Data Wrangling in R
R is a an open-source programming language that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques. However, to take advantage of the wide statistical and visualization capabilities within R, the data itself must be in the right format. This data wrangling course provides a comprehensive overview of the tools and techniques needed to effectively transform data using the R language.
An overview of data science best practices for data ingestion, preparation, and wrangling for analysis provides a strong foundation for the course content. Students will query relevant databases using R, be introduced to Base R, and explore examples of data wrangling code using specific R packages. R programming concepts focus on exercises using real data.
What you will learn:
- An introduction to data science concepts and data preparation for analysis
- An introduction to R, how R is different, basic data types, and R/RStudio installation
- Overview of base R concepts and specific data wrangling packages in R
- Connecting to databases, executing database queries in R
- Common problems experienced in data wrangling
- Demo examples using canonical datasets
- An introduction to programming in R with applied data wrangling programming
Intended Audience: Data Scientists, Data Analysts, Business Intelligence Analysts
Duration: 4 Days (can be customized based on requirements)