LibGuides: TIM-8501 v5: Lesson 2 Resources

Lesson 2 Resources

Applied Statistics with Data Analytics (R), Zylabs.com

Read 2.3 Data Frames

This section demonstrates filtering and subsetting of data in R.

Data Science Foundations with R, Zylabs.com

Read Chapter 5. Data Wrangling

This chapter discusses examining, cleaning, filtering, and general ‘data wrangling,’ which is the topic of this lesson.

ACCESS your Zylabs.com material from the Lesson module in your course.

Hands-on Exploratory Data Analysis with R: Become an Expert in Exploratory Data Analysis using R Packages
Datar, R., & Garg, H. (2019). Hands-on exploratory data analysis with R: Become an expert in exploratory data analysis using R packages. O'Reilly Media, Inc.
- Read Chapter 3. Examining, Cleaning, & Filtering Data
This chapter demonstrates how to address the specific requirements of the lesson: examine, clean, filter data.
Amelia II: A Program for Missing Data
Honaker, J., King, G., & Blackwell, M. (2011). Amelia II: A program for missing data. Journal of Statistical Software, 45, 1-47.
This article discusses one of the many tools for generating missing maps in R, a requirement for the assignment.
NIST/SEMATECH Engineering Statistics Handbook
Smeaton, A. (2003). NIST/SEMATECH Engineering Statistics Handbook. https://www.itl.nist.gov/div898/handbook/
- Read Chapter 1.2.1. Underlying Assumptions
- Read Chapter 1.2.2. Importance
These sections discuss the traditional assumptions and importance of those assumptions in EDA.
R Programming for Statistics and Data Science
R Programming for Statistics and Data Science (Media from Packt Publishing available freely through O’Reilly Media Inc.). (2018).
- Watch and Read Chapter 4 through Chapter 7, Matrices through Manipulating Data.
Helps students examine, clean, and filter data.
R Markdown: The Definitive Guide
Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. CRC Press. https://bookdown.org/yihui/rmarkdown/
- Read Chapter 3. Documents
This reading further develops your ability to build professional-grade output from the EDA process.

Optional Resources

Exploratory Data Analysis Using R & RStudio
Kumar, R. V. (2021). Exploratory data analysis using R & RStudio. Available at https://www.researchgate.net/profile/Rohit-Kumar-35/publication/354312419_Exploratory_Data_Analysis_using_R_RStudio/links/613086f0c69a4e4879735b4b/Exploratory-Data-Analysis-using-R-RStudio.pdf
This article provides an example of EDA in R and R Studio.