Build an A-Z Retail Dashboard with R & Shiny.

Explore how the R language powers modelling, data analysis, automation, and shiny apps with pipelines. Install and use thousands of packages for visualization and manipulation, including analogies to Python's pandas.

Learn about R, the free software environment for statistical computing and graphics, developed in 1993, and compare its role in data science and statistical analysis with Python.

Install the latest R 4.0.1 on Mac or Windows by downloading the installer, accepting the license, and running the setup to start coding.

install RStudio by downloading the latest version for your operating system, with mac option, and complete in one minute. explore RStudio's multiple windows and note server features for production workflows.

Explore the RStudio workflow, including the console, environment, plots, and packages, and learn to manage scripts, export outputs, and install packages for data analysis and visualization.

Set up a new project in the studio, save the workspace, and organize outputs in a dedicated directory named supply chain data science project for easy reuse.

Install and load R packages to perform data analysis and visualization, using either the install function or the RStudio user interface, then load libraries to access package functions.

Install and load required packages with the library function, and create a dedicated supply chain data science project folder to organize scripts and data import-export resources.

Explore data fundamentals, including vectors, data frames, metrics, and observations, then convert types, define factors, and use functions and loops to manipulate data.

Explore core data structures in R, including vectors, lists, matrices, and data frames, and distinguish continuous and categorical data types, with a focus on dates for time-based modeling.

Learn to perform arithmetic in R, save calculations as named variables in the environment, and work with atomic vectors and lists, including c() joins, named vectors, and index-based subsetting.

Learn to combine numeric, character vectors into a list in R, name items, and extract elements using double brackets to access specific vectors or values.

Import data from multiple formats into R using a reader package, explore a UK online retailer dataset, and summarize its structure, rename columns, and analyze numeric and categorical variables.

Learn to select data in a dataframe by indexing rows and columns, using head, slicing, and conditional filters to clean retail transactions and handle negative quantities.

Create and test an operations function for addition, subtraction, multiplication, division, and power; then use an ifelse function to flag United Kingdom purchases with a new column.

Explore how conditions drive outputs in a retail dashboard by using comparisons such as less than, greater than, and equal, and combine them with and or to validate data.

Define a function of x that uses conditions and if-else-elseif to classify people as child, teenager, or adult based on age; then consider applying this function to millions of people.

Identify names and ages, define a function to categorize each person by age, and apply the function inside a for loop to print categorized results.

Learn to apply for loops to a dataframe by taking the first 10 rows of retail transactions, and create a United Kingdom ok-or-not column while iterating over rows and columns.

Define a function to check if country equals united kingdom and return true, then apply it across a dataframe column, comparing for loop and apply-based approaches for supply chain analytics.

Apply R basics to subset the car dataset, extract names and prices, and classify each car as budget car, convenience car, or expensive car, then add a price category column.

Explore the cars dataset in R by inspecting its structure, counting cylinders with table, and summarizing speed, horsepower, and price; rename the first column and tally sports cars for dashboards.

Import the cars dataset, select name and price, create a price category function for budget, suitable, and expensive, then apply it with a for loop and tally results.

Explore data types in R: vectors, matrices, data frames, and objects. Learn to import data, inspect structure, compute summaries, filter and modify data, and create columns using $ and loops.

Discover data manipulation in R with the player and tidier packages, learning filter, group by, summarize, mutate, arrange, and slice using pipes.

Master data manipulation in R with dplyr, using select, group_by, summarize, mutate, and arrange to transform retail data. Learn left, right, inner, and full joins and pivot data for analysis.

Explore retail data with dplyr to group by country, summarize units sold, and identify outliers, then clean data by filtering negatives and removing anomalies.

Group retail lines by invoice to calculate the average revenue per invoice and understand how multi-line invoices affect metrics in a retail dashboard with R and Shiny.

Discover a two-step method to compute the average invoice value per country by deriving revenue per invoice and identifying the top 10 countries, using mutate, group_by, and summarize.

Compute the average items per invoice and summarize with mean, median, and standard deviation using group by and summarize. Demonstrate a case-based column to flag United Kingdom transactions.

Master joining data with left, inner, and full joins to combine sales, stocks, and production datasets by common keys, using practical examples to surface stock data.

Learn to reshape and pivot joined data like Excel, using the tidy up library, and convert invoice date-time to a date by day to create a net column.

Learn to pivot data between long and wide formats, separate by article and date, and prepare time-based data for forecasting in a retail dashboard.

Learn how to reshape retail data in R with pivot wider and pivot longer, creating date and value columns while preserving the description column for time series forecasting.

Separate a column into date and time using separate with a space delimiter, then paste them back to create a unified field for retail data analysis.

Combine learning to compute and display percentiles and quartiles by country, create a repeating name column for 43 unique countries, and spread the data for clear country distributions.

Practice joining flights, planes, and airlines with weather data in a New York flights case study using left and inner joins to identify most used planes and most punctual airlines.

Solve the assignment using a NYC flight dataset with airlines, planes, weather, airports, and flights. Apply group by destination, count, and left joins to identify New York's most popular destination.

