Plots Béton Point P

• Label points in the scatter plot
• Scatter plots with multiple groups

1. Plot Dalle Beton Point P
2. Plot Beton Terrasse Point P

This plot is a simple point A to point B and back to point A plot. The protagonist sets off on a journey, only to return to his or her starting point having gained wisdom and experience (and sometimes treasure too). Paulo Coelho's The Alchemist is a beloved contemporary illustration of this plot. How to Outline a Story Plot: Three-Act Structure. A decision plot can expose a model’s typical prediction paths. Here, we plot all of the predictions from the UCI Adult Income data set in the probability interval 0.98, 1.0 to see what high-scoring predictions have in common. The features are ordered using hierarchical clustering to group similar prediction paths.

A subplot is a set of events that occur within the wider narrative of your story, but a B plot is baked into its structure. A B plot is there to do a job, often altering how the reader experiences the main plot and the story as a whole. That’s why, once you start looking for them, you’ll find B plots in a lot of successful stories.

This article describes how create a scatter plot using R software and ggplot2 package. The function geom_point() is used.

Related Book:

mtcars data sets are used in the examples below.

Simple scatter plots are created using the R code below. The color, the size and the shape of points can be changed using the function geom_point() as follow :

Note that, the size of the points can be controlled by the values of a continuous variable as in the example below.

Read more on point shapes : ggplot2 point shapes

The function geom_text() can be used :

Read more on text annotations : ggplot2 - add texts to a plot

Add regression lines

The functions below can be used to add regression lines to a scatter plot :

• geom_smooth() and stat_smooth()
• geom_abline()

geom_abline() has been already described at this link : ggplot2 add straight lines to a plot.

Only the function geom_smooth() is covered in this section.

A simplified format is :

• method : smoothing method to be used. Possible values are lm, glm, gam, loess, rlm.
  • method = “loess”: This is the default value for small number of observations. It computes a smooth local regression. You can read more about loess using the R code ?loess.
  • method =“lm”: It fits a linear model. Note that, it’s also possible to indicate the formula as formula = y ~ poly(x, 3) to specify a degree 3 polynomial.
• se : logical value. If TRUE, confidence interval is displayed around smooth.
• fullrange : logical value. If TRUE, the fit spans the full range of the plot
• level : level of confidence interval to use. Default value is 0.95

Change the appearance of points and lines

This section describes how to change :

• the color and the shape of points
• the line type and color of the regression line
• the fill color of the confidence interval

Note that a transparent color is used, by default, for the confidence band. This can be changed by using the argument alpha : geom_smooth(fill=“blue”, alpha=1)

Read more on point shapes : ggplot2 point shapes

Read more on line types : ggplot2 line types

Plot Dalle Beton Point P

Plot Beton Terrasse Point P

This section describes how to change point colors and shapes automatically and manually.

Change the point color/shape/size automatically

In the R code below, point shapes, colors and sizes are controlled by the levels of the factor variable cyl :

Add regression lines

Regression lines can be added as follow :

Note that, you can also change the line type of the regression lines by using the aesthetic linetype = cyl.

The fill color of confidence bands can be changed as follow :

Change the point color/shape/size manually

The functions below are used :

• scale_shape_manual() for point shapes
• scale_color_manual() for point colors
• scale_size_manual() for point sizes

It is also possible to change manually point and line colors using the functions :

• scale_color_brewer() : to use color palettes from RColorBrewer package
• scale_color_grey() : to use grey color palettes

Read more on ggplot2 colors here : ggplot2 colors

The function geom_rug() can be used :

sides : a string that controls which sides of the plot the rugs appear on. Allowed value is a string containing any of “trbl”, for top, right, bottom, and left.

The functions geom_density_2d() or stat_density_2d() can be used :

Read more on ggplot2 colors here : ggplot2 colors

The function stat_ellipse() can be used as follow:

The number of observations is counted in each bins and displayed using any of the functions below :

• geom_bin2d() for adding a heatmap of 2d bin counts
• stat_bin_2d() for counting the number of observation in rectangular bins
• stat_summary_2d() to apply function for 2D rectangular bins

The simplified formats of these functions are :

• geom : geometrical object to display the data
• bins : Number of bins in both vertical and horizontal directions. The default value is 30
• fun : function for summary

The data sets diamonds from ggplot2 package is used :

Change the number of bins :

Or specify the width of bins :

Step 1/3. Create some data :

Step 2/3. Create the plots :

Create a blank placeholder plot :

Step 3/3. Put the plots together:

To put multiple plots on the same page, the package gridExtra can be used. Install the package as follow :

Arrange ggplot2 with adapted height and width for each row and column :

Read more on how to arrange multiple ggplots in one page : ggplot2 - Easy way to mix multiple graphs on the same page

Change colors manually :

Read more on ggplot2 colors here : ggplot2 colors

This analysis has been performed using R software (ver. 3.2.4) and ggplot2 (ver. 2.1.0)

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