Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

R packages

An R package extends the functionality of basic R. Base R, by itself, is very capable, and you can do an incredible amount of analytics without adding any additional packages. However adding a package may be beneficial if it adds a functionality which does not exist in base R, improves or builds upon an existing functionality, or just makes something that you can already do easier.

For example, there are no built in packages in base R which enable you to perform certain types of machine learning (such as Random Forests). As a result, you need to search for an add on package which performs this functionality. Fortunately you are covered. There are many packages available which implement this algorithm.

Bear in mind that there are always new packages coming out. I tend to favor packages which have been on CRAN for a long time and have large user base. When installing something new, I will try to reference the results against other packages which do similar things. Speed is another reason to consider adopting a new package.

The stargazer package

For an example of a package which can just make life easier, first lets consider the output produced by running a summary function on the regression results, as we did previously. You can run it again if you wish.


The amount of statistical information output by the summary() function can be overwhelming to the initiated. This is not only related to the amount of output, but the formatting. That is why I did not show the entire output in the previous example.

One way to make output easier to look at is to first reduce the amount of output that is presented, and then reformat it so it is easier on the eyes.

To accomplish this, we can utilize a package called stargazer, which will reformat the large volume of output produced by summary() function and simplify the presentations. Stargazer excels at reformatting the output of many regression models, and displaying the results as HTML, PDF, Latex, or as simple formatted text. By default, it will show you the most important statistical output for various models, and you can always specify the types of statistical output that you want to see.

To obtain more information on the stargazer package you can first go to CRAN, and search for documentation about stargazer package, and/or you can use the R help system:

IF you already have installed stargazer you can use the following command:


If you havent installed the package, information about stargazer, (or other packages) can also be found using R specific internet searches:


If you like searching for documentation within R, you can obtain more information about the R help system at:

Installing stargazer package

Now, on to installing stargazer:

  • First create a new R script (File | New File | R Script).
  • Enter the following lines and then select Source from the menu bar in the code pane, which will submit the entire script:
        stargazer(lm_output, , type="text")

After the script has been run, the following should appear in the Console:

Code description

Here is a line by line description of the code which you have just run:

  • install.packages("stargazer"): The line will install the package to the default package directory on your machine. If you will be rerunning this code again, you can comment out this line, since the package will have already be installed in your R repository.
  • library(stargazer): Installing a package does not make the package automatically available. You need to run a library (or require()) function in order to actually load the stargazer package.
  • stargazer(lm_output, , type="text"): This line will take the output object lm_output, that was created in the first script, condense the output, and write it out to the console in a simpler, more readable format. There are many other options in the stargazer library, which will format the output as HTML, or Latex.

Please refer to the reference manual at for more information.

The reformatted results will appear in the R Console. As you can see, the output written to the console is much cleaner and easier to read.

Saving your work

After you are done, select File | File Save from the menu bar.

Then navigate to the PracticalPredictiveAnalytics/Outputs folder that was created, and name it Chapter1_LinearRegressionOutput. Press Save.