Tableau for R Users - Explore Tableau and Embed R Code

Connect data sets to a Tableau workbook by selecting a source, loading an Excel workbook with partners, products, and sales sheets, and explore extracts, filters, and metadata.

Learn to create filtered data extracts in Tableau for R users, applying numeric and date filters. Then aggregate data for visible dimensions to reduce the data size.

Explore joining multiple tables in Tableau using inner, left, right, and outer joins to merge sales data with shop and product details via a common field.

Learn to create visualizations in Tableau by dragging fields onto shelves, using the Show Me tool, and building charts like bar charts and treemaps from a connected Excel dataset.

Plan visualizations by identifying what to show and who the audience, then select suitable chart types or Show Me options to tell a self-explanatory analytical story.

Sort data in Tableau by category and date with automatic and manual options, and compare sorting by field, by measure, or by custom order to reveal revenue and quantity patterns.

Learn to add row and column totals and subtotals in Tableau, choose aggregation, view underlying data, and export totals for multi-dimensional data.

Explore the difference between discrete and continuous data and how date fields shape granularity, axis and header displays in Tableau. Learn how hierarchies, sorting, and level changes affect visualizations.

Explore how Tableau hierarchies enable drill-down from region to shop, control levels with plus signs, and navigate visualizations by dragging fields into hierarchies.

Change the aggregation of measures in a text table to reveal insights by switching revenue from sum to average, using text marks, and applying percent of total calculations.

Tableau communicates with data sources to perform calculations and aggregate measures by category. Understand raw versus aggregated data through a category sales demo.

Create calculated fields to derive insights from raw data and aggregates. Apply aggregate calculations, like profit margin using sum of sales, and observe order of operations in visualizations.

Explore how to apply simple and advanced filters in Tableau to refine data, including field-based filters, time filters, and top 10 and conditional filters across sales, category, and salesperson.

Create and use a benchmark parameter to inject dynamic values into calculations, and visualize a reusable parameter-driven reference line with color cues to compare sales against the threshold.

Learn how table calculations operate on the entire table and differ from calculated fields, with definitions, scope, partitioning and addressing fields, and ranking examples.

Explore table calculations in Tableau for R users, learning partitioning and addressing fields, quick versus custom calculations, and how percent of total and running total reshape bar charts.

Explore proportional chart types, including pie charts, tree maps, and waterfalls, and learn when to use them for static data or time-based comparisons.

Explore how bar charts, column charts, and line graphs enable data comparisons across categories and over time, with stacked, bullet, and combined visuals for deeper insights.

Learn to add reference lines, trend lines, and drop lines in Tableau to visualize benchmarks, patterns, and axis interactions across time.

Explore how to build bullet graphs in Tableau with actual vs target measures, using reference lines, distribution bands, and region-based scopes to compare branch performance to targets.

Explore histograms, box and whisker plots, and scatterplots to visualize distributions: one-dimensional versus categorical data, quartiles and median, outliers, correlations, and the use of colors or tables for multidimensional views.

Learn to build histograms in Tableau by creating bin dimensions or manual bins for age distribution, using count as the measure, and parameterizing bin size.

Use scatterplots in Tableau to plot waiting time versus eruption duration, exploring correlation while leveraging colors, sizes, and shapes, with optional linear regression trend lines.

Master Gantt charts in Tableau to plan and monitor projects by mapping tasks to start and end dates, calculating duration, and visualizing status with color, labels, and tooltips.

Explore how maps in Tableau for R users visualize data with coordinates and gradient coloring to show proportions, distributions, and patterns, using map servers for granularity.

Explore mapping in Tableau by loading the Luse Geo workbook, organizing country, state, and city with coordinates, and building a field map with pies and color by revenue.

Organize data with tables by using columns for variables and rows for observations, learn to query, add or remove fields on demand, and explore the highlight table and heat maps.

Learn to use Tableau's format pane to apply font, alignment, shading, borders, and lines to a whole worksheet or individual rows and columns, improving readability and presentation.

Explore labels and tooltips in Tableau, learn to edit them and reveal underlying values, and use the summary card and highlighter to analyze selected marks.

Learn to enhance visualizations with tooltips, labels, and annotations in Tableau for R users, including editing tooltip content, creating permanent labels, and annotating points or areas.

Explore how to customize axes in Tableau, including starting at zero, fixed and uniform ranges, and dual axes for revenue and quantity, with filters and color distinctions to compare shops.

Explore how to use Tableau's marks shelf to place measures and dimensions, switch between bar, line, area, or shapes, and apply color, size, tooltips, and drop lines.

Explore Tableau's tooltip feature, divvies in tooltip, to embed a worksheet in a tooltip, compare category contributions with percent of total, and create elegant, contextual insights.

