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R

Basic R Workshop

Lesson 1 Lesson 2 Lesson 3

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

Data Structure

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

Decision making

  • If else statement
  • Switch statement

Loops

  • For Loop
  • While Loop
  • Repeat Loop

Functions

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

Managing Data Frame

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

 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 Lesson 5  
 Data Integration
  • Append data from another tables
  • Manage columns with data in dataframe
  • Merge data by common keys

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

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

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

Course Material: Staff / Student

 

Data visualization with R Workshop

Lesson 1 Lesson 2 Lesson 3
  1. Customizing graphics with GGPLOT2
    • Graphical Parameters
    • Axes, Text and Legend
    • Saving Graphs
    • Combining Plots
    • Themes
    • Statistical transformations
    • Position adjustments
    • Coordinate systems
  1. Other advanced techniques on graphics
    • Grouping
    • Faceting
    • Geometric objects displayed in the same plot
  2. Other common charts
    • Treemap
    • Dot chart
    • Density Plot
    • Ridgeline plot (joyplot)
    • Mean/SEM plot
  1. Other common charts
    • Strip plot
    • Jitter plot
    • Combining jitter and boxplots
    • Beeswarm Plots
  2. Introduce to create various plots in R with help on a web site
    • From Data to Viz
  3. Other advanced graphical libraries
    • Display multivariate relationships – Lattice Graphs
    • Visualizing Categorical Data – Mosaic Plots
    • Display data correlations – Correlation matrix
Lesson 4 Lesson 5  
  1. Time-dependent graphs and Maps
    • Time-dependent graphs
      • Time series
      • Area charts
      • Dumbbell charts
      • Slope graphs
    • Maps
      • Bubble map
      • Choropleth map
  1. Interactive Graphics
    • Create interactive maps using the Leaflet package
    • Create interactive dashboards using the flexdashboard package

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

 Course Material: Staff / Student

 

Intermediate R Programming for Researchers

Lesson 1 Lesson 2 Lesson 3
  • Introduction to Deep Learning in R
  • Machine Learning vs Deep Learning
  • Neural Network Basic
  • Introduction to Keras
  • Workflow of Machine Learning and Deep Learning
  • Deep Learning Toolsets
  • Elementary example
  • Convolutional Neural Networks in TensorFlow Using R
  • Optimization Techniques of Neural Network
  • Underfitting and Overfitting Solution
  • ConvNet
  • Image Augmentation
  • Transfer Learning
  • Natural Language Processing in TensorFlow Using R
  • Tokenizer and Word Embedding
  • RNN (Recurrent Neural Network)
  • LSTM (Long Short Term Memory Cell)

Lecture Videos: Staff / Student

Material - Example: Staff / Student

Lecture Videos: Staff / Student

Material - Example: Staff / Student

Lecture Videos: Staff / Student

Material - Example: Staff / Student

How to install Tensorflow and Kears in Anaconda3

 

 

Data Analysis with R

Lesson 1

Performing Data Analysis

  • Typical Data analysis process

Data requirement Gathering and Collection

Data Cleaning

  • Evaluate the quality of data
  • Handle Dirty Data
  • Implement data cleaning process

Lesson 2

Data Analysis, visualization and interpretation

  • Descriptive Statistics with graph (I)
    • Descriptive statistics by group(s)
    • Descriptive statistics by single group

Lesson 3

Data Analysis, visualization and interpretation

  • Descriptive Statistics with graph (II)
    • Descriptive statistics by multiple groups
  • More on Trending and Prediction
    • Customer segmentation analysis
      • RFM Analysis

Lesson 4

Case 1: Sales of items together (Apriori Algorithm)

  • Data preparation for modelling
  • Data modelling with testing and training data
  • Evaluation of data modelling
  • Finding the frequent itemsets

Lesson 5

Case 2: Sales Prediction (Time Series forecasting)

  • Data preparation for modelling
  • Data modelling with testing and training data
  • Evaluation of data modelling
  • Sales forecasting

 

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

  Lesson 3: Staff / Student

  Lesson 4: Staff / Student

  Lesson 5: Staff / Student

 Course Material: Staff / Student

 

Introduction to Shiny software

Lesson 1

  • What is Shiny?
  • Components of Shiny
  • Components and features of shinydashboard
  • Differences between flexdashboard and shinydashboard
  • shinydashboard makes use of Shiny to create dashboards

 

Lesson 2

shinydashboard makes use of Shiny to create dashboards

  • Interactive dashboard

Three ways to deploy a Shiny app into a web page

  • Shinyapps.io
  • Shiny Server
  • RStudio Connect

Easy web publishing for R from RStudio

  • RPubs website

Lecture Videos: 

  Lesson 1: Staff / Student

  Lesson 2: Staff / Student

 Course Material: Staff / Student