Exploratory
Exploratory Desktop provides a Simple and Easy-to-Use UI experience to access various data sources, clean and transform data, visualize and analyze data to gain deeper insights, communicate your discoveries with Notes, and monitor your business metrics with Dashboards. You can quickly extract data from various built-in data sources such as Redshift, BigQuery, PostgreSQL, MySQL, Oracle, SQL Server,
Text2Landscape: Visualize a Text in Multiple Spaces with R — Force-directed networks, Biofabric, Word Embeddings, Principal Component Analysis and Self-Organizing Maps
First Visualizations: Frequencies Let us first visualize word frequencies. We can get these frequencies with the quanteda package, which implies transforming the column of lemmas (text.lemmas$lemma) into a quanteda tokens object, then to a document-feature matrix. Doing so, we only retain significant parts of phrases (nous, proper nouns, verbs and adjectives). This only partially spares us the tas
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