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11.4 Compare the official view and the dplyr output.11.3.4 Selecting relevant tables and columns.11.3.1 Use a view just like any other table.11.3 Unpacking the elements of a view in the Tidyverse.11.2.2 Rely on – and be critical of – views.11.1 Setup our standard working environment.10.2 Disconnect from the database and stop Docker.10.1.4 Many handy R functions can’t be translated to SQL.10.1.2 Time-based, execution environment issues.10 Lazy Evaluation and Execution Environment.9.3.1 Create a black box query for experimentation.9.2 R is lazy and comes with guardrails.8.10 Disconnect from the database and stop Docker.8.9.4 Monthly Sales by Order Type with corrected dates – relative to a trend line.8.9.2 Define and store a PostgreSQL function to correct the date.8.9.1 Define a date correction function in R.8.9 Correcting the order date for Sales Reps.8.8 Detect and diagnose the day of the month problem.8.7.3 Compare monthly lagged data by Sales Channel.8.7.2 Monthly variation compared to a trend line.8.7.1 Retrieve monthly sales with the onlineorderflag.8.7 Impact of order type on monthly sales.8.6.4 Comparing average order size: Sales Reps to Online orders.8.6.1 Add onlineorderflag to our annual sales query.8.5.2 Comparing dollars and orders to a base year.
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8.4.1 Annual summary of sales, number of transactions and average sale.8.3 The overall AdventureWorks sales picture.8.1 Setup our standard working environment.8 Asking Business Questions From a Single Table.7.6 Disconnect from the database and stop Docker.7.5.5 The skim function in the skimr package.7.5.4 The glimpse function in the tibble package.7.5.2 Always look at your data with head, View, or kable.
ADVENTUREWORKSLT 2014 GITHUB CODE