# Plotting Data

Let's explore our data by visualizing relationships between our variables and `quality`.  A good place to start is the `GGally::ggpairs()` function.  We have a few too many variables to make it easy to interpet the output on the entire data set.  Let's subset our data first.  **Use the data that doesn't include the erroneous alcohol values for all plots below.**

1. Use the `ggpairs()` function on the variables containing 'acid', `alcohol`, `quality`, and `type`.  Use `aes(color = type)` in the `ggpairs()` function.
```{r, out.width = "90%"}

```
2. We see a number of `fixed_acidity` values that are too large.  Let's remove any `fixed_acidity` values larger than 200 and repeat the `ggpairs()` plot.
```{r, out.width = "90%"}

```
3. There are some interesting relationships there.  Let's view some relationships a little differently (using the subsetted data from 2).  
- Create a scatter plot of `volatile_acidity` and `citric_acid`.  
- Make the *color* of the points depend on the `quality` variable.  
- Add a smoothed trend line via the `geom_smooth()` layer.  Change the color of the lines to 'red' (doesn't need to go in an `aes()`) and make the `linetype` differ based on the `type` variable (this does go in a local `aes()`).
- Add a title to the plot. (`ggtitle()`)
```{r, out.width = "90%"}

```