README.md

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_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          9

Define metrics

As 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 
)

Just call mmetrics::add() !

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 0.114

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

It is hassle for users to re-define metrics when you would like to use these 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:  global

You 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

More examples

• As a first step, see the vignettes Introduction to {mmetrics} package