Code
# Load your data files here.
# Replace the file paths with your actual file locations.
# Example:
# my_data <- read_csv("data/my_data.csv")Project Title — Replace With Your Research Question
1 Problem Definition
1.1 Research Question
State your research question clearly and specifically here. A good research question is answerable with the available data and has implications for action.
Example: Which early indicators best predict student withdrawal in distance learning modules?
1.2 Stakeholder and Context
Who is the audience for this analysis? What decision are they trying to make? Why does this question matter to them?
Example: This analysis is intended for the advising office at a distance learning institution. Understanding early withdrawal predictors would allow advisors to proactively reach out to at-risk students before they disengage.
1.3 Hypotheses
Before looking at the data, state what you expect to find and why. This anchors your analysis in theory rather than data dredging.
- H1:
- H2:
- H3:
2 Data Understanding
2.1 Dataset Overview
Describe the dataset you are using. Where does it come from? What does it contain? What are its limitations?
2.2 Data Dictionary
Document every variable you plan to use. Explain what it measures, its data type, and any known issues or caveats.
| Variable | Description | Type | Notes |
|---|---|---|---|
2.3 Ethical Considerations
How was the data collected? Is it anonymized? Are there any ethical concerns about how it was gathered or how your analysis might be used?
3 Data Import and Preparation
3.1 Load Data
3.2 Inspect Data
Code
# Check dimensions, column names, and data types before doing anything else.
# This catches problems early — wrong column names, unexpected data types,
# columns that should be numeric but read as character, etc.
# Example:
# glimpse(my_data)
# names(my_data)
# nrow(my_data)3.3 Join and Reshape
Code
# If your data comes from multiple tables, join them here step by step.
# Document each join — why you used left_join, what the join keys are,
# and what you expect the row count to be after each step.
# Step 1 — Start with the central table
# Step 2 — Add ...
# Step 3 — Add ...3.4 Clean and Transform
Code
# Handle missing values, recode variables, create derived variables.
# Document every decision — why you dropped NAs, what a recoded variable means,
# why you created a new variable.4 Exploratory Data Analysis
Explore the data before trying to answer the research question. Look at distributions, missingness, outliers, and relationships between variables. The goal is to understand what you have and refine your hypotheses.
4.1 Missingness
Code
# How much missing data is there? Which variables? Does missingness follow
# a pattern — e.g. are missing values concentrated in withdrawn students?4.2 Distributions
Code
# Plot the distribution of your key variables.
# Are they normally distributed? Skewed? Are there outliers?4.3 Group Comparisons
Code
# Compare your outcome variable across groups.
# Example: average score by final result, VLE engagement by withdrawal status.4.4 Correlations
Code
# Examine relationships between variables before modeling.
# Are your predictors correlated with each other? With the outcome?5 Analysis
Answer your research question here using appropriate methods. The method should follow from the question — not the other way around.
5.1 Approach
Describe the analytical approach you chose and why it is appropriate for your research question.
5.2 Results
Code
# Your main analysis goes here.5.3 Summary Table
Code
# Present key results in a clean formatted table.
# gt() is a good option for polished tables.6 Interpretation
6.1 What the Results Mean
Translate your statistical findings into plain language. What do the numbers actually mean for your stakeholder? Avoid jargon — write as if explaining to a dean, not a statistician.
6.2 Limitations
Every analysis has limitations. Be honest about yours. What can’t you conclude from this analysis? What alternative explanations exist? What data would you need to be more confident?
- Limitation 1:
- Limitation 2:
- Limitation 3:
6.3 Recommendations
What should the stakeholder actually do based on your findings? Be specific and actionable.
- Recommendation 1:
- Recommendation 2:
- Recommendation 3:
7 Conclusion
Briefly summarize the research question, what you found, and why it matters. This section should stand alone — a busy stakeholder should be able to read just this section and understand the key takeaway.
8 Appendix
8.1 Session Info
# Always include session info in a reproducible document.
# It records the R version and package versions used so others can reproduce
# your work exactly.
sessionInfo()R version 4.4.3 (2025-02-28 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scales_1.3.0 gt_1.3.0 lubridate_1.9.4 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_1.9.1 compiler_4.4.3 tidyselect_1.2.1
[5] xml2_1.3.8 yaml_2.3.10 fastmap_1.2.0 R6_2.6.1
[9] generics_0.1.3 knitr_1.50 htmlwidgets_1.6.4 munsell_0.5.1
[13] pillar_1.11.0 tzdb_0.5.0 rlang_1.1.7 stringi_1.8.4
[17] xfun_0.51 fs_1.6.5 timechange_0.3.0 cli_3.6.4
[21] withr_3.0.2 magrittr_2.0.3 digest_0.6.37 grid_4.4.3
[25] rstudioapi_0.17.1 hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5
[29] evaluate_1.0.3 glue_1.8.0 colorspace_2.1-1 rmarkdown_2.29
[33] tools_4.4.3 pkgconfig_2.0.3 htmltools_0.5.8.18.2 Additional Tables or Figures
Any supplementary material that supports the analysis but would interrupt the flow of the main document goes here.