Training Course on Statistical Data Management and Analysis using R

Course Overview

The Statistical Data Management and Analysis using R training provides a comprehensive introduction to leveraging the R programming language for effective data manipulation, exploration, and statistical modeling. Through a series of hands-on exercises and real-world examples, participants will learn essential techniques such as data importation, cleaning, visualization, and modeling. By the end of the course, attendees will have the skills and confidence to tackle diverse analytical challenges, create reproducible reports using R Markdown, and apply statistical methods to extract meaningful insights from complex datasets.

Course Duration:5 days

Course Outline

Module 1: Introduction to Statistical Analysis

• Basic steps of the research process
• Difference between populations and samples
• Difference between experimental and non-experimental research designs
• Difference between independent and dependent variables

Module 2: Introduction to R software for statistical computing

• Overview of the R Studio IDE
• Installing, loading and updating R packages
• Creating objects in R
• Data types
• Data structures
• Sorting vectors and data frames
• Directory management commands
• Direct data entry in R (for small data sets)
• Importing data from other software
• Decision structures (if, if-else, if-else if-else)
• Repetitive structures (for and while loops)
• Other important programming functions (break, next, warn, stop)

Module 3: Data Wrangling and Cleaning in R

• Working with variables
• Transform continuous variables to categorical variables
• Add new variables to data frames
• Handling missing values
• Sub-setting data frames
• Appending and merging data frames
• Spit data frames
• Stack and unstack data frames

Module 4: Explanatory Data Analysis (EDA) in R

• Creating tables of frequencies and proportions
• Cross tabulations of categorical variables
• Descriptive statistics for continuous variables

Module 5: Data Visualization using R base package

• Introduction to graphs and charts in R
• Customizing graph attributes (titles, axes, text, legends)
• Graphs for categorical variables
• Graphs for continuous variables
• Graphs to investigate relationship between variables

Module 6: Mean Comparison Tests in R

• One Sample T Test
• Independent Samples T Test
• Paired Samples T Test
• One-way analysis of variance (ANOVA)

Module 7: Tests of Associations in R

• Chi-Square test of independence
• Pearsons Correlation
• Spearmans Rank-Order Correlation

Module 8: Predictive Regression Models Using R

• Linear Regression
• Multiple Linear Regression
• Binary Logistic Regression
• Ordinal Logistic Regression

Note: This outline provides a general structure for the training. The specific content, activities, and duration of each session may be adjusted based on the target audience, learning objectives, and available time.