Introduction to Data Analysis with R

Introduction to Data Analysis

Lesson Summary:
Welcome to data analysis! This week, you will:

• Understand what data analysis is and why it is important in decision-making.

• Get an overview of Excel and R as complementary tools for analyzing data.

• Install R and RStudio on your computer and prepare Excel for data tasks.

• Explore your first dataset in Excel (sorting, filtering, basic summaries) and in R (using head() and summary()).

By the end of this week, you will be able to open and explore a dataset in both Excel and R, and understand the role of these tools in solving real-world problems.

Assignment 1

Assignment 2:

1. Download the sample dataset provided.

2. In Excel:

   • Import the dataset (CSV or Excel format).

   • Check that each column has the correct data type (date, number, text).

   • Clean the dataset: remove duplicates, fill or flag missing values, and standardize entries if needed.

   • Save the cleaned dataset with a clear file name.

3. Submit the cleaned dataset and a short note describing the cleaning steps you performed.

Lesson Summary:
This week focuses on bringing data into R and preparing it for analysis. You will learn to:

·         Import CSV, Excel, and text files into R using readr and readxl.

·         Explore datasets with basic functions like head(), summary(), and str().

·         Perform basic data wrangling using dplyr functions: select(), filter(), mutate().

·         Handle missing values and perform simple transformations.

By the end of this week, you will be able to load and clean data in R, making it ready for analysis.

Assignment 3:

1.      Import the same dataset you used in Excel into R.

2.      Explore the dataset using head(), summary(), and str().

3.      Use dplyr to:

o    Select specific columns.

o    Filter rows based on conditions.

o    Create a new column with a transformation.

4.      Identify and handle missing values.

5.      Save your cleaned and transformed dataset as an R object (.RData) or CSV.

Assignment 3:

1. Import the same dataset you used in Excel into R.

2. Explore the dataset using head(), summary(), and str().

3. Use dplyr to:

   • Select specific columns.

   • Filter rows based on conditions.

   • Create a new column with a transformation.

4. Identify and handle missing values.

5. Save your cleaned and transformed dataset as an R object (.RData) or CSV.

EDA in Excel

Lesson Summary:
This week, you will explore your data in Excel to understand its structure and patterns. You will learn to:

• Calculate descriptive statistics: AVERAGE, MEDIAN, MIN, MAX.

• Summarize data using subtotals and pivot tables.

• Identify missing values and outliers visually using conditional formatting and charts.

By the end of this week, you will be able to extract key insights from your dataset in Excel and spot data issues that need cleaning or further investigation.

Assignment 4:

1. Calculate summary statistics for your dataset (AVERAGE, MEDIAN, MIN, MAX).

2. Create pivot tables to summarize data by groups.

3. Highlight missing values and outliers using conditional formatting or visual inspection.

4. Prepare a brief summary of the patterns and issues you observed in your dataset.

EDA in R

Lesson Summary:
This week, you will explore your dataset in R to understand its structure and extract meaningful insights. You will learn to:

• Calculate descriptive statistics using mean(), median(), and sd().

• Create group summaries using group_by() and summarise().

• Detect missing values and outliers programmatically.

By the end of this week, you will be able to perform a comprehensive exploratory analysis in R and identify key trends, missing values, and anomalies in your data.

Assignment 5:

1. Perform summary statistics on your dataset in R.

2. Use group_by() and summarise() to create summaries by categorical variables.

3. Identify missing values and outliers.

4. Write a short report summarizing the key findings and data issues detected.

Data Visualization in Excel

Lesson Summary:
This week, you will learn to turn raw data into clear, actionable visuals using Excel. You will:

• Create bar, line, and column charts to highlight trends.

• Apply conditional formatting to quickly spot key values.

• Format and label charts effectively for presentations.

By the end of this week, you will be able to communicate insights visually and make your data easier to interpret for decision-making.

Assignment 6:

1. Create bar, line, or column charts for your dataset in Excel.

2. Apply conditional formatting to highlight top, bottom, or outlier values.

3. Ensure charts are properly labeled and formatted for clarity.

4. Submit your Excel file with the visualizations.

Data Visualization in R

Lesson Summary:
This week, you will learn to create and customize charts in R using ggplot2. You will:

• Understand the syntax and structure of ggplot2 (aesthetics, geoms).

• Create bar, line, and scatter plots.

• Customize colors, labels, and themes to make charts presentation-ready.

By the end of this week, you will be able to recreate Excel visualizations in R and design advanced charts for data storytelling.

