R Programming Fundamentals

Data analysis is crucial to accurately predict the performance of an application. The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you will read ahead and learn all about data visualization and graphics. You will learn data management techniques such as pivots, aggregations, and dealing with missing values. With various case studies and examples, this course gives you the knowledge to confidently start your career in the field of data science.

This is a hands-on guide that walks you through concepts with examples using built-in R data. Every topic is enriched with a supporting example that highlights the concept, followed by activities that will gradually build into a full data science project to showcase skills learned. You will perform an end-to-end analysis, starting a data science portfolio.

This course is for analysts who are looking to grow their data science skills beyond the tools they have used before, such as MS Excel and other statistical tools.

Hardware:

This course will require a computer system for the instructor and one for each student. The minimum hardware requirements are as follows:

• Processor: i3
• Memory: 2 GB RAM
• Hard disk: 10 GB
• Internet connection

Software:

• Operating system: Windows 8 64–bit
• R and RStudio
• Browsers (Google Chrome and Mozilla Firefox – latest versions)

The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Here is the list of course objectives:

Use the basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control

• Import data to R from various formats, such as CSV, Excel, and SQL
• Clean data by handling missing values and standardizing fields
• Perform univariate and bivariate analysis using ggplot2
• Create statistical summary and advanced plots, such as histograms, scatter plots, box plots, and interaction plots
• Apply data management techniques, such as factors, pivots, aggregation, merging, and dealing with missing values, on the example data sets

• Using R, RStudio, and Installing Useful Packages
• Variable Types and Data Structures
• Basic Flow Control
• Data Import and Export
• Getting Help with R

LESSON 2: DATA VISUALIZATION AND GRAPHICS

• Creating Base Plots
• ggplot2
• Interactive Plots

LESSON 3: DATA MANAGEMENT

• Factor Variables
• Summarizing data
• Splitting, Combining, Merging, and Joining Datasets