priceR

Economics and Pricing in R

output: github_document

knitr::opts_chunk$set(echo = TRUE)

priceR

priceR contains 4 types of capabilties:

Exchange Rates - easily retrieve exchange rates for immediate use
Inflation - easily inflate past (nominal) values into present day (real) prices
Regular Expressions - easily extract common pricing patterns from free text
Formatting - easily handle currencies in written work, including Rmarkdown documents

Installation

Installation via CRAN install.packages("priceR")

library(priceR)
library(tidyverse)
options(scipen = 100); options(digits = 6)

Exchange rates

Setup

Set up only takes a minute and is free for 100 requests per account per calendar month.

Go to https://exchangerate.host/, create a free account, and replace 7e5e3140140bd8e4f4650cc41fc772c0 with your API key in the following, and run once per R session.

Sys.setenv("EXCHANGERATEHOST_ACCESS_KEY"="7e5e3140140bd8e4f4650cc41fc772c0")

Current exchange rates

View the current exchange rates for 170 currencies (see them all by running currencies()):


exchange_rate_latest("USD") %>% 
  head(10)

Historical exchange rates

Here’s an example of how to get exchange rates for some currency pairs:


# Retrieve AUD to USD exchange rates
au <- historical_exchange_rates("AUD", to = "USD",
                          start_date = "2013-01-01", end_date = "2023-06-30")

# Retrieve AUD to EUR exchange rates
ae <- historical_exchange_rates("AUD", to = "EUR",
                          start_date = "2013-01-01", end_date = "2023-06-30")

# Combine
cur <- au %>% left_join(ae, by = "date")

head(cur)

And to plot the exchange rate data:

library(ggplot2)
library(ggthemes)
library(ggrepel)

cur %>% 
  rename(aud_to_usd = one_AUD_equivalent_to_x_USD,
         aud_to_eur = one_AUD_equivalent_to_x_EUR) %>% 
  pivot_longer(c("aud_to_usd", "aud_to_eur")) %>% 
  mutate(date = as.Date(date)) %>% 
  ggplot(aes(x=date, y = value, colour=name)) +
  geom_line(size=1) + 
  scale_color_manual(
    breaks = c("aud_to_usd", "aud_to_eur"), # Sets order in legend
    labels = c( "AUD to USD", "AUD to EUR"), # Pretty names in legend
    values = c("#02506A", "#03A5DC") # Sets line/legend colours
    ) + 
  scale_x_date(date_labels = "%b %Y", date_breaks = "6 month") +
  scale_y_continuous(
    expand = c(0, 0), 
    limits = c(0, 1.5)
    ) +
  labs(
    title = "AUD to USD and EUR 2013 to 2023",
    subtitle = "Plotting the Australian Dollar against the USD and Euro",
    y = "Exchange Rate"
    ) +
  theme_economist() + 
  theme(
    plot.title = element_text(size = 18, margin=margin(0,0,8,0)),
    axis.title.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
    axis.title.y = element_text(vjust = 3.5),
    legend.position="bottom",
    legend.title = element_blank()
    )


cur %>% 
  tail(200) %>% 
  rename(aud_to_usd = one_AUD_equivalent_to_x_USD,
         aud_to_eur = one_AUD_equivalent_to_x_EUR) %>%  
  mutate(date = as.Date(date)) %>% 
  ggplot(aes(x = date, y = aud_to_usd, group = 1)) +
  geom_line(colour = "#F15B40") +
  geom_smooth(method = 'loess', colour="#03A5DC") + 
  scale_x_date(date_labels = "%b %Y", date_breaks = "1 month") +
  labs(
    title = "AUD to USD over last 200 days",
    subtitle = "AUD to USD Exchange Rate; Polynomial regression trendline",
    y = "Exchange Rate"
    ) +
  theme_economist() + 
  theme(
    plot.title = element_text(size = 18, margin=margin(0,0,8,0)),
    axis.title.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
    axis.title.y = element_text(vjust = 3.5),
    legend.position="bottom",
    legend.title = element_blank()
    )


