Week 1: Introduction to R and Financial Data Analysis

Version 4.0 - August 2025

Author

Affiliation

Jon Danielsson

London School of Economics

1 R, data and sample statistics

1.1 Why R and financial data analysis matter for financial risk

R is the industry standard for quantitative finance and risk management. Financial institutions use R for portfolio optimisation, risk measurement, regulatory reporting and algorithmic trading. The ability to download, manipulate and analyse financial data programmatically is essential for modern risk management practices.

Learning R and financial data fundamentals provides the foundation for all subsequent risk modelling techniques we will develop throughout this course.

1.2 Introduction

The software we use in this course is R, which is a powerful open-source language for statistics, data analysis and simulation. It is widely used in finance, economics and academia. We recommend using RStudio as a user-friendly interface that makes it easier to write, test and debug code, with built-in tools for plots, packages and version control.

Please download R and RStudio and familiarise yourself with the basic setup. This RStudio for the Total Beginner might be of help and you can find many others on YouTube.

For financial data, we will use the EOD Historical Data API (or EODHD) through the eodhdR2 package. This provides a simple interface to access stock prices, dividends and splits directly from R. More information can be found at https://eodhd.com/financial-apis/r-library-v-2-for-financial-data-by-eodhd-2024.

For more information about R and especially how it is used in forecasting risk, refer to the R risk forecasting notebook. You can find references to resources for learning R in the notebook.

1.3 The plan for this week

Familiarisation with R and RStudio
Learn basic R commands
Set up EOD Historical Data access
Download, manipulate and save financial data
Create and customise plots
Calculate basic financial statistics

1.4 Basic R commands

We can enter some basic R commands into the console and you see the output immediately printed on the console.

1+1

[1] 2

2^5

[1] 32

a=2
b=100
exp(a)+b/10

[1] 17.38906

result=exp(a)+b
cat("the answer is:",log(a),"or",result,"\n")

the answer is: 0.6931472 or 107.3891

Usually it is best to create a file for all the code you want to execute, perhaps seminar1.R. You can then execute the commands in this file either one by one or all at once. We use scripts and notebooks — not the console alone.

Also, it is best to keep the data files you create below in the same directory (folder).

R needs to be able to find your data and for that you may need to set the working directory. You can do that with the RStudio menu item Session/Set Working Directory or the R command setwd().

1.5 Setting up EOD Historical Data Access

1.5.1 Installing Required Packages

The eodhdR2 package relies on several supporting packages that need to be installed first:

You only need to run install.packages() once.

# Install supporting packages
install.packages(c("httr", "jsonlite", "lubridate", "data.table", "readr"), repos = "https://cloud.r-project.org/")

# Install the EODHD R package
install.packages('eodhdR2', repos = "https://cloud.r-project.org/")

Every time you run this code, you need to load the package (also known as a library).

# Load the package
library(eodhdR2)

1.5.2 API Token Setup

An API token is like a digital key that identifies you to the EODHD service. The token is a unique string of characters that tells EODHD who you are and what data you’re allowed to access.

If you are in my courses, you will get access to EODHD. For others, you can register at eodhistoricaldata.com for a free or paid account.

In the code below, if you have a personal API key: Replace “YOUR_API_KEY_HERE” in the code below with your actual key. Else, if you are using the demo key, you can only access basic price data for major stocks. Functions like get_splits() will not work.

1.6 Downloading Financial Data

1.6.1 Setup

Set up your API token

token = "YOUR_API_KEY_HERE"
set_token(token)

1.6.2 What works with demo vs personal tokens

Demo tokens can access: basic price data (get_prices()) and ividend data (get_dividends()), but not stock splits data (get_splits()) and some advanced market data.

Personal token can access all functions without restrictions. However, there are tiers in access.

