- Changed printing of non-baseline categorical variable levels in regression table to be cleaner #102
- Added explicit
`conf.level`

argument to`get_regression_table()`

inherited from`broom::tidy.lm()`

- Improved main package vignette based on feedback from @lwjohnst86 & @lisamr
- Added JOSE publication in
`vignettes/paper.md`

- Fixed
`pkgdown`

and`covr`

issues, defragged documentation.

- Use vdiffr conditionally

- Modified
`vignettes/why-moderndive.Rmd`

main vignette - Updated
`geom_parallel_slopes()`

with new arguments:- Use
`fullrange=TRUE`

to draw regression lines over the entire support of the x-axis (by @wjhopper) - Use
`level`

to set different level of confidence interval shading (by @echasnovski)

- Use
- Added new function
`geom_categorical_model()`

for visualizing regression models with one categorical explanatory/predictor variable (by @wjhopper) - Add deprecation warning message to
`gg_parallel_slopes()`

directing users to use`geom_parallel_slopes()`

instead (by @mariumtapal)

- Added
`geom_parallel_slopes()`

geom extension to`ggplot2`

package to plot parallel slopes regression models with one numerical and one categorical variable (this is not possible using`ggplot2::geom_smooth()`

). Note this renders`gg_parallel_slopes()`

function added in v0.3.0 obsolete. - Added example of
`geom_parallel_slopes()`

to “Why`moderndive`

?” vignette - Added student names (permission obtained in all cases) to
`pennies_resamples`

data frame columns `get_correlation()`

now:- Respects
`dplyr::group_by()`

grouping - Can handle missing data by either passing
`na.rm = TRUE`

argument or by passing standard`stats:cor(use = "complete.obs")`

argument via`...`

- Respects

- Added minimally viable “parallel slopes” regression model plotting function
`gg_parallel_slopes()`

. In the future we hope to define a new`ggplot2`

geom. - Added “Why
`moderndive`

?” vignette - Added ID argument to
`get_regression_points()`

to return a column that identifies the observational units/rows - Datasets:
- Added
`DD_vs_SB`

: Dunkin Donuts and Starbucks in Eastern Massachusetts data collected by @DelaneyMoran - Added
`promotions`

: tibble version of`openintro::gender.discrimination`

used to illustrate permutation test. - Added
`MA_schools`

: Relationship between SAT scores and socio-economic status for Massachusetts high schools. - Added
`mythbusters_yawn`

: Data from study on Mythbusters show on whether yawning is - Added
`promotions_shuffled`

: one instance of`promotions`

with`gender`

permuted/shuffled - Original
`pennies_sample`

sample of 40 pennies from`pennies`

has been renamed`orig_pennies_sample`

. New`pennies_sample`

consists of 50 pennies sampled from bank in Northampton, MA, USA on 2019/2/1. - Added
`pennies_resamples`

: 35 bootstrap resamples of new`pennies_sample`

- Added
`movies_genre`

: random sample of 32 action and 36 romance movies from`ggplot2movies::movies`

- Added
- Removed all
`assertive::assert()`

code - Converted
`house_prices$date`

from`dttm`

(date-time) to`date`

per R4DS comment on using simplest data type possible

Updated package for:

- Use in DataCamp’s Modeling with Data in the Tidyverse, in particular added
`evals`

and`house_prices`

datasets and updated`get_regression_table()`

and`get_regression_points()`

functions. - v0.4.0 of ModernDive textbook

Details:

- Created
`get_correlation()`

function to omit`$`

syntax and return a data frame - Import
`infer::rep_sample_n()`

instead of our own defined version, as this function is now included in`infer`

- Added
`evals`

,`house_prices`

,`tactile_prop_red`

,`pennies_sample`

and`mythbusters_yawn`

datasets - Added mean squared error and root mean squared error to output of
`get_regression_summaries()`

- Added
`newdata`

argument to`get_regression_points()`

. When:- Original outcome variable is included in
`newdata`

, output it as well as`residual`

(See Issue 17). - Otherwise omit
`residual`

- Original outcome variable is included in
- Removed
`tidyverse`

from Depends, Imports, or Suggests

Fixed broken url in `?bowl_samples`

- Added three
`get_regression_*`

functions meant for novice R users/regression fitters that process regression model outputs - Added datasets:
`pennies`

: 800 pennies to be treated as a population from which to simulate sampling a numerical variable from (`year`

of minting)`bowl`

: Bowl of 2400 balls of which 900 are red to be treated as a population from which to simulate sampling a categorical variable from (`color`

). Also known as the urn sampling framework .`bowl_samples`

: data from tactile version of sampling from`bowl`

done in class: 10 groups sampled n=50 balls from and counted the number red [ADD MODERNDIVE LINK]