Eve Chen - STAT 545A 2021 - Mini Data Analysis

Hello! Welcome to my channel repo!

This repository will contain Eve (Yixuan) Chen's work for Mini Data Analysis deliverables, as part of STAT 545A (Winter term 1 2021).

Files in the repository 📂:

There are three milestones for my analysis, work of which is distributed in 3 folders in this repository. Each folder would contain: 1. the R markdown files with source code, 2. the knitted markdown files, and 3. a sub-folder of attached files like figures from the knitted markdown.

Instructions on how to open and render the markdown (.md) and R markdown (.rmd) files：

• .md files : The markdown files are knitted from the R markdown, it can be read and edited with a normal text editor. But the robust and reproducible way is to edit on the source code in R markdown file of the same name, then knit and update the corresponding markdown file.
• .rmd files: The R Markdown is a file format for making dynamic documents with R. You can use Integrated Development Environment like RStudio to open the files, edit and run the code embedded. You can also render the files into markdown, HTML or PDF formats with RStudio IDE.

Deliverables 📔 :

1. Milestone 1 (tag m1.0, released on Oct 9)

   The first deliverable contains a work report of some foundamental analysis of 7 semi-tidy datasets from the datateachr package. We gradually focus on getting familiar with one dataset of choosing with the tidyverse package. And finally we come up with 4 interesting questions that can be answered in the following milestones.

   • Markdown: mini-project-1.md
   • R Markdown with source code: mini-project-1.rmd
   • Figures: mini-project-1 file folder

2. Milestone 2 (tag m2.0, released on Oct 19)

   In the second deliverable, we will explore more in depth the concept of tidy data, and investigate further into the 4 research questions that we defined in milestone 1. The questions were slightly modified here so as to optimally integrate what was learned in the class. At the end, 2 refined research questions out of 4 were selected for milestone 3.

   • Markdown: mini-project-2.md
   • R Markdown with source code: mini-project-2.rmd
   • Figures: mini-project-2 file folder

3. Milestone 3 (tag m3.1, released on Oct 27)

   In the last milestone, we’ll be sharpening some of the results we obtained from the previous milestones by: manipulating special data types in R including factors and/or dates and times, fitting a model object to the data and extract a result, and reading and writing data as separate files.

   • Markdown: mini-project-3.md
   • R Markdown with source code: mini-project-3.rmd
   • Figures: mini-project-3 file folder

4. output

   The output is a summary table where the range (max, min and contrast) , mean and median of flow across the groups of extream_type from the data was computed, to observe the overall range and statistical features of the flow rate recorded as different type.

   • csv file: flow_extreme_type.csv
   • R binary (RDS) file: flow_extreme_type.rds

Thanks for viewing and commenting on my work! 😃