#### Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Julia Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Extracting and Handling Data
Metaprogramming
Statistics with Julia
Building Data Science Models
Working with Visualizations
Parallel Computing

## Histograms

Histograms are one of the best ways for visualizing and finding out the three main statistics of a dataset: the mean, median, and mode. Histograms also help analysts get a very clear understanding of the distribution of data. The ability to plot categorical data as well as numerical data is what makes the histogram unique.

We will use the `Gadfly` library, which we used for understanding and plotting data in the preceding recipes. So, to install the library, you can follow the installation steps mentioned in the previous recipes.
1. A basic histogram is a simple set of stacked bars, which shows the distribution of a particular feature in a dataset. This can be plotted using the `plot()` function, with the `Geom.histogram` attribute as the aesthetic parameter. We will use the `diamonds` dataset for the purpose. This can be done as follows:
```plot(dataset("ggplot2", "diamonds"), x = "Price", Geom.histogram)