With EODHD, we can download data directly in R using ticker symbols. The get_prices() function takes two main parameters: the stock ticker symbol and the exchange code. For US stocks, we use “US” as the exchange. Start with a single stock, Apple, whose ticker is AAPL:

# Download Apple stock data
prices = get_prices("AAPL", "US")

── retrieving price data for ticker AAPL|US ────────────────────────────────────

! Quota status: 73|100000, refreshing in 15.3 hours

ℹ cache file AAPL_US_eodhd_prices.rds saved

✔ got 11255 rows of prices

ℹ got daily data from 1980-12-12 to 2025-08-08

1.6.3 Understanding the Data Structure

The EOD price data includes:

date: Trading date
open: Opening price for the day
high: Highest price during the day
low: Lowest price during the day
close: Closing price for the day
adjusted_close: Close price adjusted for splits and dividends
volume: Number of shares traded
ticker: Symbol
exchange: Where traded
ret_adj_close: Simple returns for the adjusted closeing price

1.7 Exploring the Data

1.7.1 Description

# Explore the data structure
dim(prices)

[1] 11255    10

head(prices)

        date    open    high     low   close adjusted_close    volume ticker
1 1980-12-12 28.7392 28.8736 28.7392 28.7392         0.0986 469033600   AAPL
2 1980-12-15 27.3728 27.3728 27.2608 27.2608         0.0935 175884800   AAPL
3 1980-12-16 25.3792 25.3792 25.2448 25.2448         0.0866 105728000   AAPL
4 1980-12-17 25.8720 26.0064 25.8720 25.8720         0.0887  86441600   AAPL
5 1980-12-18 26.6336 26.7456 26.6336 26.6336         0.0914  73449600   AAPL
6 1980-12-19 28.2464 28.3808 28.2464 28.2464         0.0969  48630400   AAPL
  exchange ret_adj_close
1       US            NA
2       US   -0.05172414
3       US   -0.07379679
4       US    0.02424942
5       US    0.03043968
6       US    0.06017505

names(prices)

 [1] "date"           "open"           "high"           "low"           
 [5] "close"          "adjusted_close" "volume"         "ticker"        
 [9] "exchange"       "ret_adj_close"

# Summary statistics
summary(prices$adjusted_close)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  0.0377   0.2449   0.4627  25.9844  20.3285 258.3967

1.7.2 Make returns

y=diff(log(prices$adjusted_close))

We could add y to prices but that will not work.

# prices$y=y  # This would cause an error

It gives an error replacement has 11236 rows, data has 11237. This occurs because the first observation in y will be

# This shows the formula for the first return (not executed):
# y[1] = log(prices$adjusted_close[2]) - log(prices$adjusted_close[1])

So the first observation in y corresponds to the second day.

One way to deal with this is by

# First, make sure y is properly calculated as the full vector of returns
y=diff(log(prices$adjusted_close))
length(c(NA,y))

[1] 11255

prices$y=c(NA,y)

And, perhaps, to then remove the first row altogether:

prices=prices[2:nrow(prices),]

summary(prices$y)

      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.7314330 -0.0126420  0.0000000  0.0006888  0.0142034  0.2868394

1.8 Visualizing Stock Data

R can easily build plots to visualise data. We use the plot command for this. See Plottin in the R Notebook.

We use base R plots instead of ggplot2 because the code is shorter and simpler for the types of plots we make in financial analysis. Base plots are built into R and do not require additional packages, while producing comparable visual quality for our purposes.

plot(prices$close)

This plot needs improvement. To begin with, we want dates on the x-axis.

plot(prices$date,prices$close)

We can do better

plot(prices$date,prices$close,
     type = 'l',
     las = 1,
     main = "Apple stock prices",
     xlab = "Date",
     ylab = "Price")

But this does not look very realistic. Why?

The regular closing price shows sudden jumps and drops that are not related to company performance — these are caused by stock splits and dividend payments. For example, Apple had a 4-for-1 stock split on August 31, 2020, where the price suddenly dropped from around $500 to $125, creating an artificial drop that has nothing to do with the company’s value.

Adjusted prices correct for these corporate actions, giving us the true economic picture of how the stock has performed. This is crucial for financial analysis because we want to measure actual returns, not accounting artifacts.

