Description

Economics and Pricing Tools.

Description

Functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020) <https://sdw.ecb.europa.eu/curConverter.do>.

README.md

cran.r-project.org

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)

##    currency one_USD_is_equivalent_to
## 1       AED                  3.67299
## 2       AFN                 77.50220
## 3       ALL                101.25030
## 4       AMD                413.44991
## 5       ANG                  1.80265
## 6       AOA                829.00004
## 7       ARS                350.01992
## 8       AUD                  1.58582
## 9       AWG                  1.80000
## 10      AZN                  1.70231

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)

##         date one_AUD_equivalent_to_x_USD one_AUD_equivalent_to_x_EUR
## 1 2013-01-01                     1.03746                    0.785518
## 2 2013-01-02                     1.04997                    0.795034
## 3 2013-01-03                     1.04722                    0.800920
## 4 2013-01-04                     1.04837                    0.800502
## 5 2013-01-05                     1.04837                    0.800502
## 6 2013-01-06                     1.04916                    0.800944

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()
    )

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

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)

## Generating URL to request all 297 results
## Retrieving inflation data for US 
## Generating URL to request all 63 results

df

##    years nominal_prices in_2008_dollars
## 1   2012        8.31857         7.66782
## 2   2008        9.30947         9.30947
## 3   2008       14.67612        14.67612
## 4   2007       10.21153        10.60356
## 5   2003       10.38786        12.15782
## 6   2015       15.14519        13.26473
## 7   2008       11.38275        11.38275
## 8   1996        6.20482         8.51713
## 9   2010        7.93944         7.67319
## 10  2004        8.66301         9.87471

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")

##   salary periodicity
## 1 105000      Annual
## 2 100000      Annual
## 3 115000      Annual
## 4 150000      Annual
## 5 225000      Annual
## 6 200000      Annual
## 7 175000       Daily
## 8 260000      Hourly
## 9     NA      Annual

Formatting helpers

Neatly format currencies

format_currency() makes nicely formats numeric data:

format_currency("22500000", "¥")

## [1] "¥22,500,000"

format_dollars() is the same but exclusively for dollars:

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

## [1] "$445.50" "$199.99"

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.

r-priceR

priceR

Installation

Exchange rates

Setup

Current exchange rates

Historical exchange rates

Inflation

Adjust prices for inflation

Extraction helpers

Extract salary from free text

Formatting helpers

Neatly format currencies

More about priceR

Research and academia

Guides and tutorials

Contributing to priceR

Version

License

Status

Source

Homepage

Platforms (75)

priceR

Installation

Exchange rates

Setup

Current exchange rates

Historical exchange rates

Inflation

Adjust prices for inflation

Extraction helpers

Extract salary from free text

Formatting helpers

Neatly format currencies

More about priceR

Research and academia

Guides and tutorials

Contributing to priceR

Version

License

Status

Source

Homepage

Platforms75 (75)

Platforms (75)