Plotting Decision trees

# Plotting Decision trees
### Dr. D’Agostino McGowan

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<div class="my-footer">
<span>
Dr. Lucy D'Agostino McGowan
</span>
</div>

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## 📖 Take 06 Assessment in Canvas

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## Plotting decision trees

There are several R packages that assist with tree plotting

* `rpart.plot`
* `partykit`
* `rattle`

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## Where to find out more about packages

1. Vignettes
2. Journal Article (R Journal is great)
3. Rstudio Community
4. StackOverflow
5. Twitter

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## <i class="fas fa-laptop"></i> `Learn about partykit`

Google `partykit` to find out how more about it (hint: use key words like `rstats` and `decision tree plots`)

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## `rpart.plot`

We're going to focus on `rpart.plot`, but feel free to try the others!

```r
install.packages("rpart.plot")
```

```r
library(rpart.plot)
```

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## `rpart.plot`

```r
tree_spec <- decision_tree(
  cost_complexity = 0.1,
  tree_depth = 10,
  mode = "regression") %>%
  set_engine("rpart")

model <- fit(tree_spec,
                   Salary ~ Hits + Years + PutOuts + RBI + Walks + Runs,
                   data = baseball)
```
]

```r
rpart.plot(model$fit,
           roundint = FALSE)
```

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## `rpart.plot`

```r
tree_spec <- decision_tree(
  cost_complexity = 0.1,
  tree_depth = 10,
  mode = "regression") %>%
  set_engine("rpart")

model <- fit(tree_spec,
                   Salary ~ Hits + Years + PutOuts + RBI + Walks + Runs,
                   data = baseball)
```
]

```r
*rpart.plot(model$fit,
           roundint = FALSE)
```

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## `rpart.plot`

```r
tree_spec <- decision_tree(
  cost_complexity = 0.1,
  tree_depth = 10,
  mode = "regression") %>%
  set_engine("rpart")

model <- fit(tree_spec,
                   Salary ~ Hits + Years + PutOuts + RBI + Walks + Runs,
                   data = baseball)
```
]

```r
rpart.plot(model$fit, 
*          roundint = FALSE)
```

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## `rpart.plot`

```r
rpart.plot(model$fit, 
*          roundint = FALSE)
```

![](17-decision-tree-plot_files/figure-html/unnamed-chunk-11-1.png)

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## <i class="fas fa-laptop"></i> `AE 05 - Regression trees`

1. Open your application exercise (05) from last week
2. Install `rpart.plot`
3. Create a plot from your final decision tree
3. Knit, Commit, Push. Be sure to have the final results pushed to GitHub by **April 8 at noon**