Imagine: You are about to sit down with a newly-fetched data set , excited about the insights it will bring you and then you find out it is no use. If you’ve been there, then you know for sure what an untidy dataset is. A statistician from New Zealand once said: Tidy are all alike, but every messy dataset is messy in its own way. Indeed, as data may come in various forms and shapes, sometimes we are inundated with it. datasets As a result, our data science team becomes shortsighted and oops.. disillusioned by mountains of . The only way data is by keeping data clean and organized. unworkable data specialists can facilitate analysis What is Tidy Data? Essentially, tidy data is a term coined by Hadley Wickham in his Tidy Data paper (remember that statistician from NZ?). He defined tidy data as data that is neatly organized and all . set for analysis This way of organizing allows you to easily produce charts, diagrams, and . As it often happens, not all data comes out of the database clean, therefore to efficiently analyze it. summary statistics cleansing it is essential Without further ado, let us break down the principles that allow you keep your data nice and clean. Tidy Data Principles 1. Each row is an Observational Unit We’ll start with one of the basic principles. When you are giving your data the once-over, you should make sure each row .By definition, observation is the individual unit under question. contains an observation If we look at the table above, an observational unit could be called ‘people’. You can see that each person has an individual row on the table and all of the information for that person is in the same row. Observations are included in rows, variables are represented as columns and there is only one observational unit per table. Now THIS is tidy data. 2. Each Column is a Variable A variable is the unit you are assessing. Again, if we turn to our table above, age, hair_color and height fall within the . category of variables In tidy data each variable is represented in a separate column. Okay, now a one-second quiz: What is wrong with this dataset? Yep, you guessed it right. Never put multiple variables in one column, . otherwise your data analysis is doomed 3. Each Cell is a Value If you have got hold of the first two principles, this one . should already be a no-brainer Anyway, we’ll make an extra effort to lay it all out. Each cell should contain only one value. It is also important that all values in the same column are formatted the same way. On this data set, you can see that we have a table with four variables and three observations. Each cell contains one piece of information and . All of our age values are digits, hair color values are whole words – you get the idea. Therefore, this . our values all match dataset is tidy and almost fit for analysis 4. Each Column has a Unique Name , columns should have specific and descriptive names. Let us demonstrate you an example of this principle. In an ideal dataset The third column is labeled hair_color. This is a more specific heading that if we were simply to call it – hair. The word ‘hair’ can refer to anything from hair length to hairstyle. This will help you speed up the analysis process. level of specificity The Final Word Tidy data is an of realizing the full data potential that exists. , it can be used as input into a wide range of other functions. essential part Once your data is tidy

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Can We Make Data Tidy?

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