#### 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

## Plotting functions

In data science and statistical modeling, there are several instances where an analyst needs to use several functions for both transforming and exploratory analytics steps. So, one can plot them in Gadfly in a very simple way, which can used to plot separate functions as well as to stack several functions in a single plot.

1. Let's start with a basic function plot to get familiar with the syntax. So, a good basic function to start is the `sin()` function, which can be invoked as sin. The function can be included directly in the plot command, along with the upper and lower limits of the x axis. The syntax is: `plot(function, lower_limt, upper_limit)`. This can be done as follows:
```plot(sin, 0, 30)