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R is a popular programming language for data analytics, statistical analysis and data visualisation. This R statistics essential course will show you how to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts, scatter plots, and histograms; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables.
Topics include:

  • Installing R on your computer
  • Using the built-in datasets
  • Importing data
  • Creating bar and pie charts for categorical variables
  • Creating histograms and box plots for quantitative variables
  • Calculating frequencies and descriptives
  • Transforming variables
  • Coding missing data
  • Analyzing by subgroups
  • Creating charts for associations
  • Calculating correlations
  • Creating charts and statistics for three or more variables
  • Creating crosstabs for categorical variables

Outline

Part 1: Basics of R

Module 1. Getting Started

  • What is R
  • Install R and RStudio
  • Explore RStudio Interface

Module 2. Data Types

  • Numbers 
  • String
  • Vector
  • Matrix
  • Array 
  • Data Frame
  • List
  • Factor

Module 3. Packages & Data Sets

  • Packages
  • Data Sets
  • R Script

Module 4. Data Input/Output

  • Read data from file
  • Read data from web 
  • Write data to file

Module 5. Basic Plots in R

  • Scatter Plot
  • Boxplot
  • Bar chart
  • Pie chart
  • Histogram

Module 6. R Programming - Control Structures

  • Conditional
  • Loop
  • Break & Next
  • Operators

Module 7: R Programming - Function

  • Function Syntax
  • Default Arguments
Part 2: Statistical Applications of R

Module 8. Basic Statistics Analysis with R

  • Basic Statistics 
  • Correlation
  • Regression
  • Hypothesis Testing
  • ANOVA

Speaker/s

Dr. Zahra Nazemi has PhD in mathematical statistics from Universiti Putra Malaysia. Her research interests are applied statistics, medical statistics, Bayesian statistics, statistical inference and Software R. She has worked as a lecturer in different universities for more than 4 years. She also consulted and worked on assignments for parametric and non-parametric analysis, univariate and multivariate regression analysis in various areas such as medical, economics and psychology. Her other skills are knowledge of research methodology, extensive experience with SPSS, AMOS, R and MINITAB and writing and presenting reports. Moreover, she conducted special training program in mathematical programming including optimization, advanced multivariate data analysis, and simulation techniques
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