Explore analyzing flight data to identify the busiest month (July) and the most punctual airline by creating an aggregate delay, grouping by carrier, and summarizing mean delay.

Build an a-z retail dashboard with R & Shiny by analyzing flights to compute longest airtime and mean duration by origin-destination, join airports data, and assess carrier seat capacity.

Identify the most used aircraft model and its manufacturer through joins and totals, with distinct models and missing data handling for a supply chain dashboard.

Master data manipulation with dplyr verbs like filter, mutate, arrange, slice, and summarise, then group_by summaries. Use left and inner joins and pivot tables to shape retail data into dashboards.

Learn the basics of Shiny for building interactive retail dashboards in R, including creating simple apps, tables, and basic visualizations.

Learn to install packages for building a retail dashboard with R and Shiny, using a script to install resources and enable database connectivity.

Create your first Shiny application by combining a fluid page UI with a server function, learning input, output, and session, and displaying hello world.

Explore building a first shiny app with a select input to choose a dataset from the data sets package, and display a reactive summary and table in the UI.

Build a Shiny app interface that lets users select datasets like CO2, view the data table and its summary, and display outputs through an active function.

Learn to build a Shiny server with a reactive expression that fetches a user selected dataset, producing a live summary and a render table with data table features.

Explore the Fibonacci sequence by building a Shiny app that takes a numeric input and returns the corresponding Fibonacci numbers, with customizable initial values and labels.

Learn to build a shiny app that generates a Fibonacci sequence from a user input, using numeric input, UI and server logic, with result filtering.

Compare imperative and declarative programming, and see how Shiny enables user-driven filtering of a retail dataset with multiple inputs to update visualizations.

Learn to set up essential libraries for a retail dashboard in R and Shiny, use vroom for fast loading, and configure date and country filters.

Explore the base retail dashboard app structure, focusing on the sidebar with country, size, and date filters that drive dynamic table, plot, analysis, and model views across multiple pages.

Design a Shiny user interface using a fluid page and flatly theme, adding a sidebar layout with multiple select inputs for country and item codes.

Configure a Shiny app structure for a retail dashboard, using a sidebar layout with date range inputs and a main panel featuring line and bar plots of revenue by country.

Build an a-z retail dashboard with R & Shiny. The lecture demonstrates a reactive server, filtering data by a date range to compute online revenue from quantity and price.

Learn to implement country and SKU reactivity in a retail dashboard built with R and Shiny, using input-driven subsetting to update datasets and visuals.

Create line plots to visualize revenue over time by country in a Shiny dashboard, color by country, and configure outputs, libraries, and axis mappings.

Debug a retail dashboard app in R and Shiny by fixing country naming, date-time to date conversion, and render settings for interactive country filtering and plotting.

Create interactive plots and a data table in a Shiny app using the DT package, rendering a transactional data table and comparing country totals.

Learn how operational metrics like conversion rate, average selling price, and average transaction value reveal weekly performance. Apply these metrics to promotions, seasonality, and live real-time insights for stakeholders.

Learn to define and monitor retail metrics in a retail app, focusing on margin, markup, inventory turnover, cash-to-cash cycle, and footfall conversion for sustainable profitability.

Analyze key retail metrics such as sales per meter, sales per employee, average transaction value, units per transaction, conversion rate, and footfall to optimize space, staff performance, and profitability.

Calculate key retail metrics—average transaction value, units per transaction, average selling price, conversion rate, sales per employee, and sales per square meter—and compare stores over time for benchmarking.

Learn the difference between margin and markup, and how each affects profit and cost in retail. Use examples to calculate and compare margins and markups.

Explore how top line sales and bottom line profit shape retail outcomes, using year-on-year growth forecasts, budgets, open to buy plans, and margins to meet targets.

Explore how stock turns influence profitability and how excessive inventory leads to expenses and markdowns. See how conversion rate, training, merchandising, assortment, and promotions shape in-store performance, with seasonal comparisons.

See how chasing 600k in sales at 25 percent margin yields the same profit as 500k at 30 percent margin, yet incurs 29 percent more logistics and storage costs.

Explore margins vs markups in retail for your A-Z retail dashboard, clarifying definitions as percentage of profit from selling price versus cost, with practical formulas and examples.

Explore how supplier discounts empower retailers to run promotions, boost margins, and drive sales, creating a win-win for suppliers, retailers, and customers.

Explore Shiny's reactive functions to create a seamless web app for retail data, using date slider widget and dropdown widgets, and build a comprehensive retail analytics application.

Explore market basket analysis and association rules, learn about confidence and support, and apply these to generate product recommendations and bundles, illustrated with a UK retailer dataset.

Explore market basket analysis to uncover associations and association rules between products that customers often buy together, using support, confidence, and lift for recommendations powered by the Priore algorithm.

Apply market basket analysis on UK retail data by cleaning, filtering UK items, ranking the top 10 products by sales, and transforming the data into transactions for visualization and insights.

Apply the flyer package to transform and read retail transaction data, build a market basket of transactions, and configure separators and descriptions for invoices, preparing for the next lecture.

Analyze and summarize the transactions and invoices dataset, highlighting item counts per invoice and key statistics such as min, median, and max, using visualization.

