Offset a vector prices or quantities

For each product, compute either the position of the previous period (back period), or the position of the first period (base period). Useful when price information is stored in a table.

Usage

back_period(period, product = gl(1, length(period)), match_first = TRUE)

base_period(period, product = gl(1, length(period)), match_first = TRUE)

Arguments

period

A factor, or something that can be coerced into one, that gives the time period for each transaction. The ordering of time periods follows the levels of period to agree with cut().

product

A factor, or something that can be coerced into one, that gives the product identifier for each transaction. The default is to assume that all transactions are for the same product.

match_first

Should products in the first period match with themselves (the default)?

Value

Both functions return a numeric vector of indices for the back/base periods. With back_period(), for all periods after the first, the resulting vector gives the location of the corresponding product in the previous period. With base_period(), the resulting vector gives the location of the corresponding product in the first period. The locations are unchanged for the first time period if match_first = TRUE, NA otherwise.

Note

By definition, there must be at most one transaction for each product in each time period to determine a back/base period. If multiple transactions correspond to a period-product pair, then the back/base period at a point in time is always the first position for that product in the previous period.

See also

outliers for common methods to detect outliers for price relatives.

rs_pairs in the rsmatrix package for making sales pairs.

Examples

df <- data.frame(
  price = 1:6,
  product = factor(c("a", "b")),
  period = factor(c(1, 1, 2, 2, 3, 3))
)

with(df, back_period(period, product))
#> [1] 1 2 1 2 3 4

# Make period-over-period price relatives

with(df, price / price[back_period(period, product)])
#> [1] 1.000000 1.000000 3.000000 2.000000 1.666667 1.500000

# Make fixed-base price relatives

with(df, price / price[base_period(period, product)])
#> [1] 1 1 3 2 5 3

# Change the base period with relevel()

with(df, price / price[base_period(relevel(period, "2"), product)])
#> [1] 0.3333333 0.5000000 1.0000000 1.0000000 1.6666667 1.5000000

# Warning is given if the same product has multiple prices
# at any point in time

with(df, back_period(period))
#> Warning: there are duplicated period-product pairs
#> [1] 1 1 1 1 3 3