No matter what the programming language a person is using, it is well known that there will be a need to organize data in as efficient way as can be done. The way to handle this is usually binning, though most people do not usually do this unless the person is heavily engaged in data science. That is the beauty of R, as it is the perfect language for either a company or individual needing to sort numbers via the program they are creating. Observations via the coding will help the process become clear with the minimal misunderstandings.
Binning in R is the same as the machine learning concept of discretization. This allows for data to be sorted into bins, which allows people and programs to understand what they have better. This sorting of data is necessary for many reasons and can make large data projects become more coherent for everyone. A person that is dealing with large projects may not have heard of the technique, but this tutorial should allow people to consider adding it to make data science easier. Optimal binning will allow someone to handle issues they have had with data before.
Using the word select allows a person to get the numeric value that is needed for the sorting that is needed. This is usually a select( ) with the name of the information value inside the parentheses.
An example would be thus, with the person naming the table.
name_table Information to be sorted %>%
select( categories ) %>%
This will give a table of the sorted information. Any of this information is quoted and evaluated. Select allows for some special functioning, which are “starts with( )”, “ends with( )”, contains( )”, and several others.
Equal frequency binning comes from several options after a person has set where the program is to get the information. This will try and put the information in an equal amount into the bins via equal_freq and the command for the number of bins is n_bins. The nbins command is teamed with var for the numeric value. It looks like equal_freq( var, n_bins). This will give a better look for what a person is able to read through with the output. The R package that the programmer gets this from is the Hmslc package.
Using numeric will allow a person to shape the numeric vector into a more manageable form in these areas. This will look like numeric( length = “how long). This will be valuable when dealing with decimal points that are gotten from any math functions when using the data, or when wanting to make a good interval.
The command pretty is another area that intervals become important when someone is looking for an equal width in the appearance. This is the command that will allow someone to sort through the integers with several arguments. The documentation and experimenting will allow someone to sort the data they need, but could require an entire tutorial.
The use of binning will allow someone to use a categorical variable for statistical modeling. It brings the information in and allows them to shape how the data is seen for each area. This may mean that bagged clustering will be mixed in via bclust when sorting through all the information when looking for a better way to sort. Missing values are usually listed via the y argument. This will allow someone to sort the information even more by combining binning and clustering in a way that shows people where all the data is coming from.
At some point, the numeric vector may need to be changed into a factor. This may then need to be become an ordered factor. All of this is to allow for a discrete classification. Data is made more clear in this manner for better binning. The command to change it into a factor is factor( ). This can then be checked to see if it is ordered or unordered.
This will look like this.
state “various states”
This may seem like a lot of information, but as someone starts binning, it will be made clear. The commands listed here are what will allow someone to sort all the data they need and will be the basic concepts that will start them on the right path to having a better presentation via the R language. Binning will allow someone to incorporate all of this into an output that is more coherent for the end user.