However, not all large changes in the closing price are due to splits. Apple’s price halved on 2000-06-21 due to a bad earnings announcement.

plot(prices$date,prices$adjusted_close,
     type = 'l',
     las = 1,
     main = "Apple stock prices",
     xlab = "Date",
     ylab = "Adjusted price"
)

Or perhaps with log y-axis:

plot(prices$date,prices$adjusted_close,
     type = 'l',
     las = 1,
     main = "Apple stock prices",
     xlab = "Date",
     ylab = "Adjusted price",
     log='y'
)

1.9 Accessing Additional Data

EOD also provides dividend and split information:

# Get dividend data for Apple
dividends = get_dividends("AAPL", "US")

── retrieving dividends for ticker AAPL|US ─────────────────────────────────────

! Quota status: 74|100000, refreshing in 15.3 hours

ℹ cache file AAPL_US_eodhd_dividends.rds saved

✔ got 88 rows of dividend data

head(dividends)

        date ticker exchange declarationDate recordDate paymentDate period
1 1987-05-11   AAPL       US            <NA>       <NA>        <NA>   <NA>
2 1987-08-10   AAPL       US            <NA>       <NA>        <NA>   <NA>
3 1987-11-17   AAPL       US            <NA>       <NA>        <NA>   <NA>
4 1988-02-12   AAPL       US            <NA>       <NA>        <NA>   <NA>
5 1988-05-16   AAPL       US            <NA>       <NA>        <NA>   <NA>
6 1988-08-15   AAPL       US            <NA>       <NA>        <NA>   <NA>
    value unadjustedValue currency
1 0.00054         0.12096      USD
2 0.00054         0.06048      USD
3 0.00071         0.07952      USD
4 0.00071         0.07952      USD
5 0.00071         0.07952      USD
6 0.00071         0.07952      USD

# splits = get_splits("AAPL", "US")  # Only works with a personal token
# head(splits)

1.10 Recap

1.10.1 In this seminar we have covered:

Installing and setting up R and RStudio
Installing and configuring the eodhdR2 package
Understanding API tokens and how to use them securely
- Understanding demo token limitations
Downloading and exploring stock price data
Understanding the difference between regular and adjusted prices
Calculating log returns from price data and handling dimension mismatches
Creating progressively better visualisations:
- Basic plots
- Adding dates to axes
- Improving labels and formatting
- Using log scale for better long-term visualisation
Accessing additional data (dividends and splits)

1.10.2 Key skills learned:

Managing package installation with install.packages() and library()
Working with data frames and handling NA values
Using diff() and log() for return calculations
Debugging common issues (like dimension mismatches)
Customizing plots with multiple parameters
Understanding why adjusted prices matter for analysis

1.10.3 Some new functions used:

install.packages() — installs R packages from CRAN
library() — loads an installed package into the current session
set_token() — configures your API authentication token
get_prices() — downloads historical price data for a stock
get_dividends() — retrieves dividend payment history
get_splits() — fetches stock split information
dim() — shows dimensions (rows and columns) of a data frame
head() — displays the first few rows of data
nrow() — returns the number of rows in a data frame
names() — lists column names of a data frame
summary() — provides statistical summary of data
diff() — calculates differences between consecutive elements
log() — computes natural logarithm
c() — combines values into a vector or list
length() — returns the number of elements in a vector
plot() — creates various types of graphs and charts
cat() — prints output to the console
exp() — calculates exponential (e^x)
setwd() — sets the working directory
help() — displays documentation for functions

1.11 Optional exercises

Data exploration: Download data for Microsoft (MSFT) and create a complete analysis:
- Calculate log returns using diff(log(adjusted_close))
- Find the highest and lowest daily returns
- Create a plot with proper labels and log scale
- Compare the results with Apple
Handling data issues:
- Download JPMorgan (JPM) data and calculate returns
- What happens if you do not use c(NA, y) when adding returns to the dataframe?
- Practice removing the first row after adding returns
- Verify your results using head() and dim()
Visualization practice:
- Create plots for any three stocks showing both regular and adjusted closing prices
- Explain why the regular closing prices might show sudden jumps
- Use different colors for each stock (hint: col = "red", etc.)
- Add a legend to identify each line
Working with dividends:
- Use get_dividends() for Apple, Microsoft and Exxon (XOM)
- Which company pays dividends most frequently?
- Create a plot showing dividend amounts over time
- Calculate the total dividends paid by each company
Summary statistics:
- For any five stocks of your choice:
- Calculate mean and standard deviation of returns
- Use summary() on both prices and returns
- Create a table comparing the volatility (standard deviation) of each stock
- Which stock would be considered most risky based on volatility?