moderndive (version 0.8.0)

moderndive: moderndive - Tidyverse-Friendly Introductory Linear Regression

Description

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 (ORCID)

Authors:

• Albert Y. Kim (ORCID)

• Chester Ismay (ORCID)

Other contributors:

• Andrew Bray (ORCID) [contributor]

• Delaney Moran [contributor]

• Evgeni Chasnovski (ORCID) [contributor]

• Will Hopper (ORCID) [contributor]

• Benjamin S. Baumer (ORCID) [contributor]

• Marium Tapal (ORCID) [contributor]

• Wayne Ndlovu [contributor]

• Catherine Peppers [contributor]

• Annah Mutaya [contributor]

• Anushree Goswami [contributor]

• Ziyue Yang (ORCID) [contributor]

• Clara Li (ORCID) [contributor]

• Caroline McKenna [contributor]

• Catherine Park (ORCID) [contributor]

• Abbie Benfield [contributor]

• Georgia Gans [contributor]

• Kacey Jean-Jacques [contributor]

• Swaha Bhattacharya [contributor]

• Vivian Almaraz [contributor]

• Elle Jo Whalen [contributor]

• Jacqueline Chen [contributor]

• Michelle Flesaker [contributor]

• Irene Foster [contributor]

• Aushanae Haller [contributor]

• Benjamin Bruncati (ORCID) [contributor]

• Quinn White (ORCID) [contributor]

• Tianshu Zhang (ORCID) [contributor]

• Katelyn Diaz (ORCID) [contributor]

• Rose Porta [contributor]

• Renee Wu [contributor]

• Arris Moise [contributor]

• Kate Phan [contributor]

• Grace Hartley [contributor]

• Silas Weden [contributor]

• Emma Vejcik [contributor]

• Nikki Schuldt [contributor]

• Tess Goldmann [contributor]

• Hongtong Lin [contributor]

• Alejandra Munoz [contributor]

• Elina Gordon-Halpern [contributor]

• Haley Schmidt (ORCID) [contributor]

See Also

Useful links:

• https://moderndive.github.io/moderndive/

• https://github.com/moderndive/moderndive/

• Report bugs at https://github.com/moderndive/moderndive/issues

Examples

library(moderndive)

# Fit regression model:
life_exp_model <- lm(
  life_expectancy_2022 ~ gdp_per_capita,
  data = un_member_states_2024
)

# Regression tables:
get_regression_table(life_exp_model)

# Information on each point in a regression:
get_regression_points(life_exp_model)

# Regression summaries
get_regression_summaries(life_exp_model)

# Plotting parallel slopes models
library(ggplot2)
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE)