# Summarizing Data

Let's start by summarizing our `quality` variable from our full wine data set.  

1. First remove the extremely high values for `alcohol` (< 132) and save this as a new object we'll use going forward. (`filter()`)
```{r}

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2. Find the mean, standard deviation, Q1, Median, and Q3 for the `quality` variable.  (`summarize()`)
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3. Find the above for the `quality` variable for the `red` and `white` wines separately. (`group_by()`)
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4. Add in a new categorical variable that identifies the wines as high alcohol, medium, alochol, and low alcohol using the `dplyr::cut()` function and `mutate()` (`data %>% mutate(alc_level = cut(alcohol, 3))`).  Find the above summary statistics at each level of `type` and `alc_level` combination. (`group_by()`)
```{r}

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5. Lastly, let's find the correlation between `alcohol` and `quality` for each level of the `type` variable.
```{r}

```