Data Analysis with R: Case Study with Real Data Using dplyr

This is a case study that allows you a glimpse into the life of a Data Analyst. The project isn’t glamorous, there are no machine learning algorithms but it reflects what a Data Analyst does most of the time. The tools we learned in the previous chapter along with some new one’s will help us prepare the analysis for an RMarkdown or Shiny App Presentation.

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Data Analysis with R: Case Study with Real Data Using dplyr

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Task List


We will cover the following tasks in 44 minutes:


Introduction

In this section we’re going to look at the Big Picture. What are we going to do to the data and why.


Colnames

Sometimes Data doesn’t come in with Colnames as is the case with this Data. We will add the colnames. Just follow me along.


More Clean Up

We’re going to discuss how to change the class of a Vector. This is important. I also will show you a quick way to look at the data to see what is needed to be changed.


Funny Data

Funny data isn’t funny if you don’t catch it. It can spell trouble down the road when others are looking at your work. Come in and we’ll learn some of the more basic ways to check for funny data.


Stripping Out Data

In this chapter we’re going to remove data the old fashion way using Base R. Come in and see how to do this and also to keep up with the lesson.


Lubridate

Finally. I told you in Chapter 2 we would do Lubridate. Better than late than ever. I’ll show you a couple useful functions within the package.


Adding a Column

In this chapter I will show you how to add a column to your data set. You can also use this for feature engineering when building models down the road.


Tables and Branches

We’re going to do some analysis using our old dplyr tool set. We’re going to group by Branch and then do some analysis to see which branches are not living up to the others.


More Analytics

Let’s dive in a little further and see how the Credit Department might freak out at some of these orders.


Final Thoughts

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Chris Shockley

About the Host (Chris Shockley)


I am an R enthusiast, hiker, and amateur astronomer. My favorite hike is located in Mt. Rainier National Park, my favorite Deep Sky Object is Alberio, and my favorite R package is dplyr (since I use it everyday). I have a dog named Coog (Lllasa Apso), and I love talking about Coog (as you'll find out soon). I work as a Data Analyst/Financial Analyst for a Metals Co. located in Seattle, WA. I have been in my current position for 4 years. I work primarily in R for Analytics and R Shiny for Deployment. I have built numerous apps over the past few years. My hope is that I can help you in learning R. Yes, the Rhyme Interface will help you. But. You also must take what you learn and practice, practice, practice. So... Let's get after it. See you soon.



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