### HTML scraping

We will scrape NFL data in this lab.

We will follow [this tutorial](https://towardsdatascience.com/scraping-nfl-stats-to-compare-quarterback-efficiencies-4989642e02fe) on scraping NFL stats (although the tutorial uses python).

We want to scrape from the webpage of [https://www.pro-football-reference.com/years/2019/passing.htm](https://www.pro-football-reference.com/years/2019/passing.htm)

Now open html with rvest

```{r}
rm(list = ls()) # clean-up workspace
```

### Parse the table

Inspect the table elements and try to find the patterns you can use to make the CSS selector for fetching the rows of the table


The [CSS selector reference](https://www.w3schools.com/cssref/css_selectors.asp) could be helpful here.

### Get column names

Find the CSS selector for the title row of the table.  Is it an element of `thead`?

Now get all the column names passing elements inside the `thead` element.

```{r, eval = FALSE}
# complete your code for getting the column names
col_names
```

### get each row of the table

Now find the CSS selector for each row of the table.

For example, one possible rule your instructor found is to look for all the \<tr\> tags inside the \<tbody\> element.

Hint: CSS selector `[target=_blank]` selects all elements with target="_blank".

What does the CSS selector `[data-stat=age]` select?


```{r, eval = FALSE}

# Finish your code for scraping the whole table

```

You may refer to the "NFL_passing_2019.csv" file which contains the scraped data by your instructor.
