myTarGetStats: Get stat by clients, campaigns or banners from 'API MyTarget'

Description

Get stat by clients, campaigns or banners from 'API MyTarget'

Usage

myTarGetStats(
  date_from = Sys.Date() - 7,
  date_to = Sys.Date(),
  object_type = "campaigns",
  object_id = NULL,
  stat_type = "day",
  metrics = "base",
  package_id = NULL,
  attribution = c("conversion", "impression", "default"),
  banner_status = NULL,
  campaign_status = NULL,
  sort_by = NULL,
  sort_direction = c("asc", "desc"),
  auth = NULL,
  login = getOption("rmytarget.login"),
  token_path = myTarTokenPath(),
  api_version = getOption("rmytarget.stat_api_version")
)

Arguments

date_from

Start date

date_to

End date

object_type

API object type, character value, one of campaigns, banners, users.

object_id

ID of API object (id campaign or any object).

stat_type

Breakdown by day, possible values: day, summary.

metrics

Set of metrics or fields, see metrics section for more ditali, default "base", but you can load any of all, base, events, video, viral, uniques, tps, or go this MyTarget Stat API v2 or MyTarget Stat API v3.

package_id

List of package identifiers. Available for banner statistics.

attribution

Attributing by event time or impression time. Available options: conversion, impression, default.

banner_status

Banner statuses list, available for campaign and banner statistics. Possible values: all, active, blocked, deleted.

campaign_status

List of campaign statuses, available for campaign and banner statistics. Possible values: all, active, blocked, deleted.

sort_by

The field by which identifiers of campaigns, banners or users will be sorted. Available metrics: base, events, video, viral, carousel, tps, moat, playable, romi.

sort_direction

Sorting direction. Available options: asc, desc

auth

MyTarget Autherization R object. See myTarAuth

Path to directory where you save credential data

token_path

Your login, or client name in MyTarget account

api_version

MyTarget Statistic API version: v2, v3

Value

Data frame with statistic.

Sets of metrics

You can load different sets of metrics, for this use metrics arguments, and set a vector containing the names of the desired metric sets. For more details of v2 API go this link. For more details of v3 API go this link. For example: metrics = c("base", "video", "viral")

base - based metrcis.

shows: Number of impressions
clicks: Number of clicks
goals: Number of goals achieved
spent: Cost sum
cpm: Average cost per 1000 views
cpc: Average cost per click
cpa: Average cost per goals
ctr: Percentage of clicks to views
cr: Percentage ratio of the number of goals achieved to the number of clicks.

events - metrics for advertised posts on social media feeds.

opening_app: Number of discoveries of the advertised social networks application
opening_post: Number of discoveries of the advertised message in the social media feed
moving_into_group: Number of transitions to the group page from the advertised message
clicks_on_external_url: Number of clicks on the external link in the advertised message
launching_video: Number of video launches in advertised message
comments: Number of comments left in the advertised message
joinings: Number of joining the group through the advertised message
likes: Number of likes of the advertised message
shares: Number of action "Share" for the advertised message.
votings: Number of voting actions in the advertised message

uniques - metrics by the number of unique users.

reach: Number of unique users who saw the ad for the specified period
total: Number of unique users who saw an ad for all time
increment: Number of new unique users who saw the ad for the specified period
frequency: Average frequency of displaying ads to one unique user

video - metrics for video ads.

started: Number of video playback starts
paused: Number of pauses of video playback
resumed_after_pause: Number of video playback after pause
fullscreen_on: Number of full-screen video playbacks
fullscreen_off: Number of shutdowns of full-screen video playback
sound_turned_off: Number of video mute
sound_turned_on: Number of video sound starts
viewed_10_seconds: Number of views of the first 10 seconds of the video
viewed_25_percent: Number of views of the first 25 percent of the video duration
viewed_50_percent: Number of views of the first 50 percent of the video duration
viewed_75_percent: Number of views of the first 75 of the video duration
viewed_100_percent: Number of views 100 percent of the video duration
viewed_10_seconds_rate: Percentage of views with the achievement of the first 10 seconds of the video
viewed_25_percent_rate: Percentage of views with the achievement of the first 25 percent of the video duration
viewed_50_percent_rate: Percentage of views with the achievement of the first 50 percent of the video duration
viewed_75_percent_rate: Percentage of views with the achievement of the first 75 percent of the video duration
viewed_100_percent_rate: Percentage of views with the achievement of the first 100 percent of the video duration
depth_of_view: Average video viewing depth (percent)
view_10_seconds_cost: Average cost of watching the first 10 seconds of a video
viewed_25_percent_cost: Average viewing cost of the first 25 percent of video length
viewed_50_percent_cost: Average viewing cost of the first 50 percent of video length
viewed_75_percent_cost: Average viewing cost of the first 75 percent of video length
viewed_100_percent_cost: Average viewing cost of the first 100 percent of video length

viral - metrics of viral events.

