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https://github.com/stevecondylios/priceR

Economics and Pricing in R
https://github.com/stevecondylios/priceR

cran data-science econometrics economics finance modeling r-programming statistics

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Economics and Pricing in R

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README 

        

---

output: github_document

---



  

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

```



# priceR 



[![CRAN status](https://www.r-pkg.org/badges/version/priceR)](https://cran.r-project.org/package=priceR)

[![R build status](https://github.com/stevecondylios/priceR/workflows/R-CMD-check/badge.svg)](https://github.com/stevecondylios/priceR/actions?workflow=R-CMD-check)



`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")`



```{r message=FALSE}

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. 



```r

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

```



### Current exchange rates



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



```{r message=FALSE}



exchange_rate_latest("USD") %>% 

  head(10)



```



### Historical exchange rates



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



```{r message=FALSE}



# 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:



```{r message=FALSE}

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()

    ) 

```



```{r message=FALSE}



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()

    ) 



```



```{r message=FALSE}



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()`). 



```{r message=FALSE}



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



```{r}



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:



```{r message=FALSE}



format_currency("22500000", "¥")



```



`format_dollars()` is the same but exclusively for dollars:



```{r}

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*](https://www.sciencedirect.com/science/article/abs/pii/S0264837722001557), 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*](https://pubmed.ncbi.nlm.nih.gov/35722703/), 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*](https://people.stat.sc.edu/hitchcock/jds1040.pdf), 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*](https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010568), 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](https://www.sciencedirect.com/science/article/pii/S1465324922007861), Cytotherapy [online] doi:10.1016/j.jcyt.2022.09.002.



### Guides and tutorials



How to [*Convert Between Currencies Using priceR*](https://www.bryanshalloway.com/2022/06/16/converting-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](https://github.com/stevecondylios/priceR/issues)) 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