R for Data Analysis
This course aims to equip attendees with the necessary skills to perform data analysis using R. Through a hands-on approach, students will learn the critical techniques and strategies for manipulating, analyzing, and interpreting data.
per person
Level
Duration
Training Delivery Format
Face-to-face / Virtual Class
per person
Level
Duration
Training Delivery Format
Face-to-face (F2F) / Virtual Class
Class types
Public Class
Private Class
In-House Training
Bespoke
About this course
R for Data Analysis is a two-day intermediate level course designed to introduce attendees to the principles and practices of data analysis using the R programming language. The course focuses on data manipulation, exploratory data analysis, and statistical inference, leveraging the power of R’s robust packages. Attendees will gain exposure to real-world data problems and how to solve them using R.
Who should attend?
This course is ideal for data analysts, researchers, statisticians, and anyone interested in enhancing their data analysis skills using R.
It’s also useful for academics and professionals who want to leverage data-driven decision-making in their respective fields.
Learning Outcome
Upon completion of this course, attendees will be able to import, clean, and manipulate data in R, perform exploratory data analysis, conduct statistical analysis, interpret the results, and present data visually. They will be capable of handling real-world data analysis tasks using R.
Prerequisites
Participants should have basic knowledge of statistics and should be familiar with any programming language, although knowledge of R is not necessary.
Course Content
Module 1: Introduction to R and RStudio
- Understanding R and RStudio Interface
- Installing Packages
- Getting help in R
Module 2: Data Structures in R
- Understanding Vectors, Matrices, Lists, and Data Frames
- Basic Operations with Data Structures
Module 3: Importing and Exporting Data
- Reading and Writing CSV Files
- Reading and Writing Excel Files
- Connecting to Databases
Module 4: Data Cleaning and Manipulation with dplyr
- Selecting and Filtering Data
- Sorting and Arranging Data
- Creating New Variables
- Grouping and Summarizing Data
Module 5: Introduction to Exploratory Data Analysis
- Understanding Basic Statistical Measures
- Dealing with Missing Values
- Outlier Detection
Module 6: Data Visualization with ggplot2
- Creating Basic Graphs (Histograms, Boxplots, Scatterplots)
- Customizing Graphs
- Faceting and Themes
Module 7: Introduction to Statistical Analysis in R
- Basic Statistical Tests (t-tests, chi-square tests)
- Correlation and Regression Analysis
- Logistic Regression
Module 8: Presentation of Analysis Results
- Reporting Results
- Creating Reproducible Reports with R Markdown
- Developing Shiny Applications for Interactive Data Presentation
Please note that the actual time spent on each module may vary depending on the class’s progress and understanding of the topics.
FAQs
Q: I have no prior experience with R. Can I still attend?
A: While knowledge of R is not a prerequisite, familiarity with any programming language is required to get the most out of this course.
Q: Will we work on real-world datasets?
A: Yes, we will be working on several real-world datasets to better understand and apply the concepts learned.
Q: Can I apply what I learn to my field of work/study?
A: Absolutely, R is versatile and widely used in various fields for data analysis. The skills learned can be transferred to a wide array of applications.
At this time, this course is available for private class and in-house training only. Please contact us for any inquiries.