packageVersion(“Robyn”)
## Power multi-core use when operating RStudio
Sys.setenv(R_FUTURE_FORK_ENABLE = “true”)
choices(future.fork.allow = TRUE)
## Verify simulated dataset or load your personal dataset
information(“dt_simulated_weekly”)
head(dt_simulated_weekly)
information(“dt_prophet_holidays”)
head(dt_prophet_holidays)
# Listing the place you wish to export outcomes to (will create new folders)
robyn_directory <- “~/Desktop”
InputCollect <- robyn_inputs(
dt_input = dt_simulated_weekly,
dt_holidays = dt_prophet_holidays,
date_var = “DATE”, # date format have to be “2020-01-01”
dep_var = “income”, # at the moment one dependent variable allowed
dep_var_type = “income”, # “income” or “conversion” allowed
prophet_vars = c(“pattern”, “season”, “vacation”), # “pattern”,”season”, “weekday” & “vacation” allowed
prophet_country = “DE”, # 123 international locations included in dt_prophet_holidays
context_vars = c(“competitor_sales_B”, “occasions”), # e.g. rivals, low cost, unemployment and so on
paid_media_spends = c(“tv_S”, “ooh_S”, “print_S”, “facebook_S”, “search_S”), # obligatory enter
paid_media_vars = c(“tv_S”, “ooh_S”, “print_S”, “facebook_I”, “search_clicks_P”), # if offered,
# Robyn will use this as an alternative of paid_media_spends for modelling. Media publicity metrics
# embody usually, however not restricted to impressions, GRP and so on. If not relevant, use spend as an alternative.
organic_vars = c(“e-newsletter”), # advertising and marketing exercise with out media spend
factor_vars = c(“occasions”), # point out categorical varibales in context_vars or organic_vars
window_start = “2016-01-01”,
window_end = “2018-12-31”,
adstock = “geometric” # geometric or weibull_pdf
)
print(InputCollect)
hyper_names(adstock = InputCollect$adstock, all_media = InputCollect$all_media)
plot_adstock(plot = FALSE)
plot_saturation(plot = FALSE)
# Instance hyperparameters ranges for Geometric adstock
hyperparameters <- record(
facebook_I_alphas = c(0.5, 3),
facebook_I_gammas = c(0.3, 1),
facebook_I_thetas = c(0, 0.3),
print_S_alphas = c(0.5, 1),
print_S_gammas = c(0.3, 1),
print_S_thetas = c(0.1, 0.4),
tv_S_alphas = c(0.5, 1),
tv_S_gammas = c(0.3, 1),
tv_S_thetas = c(0.3, 0.8),
search_clicks_P_alphas = c(0.5, 3),
search_clicks_P_gammas = c(0.3, 1),
search_clicks_P_thetas = c(0, 0.3),
ooh_S_alphas = c(0.5, 1),
ooh_S_gammas = c(0.3, 1),
ooh_S_thetas = c(0.1, 0.4),
newsletter_alphas = c(0.5, 3),
newsletter_gammas = c(0.3, 1),
newsletter_thetas = c(0.1, 0.4),
train_size = c(0.5, 0.8)
)
InputCollect <- robyn_inputs(InputCollect = InputCollect, hyperparameters = hyperparameters)
print(InputCollect)
# Verify spend publicity match and contemplate channel break up if relevant
InputCollect$ExposureCollect$plot_spend_exposure
# Run all trials and iterations. Use ?robyn_run to examine parameter definition
OutputModels <- robyn_run(
InputCollect = InputCollect, # feed in all mannequin specification
cores = NULL, # NULL defaults to (max out there – 1)
iterations = 2000, # 2000 beneficial for the dummy dataset with no calibration
trials = 5, # 5 beneficial for the dummy dataset
ts_validation = FALSE, # 3-way-split time collection for NRMSE validation.
add_penalty_factor = FALSE # Experimental characteristic so as to add extra flexibility
)
print(OutputModels)
OutputModels$convergence$moo_distrb_plot
OutputModels$convergence$moo_cloud_plot
OutputCollect <- robyn_outputs(
InputCollect, OutputModels,
pareto_fronts = “auto”, # robotically choose what number of pareto-fronts to fill min_candidates (100)
# min_candidates = 100, # high pareto fashions for clustering. Default to 100
# calibration_constraint = 0.1, # vary c(0.01, 0.1) & default at 0.1
csv_out = “pareto”, # “pareto”, “all”, or NULL (for none)
clusters = TRUE, # Set to TRUE to cluster comparable fashions by ROAS. See ?robyn_clusters
export = TRUE, # it will create recordsdata domestically
plot_folder = robyn_directory, # path for plots exports and recordsdata creation
plot_pareto = TRUE # Set to FALSE to deactivate plotting and saving mannequin one-pagers
)
print(OutputCollect)
## Examine all mannequin one-pagers and choose one which principally displays your online business actuality
print(OutputCollect)
select_model <- “5_257_2” # Choose one of many fashions from OutputCollect to proceed
#### Model >=3.7.1: JSON export and import (quicker and lighter than RDS recordsdata)
ExportedModel <- robyn_write(InputCollect, OutputCollect, select_model, export = TRUE)
print(ExportedModel)
# To plot any mannequin’s one-pager:
myOnePager <- robyn_onepagers(InputCollect, OutputCollect, select_model, export = FALSE)
print(ExportedModel)
InputCollect$paid_media_selected
AllocatorCollect1 <- robyn_allocator(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
# date_range = “all”, # Default to “all”
# total_budget = NULL, # When NULL, default is whole spend in date_range
channel_constr_low = 0.7,
