Function reference • LSTbook

All functions

AAUP: 1984 salaries in various professional fields

Anthro_F: Anthropometric data from college-aged women

Birdkeepers: Birdkeeping and Lung Cancer

Births2022: Records on births in the US in 2022

Boston_marathon: Winning times in the Boston Marathon

Butterfly: World records in the 100 & 200 m butterfly swim

Calif_precip: Annual precipitation in California locations

Callback Names_and_race: Resume Experiment Data

College_grades: Grades at a small college

Econ_outlook_poll: SIMULATED data from an economic outlook poll

FARS: Annual summaries concerning motor-vehicle related fatalities in the US#'

FEV: Smoking and lung function among youths

Framingham: Data from the Framingham heart study

Germany1933vote: Voting patterns in the 1933 German national election

Gilbert: Data from the trial of serial killer Kristen Gilbert

Go_vote: Get out the vote experiment

Hill_racing: Winning times in Scottish Hill races, 2005-2017

MPG: Fuel economy measurements on US car models

Clock_auction Geography_journals PGA_index Dowsing: Data from McClave-Sincich Statistics 11/e

McCredie_Kurtz: "Big Five" personality ratings for college first-year students

Monocacy_river: Data on run-off from the Monocacy river at Jug Bridge, Maryland.

Offspring: Relative sizes offspring/parent for many species

Orings: Space Shuttle O-Ring Failures

PIDD: Pima Indians Diabetes Database

Penguins: Body measurements on penguins

Sessions Grades Gradepoint: Sample from a college registrar's database

STAR: STAR Project Data

Shipping_losses: Shipping losses in 1941 in the Atlantic

Nats: Short, simple data frames for textbook examples

UCB_applicants: Roster of applicants to six major departments at UC Berkeley

AAUP: Monthly tallies of wildfires in the US from 2000 to 2022

Wheat: Experimental data on the yield of winter wheat

add_plot_labels(): Convenience function for adding labels to point_plot or others without needing the ggplot2 + pipe.

dag_draw(): Draw a DAG

categorical() cat2value() bernoulli() mix_with() each() block_by() random_levels(): Helpers for specifying nodes in simulations

datasim_make() datasim_to_igraph() datasim_intervene(): Construct and modify data simulations

datasim_run(): Run a datasim simulation, producing a data frame

model_eval(): Evaluate a model on inputs

model_eval_fun(): Helper functions to evaluate models

model_family(): Check model type against model specification and data

model_plot(): Graph a model function

model_skeleton(): Convert a model to a skeleton

model_train(): train a model, easily

model_values(): Construct a model and return the model values

mosaic_cull_for_do(): Cull objects used with do()

ntiles(): Create vector based on roughly equally sized groups

point_plot(): One-step data graphics

print(<datasim>): Nice printing of some internal objects

print(<model_object>): A printing method for model objects

conf_interval() R2() regression_summary() anova_summary(): Summaries of regression models

sample() resample(): Samples from various kinds of objects

sim_00 sim_01 sim_02 sim_03 sim_04 sim_05 sim_06 sim_07 sim_08 sim_09 sim_10 sim_11 sim_12 sim_flights sim_medical_observations sim_prob_21.1 sim_satgpa sim_school1 sim_school2 sim_vaccine: Simulations for use in Lessons in Statistical Thinking

split_tilde(): Evaluate a tilde expression on a data frame

add_slope_rose() add_violin_ruler(): Add a slope "rose" to a plot.

trials(): Run the left side of the pipeline multiple times.

explanatory_vars() response_var() response_values() formula_from_mod() get_training_data(): Utilities

zero_one() label_zero_one(): Zero-one transformation for categorical variable