Book Image

RStudio for R Statistical Computing Cookbook

By : Andrea Cirillo
Book Image

RStudio for R Statistical Computing Cookbook

By: Andrea Cirillo

Overview of this book

The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications.
Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Forecasting the stock market


In this recipe, we will develop a step-by-step 2-year forecast of the Fiat-Chrysler Automotive stock price.

This task will be accomplished by applying the Arima modeling technique to FCA stock time series.

Arima (Autoregressive integrated moving average) models basically involve the estimation of an autoregressive model and a moving average, employed to estimate both the stochastic part and the underlying trend.

Getting ready

This recipe is mainly based on the tseries package and forecast package, the first for Arima model fitting and the second for prediction of future values. We will also need the quantmod package in order to download stock data from Yahoo Finance.

We therefore need to install and load these three packages:

install.packages(c("tseries","forecast","quantmod"))
library(tseries)
library(forecast)
library(quantmod)

How to do it...

  1. Download data:

    sp500 <- new.env()
    stocks <- getSymbols(c("FCA"), env = sp500,
      
    from = as.Date("2015-01-01"), 
     ...