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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## moderndive R Package <img src="https://github.com/moderndive/moderndive/blob/master/images/hex_blue_text.png?raw=true" align="right" width=125 />
[](https://cran.r-project.org/package=moderndive) [](https://travis-ci.org/moderndive/moderndive) [](https://codecov.io/github/moderndive/moderndive?branch=master)[](http://www.r-pkg.org/pkg/moderndive)
An R package of datasets and wrapper functions for [tidyverse](https://www.tidyverse.org/)-friendly introductory linear regression used in
- ModernDive: An Introduction to Statistical and Data Sciences via R available at [ModernDive.com](https://moderndive.com/)
- DataCamp's [Modeling with Data in the Tidyverse](https://www.datacamp.com/courses/modeling-with-data-in-the-tidyverse)
## Installation
Get the released version from CRAN:
```{r, eval=FALSE}
install.packages("moderndive")
```
Or the development version from GitHub:
```{r, eval=FALSE}
# If you haven't installed remotes yet, do so:
# install.packages("remotes")
remotes::install_github("moderndive/moderndive")
```
## Demo
Let's fit a simple linear regression of teaching `score` (as evaluated by students) over instructor age for 463 instructors at the UT Austin:
```{r}
library(moderndive)
score_model <- lm(score ~ age, data = evals)
```
Among the many useful features of the `moderndive` package outlined in our essay ["Why should you use the moderndive package for intro linear regression?"](https://moderndive.github.io/moderndive/articles/why-moderndive.html) we highlight three functions in particular:
#### 1. Get regression tables
Get a tidy regression table **with** confidence intervals:
```{r}
get_regression_table(score_model)
```
#### 2. Get fitted/predicted values and residuals
Get information on each point/observation in your regression, including fitted/predicted values & residuals, organized in a single data frame with intuitive variable names:
```{r}
get_regression_points(score_model)
```
#### 3. Get regression fit summaries
Get all the scalar summaries of a regression fit included in `summary(score_model)` along with the mean-squared error and root mean-squared error:
```{r}
get_regression_summaries(score_model)
```
## Other features
#### 1. Print markdown friendly tables
Want to output cleanly formatted tables in an R Markdown document? Just add `print = TRUE` to any of the three `get_regression_*()` functions.
```{r}
get_regression_table(score_model, print = TRUE)
```
#### 2. Predictions on new data
Want to apply your fitted model on new data to make predictions? No problem! Include a `newdata` data frame argument to `get_regression_points()`.
For example, the Kaggle.com practice competition [House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques){target="_blank"} requires you to fit/train a model to the provided `train.csv` training set to make predictions of house prices in the provided `test.csv` test set. The following code performs these steps and outputs the predictions in `submission.csv`:
```{r, eval=FALSE}
library(tidyverse)
library(moderndive)
# Load in training and test set
train <- read_csv("https://github.com/moderndive/moderndive/raw/master/vignettes/train.csv")
test <- read_csv("https://github.com/moderndive/moderndive/raw/master/vignettes/test.csv")
# Fit model
house_model <- lm(SalePrice ~ YrSold, data = train)
# Make and submit predictions
submission <- get_regression_points(house_model, newdata = test, ID = "Id") %>%
select(Id, SalePrice = SalePrice_hat)
write_csv(submission, "submission.csv")
```
The resulting `submission.csv` is formatted such that it can be submitted on Kaggle, resulting in a "root mean squared logarithmic error" leaderboard score of 0.42918.
```{r echo=FALSE}
knitr::include_graphics("https://github.com/moderndive/moderndive/raw/master/vignettes/leaderboard_orig.png")
```
## The Details
The three `get_regression` functions are wrappers of functions from the [`broom`](https://CRAN.R-project.org/package=broom/vignettes/broom.html){target="_blank"} package for converting statistical analysis objects into tidy tibbles along with a few added tweaks:
1. `get_regression_table()` is a wrapper for `broom::tidy()`
1. `get_regression_points()` is a wrapper for `broom::augment()`
1. `get_regression_summaries()` is a wrapper for `broom::glance()`
Why did we create these wrappers?
* The `broom` package function names `tidy()`, `augment()`, and `glance()` don't mean anything to intro stats students, where as the `moderndive` package function names `get_regression_table()`, `get_regression_points()`, and `get_regression_summaries()` are more intuitive.
* The default column/variable names in the outputs of the above 3 functions are a little daunting for intro stats students to interpret. We cut out some of them and renamed many of them with more intuitive names. For example, compare the outputs of the `get_regression_points()` wrapper function and the parent `broom::augment()` function.
```{r}
get_regression_points(score_model)
library(broom)
broom::augment(score_model)
```
***
Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.