plot_trend_and_forecast {AutoAds}

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)

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