[MUSIC]
And let's next start our brows tool and we pull over brows and
let's run this again so that we can look at it.
All right, so once that is run, just drag this over and we can take a look.
So okay, what do we learn from the graph first, what probably stands out is that
most of the transactions really happen in January and February.
Those are super tall here, right here on the left of the graph.
What, this should definitely be of interest at the order.
The order would want to know why this is the case.
It's not immediately clear to me why January and
February would be the most active months for p card purchases at the university.
It might be that more transactions happen at this time or
that they're of the transactions that do happen or
of a higher amount to higher value or some combination of both of those.
Certainly January is the start of the spring or
winter semester at most universities.
But it's often the case that the fall semester which starts in September is
the busier, more active time of the year for university and for purchases.
So perhaps the university is making a lot of capital expenditures, building
materials and whatnot at the beginning of the year to fit with state budgets.
It might be that the beginning of the calendar year is a time when
the university makes a lot of purchases of hotels for air travel for conferences, for
example.
Well anyway,
more consistent with our expectations is the high amount of transactions in august.
All right, that makes sense as being close to the beginning of the fall semester.
All in all this graph does provide several new questions to the auditor that they
need to ask the management.
And indicates several additional tests that the auditor needs to take and do.
Specifically the auditor could search the descriptions of the item description
column or the merchant column of the MCC column there in January and
February to kind of find out what's going on.
They might search for terms about construction or air travel to see if
that's what's driving the high number of purchases in January and February.
For our next visualization let's use the week day variable that we created earlier
to visualize the summer purchases made based on each day of the week.
Once again we'll need the summarized tool and then the sort tool and
then the interactive chart tool and then the browse tool.
So let's go ahead and put this back.
I had to bring each one of those out individually.
So, I've got these now on my favorite, so
I'm going to bring out the summarized tool.
Let's go ahead and bring the sort tool
in here now than an interactive chart tool and
our brows tool once again.
Okay, pull on the data.
So now again our goal is to we're going to group by a week because we want
to look at the day of the week rather we're in a group by weekday because
we want to see one observation per a weekday.
So, let's go ahead and pick that one.
So we're going to pick week day, which is one of the new variables that we created,
select that here.
And then we'll add a group by group by that and
then we're going to again sum up the amount and so we'll add that.
So here's are some and I'll change the name.
Just make this lower case.
Just for my preference.
You can do whatever you want and
sort by a weekday ascending again as we did before.
And then we'll do the first part of our configuring this chart.
We're going to once again at bar chart here.
So it is the sum by the of the week.
All right and we'll pick the bar chart.
We did this before.
Well x is going to be the weekday and
then why is going to be some amount and so let's go ahead.
Run this and see what we get, make sure that there's no errors at all.
We do notice from before the interactive chart here.
There's a warning that this is back with the bar chart that the dataset is
greater than 110,000 rows that can cause a slow chart rendering.
We're not going to modify that.
We don't care.
We're not going to sample.
Hopefully your computers are quick enough to work with that.
So no air is that warning we're not going to worry about.
Let's go ahead and take a look at our chart here, pull this over and
this looks pretty good and I think we can learn some things from this.
Let's change it up just a bit.
If we can, let's go over here and go to our chart let's just change the colors.
Let's color again as we did before by month.
So if we go into layer and then we click on variable and
we select the week day we can change this around.
Look at all these different options we could pick,, so
just play around with these tools against go ahead and
run this so we can see how that looks in our brows tool.
So what do we learn from this plot first?
It's probably obvious just from looking at it.
But it's important to note that weekday equal to 0 is Sunday and
weekday equal to 6 is Saturday.
Thus, from the graph we can see that the majority of transactions happen
as we would expect during the normal five weekdays Monday through Friday.
But probably the biggest finding here is that there's a half a million transactions
on Sunday and on Saturday we have another whole bunch of transactions
over a million transactions on Saturday.
So all in all,, we'd expect a priority that there would
probably be some transactions on the weekends.
For example, academics frequently travel on the weekends and
hold conferences on the weekends.
However, 1.5 million spent on weekends over the course of the year
seems potentially like a lot.
So the auditor would definitely want to do some additional testing on transactions
that happen on weekends and
we certainly inquire about these transactions to management.
Okay, last, let's visualize the sum of transactions by the day of the month.
So maybe we have different transactions, a different behavior at the beginning of
the month versus the end of the month versus the middle of the month.
So let's visualize that and see if we find anything interesting.
Again, we're just going to use all the same tools here.
Again, I'll just kind of throw them all together at the beginning.
Once more.
Like this, pulling the data
as we've done in the past.
Okay, now we're going to summarize by that knew that last new variable that we
created, which was date a month and so well grouped by date a month.
Again, this time and we'll once again take amount and sum by amount.
Once again, we'll change this to lower case just because that's my preference.
All right, so that's done sort by day, month,
let's configure our tool again to use a very similar chart will add our layer and
this will be called some by day of the month.
And again, bar charts and
we'll click here a day of the month and so some amount.
And so that will be our chart will run that and
then let's go change the colors again because we like to do that.
No errors.
Which is great with that one morning, which are not worried about.
And we go in here and let's go down to the style and
we'll change the layer and we'll add that variable stayed a month again.
And let's go ahead and change this to something interesting.
Yeah, how about that?
All right, run this one more time so
we can look at it again in our brows tool.
So here's our brows tool, here's the chart.
So now do we learn anything from this plot?
Not really the lowest transactions appear to be at the beginning and the end of each
month with peak purchase days scattered throughout the course of the month.
None of this seems overly surprising.
Perhaps a more thorough investigation would examine how these vary by month.
In particular, the author might be interested in January and February and
august since those were there really active months that we saw in our
last graph.
All right, so there's some good visualization of our data and
we're ready to go in and do some control testing of it.