mmetrics

Easy Computation of Marketing Metrics with Different Analysis Axis.

Installation

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")

Example

Load Dummy data

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

Define metrics

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)

Just call`mmetrics::add()` !

Callmmetrics::add() with grouping key (heregender) then we will get 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

Remove aggregate function from metrics using`mmetrics::disaggregate()`

It is hassle for users to re-define metrics when you would like to use these fordplyr::mutate(). In this case, you can usemmetrics::disaggregate() to removethe first aggregation function for 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 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 usingmmetrics::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