cur %>% 
  tail(365 * 8) %>% 
  rename(aud_to_usd = one_AUD_equivalent_to_x_USD,
         aud_to_eur = one_AUD_equivalent_to_x_EUR) %>% 
  mutate(date = as.Date(date)) %>% 
  ggplot(aes(x = date, y = aud_to_eur, group = 1)) +
  geom_line() +
  geom_smooth(method = 'loess', se = TRUE) + 
  geom_line(colour = "#02506A") +
  geom_smooth(method = 'loess', colour="#03A5DC") + 
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  labs(
    title = "AUD to EUR over last 8 years",
    subtitle = "AUD to EUR Exchange Rate; Polynomial regression trendline",
    y = "Exchange Rate"
    ) +
  theme_economist() + 
  theme(
    plot.title = element_text(size = 18, margin=margin(0,0,8,0)),
    axis.title.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
    axis.title.y = element_text(vjust = 3.5),
    legend.position="bottom",
    legend.title = element_blank()
    )

Inflation

Adjust prices for inflation

adjust_for_inflation() automatically converts between nominal and real dollars,
or in/deflates prices from one year’s prices to another’s.

It works for 304 countries / areas (see them with all by running show_countries()).


set.seed(123)
nominal_prices <- rnorm(10, mean=10, sd=3)
years <- round(rnorm(10, mean=2006, sd=5))
df <- data.frame(years, nominal_prices)

df$in_2008_dollars <- adjust_for_inflation(nominal_prices, years, "US", to_date = 2008)

df

Extraction helpers

These helpers let you extract useful numeric data from messy free text (character) data.

Extract salary from free text

extract_salary() extracts salaries as useful numeric data from non-standard free text


messy_salary_data <- c(
  "$90000 - $120000 per annum",
  "$90k - $110k p.a.",
  "$110k - $120k p.a. + super + bonus + benefits",
  "$140K-$160K + Super + Bonus/Equity",
  "$200,000 - $250,000 package",
  "c$200K Package Neg",
  "$700 p/d",                                       # daily
  "$120 - $140 (Inc. Super) per hour",              # hourly
  "Competitive"                                     # nothing useful (will return NA)
)

messy_salary_data %>%
  extract_salary(include_periodicity = TRUE, 
                 salary_range_handling = "average")

Formatting helpers

Neatly format currencies

format_currency() makes nicely formats numeric data:


format_currency("22500000", "¥")

format_dollars() is the same but exclusively for dollars:

format_dollars(c("445.50", "199.99"), digits = 2)

More about priceR

Research and academia

Curran-Groome, W., Hino, M., BenDor, T. and Salvesen, D., 2022. Complexities and costs of floodplain buyout implementation, Land Use Policy, Volume 118, July 2022.

Thomas, C., Shae, W., Koestler, D., DeFor, T., Bahr, N. and Alpern, J., 2022. Antifungal drug price increases in the United States 2000–2019, Mycoses, Online Ahead of Print, June 2022.

Petitbon, A. and Hitchcock, D., 2022. What Kind of Music Do You Like? A Statistical Analysis of Music Genre Popularity Over Time, Journal of Data Science, Volume 20 (2), April 2022.

Widdicombe, J., Basáñez, M., Entezami, M., Jackson, D., Larrieu, E. and Prada, J., 2022. The economic evaluation of Cystic echinococcosis control strategies focused on zoonotic hosts: A scoping review, PLoS Neglected Tropical Diseases, Volume 16 (7), July 2022.

Thielen, F.W., Heine, R.J.S.D., Berg, S. van den, Ham, R.M.T. ten and Groot, C.A.U. (2022). Towards sustainability and affordability of expensive cell and gene therapies? Applying a cost-based pricing model to estimate prices for Libmeldy and Zolgensma, Cytotherapy [online] doi:10.1016/j.jcyt.2022.09.002.

Guides and tutorials

How to Convert Between Currencies Using priceR by Bryan Shalloway

Contributing to priceR

If your research, guide or tutorial uses priceR, please contact the priceR maintainer (or create an issue) so your work can be included here.

Issues and Feature Requests

When reporting an issue, please include:

Example code that reproduces the observed behavior.
An explanation of what the expected behavior is.

For feature requests, raise an issue with the following:

The desired functionality
Example inputs and desired output

Pull Requests

Pull requests are welcomed. Before doing so, please create an issue or email me with your idea.

Any new functions should follow the conventions established by the the package’s existing functions. Please ensure

Functions are sensibly named
The intent of the contribution is clear
At least one example is provided in the documentation