R is a free software that is capable of handling mathematical and statistical manipulations. It has its own programming language as well as built in functions to perform any specialized task. In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
The main focus will be how to use R in data analysis such as:
- Importing the data.
- Understanding the structure of the data.
- Editing output.
- Printing results.
- Creating and editing a data file.
Quantitative Analysis using R
- Introduction to estimation and hypothesis testing.
- Three or more samples.
- Correlation and simple linear regression.
Basic statistical terms and concepts
- Basic data quality checks
- Basic exploratory data analysis procedures
- Basic Descriptive Statistics
- The core functions of inferential statistics
- Common inferential statistics
Data Entry, management and Manipulation
- Replacing missing values.
- Exploring data Selecting and sorting cases.
- Restructuring data.
- Merging files.
- Syntax and output.