A run-through of the most important R coding skills to learn.
• Regular Expressions. Lists basic regular expression patterns.
• Exercises. These questions test your R skills and range from beginner to advanced.

Microbiome data

• Hierarchical clustering (Fig 3A of CID paper)
• PCOA and other ordination methods
• Phylogenetic Trees from Phyloseq
• Testing taxonomic features (LDA effect size)
• Single sample barplot

ying14/yingtools2 documentation built on April 14, 2025, 1:24 a.m.