Define the transactional data and set a 0.9 confidence and 0.001 support to discover high association rules, and refine recommendations by inspecting pink and blue vintage spot rules.

Explore the top 10 association rules in a retail dashboard built with R and Shiny, highlighting item pairs and their support and confidence.

Explore how to subset association rules from transactional data, visualize connections, and automate rule generation to power promotions and customer recommendations in a retail dashboard.

Complete the final assignment on market basket analysis using the UK media dataset, apply your newly acquired skills, run the provided script, and verify results.

Apply market basket analysis to reveal product affinities and drive targeted promotions that boost profits. Build retailer user interface, implement backend functions, and deploy an Azure-based dashboard with database access.

Outline core libraries for the retail dashboard in R and Shiny, including Shiny themes and data tables, plus cloud database plans and association rules for market basket analysis.

Define variables and parameters for the Mango stores dataset in R and Shiny, rename price to price_picked, compute revenue from quantity, price, and promotion, and convert dates for date-range filtering.

Define data variables and dimensions for a retail dashboard, including brands, SKUs, descriptions, stores, suppliers, sections, and era months, enabling min and max dates, filtering, and UI development in Shiny.

Create a simple retail analytics interface using a fluid page and Shiny theme, with a left sidebar for date range and store selection and a main tabbed panel for outputs.

Complete the sidebar panel by adding dimensions such as brands, stores, suppliers, families, and items, with sections and descriptions. Enable filters by description and revenue to refine the main panel.

Build the Mango overview page within a Mango retail dashboard using R and Shiny, configuring a sidebar and main panel, with 11 data visualizations and a ranking-focused data table.

Create supplier, brand, and item summary pages with scatterplots and brush interactivity that reveal details in a data table and enable downloading selected data.

Refine the retail dashboard UI by adding brand and supplier dimensions and handling sections and items. Use the hoverable scatterplot to view data, and utilize download and save features.

Lay out a six-page retail dashboard in Shiny with a filterable raw data table for download, and six plots including time-series revenue and sales history across categories.

Explore the price distributions tab to visualize category averages and price dispersion with histograms and kernel density plots, compare brands, and assess price gaps for retail strategy.

Finalize the user interface by implementing size and color curves, color/size pie charts, and market basket insights, plus metrics on conversion rate and average transaction value.

Create reactive expressions to filter the Mango stores dataset by date range, family, subfamily, section, and brand, handling all selections and rendering a filtered data table named filter.

Fix subfamily ratings to align activity data across stores, brands, suppliers, and descriptions, and implement separate reactive expressions with lazy loading in shiny to update only changed parameters.

Develop summary tables using a reactive function to filter data by section and store, compute total revenue, and present the results in a wide table with column totals.

Develop reactive summary functions in a Shiny app to compute store and section totals, including revenue, cost of goods sold, and total costs, using consistent data and matching names.

Explore interactive plots in an R and Shiny dashboard to analyze revenue by store and section, top 15 sales and revenue, with bar plots and filterable comparisons.

Preview and compare plots showing margins between cost of goods sold and revenue, filter by brand, and rank by attributes using a reactive function in a Shiny dashboard.

Sort and rank data by store and story with a top function inside a reactor, splitting data into frames and selecting the top 15 or fewer by input rank.

Sort by revenue or visits, select the top 15 overall, remove the store dimension, and return the first 15 rows for plotting the results.

Learn to build a group plot in R using input choice and input rank, reorder X by rank, apply color coding, and validate the plot within a Shiny dashboard.

Explore a retail dashboard in R and Shiny that presents plot-based rankings by store, including ranking by description or brand, with debugging notes on plot and table outputs.

Render the overall plot and ranking to show the top 15 items, enable downloading selected data, and present a new table by item, family, unit sales, or revenue.

Learn to create ranked data based on sales, display it in a reactive data table, and enable downloading via a download handler in a Shiny app.

Create interactive supplier and brand scatterplots with reactive data frames to summarize revenue, then use input brush to explore points and reveal top and bottom revenue suppliers and brands.

Use the input brush on a scatter plot to select suppliers and brands, view brushed points with revenue and sales, and download the summarized data.

Create an item summary by reusing ranked data workflows, include item descriptions, size, and color, enable filterable results and downloadable Excel data, and prepare for daily dynamic updates.

Learn to build line plots for revenue history, by section and by category, and turn them into interactive charts in a Shiny framework.

Calibrate time series visualizations for a retail dashboard by transforming line plots into daily sales and revenue across stores and sections, using reactive date grouping and revenue history analysis.

Analyze price, size, and color curves using histograms and kernel density plots to determine popular sizes and colors, set price buckets, and guide buyer quantities across brands and subfamilies.

Explore building size and color curves in a Shiny dashboard, creating color and size distributions with pie charts, domain setup, and a two-column grid for clear visualization.

Explore size and color preview in a retail dashboard with a pie plot; selecting a brand reveals color popularity, green, and size distribution across large and medium.

Explore how brand and supplier contributions drive revenue, margins and discounts across dimensions, and build map, metrics, and market basket analyses in a Shiny dashboard.