# Reading Data

The data set we'll use for our EDA comes from the [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/wine+quality).  There are two data sets that are 'related to red and white variants of the Prtuguese "Vinho Verde" wine.'  There are physicochemical variables and a quality score, as rated by experts.  

Input variables (based on physicochemical tests):
- fixed acidity
- volatile acidity
- citric acid
- residual sugar
- chlorides
- free sulfur dioxide
- total sulfur dioxide
- density
- pH
- sulphates
- alcohol

Output variable (based on sensory data):
- quality (score between 0 and 10)


## Reading in the White Wine Data

There is an excel version of the white wine data set available at <https://www4.stat.ncsu.edu/~online/datasets/white-wine.xlsx>.  

- Download this file
- Place it in a folder you know (such as your working directory, `getwd()`)
- Import the data from the first sheet using `readxL::read_excel()`

```{r}

```

When you print the data set out to the console, you may notice that some of the variable names are surrounded by backticks.  This is because they are non-standard (they include a space in them).  We can rename them in a number of ways.  We'll do it by reading in the variable names from the 2nd sheet of the same file.

- Read in the data from the 2nd sheet.  This should return a data frame with one column containing alternative versions of the variable names.
- Grab that column (say with `$`) and overwrite the `colnames()` of your current `tibble`.  For example,
`colnames(my_wine) <- names_df$Variables`

```{r}

```

Lastly, we'll add a column to this data set to indicate the wines are white.  Run the code below (with necessary modifications on the `tibble` object name).

`my_wine <- mutate(my_wine, type = "white")`

```{r}

```


## Reading in the Red Wine Data

There is a semi-colon delimited version of the red wine data set available at <https://www4.stat.ncsu.edu/~online/datasets/red-wine.csv>.  This can be read in directly from the URL using the `readr::read_delim()` function with the `delim = ";"` argument (`read_csv2()` would work here too but it reads in some columns as character instead of double).  

- You should replace the variables as done above
- You should append a column denoting the `type` as "red"

```{r}

```


## Combining the Two Wine Data Sets

Our final task is to combine these two data sets into one data set.  They both have the exact same columns so this is an easy append task!  

Use the `dplyr::bind_rows()` function (see the help) to create one `tibble` containing all of the wine data.  

```{r}

```