Lab Exercise – Data Summary 1. For this exercise, go online and select a comma separated value (.CSV) data file from one of these sources: The US government makes many, many data sets available for free, and all are legally free to use

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Lab Exercise – Data Summary

1. For this exercise, go online and select a comma separated value (.CSV) data file from one of these sources:

The US government makes many, many data sets available for free, and all are legally free to use and in the public domain.
https://data.gov/

Additionally, you could check one of the sources listed on these pages, I recommend Socrata, as an example.
https://www.dataquest.io/blog/free-datasets-for-projects/

This website has a great collection of useful .csv data files that are all covered by the creative commons license, and none of the files are too large.  The one problem, is that to be useful, the delimiters in these files will have to be replaced from a semicolon (;) to a comma (,).  Something that can be done easily enough in a text editor with a search and replace function. Or you can explore using the read_csv() function to load the file (see below) and make it work with a semicolon, rather than with a comma.
https://perso.telecom-paristech.fr/eagan/class/igr204/datasets

Take a look around, it might take a few minutes but find a .csv file.

2. Use the techniques described in the supplemental materials (see the STUDY section of course content) to load a .CSV file into an R data frame.  Data Frames are described in the Cotton book in chapter 5.  There is a lot more about getting and loading data in chapter 12, but we aren’t ready for all that just yet.

Load your data file into a data frame called BAN6040_DATA

For instance, if you wanted to load the .csv file called “CARS.CSV” you might use the following command…

BAN6040_DATA <- read.csv(“CARS.CSV”)

3. Once your dataset is loaded, describe a couple of things about your data set.  find out about the Structure of your dataset using the str() command.  Also take a peek at the first few rows of your data using the head() command, and finally find out some statistical information about your data (assuming it has numerical columns) using the summary() command.

Those commands were introduced in chapter 3, last week, but they make a lot more sense, and show a lot more usefulness when working with a data frame.

Copy the results of step 3, along with a description of the data file 

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