Introduction

Purpose

This document illustrates how R and RStudio can be used for the 4 steps of data analysis:

Data acquisition
Data “cleaning” (process the data to be researchable)
Analysis
Reporting (conveying your results)

If you have correctly installed R and Rstudio, this document should run and produce both graphs and regression results.

What this does

This example code uses R to

Check to see if the following packages are installed, and installs them if not: babynames, derivmkts, gapminder, GGally, ggmap, gridExtra, haven, knitr, lubridate, maps, nycflights13, purrr, quantmod, readxl, rmarkdown, stargazer, stringr, tidyquant, tidyverse
download daily stock price data using R’s tidyquant package
compute daily continuously compounded returns
plot the following:
- the stock price on a log scale
- the daily continuously compounded return
- the daily squared return
- a return histogram
compute a table of annual return volatilities
perform a regression of squared stock returns on year dummies

Output

This program uses tidyquant’s tq_get function to obtain stock price data for Ford from January 1, 2010 to December 31, 2016.

The program will write the dataset, as a CSV file, to your working directory in R, which is /datadisk/home/rmcd/tex/rclass/code

Here are the first four lines of the downloaded data:

# A tibble: 4 x 7
        date   open   high    low close    volume adjusted
      <date>  <dbl>  <dbl>  <dbl> <dbl>     <dbl>    <dbl>
1 2010-01-04 12.747 12.885 12.597 10.28  60855800 8.201456
2 2010-01-05 13.098 14.089 13.036 10.96 215620200 8.743967
3 2010-01-06 14.051 14.364 13.951 11.37 200070600 9.071067
4 2010-01-07 14.364 14.653 14.189 11.66 130201700 9.302429

We take the data and

compute continuously compounded returns
plot four representations of the ereturn
compute annual historical volatility
run regressions explaining returns using year dummies

Return Analysis

Now we analyze returns and volatility

Plots

Historical Volatility

Compute historical volatility by year.

Volatility by year
Year	Volatility (%)
2010	38.105
2011	39.982
2012	25.368
2013	23.952
2014	21.021
2015	22.155
2016	26.164

Absolute and squared returns regressed on year dummies


	Dependent variable:

	abs(return)	Squared return
	(1)	(2)

year2011	-0.001	0.0001
	(0.001)	(0.0001)

year2012	-0.007^***	-0.0003^***
	(0.001)	(0.0001)

year2013	-0.007^***	-0.0004^***
	(0.001)	(0.0001)

year2014	-0.009^***	-0.0004^***
	(0.001)	(0.0001)

year2015	-0.008^***	-0.0004^***
	(0.001)	(0.0001)

year2016	-0.007^***	-0.0003^***
	(0.001)	(0.0001)

Constant	0.019^***	0.001^***
	(0.001)	(0.0001)


Observations	1,761	1,761
R²	0.070	0.045
Adjusted R²	0.067	0.041
Residual Std. Error (df = 1754)	0.012	0.001
F Statistic (df = 6; 1754)	21.941^***	13.645^***

Note:	p<0.1; p<0.05; p<0.01

Stock Data Example