Assignment 7:

1. Use ggplot2 to create bar, line, or scatter plots from your dataset.

2. Customize your charts with appropriate colors, labels, and themes.

3. Compare your R visualizations with the ones you created in Excel.

4. Submit your R script and the resulting plots.

Statistical Analysis in Excel

Lesson Summary:
This week, you will learn how to perform basic statistical analysis using Excel. You will:

• Use the Data Analysis ToolPak for correlation, t-tests, and regression.

• Create scatterplots with regression lines.

• Interpret p-values, R-squared, and other statistical outputs.

By the end of this week, you will be able to analyze relationships between variables and interpret results effectively in Excel.

Assignment 8:

1. Use the Data Analysis ToolPak to run correlation analysis on your dataset.

2. Conduct a t-test or regression analysis as appropriate.

3. Create scatterplots with regression lines.

4. Interpret the outputs and write a short summary of your findings.

5. Submit your Excel workbook with analysis and charts.

Statistical Analysis in R

Lesson Summary:
This week, you will perform statistical analysis using R. You will:

• Calculate correlation and perform hypothesis tests using cor(), t.test(), and chisq.test().

• Fit linear regression models using lm().

• Visualize regression lines and relationships with geom_smooth() in ggplot2.

By the end of this week, you will be able to run statistical tests, interpret results, and create visualizations of relationships between variables in R.

Assignment 9:

1. Calculate correlations between variables in your dataset.

2. Perform appropriate t-tests and/or chi-square tests.

3. Fit a linear regression model using lm().

4. Visualize the regression line with ggplot2.

5. Write a brief interpretation of your results.

6. Submit your R script and any resulting plots.

Advanced Analysis Techniques

Lesson Summary:
This week focuses on more advanced tools for analyzing data in both Excel and R. You will learn:

• Excel: Advanced formulas such as IF(), VLOOKUP(), and INDEX-MATCH for dynamic data analysis.

• Excel: Time series basics, including trendlines and forecasting.

• R: Time series analysis using ts(), decomposition, and the forecast package.

• R: Introduction to ANOVA for comparing group means and clustering techniques for identifying patterns in data.

By the end of this week, you will be able to handle complex datasets, perform time-based analyses, and explore group differences and clustering in R.

Assignment 10:

1. Apply advanced formulas in Excel to analyze your dataset.

2. Create a time series plot and apply trendlines in Excel.

3. In R, create a time series object, decompose it, and forecast future values.

4. Run a simple ANOVA or clustering analysis in R.

5. Document your steps and insights clearly.

6. Submit both Excel and R outputs for review.

Model Evaluation (Conceptual)

• Lesson: Introduction to model evaluation

  • Importance of evaluating models

  • Key evaluation metrics for regression: R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE)

  • Key evaluation metrics for classification: Accuracy, Precision, Recall, F1-score (brief overview)

  • Concept of overfitting and underfitting

  • Cross-validation overview (conceptual)

Choose one regression or classification scenario from your previous work. Write a short explanation of how you would evaluate the model using the metrics discussed. No calculations required; focus on reasoning and interpretation.

Case Study & Project Prep

Lesson Summary:
This week prepares you to integrate all the skills you’ve learned into real-world applications and your final project. You will:

• Apply Excel and R techniques to a real-world dataset.

• Combine quick insights from Excel with deeper analysis in R.

• Plan your final project, including selecting a dataset, cleaning data, and outlining your analysis approach.

By the end of this week, you will have a clear roadmap for your final project and be ready to conduct comprehensive data analysis.

Assignment 11:

1. Choose a dataset relevant to your interest or field.

2. Perform initial cleaning in Excel.

3. Apply exploratory and summary analyses in Excel and R.

4. Draft a detailed plan for your final project, including:

   • Dataset description

   • Cleaning steps

   • Analysis methods

   • Visualizations to include

5. Submit your project plan and preliminary analysis for feedback.

Final Project Presentations

Lesson Summary:
This week is dedicated to completing and presenting your final project. You will:

• Finalize dataset cleaning and full analysis using both Excel and R.

• Develop comprehensive visuals, summary statistics, and interpretations.

• Present your findings clearly, including insights, recommendations, and actionable conclusions.

By the end of this week, you will demonstrate mastery of data analysis concepts and practical skills, showing your ability to solve real-world problems using data.

Assignment 12:

1. Complete all data cleaning and analysis in Excel and R.

2. Create visuals and statistical summaries for your project.

3. Compile your results into a professional report or presentation.

4. Present your findings to the class, highlighting key insights, trends, and recommendations.

5. Submit your final project for evaluation.