RStudio project and experimental data

Last updated on 2026-10-20 | Edit this page

Overview

Questions

  • How do I organize my work for this lesson?
  • How do I get the data used in this lesson?

Objectives

  • Create an RStudio project with a folder for the data.
  • Download the lesson data.
  • Check that the required packages are installed.

Create an RStudio project

It is good practice to keep all files of one analysis in one folder. An RStudio project helps with this: when you open the project, R automatically uses the project folder as its working directory. All file paths in this lesson are relative to this folder.

  1. In RStudio, choose File → New Project → New Directory → New Project.
  2. Name the project microbiome-lesson and choose where to save it.
  3. Click Create Project.

Next, create a folder called data inside the project for the input files. You can use the New Folder button in the Files pane, or run:

R 

dir.create("data")

Finally, open a new R script (File → New File → R Script) and save it in the project folder, for example as microbiome-analysis.R. Write the code of the following episodes into this script, so that you can run it again later.

Prerequisite

New to R and RStudio?

This lesson assumes you know the basics of R and RStudio. If you need a refresher, see the Introduction to data analysis with R and Bioconductor lesson.

Download the data

The data consists of the four parts of a microbiome dataset (see episode 1), each in its own file:

File Content
taxonomy_abundance.csv Abundance table: taxa × samples
taxonomy_table.csv Taxonomy of each taxon
sample_metadata.csv Information about each sample
phylogeny.tree Phylogenetic tree of the taxa

We download them into the data folder with download.file(). Here is the first one:

R 

download.file(
    url = "https://raw.githubusercontent.com/microbiome/microbiome-lesson/main/episodes/data/taxonomy_abundance.csv",
    destfile = file.path("data", "taxonomy_abundance.csv")
)
Callout

File paths with file.path()

The function file.path() joins folder and file names into a path: file.path("data", "taxonomy_abundance.csv") gives "data/taxonomy_abundance.csv". Forward slashes work on Windows, macOS and Linux alike, but file.path() saves you from typing the separators yourself, so we use it for all file paths in this lesson.

Challenge

Challenge 1: Download the other files

Download the remaining three files (taxonomy_table.csv, sample_metadata.csv and phylogeny.tree) into the data folder. Then check that all four files are there.

R 

url <- "https://raw.githubusercontent.com/microbiome/microbiome-lesson/main/episodes/data/"

download.file(paste0(url, "taxonomy_table.csv"), file.path("data", "taxonomy_table.csv"))
download.file(paste0(url, "sample_metadata.csv"), file.path("data", "sample_metadata.csv"))
download.file(paste0(url, "phylogeny.tree"), file.path("data", "phylogeny.tree"))

list.files("data")

Check the packages

We use these packages:

Package Used for Episodes
mia Microbiome analysis all
miaViz Microbiome visualization all
ape Reading the phylogenetic tree 4
scater Principal component analysis (PCA) 7
bluster Clustering 7, 10
daa Simple statistical tests (Wilcoxon test, t-test, linear model) 5, 6, 8
maaslin3 Differential abundance and prevalence 8, 10
mikropml Machine learning 9

mia, miaViz and daa belong to the OMA framework. Each episode loads the packages it needs with library().

If you followed the setup instructions, the packages are already installed. Check that they load without errors:

R 

library(mia)
library(miaViz)
library(ape)
library(scater)
library(bluster)
library(maaslin3)
library(mikropml)

We do not load the daa package with library(). Instead, we call its functions with daa:: in front, as in daa::getLm(). Check that it is installed by asking for its version:

R 

packageVersion("daa")

OUTPUT 

[1] '0.99.0'

Messages are normal. If you see an error such as there is no package called 'mia', go back to the setup instructions.

Key Points
  • Use an RStudio project to keep the data, code and results of an analysis together.
  • Use download.file() to download data from the internet, and file.path() to build file paths.
  • The main packages of this lesson are mia and miaViz from the OMA framework; later episodes also use ape, scater, bluster, daa, maaslin3 and mikropml.