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