Instead of writing a SQL query for each metric why not getting the statistics you need all at once instantly. The summary statistics and visualization get automatically updated while you transform the data, which means you can interact with the data in a super fast and iterative way.
In-Memory, Vectorized, Super Fast
The time between asking questions and getting answers is critical for an effective exploratory data analysis R’s in-memory engine and vectorized operation make it possible for you to explore data in a highly flexible way at speed of thoughts.
Quickly summarize and pivot data to get the high level insights quickly. You can aggregate data flexibly and express data with color to spot outliers quickly. Learn more.
Rich Interactive Visualization
Visualize your data with a wide selection of charts quickly. You can aggregate the data flexibly with just a click. You can share the charts with your audience through Slack, Web Site, Wiki, Blog, etc, but also with the data preparation steps so that your charts are self-explanatory for the data provenance.
Creating window operations is super easy when you have a grammar. It is just a matter of grouping the data and creating a new column with a rich collection of R functions. Learn more.
You can manage variables outside of the queries and assign the values to the queries dynamically at the execution time. This would be useful especially when your table names keep changing or you want to manage the query conditions without touching the queries themselves.
Access to thousands of algorithms
You can quickly apply various statistical and machine learning algorithms from R’s wide variety of collection as part of your data exploration. You can visualize the result flexibly and run multiple experiments with reproducible steps easily.
By using the built-in text analysis functions, you can easily quantify your text and documents and discover similarities among customers, locations, documents, etc. Oh, have I mentioned scoring sentiments for words and sentences is just a single operation?
Join with other data sources
You can quickly join the data from multiple data sources including database, files, log files, web sites, APIs, etc, a very flexible and quick way. Being able to join multiple data sets as part of your data exploration not only enriches your existing data but also help you discover hidden patterns and trends easily.
Reproducible Data Analysis
Not only you can explore data iteratively and flexibly, but also all the data analysis steps are automatically recorded and managed for their data dependency. You can easily collaborate through data also generate a reproducible scripts to deploy on production systems and
Extend and Customize
Install your favorite R packages and start using the functions right away. You can also write your own R functions and variables so that you can use them as part of the grammar based data wrangling and analysis or use it as a way to extract data from anywhere you like.
Data Analysis is a team sport, it’s much better when you collaborate with others. You can share your insights with your co-workers, or share the data and the steps to produce it so that you can collaboratively build your data analysis with others.
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