Data processing is a process, in which raw data is modified into the form needed for modeling and visualizing. It’s the inherent part of every analysis of data and requires the most time and work. This training enables you to discover the way to process and manage data in R efficiently while using the newest libraries such as dplyr, tidyr, reshape2 and lburidate.

The training is conducted by using Live script method which enhances the learning process and enables you to memorize the programming techniques by creating an easy and efficient script for repeatable analysis. Due to a number of exercises you will be able to solidify acquired knowledge and gain very useful skills which are valuable for employers.

The training is aimed at people who work with R and are willing to boost their skills of data processing.

1. Data analysis in R
   • Type and class
   • Data storage in R
   • Object structure
   • Type conversion
   • Missing values
2. Overview of libraries for data processing in R
   • data.frame
   • dplyr
   • reshape2
   • stringr
   • tidyr
3. Basic data types
   • Numbers and vectors
   • Matrix and tables
   • Factor
   • List
   • Data frame – tworzenie manipulacje
   • Date and Time
4. Accessing data from files
   • Import from – TXT
   • Import from – CSV
   • Import from – XLS
   • Binary files
   • Exporting data to other formats
5. Accessing data from databases
   • SQL basics
   • Manipulating data in R using SQL
   • RODBC, DBI, libraries
   • Connecting to Microsoft SQL Server
   • Connecting to MySQL
   • Query execution
   • Exporting data to database
   • Advanced operations
6. Indexing
   • Introduction to indexing
   • Numerical indexing
   • Text indexing
   • Logical indexing
   • Indexing different objects in R
   • Special functions from dplyr library
   • data.table functions for indexing
7. String processing in R
   • String data objects
   • Operations on strings
     • String representation in R
     • Combining text and numbers
     • Overview of useful functions
   • String processing with stringr library
   • Regular expressions in R
     • Pattern matching
     • Pattern replacement
   • Examples
8. Data aggregations
   • aggregate()
   • apply()
   • Pivot tables
   • table()
   • Aggregation with reshape2 functions
9. Transformations
   • reshape2 functions: melt, cast
   • tidyr functions: gather, spread, unite, separate
   • dplyr package
10. Variables 
    • Creating new variables
    • Subsetting variables
    • Variables transformations
    • recode
11. Joining data from different sources and types
    • Merging data with merge()
    • Joins
    • Union
12. R jobs automation
    • Scheduling analytical tasks in different systems
    • Reproducible research– Generating reports with Rmarkdown
    • Best practices
13. Big data 
    • Overview of tools and packages for big data processing
    • Hadoop and MapReduce
    • Spark integration with R