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)Retrieve current exchange rates
Works for 170 currencies
exchange_rate_latest("USD") %>%
head(10)## Daily USD exchange rate as at end of day 2020-07-31 GMT
## currency one_usd_is_equivalent_to
## 1 AED 3.67300
## 2 AFN 76.80003
## 3 ALL 105.30020
## 4 AMD 481.61635
## 5 ANG 1.79471
## 6 AOA 552.92014
## 7 ARS 72.25342
## 8 AUD 1.38494
## 9 AWG 1.80000
## 10 AZN 1.70250
View available currencies
currencies() %>%
head()## description code
## 1 United Arab Emirates Dirham AED
## 2 Afghan Afghani AFN
## 3 Albanian Lek ALL
## 4 Armenian Dram AMD
## 5 Netherlands Antillean Guilder ANG
## 6 Angolan Kwanza AOA
Retrieve historical exchange rates
# Retieve AUD to USD exchange rates
au <- historical_exchange_rates("AUD", to = "USD",
start_date = "2010-01-01", end_date = "2020-06-30")
# Retieve AUD to EUR exchange rates
ae <- historical_exchange_rates("AUD", to = "EUR",
start_date = "2010-01-01", end_date = "2020-06-30")
# Combine
cur <- au %>% left_join(ae, by = "date")
head(cur)## date one_AUD_equivalent_to_x_USD one_AUD_equivalent_to_x_EUR
## 1 2010-01-01 0.898084 0.624103
## 2 2010-01-02 0.898084 0.624103
## 3 2010-01-03 0.898084 0.624103
## 4 2010-01-04 0.912623 0.632711
## 5 2010-01-05 0.912011 0.634840
## 6 2010-01-06 0.920736 0.639223
Plot exchange rates
library(ggplot2)
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()
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() +
geom_smooth(method = 'loess') +
theme(axis.title.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_x_date(date_labels = "%b-%Y", date_breaks = "1 month") +
ggtitle("AUD to USD over last 200 days")
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) +
theme(axis.title.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
ggtitle("AUD to EUR over last 8 years")
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 any of 304 countries / areas. See them with all with
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 304 results
## Retrieving inflation data for US
## Generating URL to request all 60 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: extract useful numeric data from messy 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
)
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
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"
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.
- A specific url you’re attempting to retrieve R code from (if that’s what your issue concerns)
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