Data Analysis: R Programming Essentials-R Programming Guide

Example

Creating data frames from scratch or importing data from various sources (CSV, Excel, databases), and performing operations like adding, removing, or modifying columns.

Scenario

Analyzing a dataset of sales transactions to identify top-performing products and trends over time.

Example

Using functions like `aggregate()`, `summary()`, and `group_by()` to compute summaries (mean, median, sum) across grouped data.

Scenario

Summarizing patient data in medical research to find average treatment effects across different groups.

Example

Combining data from multiple sources using functions like `merge()` or `join()` to create comprehensive datasets for analysis.

Scenario

Integrating customer information with sales data to understand purchasing patterns.

Example

Employing strategies like imputation or removal of rows/columns with missing data to maintain the integrity of analyses.

Scenario

Cleaning a survey dataset by addressing missing responses before performing statistical analysis.

Example

Using `apply()`, `sapply()`, `lapply()`, and conditional statements to transform data based on specific criteria.

Scenario

Automating the categorization of expenses based on amounts and types for budget analysis.

Example

Creating graphs and plots with ggplot2 or base R plotting functions to visually represent data trends and distributions.

Scenario

Visualizing climate change data to highlight global temperature trends over decades.