Tuesday 13 September 2022\ #### **Require pre-session activities:** - Work through [this R tutorial](https://canvas.ubc.ca/courses/97662/files/19784375?wrap=1 "LDP-SDM-2021-lesson3_tutorial.R")[ Download this R tutorial](https://canvas.ubc.ca/courses/97662/files/19784375/download?download_frd=1), familiarizing yourself with the functions that we will mention in class. Note that you will first need to install a few packages, including: assertr, stringdist, GGally, taxize, and palmerpenguins (see pre-class [Instructions for Students: R and RStudio](https://canvas.ubc.ca/courses/97662/pages/instructions-for-students-r-and-rstudio "Instructions for students: R and RStudio") for package installation instructions).\ \ #### **Optional readings:** - Broman, K.W., and Woo, K.H. (2018). Data organization in spreadsheets. *American Statistician*, 72, 2-10. [https://doi.org/10.1080/00031305.2017.1375989 Links to an external site.](https://doi.org/10.1080/00031305.2017.1375989)[**PDF**](https://canvas.ubc.ca/courses/97662/files/19784378?wrap=1 "Broman & Woo 2018 Am. Stat.pdf")[Download PDF](https://canvas.ubc.ca/courses/97662/files/19784378/download?download_frd=1) - de Jonge, E., and van der Loo, M. (2013). An introduction to data cleaning with R. *Statistics Netherlands*. [https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf Links to an external site.](https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf)[**PDF**](https://canvas.ubc.ca/courses/97662/files/19784387?wrap=1 "de Jonge & van der Loo 2013 Stat. NL.pdf")[ Download PDF](https://canvas.ubc.ca/courses/97662/files/19784387/download?download_frd=1)\ \ #### **In-session activities:** 1. **10 min wrap-up of last Thursday's activity**\ \ 2. **Lecture (20-30 min; Rachel Germain)** - Data cleaning and quality control - Identifying outliers, typos, etc. with functions and algorithms - Data standards for species, time, and space - See: [lesson 3 lecture slides](https://canvas.ubc.ca/courses/97662/files/22428437?wrap=1 "LDP-SDM-2022-lesson3_lecture_slides.pdf")[ Download lesson 3 lecture slides](https://canvas.ubc.ca/courses/97662/files/22428437/download?download_frd=1)\ \ 3. **Break (5 min)** 4. **Activity / discussion (40 min; Mike Lavender)** - See: [lesson 3 pre-tutorial code](https://canvas.ubc.ca/courses/97662/files/19784375?wrap=1 "LDP-SDM-2021-lesson3_tutorial.R")[ Download lesson 3 pre-tutorial code](https://canvas.ubc.ca/courses/97662/files/19784375/download?download_frd=1)and [Data Cleaning and Standards Assignment](https://canvas.ubc.ca/courses/97662/assignments/1189490 "Data Cleaning and Standards (individual/group assignment)") - Break-out groups of \~4-5 people grouped according to R skill level - Choose a data cleaning task to work on - Discuss ideas for how to address the task(s), then write a short script - For references on some useful functions, see: - [assertr vignetteLinks to an external site.](https://cran.r-project.org/web/packages/assertr/vignettes/assertr.html) - [lubridate cheat sheet](https://canvas.ubc.ca/courses/97662/files/19784383?wrap=1 "lubridate_cheat_sheet.pdf")[Download lubridate cheat sheet](https://canvas.ubc.ca/courses/97662/files/19784383/download?download_frd=1) - [stringr cheatsheet](https://canvas.ubc.ca/courses/97662/files/19784382?wrap=1 "stringr_cheat_sheet.pdf")[ Download stringr cheatsheet](https://canvas.ubc.ca/courses/97662/files/19784382/download?download_frd=1)\ \ #### **Homework:** - Complete and submit your individual data cleaning task\ - You are encouraged to collaborate with your peers to find a solution, but everyone should submit their own assignment - **Due**: Friday 16 September 2022 - See: [Data Cleaning & Standards Assignment](https://canvas.ubc.ca/courses/97662/assignments/1189490 "Data Cleaning and Standards (individual/group assignment)") for instructions and rubric