## 4.4 Check the Packaging

Have you ever gotten a present before the time when you were allowed to open it? Sure, we all have. The problem is that the present is wrapped, but you desperately want to know what’s inside. What’s a person to do in those circumstances? Well, you can shake the box a bit, maybe knock it with your knuckle to see if it makes a hollow sound, or even weigh it to see how heavy it is. This is how you should think about your dataset before you start analyzing it for real.

Assuming you don’t get any warnings or errors when reading in the dataset, you should now have an object in your workspace named ozone. It’s usually a good idea to poke at that object a little bit before we break open the wrapping paper.

For example, you should check the number of rows

> nrow(ozone)
[1] 7147884

and columns.

> ncol(ozone)
[1] 23

Remember when we said there were 7,147,884 rows in the file? How does that match up with what we’ve read in? This dataset also has relatively few columns, so you might be able to check the original text file to see if the number of columns printed out (23) here matches the number of columns you see in the original file.

Another thing you can do in R is run str() on the dataset. This is usually a safe operation in the sense that even with a very large dataset, running str() shouldn’t take too long.

> str(ozone)
Classes 'tbl_df', 'tbl' and 'data.frame':   7147884 obs. of  23 variables:
$State.Code : chr "01" "01" "01" "01" ...$ County.Code        : chr  "003" "003" "003" "003" ...
$Site.Num : chr "0010" "0010" "0010" "0010" ...$ Parameter.Code     : chr  "44201" "44201" "44201" "44201" ...
$POC : int 1 1 1 1 1 1 1 1 1 1 ...$ Latitude           : num  30.5 30.5 30.5 30.5 30.5 ...
$Longitude : num -87.9 -87.9 -87.9 -87.9 -87.9 ...$ Datum              : chr  "NAD83" "NAD83" "NAD83" "NAD83" ...
$Parameter.Name : chr "Ozone" "Ozone" "Ozone" "Ozone" ...$ Date.Local         : chr  "2014-03-01" "2014-03-01" "2014-03-01" "2014-03-01" ...
$Time.Local : chr "01:00" "02:00" "03:00" "04:00" ...$ Date.GMT           : chr  "2014-03-01" "2014-03-01" "2014-03-01" "2014-03-01" ...
$Time.GMT : chr "07:00" "08:00" "09:00" "10:00" ...$ Sample.Measurement : num  0.047 0.047 0.043 0.038 0.035 0.035 0.034 0.037 0.044 0.046 ...
$Units.of.Measure : chr "Parts per million" "Parts per million" "Parts per million" "Parts per million" ...$ MDL                : num  0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 ...
$Uncertainty : num NA NA NA NA NA NA NA NA NA NA ...$ Qualifier          : chr  "" "" "" "" ...
$Method.Type : chr "FEM" "FEM" "FEM" "FEM" ...$ Method.Name        : chr  "INSTRUMENTAL - ULTRA VIOLET" "INSTRUMENTAL - ULTRA VIOLET" "INSTRUMENTAL - ULTRA VIOLET" "INSTRUMENTAL - ULTRA VIOLET" ...
$State.Name : chr "Alabama" "Alabama" "Alabama" "Alabama" ...$ County.Name        : chr  "Baldwin" "Baldwin" "Baldwin" "Baldwin" ...
\$ Date.of.Last.Change: chr  "2014-06-30" "2014-06-30" "2014-06-30" "2014-06-30" ...

The output for str() duplicates some information that we already have, like the number of rows and columns. More importantly, you can examine the classes of each of the columns to make sure they are correctly specified (i.e. numbers are numeric and strings are character, etc.). Because we pre-specified all of the column classes in read_csv(), they all should match up with what we specified.

Often, with just these simple maneuvers, you can identify potential problems with the data before plunging in head first into a complicated data analysis.