Easy Computation of Marketing Metrics with Different Analysis Axis.
You can install the released version of {mmetrics} from CRAN with:
install.packages("mmetrics")Or install the development version from github with:
# install.packages("remotes")
remotes::install_github("y-bar/mmetrics")First, we load dummy data from {mmetrics} package for this example.
df <- mmetrics::dummy_data
df
#> gender age cost impression click conversion
#> 1 M 10 51 101 0 0
#> 2 F 20 52 102 3 1
#> 3 M 30 53 103 6 2
#> 4 F 40 54 104 9 3
#> 5 M 50 55 105 12 4
#> 6 F 60 56 106 15 5
#> 7 M 70 57 107 18 6
#> 8 F 80 58 108 21 7
#> 9 M 90 59 109 24 8
#> 10 F 100 60 110 27 9As a next step, we define metrics to evaluate using mmetrics::define.
# Example metrics
metrics <- mmetrics::define(
cost = sum(cost),
ctr = sum(click)/sum(impression) # CTR, Click Through Rate
)Call mmetrics::add() with grouping key (here gender) then we will
get new data.frame with defined metrics.
mmetrics::add(df, gender, metrics = metrics)
#> # A tibble: 2 x 3
#> gender cost ctr
#> <fct> <int> <dbl>
#> 1 F 280 0.142
#> 2 M 275 0.114It is hassle for users to re-define metrics when you would like to use
these for dplyr::mutate(). In this case, you can use
mmetrics::disaggregate() to remove the first aggregation function
for the argument and return disaggregated metrics.
# Original metrics. sum() is used for this metrics
metrics
#> <list_of<quosure>>
#>
#> $cost
#> <quosure>
#> expr: ^sum(cost)
#> env: global
#>
#> $ctr
#> <quosure>
#> expr: ^sum(click) / sum(impression)
#> env: global# Disaggregate metrics!
metrics_disaggregated <- mmetrics::disaggregate(metrics)
# Woo! sum() are removed!!!
metrics_disaggregated
#> $cost
#> <quosure>
#> expr: ^cost
#> env: global
#>
#> $ctr
#> <quosure>
#> expr: ^click / impression
#> env: globalYou can use these metrics with dplyr::mutate() for row-wise metrics
computation.
dplyr::mutate(df, !!!metrics_disaggregated)
#> gender age cost impression click conversion ctr
#> 1 M 10 51 101 0 0 0.00000000
#> 2 F 20 52 102 3 1 0.02941176
#> 3 M 30 53 103 6 2 0.05825243
#> 4 F 40 54 104 9 3 0.08653846
#> 5 M 50 55 105 12 4 0.11428571
#> 6 F 60 56 106 15 5 0.14150943
#> 7 M 70 57 107 18 6 0.16822430
#> 8 F 80 58 108 21 7 0.19444444
#> 9 M 90 59 109 24 8 0.22018349
#> 10 F 100 60 110 27 9 0.24545455…or, you can do the same compucation using mmetrics::gmutate() defind
in our package. In this case, you do not need to write !!!
(bang-bang-bang) operator explicitly.
mmetrics::gmutate(df, metrics = metrics_disaggregated)
#> # A tibble: 10 x 7
#> gender age cost impression click conversion ctr
#> <fct> <dbl> <int> <int> <dbl> <int> <dbl>
#> 1 M 10 51 101 0 0 0
#> 2 F 20 52 102 3 1 0.0294
#> 3 M 30 53 103 6 2 0.0583
#> 4 F 40 54 104 9 3 0.0865
#> 5 M 50 55 105 12 4 0.114
#> 6 F 60 56 106 15 5 0.142
#> 7 M 70 57 107 18 6 0.168
#> 8 F 80 58 108 21 7 0.194
#> 9 M 90 59 109 24 8 0.220
#> 10 F 100 60 110 27 9 0.245- As a first step, see the vignettes Introduction to {mmetrics} package