'IT Support for Research' Workshop: Basic R

R is a programming language and an environment for statistical computing and graphics. This workshop, consists of five sessions, aims at introducing R for beginners and preparing for learning R at intermediate level.

The Basic R workshop covers all fundamental R knowledge, such as data types, basic string operations, basic operators, data structure, functions and etc.

All students are welcome to join.

'IT Support for Research' Workshop: Basic R

Target Audience: RPg, TPg and Ug students

Pre-requisite: Basic programming concepts

Medium of Instruction: English

Course Outline:

Lesson 1

1. Introduction to R

• What is R & Why R
• Compare R vs Python
• IDE tools for R with Anaconda
  • Anaconda
  • Rstudio
  • R command line
  • Help document of R
  • Command for Workspace
• Modules & Packages
  • Module and packages
  • Demonstration on package installation
  • Variable & R-Object
• Variables & Data Types
  • Variable and assignment
  • Data type and conversion
• Basic String Operations
  • Paste function
  • Format function
  • Strsplit function
  • Substr function
  • Nchar function
  • Toupper/ Tolower function
• Basic Operators
  • Athematic operators
  • Relational operators
  • Logical operators
  • Assignment Operators

Lesson 2

1. Data Structure

• Vectors
• Lists
• Matrices
• Arrays
• Factors
• Data Frames
• Accessing element in data structure

2. Decision making

• If else statement
• Switch statement

3. Loops

• For Loop
• While Loop
• Repeat Loop

4. Functions

Lesson 3

1. Load/Save Data in R

• Load/save CSV/ Excel/ SQLite database in R
• Install rjson package
• Load the package required to read JSON files
• Convert JSON file to a data frame

2. Managing Data Frame

• Adding on to data frames
• Adding attributes to data frames
• Subsetting data frames

3. Data Cleaning

• Prepare and import the data set
• Understanding the data set
• Impute missing values
• Find and correct invalid data

• Remove duplication records

Lesson 4

1. Data Integration

• Append data from another tables
• Manage columns with data in dataframe
• Merge data by common keys

2. Data Transformation

• Data Aggregation
• Normalization
  • Min-max normalization
• Standardization
  • Z-score standardization
  • Unit conversion
• Transform a continuous variable into a categorical variable and vice-versa
  • Conversion of a continuous variable to categorical variable
    • Data binning using R Base function cut
  • Conversion of a categorical variable to numerical variable
    • One-hot encoding
      
      

Lesson 5

1. Data Visualization with ggplot

• Selecting the right chart type
• Visualization using GGPLOT2 to gain insight and get an overview of dataset. Demonstrate specific type of datasets and design the code for below 8 charts:
  • Comparison
  • Line charts
  • Bar Charts
  • Distribution
  • Histograms
  • Boxplots
  • Composition
  • Pie charts
  • Stacked column charts
  • Relationship
  • Scatter plots
  • Heatmap