R - Deepstash

R

  • R is also is an open-source programming language.
  • It's optimised for statistical analysis and data visualisation.
  • R has a rich ecosystem with complex data models and practical tools for data reporting.
  • R is popular among data science scholars and researchers.
  • R provides various libraries and tools for cleansing and prepping data, creating visualisations, and training and evaluating machine learning and deep learning algorithms.
  • R is used within RStudio.
  • R applications can be used directly on the web via Shiny.

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MORE IDEAS FROM THEARTICLE

  • Python is a general-purpose, object-oriented programming language.
  • It emphasises code readability by using white space.
  • It is easy to learn.
  • It is a favourite of programmers and developers.
  • Python is very well suited for use in machine learning at a large scale.
  • Its suite of specialised deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow. It enables data scientists to develop sophisticated data models that plug directly into a production system.

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They approach data science differently:

  • R is used for statistical analysis.
  • Python provides a more general approach to data.
  • R is built by statisticians and leans heavily into statistical models and specialised analytics.
  • Python is a multipurpose language that can provide data analysis or machine learning in scalable production environments.
  • You might use R for customer behaviour analysis or genomics research.
  • You might use Python for developing a machine learning application. 

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Data collection

  • Python supports all kinds of data formats.
  • R is designed for data analysts to import data from Excel, CSV and text files.

Data exploration

  • In Python, you explore data with Pandas, the data analysis library for Python.
  • With R, you can build probability distributions, apply different tests, use standard machine learning and data mining techniques.

Data modelling

  • Python has standard libraries.
  • With R, you'll sometimes have to rely on packages outside of R's core functionality.

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The language to choose depends on your situation.

Points to consider:

  • Do you have programming experience? Python has a learning curve that's linear and smooth. With R, novices can Run data analysis tasks in minutes, but it takes longer to develop expertise.
  • What do your colleagues use? Academics, engineers and scientists use R. Python is used in a wide range of industries.
  • What problems are you trying to solve? R is used for statistical learning, and Python for machine learning and large-scale applications.

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As already mentioned, debugging is considered a subset of troubleshooting. However, troubleshooting does not always entail solving the problem at that moment in time. There may be procedural constraints or workflow protocols that prevent the issue from being solved immediately. Debugging, on the other hand, is meant to discover and fix a problem all in the same session, whenever possible.

People use the two terms interchangeably, which can add to the confusion

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Pandas is a Python language package, which is used for data processing. This is a very common basic programming library when we use Python language for machine learning programming. This article is an introductory tutorial to it. Pandas provide fast, flexible and expressive data structures with the goal of making the work of “relational” or “marking” data simple and intuitive. It is intended to be a high-level building block for actual data analysis in Python.

Pandas is suitable for many different types of data, including:

  • Table data with heterogeneous columns, such as SQL tables or Excel data.
  • Ordered and unordered (not necessarily fixed frequency) time series data.
  • Any matrix data with row and column labels (even type or different types)
  • Any other form of observation/statistical data set.

The Pandas Index object contains metadata describing the axis. When creating a Series or DataFrame, the array or sequence of tags is converted to Index. You can get the Index object of the DataFrame column and row in the following way:

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