# Overview:

This practice set is modeled off of the 2nd lab session. You can find the original dataset [here](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data).

Part 1.

Load the required packages and read in the nyc airbnb file and assign it to 'nyc_airbnb'. 
You do not need to use the sep = ''. argument at all because the default sep will suffice. Simply use the filepath to read the csv file in.


```{r}

```


Part 2.

1. Select the following variables using 'pipes': id, name, price, minimum_nights, number_of_reviews, room_type, neighborhood_group, neighborhood. Assign this df to 'nyc_airbnb_subset'.

```{r}


```

2.1 Create a cross-tabulation/contingency table of neighborhood_group against accommodation type using the table function.

```{r}

```

2.2 Filter nyc_airbnb_subset by the neighborhood_group 'Manhattan' using pipes again. Call this df 'manhattan'.

```{r}

```

3.1 Provide the mean price of an airbnb in Mahattan using the summarize function.

3.2 How many airbnbs are in Manhattan?

3.3 What is the maximum value of number_of_reviews? 

3.4 What is the median number of minimum_nights spent at these airbnbs?

```{r}


```

