Data Analytics Lab (CS-605) - Important Questions

Explain the role of R as a data analytics tool. In your answer describe the R execution environment, the differences between base R and popular integrated development environments such as RStudio, how to install and manage packages, and the typical workflow for an R-based data analysis project.

Demonstrate how to import and export common data formats in R. Provide example R code to read and write a CSV file, an Excel file, and an R serialized object. Explain advantages of each format and common issues to watch for (e.g., encodings, missing values, headers).

Describe the primary data structures in R: vector, factor, matrix, data.frame (or tibble), and list. For each structure give a short R code example showing how to create it, how to index/access elements, and a typical use-case in data analysis.

Using the tidyverse (dplyr) demonstrate the following data manipulation tasks on a sample data.frame: selecting a subset of columns, filtering rows based on a condition, creating a new calculated column, arranging (sorting) rows, grouping by a categorical variable and computing summary statistics. Provide the R code and explain each step briefly.

Explain how to reshape datasets between wide and long formats in R. Provide example R code using tidyr functions (for example pivot_longer and pivot_wider) and describe when each transformation is appropriate in the context of data analysis.

Compare base R plotting and ggplot2 for exploratory visualization. Using a sample dataset, provide R code to produce a histogram, boxplot, and a scatter plot with a fitted regression line using ggplot2. Explain how to customize axis labels, title, and color by a grouping variable.

Describe methods to detect and handle missing values in R. Provide example R code that identifies missing values, removes rows with missing data, and imputes missing numeric values using mean and median imputation. Discuss pros and cons of each approach.

Write an R function that takes a numeric vector and returns a named list with the mean, median, standard deviation and number of missing values. Demonstrate the use of this function on a sample vector and show how to apply it to every numeric column of a data.frame using an apply/map function.

Perform exploratory data analysis (EDA) in R on a given dataset. Your answer should include: calculation and interpretation of summary statistics for numeric and categorical variables, visualizations to show distributions and relationships (histogram, boxplot, scatter plot), identification of outliers and recommendations for preprocessing steps before modeling. Provide the R code snippets you would use for each task.

Explain how to create a reproducible analysis report using R Markdown. Describe how to include R code chunks, control chunk options (such as echo and results), knit the document to HTML or PDF, and save/share the analysis along with R session information.