Update README with examples

README.Rmd

@@ -16,40 +16,70 @@ knitr::opts_chunk$set(
 # granovaGG
 
 <!-- badges: start -->
+
 <!-- badges: end -->
 
-The goal of granovaGG is to ...
+The package granovaGG is designed to produce statistical graphics driven by the fundamental questions of analysis of variance.
+The graphics granovaGG creates can offer far more visual information than a traditional tabular model summary or significance test.
+For an example of how granovaGG can enhance analysis, see [Pruzek and Helmreich (2009)](rmpruzek.com/wp-content/uploads/2011/07/ElementalGraphicsForANOVA.finalJune11.pdf).
 
 ## Installation
 
-You can install the development version of granovaGG from [GitHub](https://github.com/) with:
+You can install the development version of granovaGG from [GitHub](https://github.com) with:
 
 ``` r
 # install.packages("devtools")
 devtools::install_github("briandk/granovaGG")
 ```
 
-## Example
+## Usage
+
+There are three main functions in this package:
+
+-   `granovagg.1w()`: for plotting one-way analysis of variance (ANOVA)
+
+-   `granovagg.ds`(): for plotting dependent-sample t-test data
+
+-   `granovagg.contr()`: for plotting contrasts
+
+## Examples
 
-This is a basic example which shows you how to solve a common problem:
+### `granovagg.1w()`
 
-```{r example}
+```{r}
 library(granovaGG)
-## basic example code
+data(poison)
+
+# Note violation of constant variance across groups in following graphic.
+granovagg.1w(poison$SurvTime, group = poison$Group, ylab = "Survival Time")
 ```
 
-What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:
+### `granovagg.ds()`
 
-```{r cars}
-summary(cars)
-```
+```{r}
+library(granovaGG)
 
-You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this.
+# This example corresponds to case 1b in Pruzek and Helmreich (2009). In this
+# graphic we're looking for the effect of Family Treatment on patients with anorexia.
+
+data(anorexia.sub)
+
+granovagg.ds(anorexia.sub,
+             revc = TRUE,
+             main = "Assessment Plot for weights to assess\nFamily Therapy treatment for Anorexia Patients",
+             xlab = "Weight after therapy (lbs.)",
+             ylab = "Weight before therapy (lbs.)"
+)    
+```
 
-You can also embed plots, for example:
+### `granovagg.contr()`
 
-```{r pressure, echo = FALSE}
-plot(pressure)
+```{r}
+data(arousal)  
+contrasts22 <- data.frame( c(-.5,-.5,.5,.5), 
+  c(-.5,.5,-.5,.5), c(.5,-.5,-.5,.5) )
+names(contrasts22) <- c("Drug.A", "Drug.B", "Drug.A.B")
+granovagg.contr(arousal, contrasts = contrasts22)
 ```
 
-In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
+## FAQs

README.md

@@ -6,47 +6,179 @@
 <!-- badges: start -->
 <!-- badges: end -->
 
-The goal of granovaGG is to …
+The package granovaGG is designed to produce statistical graphics driven
+by the fundamental questions of analysis of variance. The graphics
+granovaGG creates can offer far more visual information than a
+traditional tabular model summary or significance test. For an example
+of how granovaGG can enhance analysis, see [Pruzek and Helmreich
+(2009)](rmpruzek.com/wp-content/uploads/2011/07/ElementalGraphicsForANOVA.finalJune11.pdf).
 
 ## Installation
 
 You can install the development version of granovaGG from
-[GitHub](https://github.com/) with:
+[GitHub](https://github.com) with:
 
 ``` r
 # install.packages("devtools")
 devtools::install_github("briandk/granovaGG")
 ```
 
