Package 'AutoAds' reference manual

Title:	Advertisement Metrics Calculation
Description:	Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages 'forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/> and Rob J Hyndman et al "Documentation for 'forecast'" (2003) <https://pkg.robjhyndman.com/forecast/>, and 'ggplot2', described in Hadley Wickham et al "Documentation for 'ggplot2'" (2015) <https://ggplot2.tidyverse.org/>, and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" <https://ggplot2-book.org/>.
Authors:	Ivan Nemtsev [aut, cre, cph]
Maintainer:	Ivan Nemtsev <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2025-03-01 03:35:13 UTC
Source:	https://github.com/cran/AutoAds

All metrics

Description

A combination of functions view_percent, eCPM, CPMv, CTR, and fill_rate in one.

Usage

adstats(data)
adstats(data)

Arguments

data

A dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Requests', 'Impressions', 'Revenue', 'Viewable', and 'Clicks'.

Value

`view_percent`	A percentage of viewable impressions among the total number of impressions.
`eCPM`	Cost per thousand impressions of an ad, shown in the currency the original file had for 'Revenue'.
`CPMv`	Cost per thousand viewable impressions of an ad, shown in the currency the original file had for 'Revenue'.
`CTR`	A ratio of clicks to impressions.
`fill_rate`	A ratio of impressions to requests as a percentage.

Note

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Requests', 'Impressions', 'Revenue', 'Viewable', and 'Clicks'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
adstats <- function(data){
  data$ViewablePercent <- view_percent(data)
  data$eCPM <- eCPM(data)
  data$CPMv <- CPMv(data)
  data$CTR <- CTR(data)
  data$FillRate <- fill_rate(data)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data <- adstats(data)
  ## The function is currently defined as
adstats <- function(data){
  data$ViewablePercent <- view_percent(data)
  data$eCPM <- eCPM(data)
  data$CPMv <- CPMv(data)
  data$CTR <- CTR(data)
  data$FillRate <- fill_rate(data)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data <- adstats(data)

Cost per Click

Description

A ratio showing how much actual clicks on the ad cost.

Usage

CPC(data)
CPC(data)

Arguments

data

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Clicks' and 'Revenue'.

Value

A ratio of revenue to clicks - a cost of one click.

Note

Dataset has to be named 'data' and include columns 'Clicks' and 'Revenue'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
CPC <- function(data){
  x <- data$Clicks
  y <- data$Revenue
  round(
    y/x,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CPC <- CPC(data)
  ## The function is currently defined as
CPC <- function(data){
  x <- data$Clicks
  y <- data$Revenue
  round(
    y/x,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CPC <- CPC(data)

Cost per thousand viewable impressions

Description

Calculating CPMv based on Viewable Impressions and Revenue data. The dataset has to be named 'data' and include columns 'Viewable' and 'Revenue'.

Usage

CPMv(data)
CPMv(data)

Arguments

data

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Viewable' and 'Revenue'.

Value

Cost per thousand viewable impressions of an ad, shown in the currency the original file had for 'Revenue'.

Note

Dataset has to be named 'data' and include columns 'Viewable' and 'Revenue'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
CPMv <- function(data){
  x <- data$Viewable
  y <- data$Revenue
  round(
    y/x*1000,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CPMv <- CPMv(data)
  ## The function is currently defined as
CPMv <- function(data){
  x <- data$Viewable
  y <- data$Revenue
  round(
    y/x*1000,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CPMv <- CPMv(data)

Clickthrough rate

Description

A ratio showing how often people who see your ad end up clicking on it.

Usage

CTR(data)
CTR(data)

Arguments

data

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Impressions' and 'Clicks'.

Value

A ratio of clicks to impressions.

Note

Dataset has to be named 'data' and include columns 'Impressions' and 'Clicks'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
CTR <- function(data){
  x <- data$Impressions
  y <- data$Clicks
  round(
    y/x,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CTR <- CTR(data)
  ## The function is currently defined as
CTR <- function(data){
  x <- data$Impressions
  y <- data$Clicks
  round(
    y/x,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CTR <- CTR(data)

Effective cost per thousand impressions

Description

Calculating eCPM based on Impressions and Revenue data. The dataset has to be named 'data' and include columns 'Impressions' and 'Revenue'.

Usage

eCPM(data)
eCPM(data)

Arguments

data

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Impressions' and 'Revenue'.

Value

Cost per thousand impressions of an ad, shown in the currency the original file had for 'Revenue'.

Note

Dataset has to be named 'data' and include columns 'Impressions' and 'Revenue'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
eCPM <- function(data){
  x <- data$Impressions
  y <- data$Revenue
  round(
    y/x*1000,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$eCPM <- eCPM(data)
  ## The function is currently defined as
eCPM <- function(data){
  x <- data$Impressions
  y <- data$Revenue
  round(
    y/x*1000,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$eCPM <- eCPM(data)

Fill Rate/Show Rate

Description

A percentage of returned ads that were displayed in the app to the user out of all requested ones.

Usage

fill_rate(data)
fill_rate(data)

Arguments

data

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Requests' and 'Impressions'.

Value

A ratio of impressions to requests as a percentage.

Note

Dataset has to be named 'data' and include columns 'Requests' and 'Impressions'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
fill_rate <- function(data){
  x <- data$Requests
  y <- data$Impressions
  round(
    y/x*100,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$FillRate <- fill_rate(data)
  ## The function is currently defined as
fill_rate <- function(data){
  x <- data$Requests
  y <- data$Impressions
  round(
    y/x*100,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$FillRate <- fill_rate(data)

Plot with trend and forecast

Description

Plotting metrics of the dataset with trend and forecast, splitting them by groups of different ad blocks.

