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Easy computation of Marketing Metrics in R
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y-bar/mmetrics
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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_datadf#> 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 9
As a next step, we define metrics to evaluate usingmmetrics::define.
# Example metricsmetrics<-mmetrics::define(cost= sum(cost),ctr= sum(click)/sum(impression)# CTR, Click Through Rate)
Callmmetrics::add() with grouping key (heregender) then we willget newdata.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 0.114
It is hassle for users to re-define metrics when you would like to usethese fordplyr::mutate(). In this case, you can usemmetrics::disaggregate() to removethe first aggregation functionfor the argument and return disaggregated metrics.
# Original metrics. sum() is used for this metricsmetrics#> <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: global
You can use these metrics withdplyr::mutate() for row-wise metricscomputation.
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 usingmmetrics::gmutate() defindin 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 vignettesIntroduction to {mmetrics}package
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Easy computation of Marketing Metrics in R