add DiagrammeR to Suggests and handle within vignette

Author: njtierney

DESCRIPTION

@@ -51,6 +51,7 @@ Suggests:
     covr,
     cramer,
     DiagrammeR,
+    DiagrammeRsvg,
     extraDistr,
     fields,
     ggplot2,
@@ -73,7 +74,7 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 Language: en-GB
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.0
+RoxygenNote: 7.3.1
 SystemRequirements: Python (>= 2.7.0) with header files and shared
     library; TensorFlow (v1.14; https://www.tensorflow.org/); TensorFlow
     Probability (v0.7.0; https://www.tensorflow.org/probability/)

vignettes/get_started.Rmd

@@ -25,7 +25,7 @@ if (!file.exists("figures"))
 file.copy("../man/figures/plotlegend.png",
           "figures/plotlegend.png")
 
-diagrammer_installed <- requireNamespace("DiagrammeR", quietly = TRUE)
+diagrammer_svg_installed <- requireNamespace("DiagrammeRsvg", quietly = TRUE)
 ```
 
 ## Installation
@@ -386,7 +386,8 @@ greta provides a plot function for greta models to help you visualise and check
 ```{r plot, eval = FALSE}
 plot(m)
 ```
-```{r plot_hidden, echo = FALSE, eval = diagrammer_installed} 
+
+```{r plot_hidden, echo = FALSE, eval = diagrammer_svg_installed} 
 gr <- plot(m)
 DiagrammeR::export_graph(attr(gr, "dgr_graph"),
                          file_name = "figures/full_graph.png",
@@ -407,7 +408,7 @@ The fourth type of node (diamonds) represents probability distributions. These h
 
 For example, a plot of just the prior distribution over `coef` (defined as `coef <- normal(0, 3)`) shows the parameters as data leading into the normal distribution, and a dashed arrow leading out to the distribution's value, the variable `coef`:
 
-```{r plot_coef, echo = FALSE, eval = diagrammer_installed}
+```{r plot_coef, echo = FALSE, eval = diagrammer_svg_installed}
 coef <- normal(0, 3)
 m_coef <- model(coef)
 gr <- plot(m_coef)
@@ -421,7 +422,7 @@ DiagrammeR::export_graph(attr(gr, "dgr_graph"),
 
 It's the same for the model likelihood, but this time the distribution's parameters are a variable (`sd`) and the result of an operation (`mean`), and the distribution's value is given by data (the observed sepal lengths `y`):
 
-```{r plot_likelihood, echo = FALSE, eval = diagrammer_installed}
+```{r plot_likelihood, echo = FALSE, eval = diagrammer_svg_installed}
 sd <- variable()
 y <- as_data(iris$Sepal.Length)
 mean <- ones(150)