Using R, clean this data set to make it easier to visualize and analyze. Specifically, these are the tasks you need to do:
Save the data set as a CSV file called refine_original.csv and load it in RStudio into a data frame.
Clean up the 'company' column, so all of the misspellings of the brand names are standardized. For example, you can transform the values in the column to be: philips, akzo, van houten and unilever (all lowercase).
Separate the product code and product number into separate columns i.e. add two new columns called product_code and product_number, containing the product code and number respectively
You learn that the product codes actually represent the following product categories: p = Smartphone v = TV x = Laptop q = Tablet In order to make the data more readable, add a column with the product category for each record.
You'd like to view the customer information on a map. In order to do that, the addresses need to be in a form that can be easily geocoded. Create a new column full_address that concatenates the three address fields (address, city, country), separated by commas.
Both the company name and product category are categorical variables i.e. they take only a fixed set of values. In order to use them in further analysis you need to create dummy variables. Create dummy binary variables for each of them with the prefix company_ and product_ i.e. Add four binary (1 or 0) columns for company: company_philips, company_akzo, company_van_houten and company_unilever Add four binary (1 or 0) columns for product category: product_smartphone, product_tv, product_laptop and product_tablet