While Python is arguably the most popular programming language used in Data Science, there are still some areas where R is better. For example, R is generally better than Python for building statistical models. Likewise, R also simplifies the process of creating graphics and data visualizations. As such, R remains a valuable tool in every data scientist’s toolkit.
In addition, R has an ever-growing ecosystem of libraries in its CRAN repository that aim to make life as a data scientist easier. In this post, we’ll look at the top 10 R data science libraries.
R Data Science Libraries
Dplyr or dataframe plier is arguably one of the most popular libraries in Tidyverse’s collection of data manipulation and cleansing libraries. It’s used for data manipulation and is built around the 5 functions you’ll use most of the time to manipulate data; Select, Filter, Arrange, Mutate, and Summarize.
These functions do exactly what their names say they do, and by using them, you’ll simplify data manipulation tasks. In addition, you can also chain dplyr to other Tidyverse libraries to expand your arsenal.
Also maintained by Tidyverse, tidyr is dplyr’s cousin. While dplyr focuses on data manipulation, tidyr focuses on cleaning data from a format perspective. It’s an extremely helpful tool to unpack data from formats that aren’t ideal for data science use.
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For example, using its unnest_longer function, you can convert nested data contained in, for instance, a JSON file into rectangular data. Likewise, its complete, drop_na, fill, and replace_na functions can handle missing values by imputation, inference, or removal.
Ggplot is not only one of the most popular libraries to visualize data in R, but it’s also one of the best. It uses the Grammar of Graphics that makes it easy for you to create visualizations by expressing relationships between your data’s attributes and their graphical representation. So, it’s up to you to provide the data and map variables to aesthetics, and ggplot2 will do the rest.
And, as it’s also part of the Tidyverse suite of tools, it integrates seamlessly with the other tools in the ecosystem.
While R already contains the tools to read data, readr offers some additional advantages compared to these basic tools. For one, readr’s functions are faster than R’s functions and, by using this library, you can read data up to 100 times faster.
Readr’s functions also provide a progress bar if you’re working with a large dataset. This makes it easier to see the progress when data takes time to load.
Compared to Python that has several built-in string functions, R doesn’t perform that well when it comes to strings. Stringr aims to solve this problem by providing equivalents to Python’s functions in R.
As such, it contains functions like str_length that returns the length of the function and str_c that allows you to concatenate strings. In addition, it also provides pattern matching functions that make string search and count tasks a lot simpler.
Developed and maintained by RStudio, Shiny lets you develop and publish web applications and interactive dashboards by using your R code. This is invaluable when you want to share your work with others and make it simpler for them to understand and explore.
The library allows you to use almost all HTML and CSS to create and style your web apps, and you can extend them using themes and widgets. At its core, though, Shiny uses reactive components which means that if there’s any change in your data, the components will update to reflect these changes.
Dates are often unreliably and incorrectly parsed. This then results in errors or data that doesn’t make sense. Lubridate solves this problem by providing a range of functions that automatically parse datetime values and makes it simpler to work with dates.
As such, once dates have been parsed, you can extract the data you need by using functions like year(), month(), day(), hour(), minute(), and so on. In addition, the library also has functions like ymd(), dmy(), and mdy() that allow you to convert dates from one format to another.
RMarkdown aims to simplify the process of creating documents that allow you to document your analysis. When you do, it makes it easier for other researchers and scientists to understand what you analyzed and what your results were. RMarkdown is a variant of Markdown that has embedded R code and can be used with Knitr to create web-based reports, documents, dashboards, and presentations easier.
Caret is one of the most popular R libraries for data science. It stands for Classification and Regression Training and aims to make model building and training easier in R. As such, it contains functions that split data, train data using different classifiers, can perform hyperparameter tuning, and more.
Basically, you can almost think of Caret as the equivalent of Scikit-learn for Python, and it contains all the tools you need to solve almost any supervised machine learning problem.
The tseries library contains functions for reading timeseries, conducting tests, plotting OHLC data, and more. For example, with the plottingOHLC function, you can plot the opening price, high, low, and closing price for a stock on the stock market.
This makes it a useful tool for financial use cases like stock market analysis and trends, but it can also be valuable to chart any other timeseries or data like, for example, weather data.
R has a rich ecosystem of libraries you can use to simplify your workflows and extend the capabilities of the language. Hopefully, this post helped illustrate some of the best of these libraries that you can use as part of your R data science toolkit.
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