15 Essential packages in R for Data Science

Essential packages in R for Data Science, Do you know Most Essential packages in R for Data Science?

R is the most popular language for statistical modeling and many data scientist depending on R to solve day-to-day business problems.

R provides a diverse range of packages and more than 10,000 packages in the CRAN repository.

This will help to resolve almost all the data science problems in the research and business fields.

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Essential Packages in R

R programming language applications are used in different fields of the industry and also helping to handle day-to-day real-life problems.

In this tutorial, we are going to discuss the essential packages in R.

1. ggplot2

In the current world, visualization is everything, if you are not able to visualize then you are not able to resolve any issues.

ggplot2 is one of the most popular visualization package in R.

It is famous for its functionality and high-quality graphs that set it apart from other visualization packages.


2. ggraph

Everything has some limitations, so is an extension of ggplot2 and takes away all the limitations of ggplot2.


3. tidyr

tidyr is a new package that makes it easy to “tidy” your data. tidyr package is an evolution of Reshape2.

The data is considered tidy when each variable represents columns and each row represents an observation.


4. dplyr

dplyr facilitates several functions for the data frames in R. dplyr package is for data wrangling and data analysis purposes.

If you are working data analysis field dplyr is most essential package.


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5. tidyquant

If you are dealing with financial data then you can’t leave tidyquant package. tidyquant is considered as a financial package that is used to carry out the quantitative financial analysis.

Package tidyquant is also widely used for importing, analyzing, and visualizing data.

R is the most popular tool in the financial industry.

It provides advanced statistical analysis for almost all the necessary financial tasks.

For example, moving averages, autoregression, and time-series analysis, credit risk, risk measurement, adjust risk performance, and utilize visualizations like candlestick charts, density plots, drawdown plots, etc…


6. shiny

If you are thinking about an interactive and beautiful web interface then Shiny is the solution.

Shiny interfaces are directly written in R and provide a customizable slider widget that has built-in support for animation.


7. caret

If you are dealing with classification and regression problems then caret is one of the essential packages.

caret package is the extension of the caret is CaretEnsemble which is used for combining different models.


8. tidyverse

For data manipulation. There are a lot of new techniques available maybe users are not aware of.


9. e1071

Dealing with clustering, Fourier Transform, Naive Bayes, SVM, and other types of modeling data analysis then you can’t avoid e1071.


10. plotly

This package is mainly used for interactive and high-quality graphs then plotly is the solution for that.

It’s an extension of the JavaScript library. This package helps in embedding graphs on web applications quite easily.


11. knitr

Are you doing research?

Are you looking for reproducible results?

The solution is knit, It is reproducible, used for report creation, and integrates with various types of code structures like LaTeX, HTML, Markdown, LyX, etc.

It was inspired by Sweave and has extended the features by adding lots of packages like a weaver, animation, cacheSweave, etc

This package is an amazing one, you can make a beautiful pdf report and editable pdf forms with the help of latex coding.

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12. mlr3

Thinking about machine learning then mlr3, this package is created for doing Machine Learning.

It is also efficient, which supports Object-Oriented programming where ‘R6’ objects are being provided along with machine learning workflow.

Lots of functionality, you can deal with clustering, regression, classification, and survival analysis, etc…



XGBoost is an implementation of the gradient boosting framework.

It also provides an interface for R where the model in R’s caret package is also present.

Its speed and performance are faster than the implementation in H20, Spark, and Python. This package’s primary use case is for machine learning tasks like classification, ranking problems, and regression.


14. data.table

We can’t avoid data.table package because of its functionality.

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15. xml

If you are dealing web scraping or extracting data from online source then xlm will become handy. XML used For read and create XML documents with R.


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Here only discussed the most essential packages in R. R applications that can be used for Finance, Healthcare, Social Media, E-commerce, Manufacturing, Automation, etc…

You need to aware of some other useful packages like RMySQL, RPostgresSQL, RSQLite – For read data from a database, these packages are a good place to begin.

Choose the package accordingly based on your database.

car – For making type II and type III ANOVA tables.

httr – For working with HTTP connections

mgcv – For Generalized Additive Models

lme4/nlme- For Linear and Non-linear mixed effects models

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3 Responses

  1. Andrew says:

    dplyr is so important – it’s #4 and #14

  2. Bernhard says:

    Not only dplyr twice but also tidyverse which includes dplyr and tidyr – you really are a fanboy, aren’t you.? Honestly no mentioning of mgcv? How are you living without it? No lmer/lme4? Personally I would always choose psych for the island, but that may be because I never have to deal with financial data?

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