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workflowr

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Last updated: 2024-09-10

Checks: 2 0

Knit directory: My_Project/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: 166be7f

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 166be7f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  .Rapp.history
    Untracked:  .gitignore
    Untracked:  Stats.Rmd
    Untracked:  Stats.html
    Untracked:  data/.DS_Store
    Untracked:  data/COADREAD.clin.merged.picked.txt
    Untracked:  data/COADREAD.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data.txt

Unstaged changes:
    Modified:   .DS_Store
    Modified:   analysis/.DS_Store
    Deleted:    myproject.zip

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/about.Rmd) and HTML (docs/about.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	166be7f	oliverdesousa	2024-09-10	Start my new project
html	c3ff74c	oliverdesousa	2024-09-10	Build site.
Rmd	5e763e5	oliverdesousa	2024-09-10	Start my new project
Rmd	b77ee0a	oliverdesousa	2024-09-06	Files
html	b77ee0a	oliverdesousa	2024-09-06	Files

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This R tutorial provides a comprehensive guide to various R programming techniques and data science methods.

Module 1: Introduction

Basics:

• Introduces the RStudio interface, basic R operations, functions, package installation, data importing, indexing, and essential data manipulation skills.

Data Preprocessing:

• Covers key steps like data collection, cleaning, transformation, and reduction to prepare datasets for analysis.

Visualisation:

• Demonstrates how to create scatter plots, box plots, heatmaps, survival plots, and pie charts for effective data visualization.

Module 2: Machine Learning

Supervised Learning:

• Explains regression, classification, cross-validation, and feature transformation techniques for predictive modeling.

Unsupervised Learning:

• Explores clustering methods, including K-means, hierarchical, probabilistic, and DBSCAN, for identifying patterns in data without labels.

miRseq Analysis Tutorial:

Using what we have learnt from this tutorial we present an example analysis one could perform from a biological perspective. In this case study we investigate the differences in gene expression within a Colerectal Adenocarcinoma cohort.

Gene expression of four genes (LCN2, CXCL3, GPX2, & CXCL2) were analysed within primary Colerectal Adenocarcinoma tissue samples from patients (01A).