UI Update — DatazarAs your projects get more and more files added to it, keeping track of your datasets and analysis can become tedious. We’ve updated your project file list so you can see what type of files you have in your project before you ...

Introducing Webhooks — Fastest Way to Collect DataToday we’re introducing webhooks, the fastest way you can send data to a JSON file from anywhere.The number 1 most taxing task when it comes to data analysis or taking an initiative to become m...

With the increase in R usage over the past few years and the sheer diversity of industries employing data-science and analytics, the backgrounds of the people using R day-to-day has also changed. Different background, from hard-sciences to software-eng...

R and Python scripts you upload or create on Datazar are now executable.R files with an .r or .rscript and Python files with .pyextensions will be active for running. You can now analyze your datasets, import other people’s public data, load libra...

Have you ever wondered how you create or use Plotly (plotly.js) on Datazar? No? Well here it is anyway.Datazar’s live chart editor allows you to create custom D3js charts with a JavaScript and CSS editor. Using the DatazarJS library, you can call your datasets without having ...

In the digital age of today, data comes in many forms. Many of the more common file types like CSV, XLSX, and plain text (TXT) are easy to access and manage. Yet, sometimes, the data we need is locked away in a file format that is less accessible such as ...

Have you spent hours, pulling your hair out trying to figure out how to access datasets in R? Once imported to a variable, columns from a dataset (eg: CSV) can be very tricky to access. Sometimes columns contain spaces, funky characters or other incosistencies. Here are some examples on how ...

We get a lot of questions about the usage of R libraries. The most common question is “can I use all the R libraries in your notebooks/consoles?” Yes! You can use any of the libraries that have been published to the R package repository (CRAN). Open up your notebook/...

Adding a file to your R notebook is a simple 2 step process.1. “Import” file to notebook session.This makes sure your dataset is isolated in one state so that it doesn’t change as you analyze it. Remember, you can always load the latest content...

Getting started with notebooks is as easy as clicking a button on Datazar. Notebooks are great tools that make — creating and sharing reproducible analysis — super easy. To create R or Python notebooks, follow these directions: I. Navigate to your project, or create a new one if you don’t have ...

Today we’re introducing Datazar Community Tutorials. At Datazar, we love writing tutorials and how-tos on R, Python, D3, research and science best practices in general. So starting today, we’re extending that ability to our users so they can share ...