Week 1: Basic Statistical Concepts, Introduction to R Programming, Data Wrangling with R

Practical Skills: Basic R Programming, Data Wrangling with R

Slides

Basic Statistical Concepts and Programming I

• Basics of Statistics
• Getting started with R
• Summary statistics for univariate data

Basic Statistical Concepts and Programming II

• Summary statistics for bivariate data
• R Objects
• Data wrangling with Tidyverse
• Debugging

Performance Outcomes

You should feel confident to perform the following outcomes:

R programming

• Conduct elementary arithmetic operations using R
• Navigate the RStudio interactive development environment (IDE)
• Install external packages in R to extend functionality
• Comprehend various object types in R
• Manipulate lists, matrices, and vectors in R
• Grasp the concept of missing values within the R environment
• Decipher error messages and do basic troubleshooting

Data wrangling

• Differentiate between the Base and Tidyverse paradigms in R
• Acquire the skills to add/modify columns, subset data by rows and columns, rename column names, and perform group operations using R

Data description

• Calculate basic numerical statistics (including mean, median, mode, range, variance, standard deviation, minimum, maximum, quartiles, quantiles, correlation and interquartile range) by hand and with R
• Describe the distribution of univariate data in terms of shape (symmetry, skewness, modality), center, and spread
• Interpret common summary statistics (both numerical and graphical summaries)
• Distinguish between population parameters and sample statistics
• Identify data variable types appropriately from the context as categorical, numerical, nominal, ordinal, discrete or continuous

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.

• Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (2nd Edition)
  • Chapter 1: Getting Started with Data in R
  • Chapter 3: Data Wrangling
• R for Data Science (2nd Edition)
  • Chapter 2: Workflow basics
  • Chapter 3: Data transformation
  • Chapter 8: Workflow: getting help
  • Chapter 12: Logical vectors
  • Chapter 13: Numbers
  • Chapter 18: Missing values
  • Chapter 27: A field guide to base R
• OpenIntro Statistics (4th Edition)
  • Chapter 1: Introduction to data
  • Chapter 2: Summarizing data
• RStudio Desktop Cheatsheet
• Base R Cheatsheet

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