Compare a metric between two datasets (e.g. two events or waves)

Joins a current and a previous table on a key column and computes the difference of a metric – for example feature performance from two ipm_model() outputs, or pct from two calc_percentage() tables for two events. The result feeds straight into plot_diff().

Usage

compare_values(current, previous, by = "feature", value = "performance")

Arguments

current, previous

Data frames sharing a key column and a metric column.

by

Name of the key column to join on. Default "feature".

value

Name of the metric column to compare. Default "performance".

Value

A tibble with the key column, previous, current and difference (current - previous).

Details

The join is a left join keyed on by with previous on the left, so the row set follows the previous period and difference is current - previous (a positive number means it went up). Keys present only in current are dropped; keys missing from current get an NA difference. The inputs are deliberately generic two-column-or-more tables, so the same function compares feature performance from two ipm_model() runs, pct from two calc_percentage() tables for two events, or any other keyed metric.

See also

plot_diff(), ipm_model(), calc_percentage().

Other compare: plot_diff()

Examples

a <- data.frame(feature = c("price", "quality"), performance = c(3.1, 4.2))
b <- data.frame(feature = c("price", "quality"), performance = c(2.8, 4.4))
compare_values(current = b, previous = a)
#> # A tibble: 2 × 4
#>   feature previous current difference
#>   <chr>      <dbl>   <dbl>      <dbl>
#> 1 price        3.1     2.8     -0.300
#> 2 quality      4.2     4.4      0.200