Data Manipulation and Visualization Techniques in Julia – Part III


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Learn how to create new dataframes with Part I and how to perform basic mathematical operations in Part II.

Grouping data

Let’s look at ways to group data, which comes in handy while summarising data.

In-built datasets in Julia

The package RDatasets.jl in Julia helps you import all the in-build packages in R that can be used for testing purposes.

Here’s how you can find out the list of available datasets. It has 763 datasets.

We’ll work with one of the in-built datasets (“iris”) in this section. “iris” provides the data for multiple measurements of 3 plant species and 4 features for each of them. More details about this dataset can be found here.

The following snapshot shows the variables in the iris dataset.

Iris Dataset



Here’s the summary of this dataset.

Species setosa virginica0CategoricalValue{String, UInt8}

Let’s look at some of the questions you might want to answer using the iris dataset.

We can perform arithmetic operations by grouping data based on various columns. Here’s how we can get the answer to the following question –

What’s the mean value of the sepal length of each species?


Another package that helps make the operations more intuitive is Pipe.jl. It lets you write operations as they are performed instead of the backward approach.


Stay tuned for the next installment, in which Anshul Tayal will demonstrate how to deal with missing data.

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