In this video, we're gonna review different chart types in Data Studio and see how we
can use them to tell different stories with data.
Hi this is Ahmad from Siavak with another
lesson from Google Data Studio essentials. In this lesson, we're going to see what's
possible in Google Data Studio in terms of data visualization. And what kind of charts
and graphs to get access to, to communicate data exactly the way we want to the end user
of the report. We're going to review each chart type with several examples. We're going
to learn how and when to use it and learn about data visualization best practices. In
this first part we're going to learn about table is scorecard, time series, area chart
and bar chart. Let's go ahead and get started.
The first and simplest chart type is a scorecards. Scorecards are good for showing numbers for
showing KPIs and by KPIs I mean key performance indicators With a scorecard, you can show
a number you can show a compact number, you can show a ratio or percentage, and you can
show a currency amount. Also with these scorecards, we have the option to compare the values with
the previous period. So we can see how much the number or the ratio has changed compared
to the previous period of time. We can see the percentage of the change, or we can see
the change in the absolute numbers. Another cool feature of the scorecard is the ability
to apply conditional formatting. So for instance, here, I've said that if the conversion rate
is less than 2%, I want the background of my scorecard to be yellow. And here I've said
that if the revenue is more than $2,000, I want the background to become green. We can
set multiple criteria for a singular scorecard. So we can set it to become green for more
than $2,000 or become yellow for less than $1,500 or become red for less than $400. That's
it for the scorecard.
Let's move on to the next chart, tables. Everyone knows tables tables are good for summarizing
data in rows and columns. Dimensions they appear in rows and metrics appear in columns.
We also can see the grand total at the bottom of the table, and we have the option to paginate
the table so see the next and previous pages of state. But that's not all of it. Tables
in Data Studio have some more styling features that help us visualize data differently and
communicate different stories with our data. So for example, instead of showing numbers
in the simple way, we can apply a heat map to a column so we can easily respond to the
largest and smallest numbers in that column. We can also choose to show the numbers in
a column with a small bars. This not only helps us see the largest and smallest values,
but it can also help us to compare the values more easily. So for example, these three rows
they have about the same shade of green. So without looking at the numbers, we cannot
be sure which one has the highest value. But here when we are applying a small bars to
this column, we can immediately see that this row has the largest value. When we are using
bars to visualize data, we can also apply target line. Here I've applied a target line
at 5000. So we can quickly see which rows are below the target and which rows are above
the time. Another nice feature of tables in Data Studio is that we can apply conditional
formatting. So for example, here, I've set a criteria that if a row is for a new visitor
the background color for the whole row would be light green. And if the revenue is more
than $300, the font color of the whole row to become green. But then if the revenue is
less than $200, I want the font of just revenue column be read. So as you can see the conditional
formatting for the tables in Data Studio are quite flexible. That's it for the tables.
Let's move on to the next chart, time series. Using a time series chart, we can show the
changes of the values of the metric over time. And it helps us to see the trends and patterns
in the data. Here in this simple example of a time series chart, I'm plotting users all
the time. But we can also break down the metric over different categories, for instance, returning
visitors versus new visitors. And we can also compare the values to the previous period.
Here we can see that during the last 28 days, we had both more new visitors and more returning
visitors, compared to the 28 days before that. Here's another example of a time series chart.
Again, we are plotting users over time, but this time, instead of breaking it down per
new visitor versus returning visitor, if you're breaking it down per device category, so we
can see the number of users from desktop, mobile and tablet. And instead of plotting
it daily, so using day as the value for our date range, you're using the week of the year.
So we can see the change of the numbers from the first week of the year over to the 52nd
week. You're not limited to show only one metric on a time series chart. In this example,
I have like three metrics, users, sessions and pageviews. And I'm showing them per quarter
of the year. If we move the cursor over any of these points, we can see the actual values
for that quarter. Here's another example the same chart, but this time, I'm using months
as the dimension for the horizontal axis. And finally, it's worse to mention that a
time series chart doesn't have to be plotted using lines, we can use bars as well. So here,
I have users, so we can quickly compare how many users did we have in each quarter of
2018 and compare it with the values for the same quarter in 2017, the year before. That's
basically it for the time series.
Let's move to the next chart type, area chart. Area charts are very similar to time series
chart, with the difference that they apply shade of color to the area underneath each
series to show the volume of the data, and that it can stack different series on top
of each other. So for example here, when we see the value of returning visitors when we
are hovering over returning visitors, and we see it's close to 2.5K, it doesn't mean
that we had more than 2000 visitors on that day. But it means that the total of new visitors
and returning visitors for that day was close to 2500 and that's because the values are
stacked on each other. From here to here is the value for new visitors, and from here
to here is the value for returning visitors. This is another example of an area chart.
This time you're plotting revenue per month. And you're breaking it down per device category,
mobile, desktop and tablet. And the values are again a stacked on top of each other.
