Hands-on Activity 2 - Theory

Stéphane Tuffier

2024-06-05

Plan

1. Functional programming with purrr
2. Report generation with Quarto
3. Table creation with gt
4. Bonus: Git branch and remote repo

Functional Programming in R with purrr

Functions can be easily applied to multiple elements using functional functions: special functions that uses another function as one of its arguments.

purrr:

• Part of the tidyverse
• Focus on functional programming
• Simplify complex data manipulations

The purrr package is a part of the tidyverse suite of packages, which is designed to make data science easier and more consistent in R. purrr focuses on functional programming and helps simplify complex data manipulations, especially with lists and data frames.

Functional Programming in R with purrr

• The map family of functions: works by taking a vector (or list), applying a function to each of those items, and outputting the results from each function call.

Functional Programming in R with purrr

paste_text <- function(x) { 
  paste(x, "seconds have passed")
}
purrr::map(1:5, paste_text)

[[1]]
[1] "1 seconds have passed"

[[2]]
[1] "2 seconds have passed"

[[3]]
[1] "3 seconds have passed"

[[4]]
[1] "4 seconds have passed"

[[5]]
[1] "5 seconds have passed"

https://r-cubed-intermediate.rostools.org/sessions/functions

The name map() doesn’t mean a geographic map, it is the mathematical meaning of map: To use a function on each item in a set of items.

• map(): Applies a function to each element of a list and returns a list
• map_lgl(): Returns a logical vector
• map_int(): Returns an integer vector
• map_dbl(): Returns a double vector
• map_chr(): Returns a character vector
• map_df(): Applies a function to each element and returns a data frame

Functional Programming in R with purrr

Benefits of Using purrr

• Reduces redundancy
  • Write less code by avoiding repetitive loops
• Increases code readability
  • Clear and concise syntax
• Easier to update and debug

Dynamic Report with Quarto

What is Quarto?

• Open-source scientific publishing system

• Supports R and Python

• Integrates code, text, and outputs in one document (HTML, DOCX)

Quarto by Allison Horst

• Produces HTML, PDF, Word documents, presentations, etc.

Benefits:

• Reproducible research
• Automated reporting

Quarto Document Structure

  ---
  title: "Descriptive Statistics Report"
  author: "Your Name"
  format: html # or docx 
  ---
  
  # Results
  
  ```{r}
library(purrr)
numeric_vars <- c("creatinine", "lead", "barium", "cadmium")
descriptive_stats <- map(numeric_vars, ~ compute_numeric_summaries(nh2007[[.x]]))
descriptive_stats
```
  

• YAML header defines document metadata.
• R code chunk for analysis.
• Results section for displaying outputs.

Markdown

Text formatting:

• **bold**,
• _italic_,
• # Headers 1,
• ## Headers 2

Lists:

• Unordered lists with - or *

• Ordered lists with 1.

R Code chunks:

```{r}
summary(cars)
```

Table creation with gt

Create publication ready tables in R directly from your data.

There is many functions to style and format tables to match your needs:

• Automatic number formating
• Nice labels with special characters
• Table header spanner
• Cells highlight

Tables can be exported in many format including html or MS Word.

Table creation with gt

Bonus: Git branching

Basic Git Workflow:

• Create a branch: git branch new-branch-name
• Switch to new branch: git checkout new-branch-name
• Commit changes: git add ., git commit -m "message"
• Merge branch: git checkout main, git merge new-branch-name

• Isolates development work
• Allows multiple features/bug fixes to be developed concurrently
• Simplifies collaboration and code review

Bonus: Git local and remote