Perform function on subset of data - r

Question

I have a set of data below which shows a number of cars at an imaginary car dealership. The 'current_price' variable is obviously the price the car is currently set to sell at. The 'minimum_price' variable shows the hard floor price that the car must not, under any circumstance, sell at. (This can be assumed to be the purchase price).
I'm trying to create a function where the user can select a subset of the cars in the database (using the 'user defined parameters' as noted below), and then reduce or increase the 'Current_Price' by either a percentage or pound(£) value.
The 'minimum profit parameters' set the minimum profit which is to be made on all cars. In this example they have been set to £10 and 10%. Meaning the profit for each car must be either £10, or 10% of the current price - whichever is larger.
The price change parameters set how much the price is to move and whether it should move up or down.
# Dummy data
Type <- rep(c("Car", "Van"),each=3)
Age <- as.numeric(c(2, 2, 5, 4, 8,1))
Colour <- c("Red", "Red", "Yellow", "Red", "Black", "Red")
Make <- c("Ford", "VW", "VW", "VW", "BMW", "Ford")
Current_Price <- as.numeric(c(1050, 1000, 1500, 995, 2200, 2100))
Minimum_Price <- as.numeric(c(900, 600, 500, 850, 1900, 1950))
df1 <- data.frame(Type, Age, Colour, Make, Current_Price, Minimum_Price)
# User defined parameters - price to be changed for all cars which fit below selection
Input_Type <- "Car"
Input_Min_Age <- 2 # All cars this age and above
Input_Max_Age <- 10 # All cars this age and below
Input_Colour <- "Red"
Input_Make <- c("Ford", "VW")
# Minimum profit parameters
Input_Min_Pounds <- 10
Input_Min_Percentage <- 0.10
# Price change parameters
Input_Change_Type <- "Percentage" # "Percentage" or "Pound"
Input_Change_Value <- -0.10 # "-" sign to represent price reduction
Given the above, I would expect lines 1 & 2 to be effected by the change. Line 1's price should move down from £1,050 to £1,000. This is because £1,000 is the lowest price possible where 10% of the price is profit (900/(1-0.10) = 1000).
Line 2's price should simply move down by 10% to 900.
Has anyone got an idea how to put this into a function which will be fairly intuitive to use for someone who is not used to using R?

Answer 1

This answer uses a data.table to support the price changes in the original "data.frame" (as you have explained in the comments to your question) the solution could look like this.
I am still ignoring the pricing logic because I want to focus on the usability aspect
(this specialized pricing logic is arbitrary and not of particular interest for anybody else here at SO; if you have a specific problem to implement it yourself please open a new question and explain the problem in detail).
library(data.table)
data <- as.data.table(df1)
calc_price <- function(Current_Price,
Minimum_Price,
price_change_type,
price_change_value,
min_profit_pounds,
min_profit_percentage) {
# TODO implement your pricing logic here...
return(Current_Price + 1)
}
update_car_prices <- function(data,
filter,
price_change_type = c("Percentage", "Pound"),
price_change_value = 0,
min_profit_pounds = 10,
min_profit_percentage = 0.10) {
stopifnot(is.data.table(data))
price_change_type <- match.arg(price_change_type) # use the first value if none was provided
filter_exp <- substitute(filter) # "parse" the passed value as expression
# date the price using a separate function to encapsulate the logic
data[eval(filter_exp), Current_Price := calc_price(Current_Price,
Minimum_Price,
price_change_type,
price_change_value,
min_profit_pounds,
min_profit_percentage)][]
return(data)
}
The usage is still similar to my data.frame answer, e. g.:
update_car_prices(data, Type == "Car" & Age >= 2 & Age <= 10 & Colour == "Red" & Make %in% c("Ford", "VW"))
update_car_prices(data, Colour == "Red")
update_car_prices(data, Colour == "Red", "Pound", 500)
The differences are:
The whole data.table (data) is returned to see the impact
The original data is changed since data.tables are passed by reference
and I am updating the price "by reference" using the data.table syntax :=

Answer 2

This answer is based on a data.frame...
Your questions addresses multiple aspects (pricing logic, filtering logic and usability).
I am focusing on the usability (and ignoring the pricing logic since this is just a deliberate detail).
I see at least three options:
Use a strongly typed function:
get_car_prices1 <- function(data, Input_Type, Input_Min_Age, Input_Max_Age, Input_Colour, Input_Make, Input_Min_Pounds, Input_Min_Percentage)
Use an "untyped" function with a deliberate number of arguments via ... to support filtering by passing only the required arguments:
get_car_prices2 <- function(data, Input_Min_Pounds, Input_Min_Percentage, ...)
Use meta programming with substitute + eval
I have decided for option 3 as being the best (user friendly + flexible) option IMHO:
get_car_prices <- function(data,
filter,
price_change_type = c("Percentage", "Pound"),
price_change_value = 1)
{
price_change_type <- match.arg(price_change_type) # use the first value if none was provided
filter_exp <- substitute(filter) # "parse" the passed value as expression
data_subset <- subset(data, eval(filter_exp))
# TODO add your pricing logic here (e. g. using "ifelse")
return(data_subset)
}
# Usage examples:
get_car_prices(df1, Colour == "Red")
# Type Age Colour Make Current_Price Minimum_Price
# 1 Car 2 Red Ford 1050 900
# 2 Car 2 Red VW 1000 600
# 4 Van 4 Red VW 995 850
# 6 Van 1 Red Ford 2100 1950
get_car_prices(df1, Type == "Car" & Age >= 2 & Age <= 10 & Colour == "Red" & Make %in% c("Ford", "VW"))
# Type Age Colour Make Current_Price Minimum_Price
# 1 Car 2 Red Ford 1050 900
# 2 Car 2 Red VW 1000 600
get_car_prices(df1, Colour == "Red", "Pound", 500)
# ...
get_car_prices(df1, Colour == "Red", "dumping price", 1)
# Error in match.arg(price_change_type) :
# 'arg' should be one of “Percentage”, “Pound”
# But: The user has to learn at least the expression logic of R and that variables (and values) are case-sensitive:
get_car_prices(df1, Colour == "red")
# [1] Type Age Colour Make Current_Price Minimum_Price
# <0 rows> (or 0-length row.names)
# Error: Assignment operator (=) used instead of comparison operator (==)
get_car_prices(df1, Colour = "Red")
# Error in get_car_prices(df1, Colour = "Red") :
# unused argument (Colour = "Red")