27 R Functions in One Real-Life Student Performance Project | Days 60–86

27 R Functions in One Real-Life Student Performance Project | Days 60–86 Welcome to the Days 60–86 monthly recap of our 365 Days of R Functions series by Stats Made Simple. This time, instead of simply revising individual R functions, we bring them together in one practical real-life project: Student Performance Analysis. Imagine you are a data analyst working with a college dataset containing student information, departments, subject marks, attendance, study hours, and internal assessment scores. Your goal is to turn this raw dataset into meaningful insights. In this project, we explore how different R functions can work together as part of a complete data analysis workflow. PROJECT OBJECTIVES We will answer questions such as: • Which department has the highest average performance? • Which student has the highest average score? • Which subject has the highest average? • How does attendance differ across departments? • How do study hours vary across performance categories? • How can we identify students who may need additional academic support? • How can we restructure the data for easier analysis? FUNCTIONS COVERED Numerical and mathematical functions: • round() — round numerical values for cleaner reporting • ceiling() — always round values upward • floor() — always round values downward • trunc() — remove the decimal portion • sqrt() — calculate square roots • exp() — perform exponential transformations • log() — perform logarithmic transformations • factorial() — perform factorial calculations Data transformation functions: • transform() — create or modify variables • within() — perform multiple transformations within a data frame Group-wise analysis: • split() — divide data into groups • by() — apply a function separately to groups • aggregate() — create group-wise summary statistics Data reshaping: • reshape() — change data between wide and long structures • reshape2::melt() — convert wide data into long format • reshape2::dcast() — convert long data into wide format • stack() — stack selected columns into a long-style structure THE PROJECT WORKFLOW We begin with raw student data. Then we inspect the structure and understand our variables. Next, we perform numerical transformations and create useful calculated variables such as Total Marks, Average Marks, and Performance Category. After that, we divide students into groups and calculate department-level statistics. Finally, we reshape the subject data between wide and long formats so that it becomes easier to summarize and analyze. The most important lesson is that R functions become much more powerful when they are combined. One function may solve one small problem. But several functions working together can take you from raw data to meaningful insights. The goal is not to memorize 27 function names. The real skill is understanding the problem you have and choosing the right R tool to solve it. INTERACTIVE CHALLENGE Imagine you are the data analyst for this college. What would you analyze next? Would you investigate the relationship between attendance and performance? Would you compare study hours across departments? Would you identify students who need academic support? Or would you build a complete student performance dashboard? Share your answer in the comments. Also tell us which function from Days 60–86 you found the most useful. IMPORTANT NOTE reshape2 is an older R package. In modern R workflows, functions such as tidyr::pivot_longer() and tidyr::pivot_wider() are generally preferred for many data-reshaping tasks. However, learning reshape2::melt() and reshape2::dcast() remains useful for understanding older R code and the fundamental concepts of wide and long data. This project demonstrates an exploratory data-analysis workflow. Observed patterns, such as higher study hours among higher-performing students, should not automatically be interpreted as causal relationships without appropriate statistical testing. 365 DAYS OF R FUNCTIONS Day 60–86 Monthly Recap From raw data to transformation. From transformation to grouping. From grouping to summarization. From reshaping to insights. That's the real power of R. If you enjoyed this project, LIKE, SAVE, SHARE it with someone learning R, and SUBSCRIBE to Stats Made Simple for more practical R programming, statistics, and data-analysis tutorials. Keep Learning. Keep Practicing. Keep Analyzing. Keep Growing. #RProgramming #RStats #LearnR #RFunctions #RDataAnalysis #RDataScience #StatisticsWithR #RStatistics #DataAnalysis #DataScience #RForBeginners #ProgrammingWithR #RStudio #StudentPerformanceAnalysis #StudentDataAnalysis #DataAnalysisProject #RealWorldDataAnalysis #DataVisualization #DataTransformation #DataReshaping #TidyData #StatisticalProgramming #MachineLearning #Analytics #365DaysOfRFunctions #StatsMadeSimple