Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the tidyverse" available at https://moderndive.com/.
Author
Maintainer: Albert Y. Kim albert.ys.kim@gmail.com (ORCID)
Authors:
Chester Ismay chester.ismay@gmail.com (ORCID)
Other contributors:
Andrew Bray abray@reed.edu (ORCID) [contributor]
Delaney Moran delaneyemoran@gmail.com [contributor]
Evgeni Chasnovski evgeni.chasnovski@gmail.com (ORCID) [contributor]
Will Hopper wjhopper510@gmail.com (ORCID) [contributor]
Benjamin S. Baumer ben.baumer@gmail.com (ORCID) [contributor]
Marium Tapal mariumtapal@gmail.com (ORCID) [contributor]
Wayne Ndlovu waynedndlovu5@gmail.com [contributor]
Catherine Peppers cpeppers@smith.edu [contributor]
Annah Mutaya annahmutaya18@gmail.com [contributor]
Anushree Goswami anushreeegoswami@gmail.com [contributor]
Ziyue Yang zyang2k@gmail.com (ORCID) [contributor]
Clara Li clarasepianli@gmail.com (ORCID) [contributor]
Caroline McKenna carolinemckenna101@gmail.com [contributor]
Catherine Park jcathyp@gmail.com (ORCID) [contributor]
Abbie Benfield abbidabbers@gmail.com [contributor]
Georgia Gans georgiagans@live.com [contributor]
Kacey Jean-Jacques kjeanjacques@smith.edu [contributor]
Swaha Bhattacharya itsswahabhattacharya@gmail.com [contributor]
Vivian Almaraz viviansofia101@gmail.com [contributor]
Elle Jo Whalen ewhalen@smith.edu [contributor]
Jacqueline Chen jchen76@smith.edu [contributor]
Michelle Flesaker mflesaker@smith.edu [contributor]
Irene Foster ifoster25@smith.edu [contributor]
Aushanae Haller aushanaenhaller@gmail.com [contributor]
Benjamin Bruncati kbruncati@smith.edu (ORCID) [contributor]
Quinn White quinnarlise@gmail.com (ORCID) [contributor]
Tianshu Zhang tzhang26@smith.edu (ORCID) [contributor]
Katelyn Diaz katndiaz@gmail.com (ORCID) [contributor]
Rose Porta rporta@smith.edu [contributor]
Renee Wu rwu30@smith.edu [contributor]
Arris Moise amoise@smith.edu [contributor]
Kate Phan kphan@smith.edu [contributor]
Grace Hartley grace.hartley@gmail.com [contributor]
Silas Weden silasweden@gmail.com [contributor]
Emma Vejcik evejcik@gmail.com [contributor]
Nikki Schuldt nikkischuldt@gmail.com [contributor]
Tess Goldmann tessgoldmann@aol.com [contributor]
Hongtong Lin cccynthialht@gmail.com [contributor]
Alejandra Munoz amunozgarcia@smith.edu [contributor]
Elina Gordon-Halpern egordonhalpern@smith.edu [contributor]
Haley Schmidt heschmidt00@gmail.com (ORCID) [contributor]
Examples
library(moderndive)
# Fit regression model:
mpg_model <- lm(mpg ~ hp, data = mtcars)
# Regression tables:
get_regression_table(mpg_model)
#> # A tibble: 2 × 7
#> term estimate std_error statistic p_value lower_ci upper_ci
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 intercept 30.1 1.63 18.4 0 26.8 33.4
#> 2 hp -0.068 0.01 -6.74 0 -0.089 -0.048
# Information on each point in a regression:
get_regression_points(mpg_model)
#> # A tibble: 32 × 5
#> ID mpg hp mpg_hat residual
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 21 110 22.6 -1.59
#> 2 2 21 110 22.6 -1.59
#> 3 3 22.8 93 23.8 -0.954
#> 4 4 21.4 110 22.6 -1.19
#> 5 5 18.7 175 18.2 0.541
#> 6 6 18.1 105 22.9 -4.84
#> 7 7 14.3 245 13.4 0.917
#> 8 8 24.4 62 25.9 -1.47
#> 9 9 22.8 95 23.6 -0.817
#> 10 10 19.2 123 21.7 -2.51
#> # ℹ 22 more rows
# Regression summaries
get_regression_summaries(mpg_model)
#> # A tibble: 1 × 9
#> r_squared adj_r_squared mse rmse sigma statistic p_value df nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.602 0.589 14.0 3.74 3.86 45.5 0 1 32
# Plotting parallel slopes models
library(ggplot2)
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
geom_point() +
geom_parallel_slopes(se = FALSE)
