Read in the data:
```{r read_in_the_data}
airbnb <- read.csv("../Datasets/portland-airbnb-listings.csv")
airbnb$price <- as.numeric(gsub("$", "", airbnb$price, fixed=TRUE))
airbnb$instant_bookable <- (airbnb$instant_bookable == "t")
```

First, how many prices didn't convert?
Well, we have only `r sum(is.na(airbnb$price))` NAs in the dataset -
so, not very many.

The mean price is $`r mean(airbnb$price, na.rm=TRUE)`,
most prices are below $300,
although the highest price is $999/night.
Here is the distribution of prices:
```{r hist_prices}
hist(airbnb$price, breaks=40,
     main="price per night of AirBnB rooms in Portland",
     xlab='price, dollars')
abline(v=mean(airbnb$price, na.rm=TRUE), col='red', lty=2, lwd=2)
```

Now let's compare the instant bookable
to the not-instant bookable ones:
```{r compare}
instant_bookable <- airbnb$price[airbnb$instant_bookable]
not_instant_bookable <- airbnb$price[!airbnb$instant_bookable]

layout(1:2)
hist(instant_bookable,
     breaks=40)
hist(not_instant_bookable,
     breaks=40)
```
