janitor | simple tools for data cleaning in R | Data Visualization library

I'm building a WebApp with a SQL DB as Backend. I'm Deploying the both parts on Azure, as Azure Webapp and SQL Server.

The SQL server is sercured with Azure AD (AAD). So only Users in a Group can access the DB.

So I'm trying to setup a workflow where the Webapp login the user and collect his Access token. And then uses the token to Query the SQL server.

I've registreted the App in AAD, where it is authorized to read the user ID and impersonate as the user.

I've the following code which is working local. But I can't get it to work deployed locally in a Docker Image.

This is what I'm trying to do:

1. I have a large excel sheet I'm importing to R.
2. The data needs to be cleaned so one of the procedures is to test for character length.
3. Once the program finds a string that is too long, it needs to prompt the operator for a replacement
4. The operator inputs an alternative, and the program replaces the original with the input text.

The code I have seems to work procedurally, but the variable I have is not overwriting the original value.

Assume that we have a data frame with hundreds of observations of cars. Each observation can be grouped by model, brand and country.

How can we count how many models of cars are produced in each country?

I tried:

I have a data frame with columns generated from scraping the web for info in which several columns get the same name, e.g.

I have this sort of data:

To streamline data wrangling, I write a wrapper function consisted of several "verb functions" that process the data. Each one performs one task on the data. However, not all tasks are applicable to all datasets that pass through this process, and sometimes, for certain data, I might want to switch off some "verb functions", and skip them.

I'm trying to understand whether there's a conventional/canonical way to build such workflow within a wrapper function in R. Importantly, a way that will be efficient, both performance-wise and concise code.

As part of data wrangling, I want to carry out several steps:

1. Clean up column headers (using janitor::clean_names())
2. Recode values in the data, such that TRUE and FALSE are replaced with 1 and 0 (using gsub()).
3. Recode string values to lowercase (using tolower()).
4. Pivot wider based on specific id column (using tidyr::pivot_wider)
5. Drop rows with NA values (using dplyr::drop_na())

Toy data

Suppose I need to use add_row at last of a data set say iris like this!

I have below-mentioned dataframe in R:

According to the tabyl documentation:

However, I can't figure out how to suppress the NA from the denominator!

Please see here for the data: