R for Data Science: this is a great online book that teaches you to do data science with R. Covering a wide range of topics using the tidyverse packages.
The R Gallery: this shows a range of plots that can be created in R using ggplot2 with associated code for each plot.
R-bloggers: this is a blog that aggregates posts from a wide range of R bloggers. It is a great resource for finding out about new packages and techniques in R.
Datacamp R documentation: this provides a reference to a lot of programming and data analysis in R using base R functionality.
Limma user’s guide: this is a comprehensive guide to using the limma package for differential expression analysis.
A guide to creating design matrices for gene expression experiments: this article explains design matrices for gene expression experiments in great detail and is written to be highly accessible to readers without a mathematical background.

Paths

We briefly touched on paths in the first session, but it is an important and complex topic that with worth digging into further. Paths are a way to specify the location of a file or directory on your computer, they are important whenever data needs to be read from or written to a file. This makes it a crucial concept for data analysis in R.

Absolute paths

An absolute path is defined as the full path from the root directory of the computer. These paths start with the root directory, which is / on Unix-based systems (like Linux and MacOS) and C:\ on Windows systems. For example, the absolute path to the home directory on a Unix-based system would be /home/my_username/analysis/data/file.txt. These paths should be used when the location of the file is not going to change and is in some shared location external to the project.

Relative paths

Relative paths are defined as a path relative to the current working directory. If you are already in the directory /home/users/my_username/analysis/, the relative path to the file data/file.txt would be have the same meaning as the absolute path /home/users/my_username/analysis/data/file.txt. Relative paths are useful when the location of the file is likely to change, for example if the whole analysis folder might be moved around with its included data.

Users tend to have a home directory, which is the private directory each user is assigned. This directory is often located at /home/my_username/ on Unix-based systems and C:\Users\my_username\ on Windows systems. This directory commonly acts as the starting point for many paths local to the user, and can be referred to using the shortcut ~ (tilde). For example, the path ~/analysis/data/file.txt would be equivalent to /home/my_username/analysis/data/file.txt on a Unix-based system. The exact path that is referred to by ~ can be found by running the command Sys.getenv("HOME") in R, and is relative to the user running the R session.

The `here` package

There are situations where you want to organise your scripts into folders, if you navigate to these folders and run the scripts, they will run within the folder they are in. But often you will want to run the script from the root folder of the project. For example if you have a script in ~/analysis/scripts/plot.R and inside you use data/file.txt to reference ~/analysis/data/file.txt, if you run the script from ~/analysis/scripts/ it will not find the file. The here package solved this problem by anchoring your paths relative to the root folder of the project. Details about the package can be found here.

Using AI helpers

There are a range of useful AI helpers available that can help solve problems with R. These include ChatGPT, Claude and more. These can be useful for debugging, finding solutions to problems, and more. However, they it is important to remember that they are not perfect and do not always provide the correct answer. This can have serious consequences if code provided by an AI helper contains a mistake that ends up affecting the analysis. It is therefore very important to both check the code provided by an AI as before you run it and inspect the result it produces to ensure it is reliable.

You should avoid:

Running code from an AI helper without checking it first
Running code from an AI helper without understanding the result it produces
Copy and pasting large amounts of code from an AI helper without understanding it
Using an AI helper as a substitute for learning how to code
Using an AI helper as a substitute for understanding the problem you are trying to solve

Because of the way AI helpers work, if you don’t understand a particular bit of code it produces, you can simply ask it to explain what it’s doing. If you don’t understand the explanation, or disagree with it, you should confront the AI helper with your concerns. Only run the code once you are sufficiently convinced that it’s doing what you want.

To use AI effectively, it helps to provide as much context as possible. This includes but is not limited to:

The problem you are trying to solve
The data you are working with
The output you are expecting
The code you have already tried
The error messages you are getting

When asking questions of the AI, you should be as specific as possible while providing room for flexibility in the answer. It often helps to start with a broader question before narrowing in on the specific problem you are facing. For example a poor question would be too vague without enough context, such as “How do I plot differential expression data?” A better question could be “What plots are commonly used to visualise differential expression data in RNA-seq analysis?”. Followed by “How do I create a volcano plot in R using ggplot2?”. If you have differential expression data ready, you can also describe it to the AI helper to get more specific advice. For example “I have a a data frame with the columns gene, log2FoldChange and pvalue, how can I create a volcano plot from this data?”.

The more information you provide the AI, the more likely it is to provide a useful answer. If you are confused about how to use a specific function, instead of asking the AI “How do I use the … function from the … package?”, you can copy and paste the entire help page for the function into the AI chat along with a description of what you’re trying to do.

