Code I wrote for the Fall 2012 Coursera Computing for Data Analysis (R) class. Below, I copied the syllabus from the course website.
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, discuss 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, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.
After taking this course you should be able to
Read formatted data into R Subset, remove missing values from, and clean tabular data Write custom functions in R to implement new functionality and making use of control structures such as loops and conditonals Use the R code debugger to identify problems in R functions Make a scatterplot/boxplot/histogram/image plot and modify a plot with custom annotations Define a new data class in R and write methods for that class
Your grade in this course will consist of performance on four weekly quizzes and two programming assignments. The breakdown of the weighting for these elements is
Week 1 Quiz: 10 points Week 2 Quiz: 10 points Week 3 Quiz: 10 points Week 4 Quiz: 10 points Programming assignment 1: 30 points Programming assignment 2: 30 points There is a maximum of 100 points to obtain in this course through the four quizzes and the two programming assignments.
Performance in this course will be evaluated on a pass/fail basis. The final grade for the course will be based on the total number of points earned across the four quizzes and two programming assignments. In order to receive a passing grade, you must have a earned a total number of points of 70 or more.
- Introduction and overview
- Installing R
- Data types, subsetting
- Reading/writing data
- Control structures
- Functions
- Loop functions
- Debugging
- Simulation
- Plotting, visualizing data
- Priniciples of data graphics
- Objected oriented programming
- Data abstraction
- Statistical modeling