channel_constr_up = c(1.2, 1.5, 1.5, 1.5, 1.5),
# channel_constr_multiplier = 3,
situation = “max_response”
)
# Print & plot allocator’s output
print(AllocatorCollect1)
plot(AllocatorCollect1)
AllocatorCollect2 <- robyn_allocator(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
date_range = “last_10”, # Final 10 durations, identical as c(“2018-10-22”, “2018-12-31”)
total_budget = 1500000, # Whole funds for date_range interval simulation
channel_constr_low = c(0.8, 0.7, 0.7, 0.7, 0.7),
channel_constr_up = 1.5,
channel_constr_multiplier = 5, # Customise sure extension for wider insights
situation = “max_response”
)
print(AllocatorCollect2)
plot(AllocatorCollect2)
AllocatorCollect3 <- robyn_allocator(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
#channel_constr_low = 0.1,
#channel_constr_up = 10,
# date_range = NULL, # Default: “all” out there dates
situation = “target_efficiency”,
# target_value = 5 # Customise goal ROAS or CPA worth
)
print(AllocatorCollect3)
plot(AllocatorCollect3)
json_file = “~/Desktop/Robyn_202412181043_init/RobynModel-5_257_2.json”
AllocatorCollect4 <- robyn_allocator(
json_file = json_file, # Utilizing json file from robyn_write() for allocation
dt_input = dt_simulated_weekly,
dt_holidays = dt_prophet_holidays,
# date_range = NULL,
situation = “target_efficiency”,
target_value = 2, # Customise goal ROAS or CPA worth
plot_folder = “~/Desktop”,
plot_folder_sub = “my_subdir”
)
RobynRefresh <- robyn_refresh(
json_file = json_file,
dt_input = dt_simulated_weekly,
dt_holidays = dt_prophet_holidays,
refresh_steps = 4,
refresh_iters = 2000,
refresh_trials = 5
)
json_file_rf1 <- “~/Desktop/Robyn_202412181043_init/Robyn_202412181054_rf1/RobynModel-1_133_7.json”
RobynRefresh <- robyn_refresh(
json_file = json_file_rf1,
dt_input = dt_simulated_weekly,
dt_holidays = dt_prophet_holidays,
refresh_steps = 4,
refresh_iters = 2000,
refresh_trials = 5
)
InputCollectX <- RobynRefresh$listRefresh1$InputCollect
OutputCollectX <- RobynRefresh$listRefresh1$OutputCollect
select_modelX <- RobynRefresh$listRefresh1$OutputCollect$selectID
Response <- robyn_response(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
metric_name = “facebook_I”
)
Response$plot
Spend1 <- 80000
Response1 <- robyn_response(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
metric_name = “facebook_I”,
metric_value = Spend1, # whole funds for date_range
date_range = “last_10” # final two durations
)
Response1$plot
Spend2 <- Spend1 + 100
Response2 <- robyn_response(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
metric_name = “facebook_I”,
metric_value = Spend2,
date_range = “last_10”
)
# ROAS for the 100$ from Spend1 stage
(Response2$sim_mean_response – Response1$sim_mean_response) /
(Response2$sim_mean_spend – Response1$sim_mean_spend)
# Instance of getting natural media publicity response curves
sendings <- 30000
response_sending <- robyn_response(
InputCollect = InputCollect,
OutputCollect = OutputCollect,
select_model = select_model,
metric_name = “e-newsletter”,
metric_value = sendings,
date_range = “last_10”
)
# Simulated price per thousand sendings
response_sending$sim_mean_spend / response_sending$sim_mean_response * 1000
# Manually create JSON file with inputs information solely
robyn_write(InputCollect, dir = “~/Desktop”)
# Manually create JSON file with inputs and particular mannequin outcomes
robyn_write(InputCollect, OutputCollect, select_model)
# Choose any exported mannequin (preliminary or refreshed)
json_file <- “~/Desktop/Robyn_202412181043_init/RobynModel-5_257_2.json”
# Non-compulsory: Manually learn and examine information saved in file
json_data <- robyn_read(json_file)
print(json_data)
# Re-create InputCollect
InputCollectX <- robyn_inputs(
dt_input = dt_simulated_weekly,
dt_holidays = dt_prophet_holidays,
json_file = json_file)
# Re-create OutputCollect
OutputCollectX <- robyn_run(
InputCollect = InputCollectX,
json_file = json_file
)
# Or re-create each by merely utilizing robyn_recreate()
RobynRecreated <- robyn_recreate(
json_file = json_file,
dt_input = dt_simulated_weekly,
dt_holidays = dt_prophet_holidays,
quiet = FALSE)
InputCollectX <- RobynRecreated$InputCollect
OutputCollectX <- RobynRecreated$OutputCollect
# Re-export or rebuild a mannequin and examine abstract
myModel <- robyn_write(InputCollectX, OutputCollectX, export = FALSE, dir = “~/Desktop”)
print(myModel)
# Re-create one-pager
myModelPlot <- robyn_onepagers(InputCollectX, OutputCollectX, export = FALSE)
# myModelPlot[[1]]$patches$plots[[7]]
# Refresh any imported mannequin
RobynRefresh <- robyn_refresh(
json_file = json_file,
dt_input = InputCollectX$dt_input,
dt_holidays = InputCollectX$dt_holidays,
refresh_steps = 6,
refresh_mode = “handbook”,
refresh_iters = 1000,
refresh_trials = 1
)
# Recreate response curves
robyn_response(
InputCollect = InputCollectX,
OutputCollect = OutputCollectX,
metric_name = “e-newsletter”,
metric_value = 50000
)
