Generalize IDE references and add IDE comparison

- Changed 'RStudio' to 'Development environments (IDEs)' in schedule
- Expanded IDE acronym early for clarity
- Added IDE comparison section with image from tdscience
- Mentioned VS Code and Positron alongside RStudio
- Added link to detailed IDE comparison at tdscience.github.io/course
- Created language-comparison.csv comparing R and Python
- Updated prerequisites to mention multiple IDE options
- Made tasks.qmd IDE-agnostic where appropriate

Author: Robinlovelace

00_data/language-comparison.csv

@@ -0,0 +1,15 @@
+Attribute,R,Python
+Primary use,Statistical computing & data analysis,General purpose programming & data science
+Syntax,Domain-specific for statistics,"General purpose, readable"
+Data structures,"Data frames, vectors, lists","Pandas DataFrames, NumPy arrays, lists"
+Package ecosystem,CRAN (~20k packages),PyPI (~500k packages)
+IDE support,"RStudio, Positron, VS Code","VS Code, PyCharm, Jupyter, Positron"
+Learning curve,Moderate (statistics focus),Moderate (programming focus)
+Visualization,"ggplot2, base R graphics","matplotlib, seaborn, plotly"
+Statistical analysis,Excellent built-in support,Requires packages (scipy, statsmodels)
+Machine learning,"tidymodels, caret",scikit-learn
+Community,Academic & statistical focus,Broad & diverse
+Performance,Vectorized operations fast,Vectorized operations fast (NumPy)
+Quarto support,Native integration,Native integration
+Reproducibility,R Markdown/Quarto,Jupyter notebooks/Quarto
+Data manipulation,"dplyr, data.table","pandas, polars"

1-basics/slides.qmd

@@ -10,12 +10,14 @@ This section covers the essentials you need to get started with the concepts and
 
 ## Integrated Development Environments (IDEs)
 
-Your toolkit for writing code
+An **IDE** (Integrated Development Environment) is your toolkit for writing code efficiently.
 
 ------------------------------------------------------------------------
 
 ### RStudio
 
+Popular IDE specifically designed for R programming
+
 ![](images/paste-1.png)
 
 ----
@@ -31,6 +33,8 @@ Your toolkit for writing code
 
 ### VS Code
 
+Versatile IDE supporting multiple languages including R and Python
+
 ![](images/paste-2.png)
 
 ----
@@ -45,6 +49,14 @@ Your toolkit for writing code
 
 ------------------------------------------------------------------------
 
+### Positron
+
+New IDE from Posit (makers of RStudio) supporting R and Python equally
+
+**Note**: Currently in beta but shows great promise for bilingual data scientists
+
+------------------------------------------------------------------------
+
 ## The fundamentals of R
 
 - how to organize your work

1-basics/tasks.qmd

@@ -5,17 +5,19 @@ title: "The Basics - Tasks"
 We are going to start from scratch and build up some basic skills in R. Follow the steps below to get started.
 
 ::: {.callout-tip}
-Get R and RStudio running (see Prerequisites if needed) before.
+Get R and your IDE (RStudio, VS Code, or Positron) running (see Prerequisites if needed) before starting.
 :::
 
 Setting up your first(?) project:
 
-1. In RStudio, go to `File` > `New Project...`.
+1. In your IDE (RStudio example shown), go to `File` > `New Project...`.
 
    - Select `New Directory`.
    - Select `New Project`.
    - Name the project `DS-intro-2025` and choose a directory to save it in (your OneDrive might be a good choice).
    - Click `Create Project`.
+   
+   **Note**: The exact steps may vary slightly in VS Code or Positron, but the concept of project organization remains the same.
 
 2. Navigate to the `Files` tab, and create two folders in the project directory: `data` and `scripts`.
 

collaboration.qmd

@@ -40,7 +40,7 @@ GitHub isn't just for storing code; it provides powerful tools for teamwork:
 
 ## Getting Started
 
-To start using Git and GitHub, we recommend installing [GitHub Desktop](https://desktop.github.com/) or using the Git integration within RStudio or VS Code.
+To start using Git and GitHub, we recommend installing [GitHub Desktop](https://desktop.github.com/) or using the Git integration within your IDE (RStudio, VS Code, or Positron).
 