Usage

plot_trend_and_forecast(data, x_col, y_col, group_col)
plot_trend_and_forecast(data, x_col, y_col, group_col)

Arguments

`data`	Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Date', 'Block', and at least one with the data needed to plot ('Requests', 'Impressions', 'Revenue', 'Viewable', 'Clicks').
`x_col`	Column of the dataset 'data' with the data required for the x-axis of the plot. Preferrably 'Date'.
`y_col`	Column of the dataset 'data' with the data required for the y-axis of the plot. Preferrably the one with the data needed to plot ('Requests', 'Impressions', 'Revenue', 'Viewable', 'Clicks').
`group_col`	Column of the dataset 'data' with the data required for grouping the data. Preferrably 'Block'.

Value

A plot of the chosen data with trend and forecast.

Note

This function also requires packages 'ggplot2', and 'forecast'. Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Date', 'Block', and at least one with the data needed to plot ('Requests', 'Impressions', 'Revenue', 'Viewable', 'Clicks').

Author(s)

Ivan Nemtsev

References

Hyndman, R. J., & Athanasopoulos, G. (2018). "Forecasting: Principles and Practice" - https://otexts.com/fpp3/ Documentation for 'forecast' - https://pkg.robjhyndman.com/forecast/ Documentation for 'ggplot2' - https://ggplot2.tidyverse.org/ Wickham, H. (2016). "ggplot2: Elegant Graphics for Data Analysis" - https://ggplot2-book.org/

Examples


## The function is currently defined as
plot_trend_and_forecast <- function(data, x_col, y_col, group_col) {
  data$Date <- as.Date(data$Date)
  data_groups <- split(data, data[[group_col]])
  for (group in names(data_groups)) {
    group_data <- data_groups[[group]]
    time_series <- ts(group_data[[y_col]], frequency = 1, start = min(group_data[[x_col]]))
    arima_model <- auto.arima(time_series)
    forecast <- forecast(arima_model, h = 6)
    forecast_dates <- seq(max(group_data[[x_col]]) + 1,
    to = max(group_data[[x_col]]) + length(forecast$mean),
    by = "day")
    forecast_data <- data.frame(Date = forecast_dates, Forecast = forecast$mean)
    print(ggplot() +
            geom_line(data = group_data, aes(x = !!rlang::sym(x_col), y = !!rlang::sym(y_col))) +
            geom_line(data = forecast_data, aes(x = Date, y = Forecast), color = "blue") +
            geom_ribbon(data = forecast_data, aes(x = Date, ymin = forecast$lower[, 2],
            ymax = forecast$upper[, 2]), fill = "blue", alpha = 0.2) +
            labs(x = x_col, y = y_col, title = group) +
            theme_minimal())
  }
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
plot_trend_and_forecast(data, "Date", "Impressions", "Block")
#Any value instead of Impressions can be here
  ## The function is currently defined as
plot_trend_and_forecast <- function(data, x_col, y_col, group_col) {
  data$Date <- as.Date(data$Date)
  data_groups <- split(data, data[[group_col]])
  for (group in names(data_groups)) {
    group_data <- data_groups[[group]]
    time_series <- ts(group_data[[y_col]], frequency = 1, start = min(group_data[[x_col]]))
    arima_model <- auto.arima(time_series)
    forecast <- forecast(arima_model, h = 6)
    forecast_dates <- seq(max(group_data[[x_col]]) + 1,
    to = max(group_data[[x_col]]) + length(forecast$mean),
    by = "day")
    forecast_data <- data.frame(Date = forecast_dates, Forecast = forecast$mean)
    print(ggplot() +
            geom_line(data = group_data, aes(x = !!rlang::sym(x_col), y = !!rlang::sym(y_col))) +
            geom_line(data = forecast_data, aes(x = Date, y = Forecast), color = "blue") +
            geom_ribbon(data = forecast_data, aes(x = Date, ymin = forecast$lower[, 2],
            ymax = forecast$upper[, 2]), fill = "blue", alpha = 0.2) +
            labs(x = x_col, y = y_col, title = group) +
            theme_minimal())
  }
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
plot_trend_and_forecast(data, "Date", "Impressions", "Block")
#Any value instead of Impressions can be here

Percentage of views

Description

A ratio of viewable impressions to all impressions, shown as a percent.

Usage

view_percent(data)
view_percent(data)

Arguments

data

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Impressions' and 'Viewable'.

Value

A percentage of viewable impressions among the total number of impressions.

Note

Dataset has to be named 'data' and include columns 'Impressions' and 'Viewable'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
view_percent <- function(data){
  x <- data$Impressions
  y <- data$Viewable
  round(
    y/x*100,
  digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$ViewablePercent <- view_percent(data)
  ## The function is currently defined as
view_percent <- function(data){
  x <- data$Impressions
  y <- data$Viewable
  round(
    y/x*100,
  digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$ViewablePercent <- view_percent(data)

Package 'AutoAds'

Help Index

All metrics

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Cost per Click

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Cost per thousand viewable impressions

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Clickthrough rate

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Effective cost per thousand impressions

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Fill Rate/Show Rate

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Plot with trend and forecast

Description

Usage

Arguments

Value

Note

Author(s)

References

Examples

Percentage of views

Description

Usage

Arguments

Value

Note

Author(s)

Examples