Need to get up to speed with R in a hurry? Join SSDS as we spend the day learning R! This fast-paced bootcamp will introduce you to many aspects of R, including the basics of research design, navigating R and RStudio, data types and structures, wrangling and reshaping, visualization, exploratory data analysis, statistical tests, presentation of your research, and more.

Click here for the workshop materials.

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9:15 - 9:30AM PST: Installations (optional). Show up early if you need installation help
Click this text to download R
Click this text to download RStudio

9:30 - 9:45: The modern academic research workflow
Researcher responsibilities
R and RStudio, navigating RStudio

9:45 - 10:45: R basics
Variable assignment; functions, parameters, and arguments
Data types: numeric, character, logical, integer, factor
Data structures: vectors and data frames (and lists and matrices)

10:45 - 11:00: Break

11:00-12:00PM: Data wrangling and reshaping
Indexing, the dollar sign operator, and backet notation
The dplyr and tidyr R packages
Importing files
Package installations and finding help
Working with text data

12:00 - 1:00: Lunch on your own

1:00 - 2:00: Data visualizaiton with ggplot2
ggplot2 requirements: layers, the plus symbol, data, aes, geom_, and theme_
Histogram: hist and geom_histogram
Boxplot: boxplot and geom_boxplot
Barchart: geom_col
Scatterplot: plot, geom_point, and geom_jitter
Line plot: geom_line

2:00 - 2:45: More exploratory data analysis
Summary statistics: mean, standard deviation, median, skew, and kurtosis
Principal component analysis
                    
2:45 - 3:15: Hypotheses, p-values, and confidence intervals
Sums of squares tests: t.test, aov, cor.test, lm

3:15 - 3:30: Break

3:30 - 4:30: Presenting your research
R Markdown, knit, html, ioslides
Dashboards with the flexdashboard package
Reproducible research and the targets package
Tips for writing

4:30 - 4:45: Wrap-up
Machine learning, Green Library, SSDS, and other resources