R Programming: Data Manipulation with dplyr (Step-by-Step)

Key Takeaways

Welcome. I'm glad you're here and ready to explore some incredibly useful R functions from the dlier package. Filter function, select function, and arrange function. By the end of this screencast, you'll be comfortable using these core functions to handle common data tasks like isolating relevant records from your data set, selecting only the columns required for analysis, and effectively sorting your records. I promise this is simpler than it may initially seem. By the end of our time together, you'll feel confident applying these methods to scenarios you regularly encounter and work. To start, let's quickly confirm you have everything you need. If you don't yet have the deeply package installed, that's perfectly okay. You can install it easily by using install.packages command like this. Type in dlier in the argument. As you can see, it is downloading. Once installed, go ahead and load the package using the library function as you can see above. This will help you proceed. In this demonstration, we'll use the built-in empty cars data set. It's a compact, straightforward data set included in R, ideal for practicing new techniques. Let's take a brief look at how it looks. As you see, we've got columns like MPG or miles per gallon, CL, number of cylinders, and HP horsepower. You may spot a lot of similarity with data from your own work. Perhaps product specifications, employee performance metrics, or even monthly sales figures. Let's start with the filter function. This function allows you to keep only those rows that satisfy certain conditions. For example, if you're interested in vehicles that have an miles per gallon value higher than 20, you'd run the following. Let's define that as high efficiency cars. Assign it the filter function. First we put our data set name empty cars followed by a comma and then the condition miles per gallon greater than 20. Now we print it afterwards. Let's see how it looks. Let's briefly unpack that. The first argument is always the original data set empty cars. And the second argument defines our condition miles per gallon greater than 20. In other words, we're instructing R to return only rows greater than 20 for miles per gallon. This is simpler than it may initially look. And after trying it once or twice yourself, you'll see it's straightforward intuitive. Here is the data set. Now, a quick tip to help prevent common mistakes here. When setting equality conditions in R, always use a double equal sign. In our current example, comparing with greater than, this is not necessary, but it's worth remembering for future cases. Next, let's talk about the select function. The select function simplifies your data by keeping only the columns needed for your analysis. Suppose you're preparing a quick efficiency report or only need miles per gallon, cylinder, and horsepower columns from the original data set. To achieve this, you specify the columns in select function. Let's call this new data set or subset of data car summary. We use a select function followed by data set empty cars then all the columns that we're interested in mpg c and hp. Let's print this. See how the data only selected miles per gallon, cylinder, and horsepower. In practice, select helps cut down clutter, simplifying your reports and making large data sets more manageable. A small but useful condition, additional tip here, to remove unwanted columns instead of having to pick any columns manually, you can use the minus sign in front of column names you want to exclude. For example, select empty cars. Mine has gear. Let's type that out. Select empty cars followed by minus gear minus am. Excellent. Now moving on to the range function. The range function sorts your rows based on a specified column. Suppose you want to organize your data by miles per gallon in ascending order. You type sorted cars, assign it the arrange function, specify our data empty cars, followed by miles per gallon. Now let's print and see how this looks. See how miles per gallon is sorted in an ascending manner. But what if you need to determine cars with the highest miles per gallon at the tops? Perhaps to respond directly to a manager's inquiry about top performer vehicles. Arranging in descending order is just one simple step away and again simpler than it may initially sound. Let's clear our terminal and write our query in a different way. Let's call it sorted cars descending. assign it the arrange function. Select our data set empty cars but adding the descending function to our miles per gallon. Let's print this and see how it looks. See how it's now in descending order with the Toyota Corolla being at the top. Remember the arrange sorts ascending by default, but you only need to wrap your chosen column in descending function to reverse this order. It becomes intuitive quickly with practice. I'm confident you'll master this comfortably. Let's briefly touch on a best practice that's coming among our professionals. Combining multiple commands into a logical pipeline using what's known as a pipe operator. percent sign greater than percent sign. This operator helps you write your data task cleanly, almost as though you're guiding a colleague step by step through the analysis. Let's combine all the steps we practiced earlier in one clear sequence. We'll filter for cars with miles per gallon above 20. Select the columns miles per gallon, cylinder, and horsepower. And then arrange the resulting subset by horsepower highest first. First, let's define our subset empty cars adj for adjusted. We will do our assignment operator. Use our data set empty cars. Use the pipe operator followed by the filter function with miles per gallon over 20. Use another pipe operator followed by the select function with miles per gallon cy lp. Then we will use a final pipe operator to use the arrange function and put our condition of descending horsepower. Let's see how this looks. Notice that we only have our three columns that we selected where miles per gallon is above 20 and it's sorted in descending order by the horsepower column. As you see, writing code this way greatly improves readability for yourself and other colleagues who might review it. It even helps those developing familiarity with R quick quickly grasp your overall intention. A brief caution that can save you from frustration later. Ensure your pipe operator appears at the end of each line except the final operation of your sequence. If you encounter any errors running code like this, that's a first thing to double check, an easy fix. Before wrapping up, another brief practical tip. If your workflow involves typing repetitive commands, using a code assisted tool such as GitHub Copilot may help accelerate your scripting. Remember to always confirm carefully that your final code aligns precisely with your intended analysis. In this screencast, you've been introduced to practical ways of leveraging filter function, select function, and arrange function with confidence and clarity. You're now positioned to apply those valuable tools directly to your task at work, strengthening your data skills step by step. Keep going and practice applying these functions to realistic scenarios to consolidate your understanding. Soon enough, you'll find yourself handling your data tasks clearly and efficiently. You've got this.