1. First, you have to fetch the data into R. The code for that might look like this. Here you are reading a csv file from your working directory and loading it into a dataframe called housing.
housing <- read.csv("us-housing-data.csv", stringsAsFactors = FALSE)
The sample code above shows how to fetch data from a CSV file in a local directory. Similarly there are functions to fetch data from an XML file, an EXCEL file, a JSON file, and an HTML web page. Once you understand the fundamentals of fetching data, it is only a matter of knowing the function.
2. Second, you may want to remove some rows where data is missing for a particular column. So you create another dataframe which only has the rows where column 37 has some valid data.
cleanhousingdata <- housing[complete.cases(housing[,37]),]
3. In the third step you may want to filter that column for a certain condition. In this case, I am looking for homes that are valued at more than 1 million USD. VAL is the name of the column.
costlyhouses <- subset(cleanhousingdata, VAL >1000000)
Once you do these basic steps, you can start looking for answers to you questions in the data. Coming up with questions is another interesting area.