Explore practical techniques for embedding multi-chart tooltips in Tableau, using worksheets, maps, and custom shapes, with tips for layout, sizing, and side-by-side visuals.

Explore worksheets in Tableau and manage tabs with rename, duplicate, color-code, and reordering. Export sheets to Excel, Access, or image formats, and differentiate between clear and delete worksheets.

Create interactive dashboards in Tableau by combining multiple worksheets with filters and supporting objects, arranging containers, text, images, and web pages, while syncing changes with source sheets.

Create data stories in Tableau using the story feature, adding sheets as points with captions and interactive filters. Format the story and navigate in presentation mode with F7.

Understand data blending across multiple sources, its differences from joins, primary and secondary sources, and how joint keys affect granularity, plan, test, and validate visuals.

Blend two data sources in Tableau, set join keys on date and category, and pull only needed sales from secondary source; handle nulls with set.

Learn how to blend two data sources in Tableau using multiple linking keys, including product and customer fields, and how double linking affects charts, filtering, and interpreting vouchers and revenue.

Explore domain padding in R to prevent data suppression when blending data with left joins, by creating complete customer–product grids with zero values for missing sales.

learn how to create a union in Tableau for R users, stacking tables with the same structure, resolve mismatched fields, and track origins with sheet and table name fields.

Master data preparation in Tableau by turning messy Excel sheets into tidy, usable data. Identify header issues, remove totals, normalize measures, and pivot to create month and sales fields.

Split names into first name and surname using the split function, concatenate to form full names, and convert date strings to actual dates with left, right, case, and make date.

Explore logical statements in Tableau, including if then else, if and only if, and case. Learn when to nest, handle nulls, and apply logical operators for strings and numbers.

Explore Tableau’s date functions, including today, now, day, month, year, date part, and date name, plus make date time conversions and min max comparisons.

Explore calculation types in Tableau for R users, including row-level calculations, aggregated expressions, and table calculations; understand granularity, aggregation, and the role of level of detail expressions.

Learn how level of detail expressions in Tableau control aggregation and granularity of dimensions and measures, enabling calculations beyond the view and solving mixed level of detail challenges.

Master level of detail (LOD) expressions to control aggregation and granularity beyond the view, using include, exclude, and fixed to compute across dimensions like category, product, or country.

Explore the general syntax of level of detail expressions in Tableau, including fixed, include, and exclude, with a two-part structure: dimension declaration and an aggregation-based calculation.

Explore how the include function in Tableau's level of detail expressions adds a dimension to a view, computing at a finer granularity before re-aggregating to the view level.

Learn how the exclude expression ignores a dimension to compute a more aggregated result than the view's level of detail, duplicating values across the scope and contrasting with include.

Use the fixed expression to compute values independently of the view's level of detail, enabling precise aggregation across dimensions. Explore profitability by city with counts and boolean tests.

Learn to use level of detail expressions to compare product vs category profit ratio, using a fixed expression and calculated fields for a clear difference visualization.

Compare include, exclude, and fixed level of detail expressions in Tableau, focusing on context and filters. Learn how they affect sales aggregation across city, state, category, and subcategory.

Master Élodie expressions and filters in Tableau, comparing fixed with include and exclude, and showing how dimension and context filters affect calculations, with the superstore dataset.

Establish a connection between R and Tableau by activating the library and running the surf command, then configure a local localhost connection on port 6311 for testing.

Embed R within Tableau using the four scripts—script int, script real, script string, and script boolean—to create calculated fields that reveal outliers and classifications in multivariate data, color-coded in visuals.

Learn to identify multivariate outliers by embedding R code in Tableau using the outlier package and the sine function, producing a binary 0/1 vector for plotting aluminium versus iron.

Explore k-means clustering in Tableau and R by computing four clusters on a dataset (iris) and visualizing them in a scatterplot colored and shaped by cluster labels.

Learn how to embed R code in Tableau and test in R first, handle randomness in kmeans, and ensure reproducible colors across scatter plots by using set.seed.

Learn to build a linear model in Tableau with an R script, relating aluminium to iron and nickel, then visualize fitted values and color groups for overestimated and underestimated observations.

Learn how to select chart types for datasets, from pie and doughnut charts to bar and column charts, using categorical data and 100 percent totals.

Compare histograms and other visualizations to reveal distributions and relationships; learn how bins, axes, and color or size add dimensions across scatter, line, area, bubble, and box plots.

Explore how outliers influence analysis and compare simple and multivariate detection methods, including the three-sigma rule, box plots, and proximity or model-based approaches in Tableau and R.

Identify univariate outliers using simple methods such as the ESD method and box plot, then apply Dixon and Graps tests from the outliers package in R to confirm results.

Explore multivariate outlier detection using the outlier package in R, focusing on sign1, sign2, and pick out methods that rely on PCA to identify outliers in high-dimensional data.