Exploratory Desktop
AnalyticsData WranglingVisualizationReportingData Sources
Join / Merge
You can join multiple data sets together with various options like Left Join, Right Join, Inner Join, Full Join, or you can filter your data by using other data sets with Semi Join or Anti Join.
You can merge multiple data sets together with various options like Union, Union All, Setdiff, Intersect.
Create Buckets (Binning)
Simple and Intuitive UI helps you create buckets or categories for numeric columns with various methods, such as ‘equal length’, ‘quantile’, ‘clustering’, ‘outlier’, etc.
Assign Values
You can quickly assign new values to the existing values.
Work with NA Values
You can quickly find out how much NA or Missing values exist in your data with Summary view, and filter them out or impute NAs by replacing with static values or dynamically calculated values, or even predicting the most likely values.
The order of the categorical values is sometimes important for building Machine Learning models and Data Visualization. Exploratory makes it easy to create and manage your own categorical data by taking advantage of R’s built-in system called Factor.
Long to Wide or Wide to Long
Sometimes, it’s much easier to work with Long data, but other times Wide data could be easier. Exploratory’s simple UI makes it easy to transform between Long and Wide quickly and flexibly.
Wide to Long
Long to Wide
You might want to separate the addresses to city, state, and country, or want to separate email addresses to userid and domain name. Being able to separate the text data flexibly means you need to deal with regular expressions less. In Exploratory, you can separate text data in an intuitive way with great performance.
Tokenize Text
You can quickly tokenize the text data in a ‘tidy’ way, which was introduced by an R package called 'tidytext' in 2016 and has revolutionized the way we analyze text data ever since.