qprc_short_course_2021
Notebooks for the Quality and Productivity Research Conference 2021 short course.
Learning Objectives
By the end of this short course, you should be able to:
Part1.Rmd
- Explain the windows and layout of RStudio
- Read in Excel Data and confirm that it was loaded correctly into R
- Build a
ggplot2statement that maps data to aesthetics - Style your ggplot with built in themes to R
- Facet your plot by a categorical variable in your dataset
Part2.Rmd
- List 5 key
{dplyr}verbs for data manipulation - Sort a dataset by columns using
arrange() - Utilize the pipe (
%>%) to chain{dplyr}verbs together - Select columns from a dataset using
select() - Learn and apply
mutate()to change the data type of a variable - Apply
mutate()to calculate a new variable based on other variables in adata.frame - Apply
case_whenin amutate()statement to make a continuous variable categorical - Apply
group_by()/summarize()as a pattern to get summary statistics, including counts, means, and standard deviations within a category - Standardize variable names using
clean_names()
Part3.Rmd
- Incorporate data sources into your RMarkdown Report
- Build and reuse a parametrized RMarkdown report
- Visualize time-series data from a data stream
Dataset Used
We use the 80 cereals dataset from Kaggle: https://www.kaggle.com/crawford/80-cereals
Posit Cloud Project
You can clone this project on Posit Cloud here: https://posit.cloud/content/2728260
Get started by opening part1.Rmd and start executing code cells.