And as a result, we can see the trend of revenue. So it started at $4,000. And at the end in
December, it was close to $8,000. But what if you're not interested in seeing the trend,
and instead, you're more interested in seeing the distribution of revenue between these
different categories. In other words, we don't care about the total. But instead, we want
to see which device category brings in more revenue compared to the others. To do that,
instead of a stacking series on top of each other. We can use hundred percent of stalking
which is shown in this chart. Here I'm visualizing the same data set, but this time we are focusing
on the distribution of the revenue. So for example, here, we can see that October, desktop
was responsible for more than 70% of revenue, while in the months before and after it was
down to around 24% or 34%. And then we can also see the change in the distribution over
time. So that's basically it about the area chart.
Let's move on to the next chart type the line chart. The line chart is very similar to the
time series with the difference that with time series chart, we could only have a time
dimension for the values on the horizontal x axis. But with the line chart, we can have
any dimension with any type of categories. Here we have countries as the dimension for
the x axis and we are plotting users and revenue. Now this is not a good data visualization
because it suggests that there is continuity between these different endpoints because
they are connected to each other with the line, it suggests that there is a connection
between the United States and Canada in the United Kingdom. Why? Actually there isn't.
This is not a continuous value like time that this is the day before this today, and this
is the day after. These are different categories and D are not connected to each other in any
way. A better way of visualizing the same data set would be using combo charts. With
combo charts, we can have different series represented either as lines, or bars. Here
I've selected the users to be represented as bars and be plotted against left axis and
revenue to be represented as a blank line and be plotted against right axis. We're not
limited to using only two metrics on a combo chart either. Here I've added it said metric
sessions, and I've set it to be represented by bars and plotted against the left axis.
So with the combo chart, we can add Multiple series, multiple metrics, and choose any of
them to be represented at bars, or lines or be plotted against the right axis or left
axis. Plus, we can use any dimensions as our categories over the horizontal axis. So that's
basically about combo charts.
Let's move on and see the next chart type bar chart. bar chart a simple, they use bonds
to plot the values of one metric or multiple metrics on the y axis, the vertical axis,
and then we can have a category or a dimension applied to the horizontal axis, the x axis.
Here I have users sessions and pageviews by country. Now, if we have just one metric,
instead of multiple metrics, okay, just like here that we have just users per country,
okay. Then we can apply a breakdown dimension. So, either multiple metrics or if there is
one metric we can apply a breakdown dimension. Now I have users broken down by new visitors
versus returning visitors so for example, for United States, I can see that during the
time from the report, I had this many new visitors, and this many returning visitors,
I cannot see the total of these two numbers. And the next example of a bar chart, I have
used this tact bar chart. Now, instead of the values of a breakdown dimension, new visitors
versus returning visitors be shown side by side, they are stacked on top of each other.
So we can see that again, we've had this number of new visitors during the timeframe, this
number of returning visitors during the same timeframe. And here is the total of these
two values more than 300,000 users, whereas here, we couldn't see that at all. We could
only see each value plotted against the y axis Okay. Now, there is a Another category
of bar charts and data, a horizontal bar chart. In the example above, we've had only six categories.
But there are times that we would like to visualize more categories. And there's just
not enough room for all of them to be shown side by side. In those cases, we could use
a horizontal bar chart. Here I have 16 categories. Now again, because I'm just plotting one metric
users, I can apply a breakdown dimension to this chart as well. Let's see how it looks
like. This one is the same chart, but now I applied the age range of the user as the
breakdown dimension. So the length of the horizontal bars are the same, but I can see
how many of those were between 35 and 44 years old, how many of those were between 25 and
34 years old, right. And this is called a horizontal stacked bar chart. Now, just like
in the example of the area chart if you are not at all interested in seeing the trend
of the users, okay, from California to the rest of the states, and we don't care about
the totals, but instead we care about the distribution of the age ranges for each of
these states, we can use a stacked bar chart. But with hundred percent of stacking, like
this. This one doesn't show us the totals. Instead, it shows percentages. And we can
easily see the distribution to change of the distribution of age ranges across different
user states on this chart. We can easily see that states like California, New York and
DC, have the oldest audience and then Florida has the youngest audience of all. Okay, so
that's bar chart. I guess I've bored you enough with different kind of boring chart types.
Let's go see one that is a bit more visual. Okay, let's take a break. In the next part
of this lesson, we're going to learn how to plug location data using maps, how to use
a scatter chart and bubble charts, why it's a good idea to stay away from pie charts.
And we're also going to review some word vast chart types like pivot tables, and color charts.
But that's it for today. I hope you'll learn something new. Feel free to test your knowledge
by taking the quiz for this lesson or continue to the next. You can find both links in the
descriptions below. If you're new to measure school, make sure to subscribe so you can
get notified of future videos that we publish. And if you have any questions, feel free to
leave a comment. I look forward to seeing you in the next lesson.