priceR contains 4 types of capabilties:
Installation via CRAN install.packages("priceR")
library(priceR)
library(tidyverse)
options(scipen = 100); options(digits = 6)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")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 1.70231Here’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 0.800944And 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()
) 
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 resultsdf## 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 9.87471These helpers let you extract useful numeric data from messy free text (character) data.
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 Annualformat_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"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.
How to Convert Between Currencies Using priceR by Bryan Shalloway
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