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.

Build internal capacity at for using R as an analysis platform to
increase efficiency of data management.

Instructor:

Data Sets:

Introduction to R and
RStudio

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.

Character Data and
Basic Function Usage

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.

Basic Data Manipulation -
Tidyverse

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.