Esmayeel Vaghani

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Data science can be a bit difficult and boring for newcomers. The scheme of the previous section shows that when entering data science, it is best to start at the beginning, import, order and transform the data. The task can be a bit frustrating. To begin the study of this discipline, Tidyverse proposes to visualize data that has already been ordered and transformed, a much friendlier task for beginners.

The ggplot2 package is responsible for generating plots. We open RStudio and use the library.

We will see a message in the console.

The rest of the Tidyverse packages are ready to plot with. Since we don't know how to import our own data, we're going to use a number of data sets included out-of-the-box with this package that we can practice with.

One of them has the mpg command.

The data set that gathers information on the fuel consumption of different vehicles is called mpg.

Knowing what each variable means, we can begin to represent them graphically. We are going to follow the following scheme.

The coordinate system in which the data is plotted is created by the ggplot function. We need to include the data set to which the variables we want to represent belong in this function.

The graph is blank since we haven't chosen the variables yet.

We need to choose the type of graph that we want to create, this is dependent on the type of variable that we are going to represent, if they are continuous or discontinuous. We will see the different types of graphs as we progress in the lesson.

Some of them are not.

We are going to use a scatterplot.

We have to choose the variables that we will display.

The mapping function defines the variables to be represented. The variables x and y must be accompanied by the aes() argument.

Let's see how this example looks.

We have a graph like this.

The main variables x and y are introduced with the aes() command, which is used to modify how they will be seen on the graph.

We can see how the graph shows the relationship between the consumption of a vehicle and its displacement, by showing ourselves the previous example. We can introduce the relationship between these results and the class variable as a visual property if we want to observe it.

The car class is represented by changing the color of the points using the color argument.

The cost on the road with respect to the displacement and the different types of cars that exist with each color reveal more information.

We can include any variable in the graph.

We can assign a visual property to it based on its size and color. The type of vehicles can be represented by changing the size of the points.

We can change its transparency with alpha.

The points have a shape.

There are a number of problems in these examples.

In the first two and the third ggplot2 only represents six shapes of points, which are easily distinguishable from each other, it is difficult to see some points. Some of the values are no longer represented when the number is greater than six.

Only very specific cases are where these aesthetic are used.

Aes is only good for modifying graphs with respect to a variable. We can modify it as long as the values are the same.

Pulling can change the color of points. Command color: aes.

ggplot2 interprets that there is no variable named blue, so a meaningless plot is displayed.

Statistical graphs are used to represent quantitative data. Among them we find scatter charts (reviewed in the previous section), bar charts, box charts, histograms, etc. To study these plots we are going to use a new data set called "diamonds", included in ggplot2, which contains information about different properties of a large number of diamonds.

The same way as with scatter charts, we will use the bar chart command.

We want to classify the diamonds according to the quality of the cut. We use:

The total number of diamonds should be represented by cut quality by not specifying the variable y, according to ggplot2. ggplot2 uses statistical transformations to modify existing variables. The number of diamonds within each type of cut is counted.

You can modify the appearance of the bar graph using aes. The color of the border of the bars can be changed by the fill.

We can represent the clarity of diamonds as follows.

We can use thegeom_box function. The cost on the highway can be represented by the type of car:

If we need to flip the graph, we just add coord_flip

It is necessary to represent several graphs at the same time in order to facilitate visualization of the data.

There are two ways to do this.

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