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section-useful-skills.qmd
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---
title: Useful Skills
---
In this section, we will cover some useful computational skills that will likely be important for executing the analysis phase of any ancient DNA projects. With a focus on open- and reproducible science, we will cover introducing the command line, common programming languages to help automate your analyses, and also how to use Git(Hub) for sharing code.
## [Introduction to the Command Line (Bare Bones Bash)](bare-bones-bash.qmd)
Computational work in metagenomics often involves connecting to remote servers to run analyses via the use of command line tools. Bash is a programming language that is used as the main command line interface of most UNIX systems, and hence most remote servers a user will encounter. By learning bash, users can work more efficiently and reproducibly on these remote servers.
In this chapter we will introduce the basic concepts of bash and the command line. Students will learn how to move around the filesystem and interact with files, how to chain multiple commands together using "pipes", and how to use loops and regular expressions to simplify the running of repetitive tasks.
Finally, readers will learn how to create a bash script of their own, that can run a set of commands in sequence. This session requires no prior knowledge of bash or the command line and is meant to serve as an entry-level introduction to basic programming concepts that can be applicable in other programming languages too.
## [Introduction to R](r-tidyverse.qmd)
R is an interpreted programming language with a particular focus on data manipulation and analysis. It is very well established for scientific computing and supported by an active community developing and maintaining a huge ecosystem of software packages for both general and highly derived applications.
In this chapter we will explore how to use R for a simple, standard data science workflow. We will import, clean, and visualise context and summary data for and from our ancient metagenomics analysis workflow. On the way we will learn about the RStudio integrated development environment, dip into the basic logic and syntax of R and finally write some first useful code within the tidyverse framework for tidy, readable and reproducible data analysis.
This chapter will be targeted at beginners without much previous experience with R or programming and will kickstart your journey to master this powerful tool.
## [Introduction to Python](python-pandas.qmd)
While R has traditionally been the language of choice for statistical programming for many years, Python has taken away some of the hegemony thanks to its numerous available libraries for machine and deep learning. With its ever increasing collection of libraries for statistics and bioinformatics, Python has now become one the most used language in the bioinformatics community.
In this tutorial, mirroring to the R session, we will learn how to use the Python libraries Pandas for importing, cleaning, and manipulating data tables, and producing simple plots with the Python sister library of ggplot2, plotnine.
We will also get ourselves familiar with the Jupyter notebook environment, often used by many high performance computing clusters as an interactive scripting interface.
This chapter is meant for participants with a basic experience in R/tidyverse, but assumes no prior knowledge of Python/Jupyter.
## [Introduction to Git and GitHub](git-github.qmd)
As the size and complexity of metagenomic analyses continues to expand, effectively organizing and tracking changes to scripts, code, and even data, continues to be a critical part of ancient metagenomic analyses. Furthermore, this complexity is leading to ever more collaborative projects, with input from multiple researchers.
In this chapter, we will introduce 'Git', an extremely popular version control system used in bioinformatics and software development to store, track changes, and collaborate on scripts and code. We will also introduce, GitHub, a cloud-based service for Git repositories for sharing data and code, and where many bioinformatic tools are stored. We will learn how to access and navigate course materials stored on GitHub through the web interface as well as the command line, and we will create our own repositories to store and share the output of upcoming sessions.