Agenda

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David Kahler edited this page Aug 16, 2021 · 2 revisions

Before Workshop

Install R and RStudio
- video
- additional resource from edx
- video from last workshop
Run test.R, as explained in the video, as your assignment
- test.R will also install additional packages for R that may take some time to install
Download workshop materials from this repository, select "Download ZIP" from the green "code" button on the main, "Code" page

Day 1 – Introduction to R and Data Wrangling

Morning – Basics and Introduction to R

Installing R, RStudio, & packages Introduction to R, RStudio interface, components of R code

Basic coding, types of data (character, numeric, etc.) & objects (vectors, matrices, data frames, etc.), warnings & errors (coding style, case, object naming)
Getting help & troubleshooting in R (help files, stackoverflow, Google search)
Importing & viewing data in RStudio

Afternoon – Data Wrangling with R

Importing data into RStudio
Indexing, subsetting, & summarizing data
“dplyr” functions (select, filter, mutate, summarize)
“tidyr” functions (pivot_wider, pivot_longer)
Dates and times with “lubridate”

Day 2 – Visualizing Data, Data Analysis, and Statistics

Morning – Exploring Graphs and Plots in R

Plotting in base-R (scatterplots, line plots, histogram, bar plots, boxplots)
Introduction to “ggplot2”
Plotting in “ggplot2” (scatterplots, line plots, histogram, bar plots, boxplots)
Customizing & advanced plotting (scales, colors, labels, trendlines, faceting, annotations, saving hi-res figures)

Afternoon – Data Analysis and Statistics in R

Descriptive & summary data analysis
Statistical analysis in R (correlation, t-test, ANOVA, simple linear regression)
Reading and extracting statistical output
Data assumptions
Checking normality and residuals

Day 3 – Advanced Statistics, Additional Examples, and Review (Half-day)

Morning – Data Analysis and Statistics in R

Specific examples with hydrological data
Practice data wrangling, analysis, statistics, & plotting
Additional resources, cheat sheets, & guides to working in R (“swirl”, “tidyverse” cheat sheets, R Graphics Cookbook, Storytelling with Data)
Tips on teaching R
Google Earth Engine demonstration (if time)

NOTE: Each day will include breaks between morning and afternoon sessions, as well as shorter breaks during each session

Limpopo Resilience Lab is a collaboration between University of Venda, Duquesne University, and Rensselaer Polytechnic Institute. More information on the project website.