Statistical Data Management and Analysis using R
Our Statistical Data Management and Analysis using R course offers a comprehensive approach to mastering the essential skills and techniques for managing and analyzing statistical data using the R programming language.
In this training, participants will learn how to efficiently handle and manipulate data, perform advanced statistical analysis, and generate meaningful insights using R’s powerful features and packages.
This course is designed for individuals who want to enhance their data management and analysis capabilities in R and gain insights from complex datasets.
Course outline
Day 1: Introduction to R and Data Import
- Introduction to R programming language and its features
- Setting up the R environment and understanding the interface
- Managing and organizing data files in R
- Importing data from different file formats (CSV, Excel, SPSS)
- Exploring and summarizing data using descriptive statistics in R
Day 2: Data Manipulation and Transformation
- Data cleaning techniques in R (handling missing values, outliers)
- Data transformations and recoding variables
- Merging and reshaping datasets in R
- Subsetting and filtering data in R
- Handling categorical and continuous variables in R
Day 3: Statistical Analysis with R
- Conducting basic statistical analysis in R (t-tests, correlations)
- Introduction to exploratory data analysis (EDA) in R
- Linear regression analysis and interpretation of results in R
- Advanced statistical techniques (ANOVA, chi-square tests) in R
- Model diagnostics and interpretation in R
Day 4: Data Visualization with R
- Creating effective data visualizations using R’s graphics capabilities
- Customizing plots and charts for better presentation
- Exploring advanced visualization packages in R (ggplot2, Plotly)
- Creating interactive and dynamic visualizations in R
- Exporting and saving visualizations in various formats in R
Day 5: Advanced Topics in Statistical Analysis
- Introduction to advanced statistical techniques in R (e.g., logistic regression, time series analysis)
- Multivariate analysis and dimensionality reduction techniques in R
- Handling big data and performing parallel computing in R
- Applying Machine learning algorithms in R
- Reporting and presenting statistical results using Rmarkdown
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