added readme

.Rbuildignore

@@ -1,2 +1,3 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^README\.Rmd$

DESCRIPTION

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 Package: educ7610
 Type: Package
 Title: Regressional Analysis and Linear Models
-Version: 0.2.1
+Version: 0.2.2
 Author: Tyson S. Barrett
 Maintainer: The package maintainer <[email protected]>
 Description: Useful functions and the data required for USU's EDUC 7610 course. 

README.Rmd

@@ -0,0 +1,41 @@
+---
+output: github_document
+---
+
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# `educ7610`
+
+
+The goal of `educ7610` is to make several aspects of the Regression course (EDUC/PSY 7610) at Utah State University more accessible. Specifically, it provides the data from Darlington and Hayes' book "Regression Analysis and Linear Models", a syntax to perform diagnostics and Johnson-Neyman, odds ratios from logistic regression, among other things. In conjunction with packages like `interactions` and the `easystats` group of packages, this package can make regression analyses more straightforward.
+
+## Installation
+
+You can install the development version from GitHub with:
+
+``` r
+# install.packages("remotes")
+remotes::install_github("tysonstanley/educ7610")
+```
+
+## Example
+
+This is a basic example which shows you how to solve a common problem:
+
+```{r example}
+library(educ7610)
+data("poverty")  ## load the poverty data set
+
+model <- lm(TeenBirth ~ ViolentCrime + poverty_pct,
+           data = poverty)
+diagnostics(model) %>% head()
+```
+

README.md

@@ -1,3 +1,50 @@
-# educ7610
 
-Package created for use at Utah State University's EDUC 7610 Regression course. Provides data and helper functions.
+# `educ7610`
+
+The goal of `educ7610` is to make several aspects of the Regression
+course (EDUC/PSY 7610) at Utah State University more accessible.
+Specifically, it provides the data from Darlington and Hayes’ book
+“Regression Analysis and Linear Models”, a syntax to perform
+diagnostics and Johnson-Neyman, odds ratios from logistic regression,
+among other things. In conjunction with packages like `interactions` and
+the `easystats` group of packages, this package can make regression
+analyses more straightforward.
+
+## Installation
+
+You can install the development version from GitHub with:
+
+``` r
+# install.packages("remotes")
+remotes::install_github("tysonstanley/educ7610")
+```
+
+## Example
+
+This is a basic example which shows you how to solve a common problem:
+
+``` r
+library(educ7610)
+#> ── educ7610 0.2.0 ────────────────────────────────────────────────── learn more at tysonbarrett.com ──
+#> ✔ educ7610 attached
+#> ✔ No potential conflicts found
+data("poverty")  ## load the poverty data set
+
+model <- lm(TeenBirth ~ ViolentCrime + poverty_pct,
+           data = poverty)
+diagnostics(model) %>% head()
+#>   TeenBirth ViolentCrime poverty_pct     pred      resid      dfb.1_
+#> 1      54.5         11.2        20.1 54.97777 -0.4777688  0.01140240
+#> 2      39.5          9.1         7.1 32.93430  6.5656964  0.21476505
+#> 3      61.2         10.4        16.1 48.13306 13.0669381 -0.08407140
+#> 4      59.9         10.4        14.9 46.17653 13.7234707 -0.01726762
+#> 5      41.1         11.2        16.7 49.43426 -8.3342598  0.07141738
+#> 6      47.0          5.8         8.8 34.37397 12.6260347  0.29388664
+#>       dfb.VlnC    dfb.pvr_       dffit     cov.r       cook.d        hat
+#> 1  0.003853716 -0.01434251 -0.01704369 1.1535952 9.888197e-05 0.07683369
+#> 2  0.108941094 -0.20044139  0.23370808 1.1046067 1.833345e-02 0.07562858
+#> 3 -0.011028702  0.14825406  0.28167070 0.9325356 2.558198e-02 0.02937639
+#> 4  0.024749616  0.07786167  0.26245140 0.9118951 2.209091e-02 0.02328784
+#> 5  0.002990049 -0.10973056 -0.19000614 1.0324334 1.202486e-02 0.03364566
+#> 6  0.062618745 -0.23177500  0.32712068 0.9529815 3.460873e-02 0.04150863
+```