When calling the ggplot function, we create a coordinate system on which we can add layers on top of it . The first argument we give to the ggplot function is the dataset we are going to use and the second one is instead an aesthetic function in which we define the variables we want to plot. We can then go on adding other additional arguments such us defining a desired geometric function eg.
Taking our analysis a step further, we can now calculate the precise percentages of the difference between the different cases using the prop. As we can see from the resulting output Figure 7 , We can now go on creating 3 different Box Plots using the same technique used before. In this Dataset, we are not given the actual phone prices but a price range indicating how high the price is four different levels from 0 to 3.
The results are summarised in Figure 8. Instead, more expensive phones seem to be overall more lightweight. In the RAM vs Price Range plot have interestingly been registred some outliers values in the overall distribution.
Learning the shortcuts
Finally, we are now going to examine the distribution of camera quality in Megapixels for both the Front and Primary cameras Figure 9. Interestingly, the Front camera distribution seems to follow an exponentially decaying distribution while the Primary camera roughly follows a uniform distribution. If you are interested in finding out more about Probability Distributions, you can find more information here. In order to perform our Machine Learning analysis, we need first to convert our Factor variables in Numeric form and then divide our dataset into train and test sets ratios.
Using R summary we can then inspect the parameters of our trained model Figure Finally, we can now test our model making some predictions on the test set.
Using R confusionMatrix function we can then get a complete report of our model accuracy Figure In this case, an Accuracy of I hope you enjoyed this article, thank you for reading! If you want to keep updated with my latest articles and projects follow me on Medium and subscribe to my mailing list. These are some of my contacts details:.
Getting started with the BrailleR package
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Towards Data Science Sharing concepts, ideas, and codes. Ubuntu or Debian itself is as simple as to type in sudo aptitude install r-base or sudo apt-get install r-base don't forget that this has to be done as root , or installing the package r-base using your favourite package manager, for example Synaptic. There is also a bunch of packages extending R to different purposes.
Their names begin with r-. Take a closer look at the package r-recommended. It is a metapackage that depends on a set of packages that are recommended by the upstream R core team as part of a complete R distribution.
It is possible to install R by installing just this package, as it depends on r-base. Download the disk image dmg file and install R. The default graphical user interface for Mac is much better than the one for Windows. It includes.
What is Kobo Super Points?
Section source . To install R under Windows operating system you have to download the binaries from the web. First go to r-project. The best idea is pick a mirror closest to your actual geographical location, but other ones should work as well. The click Windows and in subdirectories base. The windows binary is the exe file, in form R- x. Regardless of the version the setup has the same steps. As usual in Windows, if you just keep clicking the Next button, you will install the program without any problems.
However, there are few things that you can alter. Both of these steps can easily be done using the installr package, by running the following command which would both install the package, and update R  :.
The difference between R and RStudio… · GitHub
There is also the possibility of using a "global" package library, see here for more details. You have a portable version if you want to install R on your USB stick . This is useful if you don't have admin rights on a computer. The basic installation requires something like mb but you may need more if you want to install add-on packages.
Once you have installed R , you need to choose a working environment.
In this section, we review all possible working environment. This include a basic terminal as well as integrated development environment therefore IDE , text editors or graphical user interface therefore GUI. Note that there are some task specific GUIs.