# Notes Section (only for reference):

# The next chunk is the set up chunk, which represents more configurations to the file. 

# (THIS LINE AND BELOW UNTIL THE WHITE DASH ARE NOT TO BE INCLUDED WITHIN THE TEMPLATE, IT IS JUST FOR NOTETAKING PURPOSES)

# This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

# When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. 

# echo = FALSE parameter can be added to the code chunk to prevent printing of the R code that generated the plot. Example below:

# {r pressure, echo=FALSE}
# plot(pressure)

# 
# Key Options Explained:

#toc: Table of contents on the left side.

#code_folding: hide: Code is hidden by default with a "Show Code" toggle.

#theme: Use a professional HTML theme (e.g., flatly, cosmo, journal).

#fig_caption: Adds captions to plots when used with fig.cap.

#params: Useful for parameterized reports.


### ✅ **3. Report Structure**

# Use proper section headers (`#`, `##`, `###` or #### or even higher for smaller font) for clarity and automatic TOC generation. Type your chosen number of hashtags, then press space to lock it in. 

#### Example:

# ```markdown
# Executive Summary

# Briefly summarize goals, key findings, and recommendations.

# Introduction

# Describe the objective of the analysis, the context, and the data sources.

# Data Import & Preprocessing

# ```{r load-data}
# library(tidyverse)
# df <- read_csv(params$dataset)
# df_clean <- df %>%
# filter(!is.na(target_variable)) %>%
# mutate(category = as.factor(category))


# EDA:

# summary(df_clean)
# ggplot(df_clean, aes(x = category, y = target_variable)) +
#  geom_boxplot() +
#  labs(title = "Target Variable by Category", y = "Target", x = "Category")

# Statistical Analysis / Modeling

# model <- lm(target_variable ~ category + predictor1 + predictor2, data = df_clean)
# summary(model)

# Discussion

#Interpret the model, discuss implications, and explain limitations.

# Conclusion

# Summarize findings and next steps.

# References
#Dataset Source: [Link]
#R Packages: tidyverse, knitr, ggplot2

# Appendix:

# Include any additional code, diagnostics, or visuals not shown in the main body.

### *4. Best Practices**
#- **Comment your code** with `#` inside chunks.
#- **Use section headers** to logically break up the report.
#- **Use `fig.cap =`** in plots for clear, automatic captions.
#- Always include:
#  - `setup` chunk with global options
#  - `library()` calls at the top
#  - Minimal inline `print()`—let plots and outputs render naturally
#- **Parameterize** if you're running similar reports on different inputs.
#- Avoid clutter by **suppressing warnings/messages** unless important.

### *5. How to Knit**
# - Click **"Knit"** → Select **HTML** (or PDF/Word if configured).
# - This will generate a clean, standalone report file.

# the above includes the entirety of the template of an industry standard Rmarkdown file, simply copy it please, also it has been reflected down below as well. 

# NEXT TO THE ITALICS AND UNDER THE KNIT ON SAVE OPTION, THERE IS A CODE TOGGLE BUTTON TO MAKE CODE BOXES, TOGGLE IT WHEN CODING. 

# you can also use insert to insert executable cells of other languages

#

1 Introduction/Summary:

2 Data Import & Preprocessing:

Cleaning data and filtering the selected data types/columns, null values, etc….

3 EDA:

Where graphs and data analysis occur along with summaries, etc….

4 Discussion:

Interpret the model, discuss implications, and explain limitations.

5 Conclusion:

Summarize findings and next steps.

6 References:

Dataset Source: [Link]

R Packages: tidyverse, knitr, ggplot2, or whatever used.

7 Appendix:

Include any additional code, diagnostics, or visuals not shown in the main body.