Next: Coverage. Datasets. (2) Generate an overview report of searching results. Here are some of the most common commands for obtaining information about objects.This problem does not apply in the case of our Google data because the only applicant listed in that data is Google (excluding co-applicants). Most of these challenges involve cleaning inventor and applicant names or cleaning text fields prior to analysis.One of our aims is to generate an open access list of tools and resources that others working on patent analysis find useful. We can therefore safely use the Google dataset to identify the IPC codes.To see our .csv file we can head over to the Files tab and, assuming that we have created a project, the file will now appear in the list of project files. We will call the function patent_count().We often want to know what type of object we are working with and more details about the object so we know what to do later. Here we will be looking at options for storing and sharing patent data with others, taking into account issues around confidentiality.###Cleaning and Tidying Patent DataPatent activity can also be controversial. Copy and paste the code into the Console and press enter.We now want to generate three IPC tables.Packages can also be installed by selecting the Packages tab and typing the name of the package.When we now examine pizza_total, we will see the publication year and a summed value for the records in that year.We are now in a position to create our country trends table.To tidy patent data we will typically need to do two things.The plot reveals a data cliff in recent years. 12.2 Load a .csv file using readr. However, one of the main issues we encounter with patent data is that our data is not tidy because various fields are concatenated.We now have the following .csv files.One useful approach to developing an infographic is to start by adding the images and then add titles and text boxes to raise key points. Depending on our purposes with the analysis we might want to keep this data (for historical analysis) or to focus in on a more recent period.If possible use the View() command or environment. We might want to dig into this in a little more detail and so let’s also create an IPC subclass field.We can tie the steps so far together using pipes into the following simpler code that we will become the applicants table for use in the infographic. Download US patent grant texts from Google patents - lpuettmann/get-patents. We will therefore live with this.In this chapter we will use RStudio to prepare patent data for visualisation in an infographic using online software tools.This is a big issue because any counts that we make later on using the applicants_cleaned field will treat “Oppenheimer Alan A” and " Oppenheimer Alan A" as separate names when they should be grouped together.One nice feature of infogram is that it is easy to share the infographic with others through a url, an embed code or on facebook or via twitter.There are only 7 classes and as we might expect they are dominated by computing. In particular data from different patent databases typically involves different cleaning challenges. Note that the match is not always perfect as the final production lens can sometimes be different from the patent that is identified as the best match. The next and final step is to generate data from the text fields.We will want to create a plot with the applicants data in our infographic software.
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