Exercise

Visualizing missing data

Dealing with missing data is one of the most common tasks in data science. There are a variety of types of missingness, as well as a variety of types of solutions to missing data.

You just received a new version of the accounts data frame containing data on the amount held and amount invested for new and existing customers. However, there are rows with missing inv_amount values.

You know for a fact that most customers below 25 do not have investment accounts yet, and suspect it could be driving the missingness. The dplyr and visdat packages have been loaded and accounts is available.

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Visualize the missing values in accounts by column using a function from the visdat package.