Crisis management. How Open Source Can Help

If you came to Linux Adictos Trying to escape the media bombardment about the Coronavirus, you entered the wrong post. The opportunity to describe the great tools available under free and open source software licenses, it's too good to pass up.

The truth is that Among the consequences of climate change, human error, and mutant viruses, one should always have this list at hand.

In this first article we are going to dedicate ourselves to General-purpose or specific-purpose programming languages ​​that can come in handy in times of danger. In those that continue we will go up in specificity.

Crisis management. Why it is important to use free and open source software

Years ago I had the opportunity to meet the person in charge of compiling statistics for a general medicine service. We are talking about one of the most important university hospitals in Argentina. This person proudly showed me his handling of Excel database functions. When I asked him why he wasn't using Access, he looked at me like I'd made some particularly degenerate sexual proposition.

It goes without saying that the information that the service obtained was incomplete, late and impossible to share easily.

The truth is that in times of crisis, the speed in obtaining, processing and distributing information It's essential. This includes

• Easy to fill out forms
• Database interoperability
• Flexibility in information processing
• Ability to represent the results in a legible way

I am not going to fall into the obviousness of proposing to replace Microsoft Office with LibreOffice, you don't need me to figure that out. What I'm talking about is that those responsible for itcompiling statistics should have a basic understanding of some of the programming languages ​​with statistical functions. For example: uterine

R

R is a language and an environment pTo be used in statistical computing and graphing.

R provides a wide variety of statistical techniquess (linear and nonlinear modeling, classical statistical tests, time series analysis, classification, grouping and graphing.

Python

Python is the Aloe Vera of programming languages. ANDIt is very easy to learn, it is supported by all platforms and has a lot of libraries to work with data. Some of them are:

pandas

pandas is a library for Python that provides data structures and high-performance data analysis tools and easy to use. The name has nothing to do with bears. Pandas is the acronym for Data Analysis Library in Python.

Bookseller It is designed for quick and easy data manipulation, reading, aggregation and visualization. Using Pandas it is possible to collect the data from a CSV or TSV file or an SQL database and create a Python object with rows and columns called a data frame. The data frame is very similar to a table in statistical software.

Seaborn

Here we have a data visualization library that provides a high-level interface to draw attractive and informative statistical graphs.

We can use Seaborn to:

• Determine relationships between multiple variables (correlation)
• Look at categorical variables for aggregate statistics
• Analyze univariate or bivariate distributions and compare them between different subsets of data
• Draw linear regression models for the dependent variables.

Julia

Julia is a general-purpose programming language, although eIncreasingly used by scientists and engineers for its data management capabilities and friendly, feature-rich environment which includes: many options for data visualization and plotting, easy handling of data sets with interactive options in real time, versatile libraries for machine learning and parallel computing features.

In addition to the libraries created natively for data manipulation, mathematical operations, and those specific to BigData, the Julia programming language,  it also supports the use of Python, R, C / Fortran, C ++, and Java packages.