--- format: html: toc: true toc-location: left toc-title: "Contents:" --- # Further reading {.unnumbered} ## Useful references {#sec-useful-references} - [R for Data Science](https://r4ds.had.co.nz): this is a great online book that teaches you to do data science with R. Covering a wide range of topics using the `tidyverse` packages. - [The R Gallery](https://r-graph-gallery.com): this shows a range of plots that can be created in R using `ggplot2` with associated code for each plot. - [R-bloggers](https://www.r-bloggers.com): this is a blog that aggregates posts from a wide range of R bloggers. It is a great resource for finding out about new packages and techniques in R. - [Datacamp R documentation](https://www.datacamp.com/doc/r): this provides a reference to a lot of programming and data analysis in R using base R functionality. - [Limma user's guide](https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf): this is a comprehensive guide to using the `limma` package for differential expression analysis. - [A guide to creating design matrices for gene expression experiments](https://f1000research.com/articles/9-1444): this article explains design matrices for gene expression experiments in great detail and is written to be highly accessible to readers without a mathematical background. ## Paths We briefly touched on paths in the first session, but it is an important and complex topic that with worth digging into further. Paths are a way to specify the location of a file or directory on your computer, they are important whenever data needs to be read from or written to a file. This makes it a crucial concept for data analysis in R. ### Absolute paths An absolute path is defined as the full path from the root directory of the computer. These paths start with the root directory, which is `/` on Unix-based systems (like Linux and MacOS) and `C:\` on Windows systems. For example, the absolute path to the home directory on a Unix-based system would be `/home/my_username/analysis/data/file.txt`. These paths should be used when the location of the file is not going to change and is in some shared location external to the project. ### Relative paths Relative paths are defined as a path relative to the current working directory. If you are already in the directory `/home/users/my_username/analysis/`, the relative path to the file `data/file.txt` would be have the same meaning as the absolute path `/home/users/my_username/analysis/data/file.txt`. Relative paths are useful when the location of the file is likely to change, for example if the whole analysis folder might be moved around with its included data. Users tend to have a home directory, which is the private directory each user is assigned. This directory is often located at `/home/my_username/` on Unix-based systems and `C:\Users\my_username\` on Windows systems. This directory commonly acts as the starting point for many paths local to the user, and can be referred to using the shortcut `~` (tilde). For example, the path `~/analysis/data/file.txt` would be equivalent to `/home/my_username/analysis/data/file.txt` on a Unix-based system. The exact path that is referred to by `~` can be found by running the command `Sys.getenv("HOME")` in R, and is relative to the user running the R session. ### The `here` package There are situations where you want to organise your scripts into folders, if you navigate to these folders and run the scripts, they will run within the folder they are in. But often you will want to run the script from the root folder of the project. For example if you have a script in `~/analysis/scripts/plot.R` and inside you use `data/file.txt` to reference `~/analysis/data/file.txt`, if you run the script from `~/analysis/scripts/` it will not find the file. The `here` package solved this problem by anchoring your paths relative to the root folder of the project. Details about the package can be found [here](https://here.r-lib.org). ## Using AI helpers There are a range of useful AI helpers available that can help solve problems with R. These include ChatGPT, Claude and more. These can be useful for debugging, finding solutions to problems, and more. However, they it is important to remember that they are not perfect and do not always provide the correct answer. This can have serious consequences if code provided by an AI helper contains a mistake that ends up affecting the analysis. It is therefore very important to both check the code provided by an AI as before you run it and inspect the result it produces to ensure it is reliable. You should avoid: - Running code from an AI helper without checking it first - Running code from an AI helper without understanding the result it produces - Copy and pasting large amounts of code from an AI helper without understanding it - Using an AI helper as a substitute for learning how to code - Using an AI helper as a substitute for understanding the problem you are trying to solve Because of the way AI helpers work, if you don't understand a particular bit of code it produces, you can simply ask it to explain what it's doing. If you don't understand the explanation, or disagree with it, you should confront the AI helper with your concerns. Only run the code once you are sufficiently convinced that it's doing what you want. To use AI effectively, it helps to provide as much context as possible. This includes but is not limited to: - The problem you are trying to solve - The data you are working with - The output you are expecting - The code you have already tried - The error messages you are getting When asking questions of the AI, you should be as specific as possible while providing room for flexibility in the answer. It often helps to start with a broader question before narrowing in on the specific problem you are facing. For example a poor question would be too vague without enough context, such as "How do I plot differential expression data?" A better question could be "What plots are commonly used to visualise differential expression data in RNA-seq analysis?". Followed by "How do I create a volcano plot in R using ggplot2?". If you have differential expression data ready, you can also describe it to the AI helper to get more specific advice. For example "I have a a data frame with the columns gene, log2FoldChange and pvalue, how can I create a volcano plot from this data?". The more information you provide the AI, the more likely it is to provide a useful answer. If you are confused about how to use a specific function, instead of asking the AI "How do I use the ... function from the ... package?", you can copy and paste the entire help page for the function into the AI chat along with a description of what you're trying to do.

Useful references

Paths

Absolute paths

Relative paths

The here package

Using AI helpers

The `here` package