 For an ideal next step in your learning journey, check out the courses available at [GitHub Skills](https://skills.github.com/).
 

images/ide-comparison.png

@@ -17,6 +17,10 @@ In this practical, you'll get hands-on experience with data science tools. We'll
 
 There are a number of languages that can be used for data science, including JavaScript/TypeScript, Julia, and MATLAB. However, the two most popular languages are **R** and **Python**. Both are excellent choices for data science, and each has its own strengths, as outlined below.
 
+**Integrated Development Environments (IDEs):** An IDE is a software application that provides comprehensive facilities for writing, testing, and debugging code. Popular IDEs for data science include RStudio, VS Code, and Positron. See the [detailed IDE comparison](https://tdscience.github.io/course/s1.html) for more information.
+
+![IDE Comparison: RStudio, Positron, and VS Code](images/ide-comparison.png){#fig-ide-comparison fig-align="center" width="80%"}
+
 ::: {.callout-tip}
 ## R vs Python
 
@@ -25,7 +29,7 @@ If you are unsure which language to pick, we recommend trying both for 10 minute
 **Why choose R?**
 
 -   **"Batteries included":** Base R has built-in support for data frames, reading data from URLs, and statistical models (like linear regression) without needing extra packages.
--   **RStudio IDE:** The premier environment for R is user-friendly and often feels familiar to those coming from MATLAB.
+-   **Development environments:** RStudio and Positron provide excellent Integrated Development Environments (IDEs) for R that are user-friendly and often feel familiar to those coming from MATLAB.
 -   **Stability:** You are less likely to encounter "dependency hell" because CRAN enforces strict checks on package compatibility.
 -   **Community:** R has a massive community specifically focused on statistics and data visualization.
 
@@ -49,7 +53,7 @@ If you are unsure which language to pick, we recommend trying both for 10 minute
 | Time | Activity |
 |------|----------|
 | 09:00 - 09:15 | **Welcome & Setup**: Introduction and getting ready |
-| 09:15 - 09:45 | **Basics**: RStudio, Quarto, and basic syntax |
+| 09:15 - 09:45 | **Basics**: Development environments (IDEs), Quarto, and basic syntax |
 | 09:45 - 10:30 | **Manipulation**: Cleaning and transforming data with dplyr |
 | 10:30 - 10:45 | *Break* |
 | 10:45 - 11:30 | **Visualisation**: Creating plots with ggplot2 |

index.qmd

@@ -19,16 +19,24 @@ If you're using AppsAnywhere (available on university computers or your own lapt
 
 This approach is recommended if you want more control over your installations and if you plan to continue using these tools after the session:
 
-#### 1. R and RStudio
+#### 1. Development Environment (IDE)
+
+An **IDE** (Integrated Development Environment) provides a comprehensive workspace for coding. Popular options include:
+
+**For R:**
+- **RStudio Desktop** (free) from [Posit](https://posit.co/download/rstudio-desktop/) - Specifically designed for R
+- **Positron** from [Posit](https://github.com/posit-dev/positron) - New IDE supporting both R and Python (beta)
+
+**For multiple languages:**
+- **VS Code** from [Microsoft](https://code.visualstudio.com/) - Versatile IDE with extensions for R, Python, and more
+
+#### 2. Programming Languages
 
 **R** is a programming language widely used for statistical computing and data science.
 
 - Download R from [CRAN](https://cran.r-project.org/)
-- Download RStudio Desktop (free) from [Posit](https://posit.co/download/rstudio-desktop/)
-
-#### 2. Python (Optional but Recommended)
 
-**Python** is another popular language for data science.
+**Python** (Optional but Recommended) is another popular language for data science.
 
 - Download Python from [python.org](https://www.python.org/downloads/) (version 3.8 or higher)
 - Or install via [Anaconda](https://www.anaconda.com/download) (includes many data science packages)