#### 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

## Line plots

Line plots, as we have already seen in the preceding examples, are very effective when it comes to exploratory data analytics. They can be used both to understand correlations and look at data trends. So, by further making use of aesthetics, we can make them more interesting and informative.

We will use the `Gadfly` library, which we have used in the preceding recipes. So, to install the library, you can follow the installation steps mentioned in the previous recipes.
1. Let's start with a basic line plot, which plots their incidences of melanoma in the respective years. So, this plot can be seen as a typical time series plot, where the x axis is a time variable and the y axis is the variable that is parameterized by time. So, to plot this, we simply need to include the dataset in the `plot()` function and include the `Geom.line` aesthetic, as follows:
```plot(dataset("Lattice", "melanoma"), x = "Year", y = "Incidence", Geom.line)