Week 4: Discrete Random Variables

Practical Skills: Data Manipulation

Slides

Discrete Random Variables

• Random variables
• Discrete random variables
• Bernoulli and Binomial Distribution
• Poission Distribution

Data manipulation

• Manipulating strings
• Formatting factors
• Dealing with dates and time

Performance Outcomes

You should feel confident to perform the following outcomes:

Discrete random variables

• Define and identify discrete random variables
• Interpret and use the probability mass function (pmf) to calculate probabilities
• Calculate expected values and variances for discrete random variables
• Describe and apply common discrete distributions (e.g., Bernoulli, Binomial, Poisson) to model data
• Use R to compute probabilities and summary statistics for discrete random variables
• Use R to generate random data, plot distributions, and compute probabilities and summary statistics for discrete random variables

Data wrangling

• Extract, combine, or replace parts of text data using appropriate string functions or regular expressions using R
• Reorder, relabel, or collapse factor levels using R
• Parse and convert text data to date or datetime objects using correct formats using R
• Extract, add, or calculate date and time components (e.g., year, month, day, duration) using R

Resources

If you don’t feel yet confident after going through the teaching materials or would like further resources, then you are also encouraged to go through the following materials.

• R for Data Science (2nd Edition)
  • Chapter 14: Strings
  • Chapter 15: Regular expressions
  • Chapter 16: Factors
  • Chapter 17: Dates and times
• OpenIntro Statistics (4th Edition)
  • Chapter 4.2.1 Bernoulli distribution
  • Chapter 4.3.1 The binomial distribution
  • Chapter 4.5 Poisson distribution
• Factors with forcats Cheatsheet
• Dates and times with lubridate Cheatsheet
• String manipulation with stringr Cheatsheet

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