Is there such a thing as a tidy dataset?
“Tidy datasets are all alike, but every messy dataset is messy in its own way.” –– Hadley Wickham In this chapter, you will learn a consistent way to organise your data in R, an organisation called tidy data. Getting your data into this format requires some upfront work, but that work pays off in the long term.
Why do you need tidyverse for data analysis?
Getting your data into this format requires some upfront work, but that work pays off in the long term. Once you have tidy data and the tidy tools provided by packages in the tidyverse, you will spend much less time munging data from one representation to another, allowing you to spend more time on the analytic questions at hand.
Are there any your functions that work with tidy data?
As you learned in mutate and summary functions, most built-in R functions work with vectors of values. That makes transforming tidy data feel particularly natural. dplyr, ggplot2, and all the other packages in the tidyverse are designed to work with tidy data. Here are a couple of small examples showing how you might work with table1.
Can you represent the same data in different ways?
You can represent the same underlying data in multiple ways. The example below shows the same data organised in four different ways. Each dataset shows the same values of four variables country, year, population, and cases, but each dataset organises the values in a different way.
What’s the difference between array tidy and Dataframe tidy?
TL;DR: Array-tidy is extending tidiness across multiple data frames that are indexed by an additional shared dimension, such as time. From what I understand, array-tidy and dataframe-tidy refer to the underlying data models that are used to achieve your tidy data.
Which is a graphing package in the tidyverse?
Note that the tidyverse also includes a graphing package, ggplot2, which we introduce later in Chapter 7 in the Data Visualization part of the book; the readr package discussed in Chapter 5; and many others.