-## Example
+## Usage
 
-This is a basic example which shows you how to solve a common problem:
+There are three main functions in this package:
+
+- `granovagg.1w()`: for plotting one-way analysis of variance (ANOVA)
+
+- `granovagg.ds`(): for plotting dependent-sample t-test data
+
+- `granovagg.contr()`: for plotting contrasts
+
+## Examples
+
+### `granovagg.1w()`
+
+``` r
+library(granovaGG)
+data(poison)
+
+# Note violation of constant variance across groups in following graphic.
+granovagg.1w(poison$SurvTime, group = poison$Group, ylab = "Survival Time")
+#> 
+#> By-group summary statistics for your input data (ordered by group means)
+#>    group group.mean trimmed.mean contrast variance standard.deviation
+#> 3      3       0.21         0.21    -0.27     0.00               0.02
+#> 9      9       0.23         0.23    -0.24     0.00               0.01
+#> 2      2       0.32         0.32    -0.16     0.01               0.08
+#> 12    12       0.32         0.32    -0.15     0.00               0.03
+#> 6      6       0.33         0.33    -0.14     0.00               0.05
+#> 8      8       0.38         0.38    -0.10     0.00               0.06
+#> 1      1       0.41         0.41    -0.07     0.00               0.07
+#> 7      7       0.57         0.57     0.09     0.02               0.16
+#> 10    10       0.61         0.61     0.13     0.01               0.11
+#> 11    11       0.67         0.67     0.19     0.07               0.27
+#> 5      5       0.81         0.81     0.34     0.11               0.34
+#> 4      4       0.88         0.88     0.40     0.03               0.16
+#>    group.size
+#> 3           4
+#> 9           4
+#> 2           4
+#> 12          4
+#> 6           4
+#> 8           4
+#> 1           4
+#> 7           4
+#> 10          4
+#> 11          4
+#> 5           4
+#> 4           4
+#> 
+#> The following groups are likely to be overplotted
+#>   group group.mean contrast
+#> 3     2       0.32    -0.16
+#> 4    12       0.32    -0.15
+#> 5     6       0.33    -0.14
+#> 
+#> Below is a linear model summary of your input data
+#> 
+#> Call:
+#> lm(formula = score ~ group, data = owp$data)
+#> 
+#> Residuals:
+#>      Min       1Q   Median       3Q      Max 
+#> -0.32500 -0.04875  0.00500  0.04312  0.42500 
+#> 
+#> Coefficients:
+#>             Estimate Std. Error t value Pr(>|t|)    
+#> (Intercept)  0.41250    0.07457   5.532 2.94e-06 ***
+#> group2      -0.09250    0.10546  -0.877 0.386230    
+#> group3      -0.20250    0.10546  -1.920 0.062781 .  
+#> group4       0.46750    0.10546   4.433 8.37e-05 ***
+#> group5       0.40250    0.10546   3.817 0.000513 ***
+#> group6      -0.07750    0.10546  -0.735 0.467163    
+#> group7       0.15500    0.10546   1.470 0.150304    
+#> group8      -0.03750    0.10546  -0.356 0.724219    
+#> group9      -0.17750    0.10546  -1.683 0.101000    
+#> group10      0.19750    0.10546   1.873 0.069235 .  
+#> group11      0.25500    0.10546   2.418 0.020791 *  
+#> group12     -0.08750    0.10546  -0.830 0.412164    
+#> ---
+#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+#> 
+#> Residual standard error: 0.1491 on 36 degrees of freedom
+#> Multiple R-squared:  0.7335, Adjusted R-squared:  0.6521 
+#> F-statistic:  9.01 on 11 and 36 DF,  p-value: 1.986e-07
+```
+
+<img src="man/figures/README-unnamed-chunk-2-1.png" width="100%" />
+
+### `granovagg.ds()`
 
 ``` r
 library(granovaGG)
-## basic example code
+
+# This example corresponds to case 1b in Pruzek and Helmreich (2009). In this
+# graphic we're looking for the effect of Family Treatment on patients with anorexia.
+
+data(anorexia.sub)
+
+granovagg.ds(anorexia.sub,
+             revc = TRUE,
+             main = "Assessment Plot for weights to assess\nFamily Therapy treatment for Anorexia Patients",
+             xlab = "Weight after therapy (lbs.)",
+             ylab = "Weight before therapy (lbs.)"
+)    
+#>                               Summary Statistics
+#> n                                         17.000
+#> Postwt mean                               90.494
+#> Prewt mean                                83.229
+#> mean(D = Postwt - Prewt)                   7.265
+#> SD(D)                                      7.157
+#> Effect Size                                1.015
+#> r(Postwt, Prewt)                           0.538
+#> r(Postwt + Prewt, D)                       0.546
+#> Lower 95% Confidence Interval             10.945
+#> Upper 95% Confidence Interval              3.585
+#> t (D-bar)                                  4.185
+#> df.t                                      16.000
+#> p-value (t-statistic)                      0.001
+#> Coordinate system already present. Adding new coordinate system, which will
+#> replace the existing one.
 ```
 
-What is special about using `README.Rmd` instead of just `README.md`?
-You can include R chunks like so:
+<img src="man/figures/README-unnamed-chunk-3-1.png" width="100%" />
+
+### `granovagg.contr()`
 
 ``` r
-summary(cars)
-#>      speed           dist       
-#>  Min.   : 4.0   Min.   :  2.00  
-#>  1st Qu.:12.0   1st Qu.: 26.00  
-#>  Median :15.0   Median : 36.00  
-#>  Mean   :15.4   Mean   : 42.98  
-#>  3rd Qu.:19.0   3rd Qu.: 56.00  
-#>  Max.   :25.0   Max.   :120.00
+data(arousal)  
+contrasts22 <- data.frame( c(-.5,-.5,.5,.5), 
+  c(-.5,.5,-.5,.5), c(.5,-.5,-.5,.5) )
+names(contrasts22) <- c("Drug.A", "Drug.B", "Drug.A.B")
+granovagg.contr(arousal, contrasts = contrasts22)
+#> [[1]]
 ```
 
-You’ll still need to render `README.Rmd` regularly, to keep `README.md`
-up-to-date. `devtools::build_readme()` is handy for this.
+<img src="man/figures/README-unnamed-chunk-4-1.png" width="100%" />
+
+    #> 
+    #> [[2]]
+
+<img src="man/figures/README-unnamed-chunk-4-2.png" width="100%" />
+
+    #> 
+    #> [[3]]
+
+<img src="man/figures/README-unnamed-chunk-4-3.png" width="100%" />
+
+    #> 
+    #> [[4]]
+    #> Warning in mean.default(response): argument is not numeric or logical:
+    #> returning NA
 
-You can also embed plots, for example:
+    #> Warning in mean.default(response): argument is not numeric or logical:
+    #> returning NA
+    #> Warning: Removed 10 rows containing missing values (`geom_hline()`).
 
-<img src="man/figures/README-pressure-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-4-4.png" width="100%" />
 
-In that case, don’t forget to commit and push the resulting figure
-files, so they display on GitHub and CRAN.
+## FAQs