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8 Useful R Packages for Data Science You Aren’t Using (But Should!)

来源:分析大师 | 2019-04-17 | 发布:经管之家

I’m a big fan of R – it’s no secret. I have relied on it since my days of learning statistics back in university. In fact, R is still my go-to language for machine learning projects.Three things primarily attracted me to R:R offers a plethora of packages for performing machine learning tasks, including ‘dplyr’ for data manipulation, ‘ggplot2’ for data visualization, ‘caret’ for building ML models, etc.There are even R packages for specific functions, including credit risk scoring, scraping data from websites, econometrics, etc. There’s a reason why R is beloved among statisticians worldwide – the sheer amount of R packages available makes life so much easier.In this article, I will showcase eight R packages that have gone under the radar among data scientists but are incredibly useful for performing specific machine learning tasks. To get you started, I have included an example along with the code for each package.Trust me, your love for R is about to undergo another revolution!I have broadly divided these R packages into three categories:R is an amazing tool for visualizing data.The ease with which we can generate all kinds of plots with just one or two lines of code? Truly a time saver.R provides seemingly countless ways to visualize your data. Even when I’m using Python for a certain task, I come back to R for exploring and visualizing my data. I’m sure most R users feel the same way!Let’s look at a few awesome but lesser-known R packages for performing exploratory data analysis.This is my go-to package for performing exploratory data analysis. From plotting the structure of the data to Q-Q plots and even creating reports for your dataset, this package does it all.Let’s see what DataExplorer can do using an example. Consider that we have stored our data in the data variable. Now, we want to figure out the percentage of missing values in every feature present. This is extremely useful when we’re working with massive datasets and computing the sum of missing values might be time-consuming.You can install DataExplorer using the below code:Now let’s see what DataExplorer can do for us:We get a really intuitive plot for missing values:One of my favorite aspects of DataExplorer is the comprehensive report we can generate using just one line of code:Below are the different kinds of factors we get in this report:You can access the full report throughthis link. A VERY useful package.How about a ‘drag-and-drop’ add-in for generating plots in R? That’s right – esquisse is a package that lets you get on with creating plots without having to code them.Esquisse is built on top of the ggplot2 package. That means you can interactively explore your data in the esquisse environment by generating ggplot2 graphs.Use the below code to install and load up esquisse on your machine:You can also launch the esquisse add-in via the RStudio menu. The user interface of esquisse looks like this:Pretty cool, right? Go ahead and play around with different types of plots – it’s an eye-opening experience.Ah, building machine learning models in R. The holy grail we data scientists strive for when we take up new machine learning projects. You might have used the ‘caret’ package for building models before.Now, let me introduce you to a few under-the-radar R packages that might change the way you approach the model building process.One of the biggest reasons Python surged ahead of R was thanks to its machine learning focused libraries (like scikit-learn). For a long time, R lacked this ability. Sure you could use different packages for performing different ML tasks but there was no one package that could do it all. We had to call three different libraries for building three different models.Not ideal.And then the MLR package came along. It is an incredible package which allows us to perform all sorts of machine learning tasks. MLR includes all the popular machine learning algorithms we use in our projects.I strongly recommend going through the below article to deep dive into MLR:Let’s see how to install MLR and
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