R package for Math 300Z

Most students will want to use POSIT.cloud. Use the “Z-Section” project.

• You will use the same project for all the Lessons adding a new file each class day.
• The {math300} R package will already be installed. If you find otherwise, use the commands given in the laptop section to install/update the needed packages for the Z-section package.

Accessing the daily worksheet

Almost all class days there will be a worksheet in the form of an Rmd file.

From within your POSIT.cloud project, download the Rmd file using this command, substituting the number of the relevant Lesson:

math300::get_lesson_worksheet(19)

• Make a habit of reading each day’s Rmd file before class. This way you can note what doesn’t yet make sense so that you can be receptive to the topic in class.
• Complete the worksheet after class.

Math 300Z R commands

There will be only a dozen commands that you will be using in the second half of Math 300Z. Almost all of them involve constructing or summarizing models.

As a reminder, here are some of the commands/syntax that should be familiar to you from the first half of the course.

• lm() fits (or “trains”) a “linear model” on data from a data frame.
• ggplot() sets things up for a new graphic. Use aes() as an argument.
• graphics layers to add onto the output of ggplot():
  • geom_point(), geom_jitter(alpha=0.5)
• filter(), mutate() and summarize() are basic data-wrangling commands we will use often.

New commands in the second half of the course:

• summarize a model: conf_intervals(), R2()
• evaluate a model: model_eval()
• graphic of a model: model_plot() (This replaces the geom_smooth() used in the first half of the course.)
• variance of a variable in a data frame: DF %>% summarize(NM = var(VAR))
• draw a DAG: dag_draw()
• sample from a DAG: sample(DAG, size=100)

A few other commands will be used occasionally in examples and demonstrations. You should know what they do, but typically there will be a reminder of the syntax: zero_one(), shuffle(), do() * {}, dag_intervene(), tibble(), regression_summary(), anova_summary().

Running R/RStudio on your laptop?

Install the {math300} and other packages with these two commands:

install.packages(c("mosaic", "ggplot", "dplyr", "openintro", "moderndive", "nycflights13", "knitr"))
# additional package for Math 300Z
remotes::install_github("dtkaplan/math300")