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In this video, we'll discuss the
meaning of data visualization,

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and why it's often useful to
visualize your data to discover

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hidden trends and properties.

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Data visualization is defined
as a mapping of data properties

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to visual properties.

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Data properties are usually
numerical or categorical,

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like the mean of a variable,
the maximum value of a variable,

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or the number of observations
with a certain property.

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Visual properties can be (x,y)
coordinates to plot points

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on a graph, colors to assign
labels, sizes, shapes, heights,

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etc.

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Both types of properties are
used to better understand

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the data, but in different ways.

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To motivate the need
for data visualization,

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let's look at a famous example
called Anscombe's Quartet.

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Each of these tables corresponds
to a different data set.

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We have four data sets, each
with two variables, x and y.

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Just looking at
the tables of data,

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it's hard to notice
anything special about it.

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It turns out that the mean
and variance of the x variable

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is the same for
all four data sets,

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the mean and variance
of the y variable

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is the same for
all four data sets,

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and the correlation
between x and y, as well as

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the regression
equation to predict y

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from x, is the exact same
for all four data sets.

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So just by looking
at data properties,

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we might conclude that these
data sets are very similar.

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But if we plot the four data
sets, they're very different.

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These plots show
the four data sets,

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with the x variable
on the x-axis,

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and the y variable
on the y-axis.

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Visually, these data
sets look very different.

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But without visualizing them,
we might not have noticed this.

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This is one example of
why visualizing data can

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be very important.

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We'll use the
ggplot2 package in R

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to create data visualizations.

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This package was created
by Hadley Wickham, who

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described ggplot as "a
plotting system for R

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based on the grammar
of graphics, which

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tries to take the good parts
of base and lattice graphics

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and none of the bad parts.

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It takes care of many of
the fiddly details that

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make plotting a hassle
(like drawing legends)

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as well as providing a powerful
model of graphics that makes it

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easy to produce complex
multi-layered graphics."

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So what do we gain
from using ggplot

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over just making plots
using the basic R functions,

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or what's referred to as base R?

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Well, in base R, each
mapping of data properties

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to visual properties is
its own special case.

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When we create a scatter plot,
or a box plot, or a histogram,

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we have to use a completely
different function.

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Additionally, the graphics are
composed of simple elements,

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like points or lines.

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It's challenging to create any
sophisticated visualizations.

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It's also difficult to add
elements to existing plots.

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But in ggplot, the
mapping of data properties

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to visual properties is done by
just adding layers to the plot.

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This makes it much easier to
create sophisticated plots

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and to add to existing plots.

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So what is the
grammar of graphics

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that ggplot is based on?

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All ggplot graphics
consist of three elements.

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The first is data,
in a data frame.

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The second is an
aesthetic mapping,

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which describes how
variables in the data frame

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are mapped to
graphical attributes.

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This is where we'll define
which variables are on the x-

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and y-axes, whether or not
points should be colored

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or shaped by certain
attributes, etc.

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The third element is
which geometric objects

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we want to determine
how the data values

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are rendered graphically.

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This is where we indicate if the
plot should have points, lines,

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bars, boxes, etc.

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In the next video, we'll
load the WHO data into R

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and create some data
visualizations using ggplot.