Duration
4 days
OVERVIEW
This course aims at providing participants with an overview of what R can offer in real-world data analysis and analytics tasks.
AUDIENCE
This short course intended audiences are IT professionals, Data Analyst, Data Scientists and professionals who want to learn about programming in R. The programme is suitable for personnel with little or some experience in programming, data management and business reporting.
PREREQUISITE
Basic knowledge of programming is preferable.
OBJECTIVES
After completing this module, participants will learn the following
- Identify types of data that can be extracted
- Define steps in cleaning data
- Deploy R language to clean data
- Define the common approach in analyzing data.
- Using R for analytics tasks.
COURSE OUTLINE
The course content will be delivered according to this schedule (E.g. typically a short course has a maximum of 7 hours per day, 9:00 am to 5:00 pm with an hour for lunch)
DAY1
0900 – 0930
Overview of R and Data Analytics
0930 – 1040
Installing and setting up R programming environment
1040 – 1100
Morning Break
1100 – 1200
R data types and objects (Vector, List, Matrix and Data Frame)
1200 – 1300
Hands-On for familiarization of RStudio and R objects
1300 – 1400
Lunch
1400 – 1440
Reading and writing data
1440 – 1540
Basic data sub-setting
1540 – 1600
Afternoon Break
1600 – 1700
Laboratory exercise – Working with data frame
DAY2
0900-1000
R control structures (Conditions and Iterations)
1000-1040
Creating and using R functions
1040-1100
Morning Break
1100-1200
Lexical scoping rules, manipulating dates and times
1200-1300
Hands-On for RScript and using R control structures
1300-1400
Lunch
1400-1500
Using the R “apply” functions
1500-1540
Debugging and profiling in R
1540-1600
Afternoon Break
1600-1700
Laboratory exercise – Basic data observation
DAY3
0900-1000
Obtaining data: Getting data from databases, web, and CSV files in R.
1000-1040
Hands-On for obtaining data from web
1040-1100
Morning Break
1100-1200
Reshaping data, sub-setting observations & variables, summarizing data.
1200-1300
Laboratory exercise – Getting, format (sub-setting) and store data.
1300-1400
Lunch
1400-1500
Data preparation: Creating new variables, grouping and merging data.
1500-1540
Hands-On on preparing data for analytics
1540-1600
Afternoon Break
1600-1700
Laboratory exercise – Preparing extracted data analysis
DAY4
0900-1000
Introduction to base plotting system
1000-1040
Laboratory exercise – base plot
1040-1100
Morning Break
1100-1200
Introduction to ggplot plotting system
1200-1300
Laboratory exercise – ggplot Plot
1300-1400
Lunch
1400-1500
Introduction to Shiny
1500-1540
Using Rpubs for sharing
1540-1600
Afternoon Break
1600-1700
Laboratory exercise – Presenting analytics using R and wrap-up
Programme scope, delivery arrangements and suitability are confirmed during enquiry.