This course includes:
You just downloaded datasets online. They came in a zip file. The first thing you do after downloading it, you extract the data with your favorite unzipping software such as WinZip or WinRar. Then you proceed with reading the data in with your analysis software (hopefully R). OK! Where is he going with this, you may wonder.
You are starting a project in R and realize your files are scattered in different paths on your computer. Your immediate reflex is to open the folders involved (say with windows explorer) and proceed to gather those files in one place before starting R. Wait, what's wrong with that? Hold that thought.
You visit a website frequently. This website is full of data—numbers, downloadable documents, and pictures alike. It may or may not have occurred to you that you can access the data programmatically and visualize it differently. Perhaps you had ideas about it but didn’t know how to get it done. Hold this thought also.
There is nothing wrong with unzipping files with a WinZip or WinRar. Still, it can be beneficial to do unzip files within R. After downloading a dataset or any zip files; you can go directly into R and manage your files there before your analysis. You ever thought about unzipping, copying and pasting, deleting files within R? This course will show you examples of that.
One of the goals of this course is to implant in you the thought of scraping data with ease. I want you to think you can scrape data and visualize it differently and doing so promptly. I will show you the commonly used web scraping techniques in R.
With APIs, you go a step further than scraping. In this course, I teach you how to retrieve data using HTTR and jsonlite packages. Specifically, use the GET function to retrieve data and the POST function to update your account. All this without logging onto your account. I use the peer-to-peer lending platform Lending Club to showcase the use of an API. The API, therefore, allows you to interact with your account programmatically. Combining this with a scheduler can prove highly efficient. A well-thought-out algorithm can be automated and handle repetitive tasks that would otherwise be routine.
This course will also introduce you to the version control system Git. You will learn the power of R Studio combined with Git and GitHub. I teach how to keep different versions of your script with Git and push files, including R scripts, datasets, and other files to the GitHub platform. You will also learn how to revert to previous versions of your code if you make mistakes in later versions. When you master this, you will no longer have to save different versions of your scripts in your directory.
To become an efficient data analyst, you have to be skilled at one or more programming languages. Why not R? This course should also serve as a barometer. If you feel comfortable with the material in this course, you should understand most R scripts you will encounter.
This course will not teach you how to hack into servers. The intent here is not to sway you towards criminal activities.
Who this course is for:
5 sections - 37 lectures - 07:12:09 total length
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One of my professors in college used to say, “statisticians don’t argue, they collect data.” In the world we are living in today, this statement is not only true for statisticians but everyone. Almost every field and industry alike make use of data to make informed decisions.
I earned a bachelor’s degree in mathematics and actuarial science in 2008, and a master’s degree in statistics in 2010 from the University of Nebraska in Lincoln. I have been a statistician with the United States Department of Agriculture since. R was introduced to me in the summer of 2008 during an internship at a marketing firm. Before that, I used SAS and Excel. I still use SAS heavily, but I am a far better R programmer. I believe R’s intuitiveness makes it easy to learn.
My line of work involves collecting, storing, and analyzing data from hundreds of agricultural surveys. In my day to day work, I use R for ad-hoc data analysis and reporting. I create data visualization applications with R shiny that help identify outliers. I have also built a few personal R shiny applications.
I am passionate about R, I use it every day and I look forward to showing you how to get the most out of your data.