The lecture content from a 2-day workshop I delivered for individuals working at the Virginia Department of Environmental Quality tht focused on building a foundation of data analysis using R.
When: | March 7-8, 2022 |
Where: | 1111 East Main Street, Richmond Virginia 37.5367, -77.4350 |
Impetus: | Build internal capacity at for using R as an analysis platform to increase efficiency of data management. |
Instructor: | ![]() |
Data Sets: |
Learning Objectives:
- Learning about the R environment,
- Understand differences between coding to the Console versus making
scripts,
- Use a Project to organize code, data, analyses, &
narratives,
- Personalize the RStudio GUI for success.
Learning Objectives:
- Learn about basic function structure,
- Explore the built-in help system,
- Practice operations using the fundamental data type
character
,
- Manipulate data in vector
formats,
- Perform textual analyses using the stringr
library.
Learning Objectives:
- Explore numeric data and mathematical operations.
- Create and manipulate data within data.frame
objects.
Learning Objectives:
- Understand data manipulation verbs,
- Pipe data through several modifier functions to derive
inferences,
- Filter and select subsets of a larger data set,
- Group and summarize measurements to derive summary parameters
Learning Objectives:
- Apply the mutate
operator to create derived data
columns.
- Demonstrate the use of unordered and ordered factor data.
- Convert textual representations of dates and times into date
objects.
- Derive temporal inferences from date objects
Learning Objectives:
- Learn about joins to merge data from two or more data.frames. -
Develop your first function for consistent data formatting prior to
visualization.
- Understand and implement basic plotting routines provided in
R::graphics
- Convert raw data into high-quality graphical output using a variety of
ggplot2
routines.
Learning Objectives:
- Understand how to create a viable map display.
- Apply differential tile providers to an interactive map.
- Create markers on a map representing data found within the data
frame.
Learning Objectives:
- Understand basic markup to represent common textual components.
- Insert graphical output (figures, maps, etc) into a markdown
document.
- Inject components of statistical inferences into the text of a
markdown document.