Coding Environment

The ability to work with multiple programming languages and pre-installed packages is crucial in data science. DataMotto offers a default environment that supports the most popular statistical languages - Python, R, and SQL - along with a collection of pre-installed packages that satisfy the needs of most users. However, if you require a specific package that is not already installed, you can manually install it in a reproducible environment to ensure consistency across different sessions. In this section, we will explore the default environment, pre-defined packages, and steps to install additional packages in R and Python.

Default Environment:

DataMotto pages support the three most popular statistical languages - Python, R, and Julia - by default. This allows a page to execute code in multiple languages simultaneously, but in separate sessions. For instance, if a variable is created in Python, it cannot be accessed in the R session.

Disk Space

Each page (notebook) in DataMotto has a capacity of 10GB for all the data added into its session environment. This includes any data you upload or generate within the page. If you need to work with larger datasets, we recommend using our data integration feature or connecting to external databases.

User Storage

User storage refers to the total amount of data and resources a user has added to their workspace, which includes all of their pages. In DataMotto's free plan, users have access to a total storage limit of 5GB. For users with larger storage needs, we offer the Pro plan, which allows for unlimited data storage. However, it's important to note that each page has a disk space limit of 10GB, even in the Pro plan. Therefore, while users can store as much data as they need, they must ensure that they do not exceed the 10GB limit for each page.

Pre-defined Packages:

We have installed a collection of pre-defined packages that cater to a wide range of user needs by default. As a beginner, you most likely won't need to install any new packages. However, advanced users can certainly install any packages they require.

Predefined R Libraries:

The current list in Python session environemt is as follows, but to obtain the latest version, you may attempt installed.packages().

The current list in Python session environemt is as follows, but to obtain the latest version, you may attempt !pip list.

Reproducible Environment:

If the package you want to use is not already installed, you can manually install it. It is recommended that you install your package in a reproducible environment; otherwise, you will need to reinstall packages each time your page session is restarted.

Install R Packages:

To create a page-local R dependency management, initialize the renv in R.

renv::init(force = TRUE)

You can now easily install packages using

install.package('NAME') or renv::install('NAME').

If you wish to install a particular version, use

renv::install('NAME@VERSION'), such as renv::install('[email protected]').

For more information about the renv package and creating a reproducible environment, we recommend reading the documentation available here.

Install Python Packages:

You can install a permanent package in Python as shown below:

!pip install --user YourPkgName

Having trouble installing or need a package pre-installed? If you encounter issues while installing a package, please contact us via [email protected], and we'll assist you promptly.

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