viral_impressions: Number of impressions of the shared advertising message in social networks
viral_reach: Number of unique users who saw the shared advertising message for the specified period
viral_total: Total number of unique users who have seen the shared advertising message for all time
viral_increment: Number of new unique users who saw the shared advertising message for the specified period
viral_frequency: Average frequency of displaying a shared advertising message to one unique user
viral_opening_app: Number of openings of the advertised application from the shared advertising message
viral_opening_post: Number of discoveries of the shared advertised message in the social media feed
viral_moving_into_group: number of transitions to the group page from the shared advertised message
viral_clicks_on_external_url: Number of clicks on the external link in the shared advertised message
viral_launching_video: Number of video launches in the shared advertised message
viral_comments: Number of comments left in the shared advertised message
viral_joinings: Number of joining the group through the shared advertised message
viral_likes: Number of likes of the shared advertised message
viral_shares: Number of actions "Share" for the shared advertised message
viral_votings: Number of voting actions in the shared advertised message

carousel - statistics on individual slides of the advertising carousel (N - from 1 to the number of slides).

slide_N_shows: Number of N slide shows
slide_N_clicks: Number of clicks on slide N
slide_N_ctr: Percentage ratio of clicks to the number of views on slide N

tps - additional write-off statistics.

tps: Additional charges for using the moat service
tpd: Additional charges for using third-party data (from dmp).

moat - statistics according to the moat service.

impressions: Number of impressions
in_view: Number of visible shows
never_focused: Number of impressions in the inactive tab
never_visible: Number of impressions out of sight
never_50_perc_visible: Additional charges for using third-party data (from dmp).
never_1_sec_visible: Number of impressions with visibility duration less than 1 second
human_impressions: Number of verified impressions
impressions_analyzed: number of impressions analyzed
in_view_percent: Number of impressions analyzed
human_and_viewable_perc: Percentage of visible hits
never_focused_percent: Verified impression percentage
never_visible_percent: Impression percentage in inactive tab
never_50_perc_visible_percent: The percentage of orders with a zone of visibility of the ad is less than 50 percent
never_1_sec_visible_percent: Percentage of impressions with visibility duration less than 1 second
in_view_diff_percent: Visible impression difference
active_in_view_time: Average time the ad is in view
attention_quality: Engagement level

playable - Playable Ads metrics.

playable_game_open: Opening the game
playable_game_close: Closing the game
playable_call_to_action: Clicks

romi - Playable Ads metrics.

value: The given value of the event
romi: Return on investment
adv_cost_share: Ad spend share

Examples

Run this code

# NOT RUN {
# base metrics by campaigns
base_data <- myTarGetStats(date_from   = Sys.Date() - 7,
                           date_to     = Sys.Date(),
                           object_type = "campaigns",
                           metrics = "base",
                           stat_type = "day",
                           login = "client_login", 
                           token_path = "D:\\mytarget_token")

# all metrics by campaigns
all_data <- myTarGetStats(date_from   = Sys.Date() - 7,
                          date_to     = Sys.Date(),
                          object_type = "campaigns",
                          metrics = "all",
                          stat_type = "day",
                          login = "client_login", 
                          token_path = "D:\\mytarget_token")

# custom set of metric by campaigns
custom_data <- myTarGetStats(date_from   = Sys.Date() - 7,
                             date_to     = Sys.Date(),
                             object_type = "campaigns",
                             metrics = c("base", "tps", "viral"),
                             stat_type = "day",
                           login = "client_login", 
                             token_path = "D:\\mytarget_token")


# if have note objects id, base metrics by ads
base_data2 <- myTarGetStats(date_from   = as.Date("2013-01-01"),
                            date_to     = Sys.Date(),
                            object_type = "banners",
                            metrics = "base",
                            stat_type = "day",
                            login = "client_login", 
                            token_path = "D:\\mytarget_token")

# get all stats group by clients, only for agency account
client_stat <-  myTarGetStats(date_from   = Sys.Date() - 7,
                              date_to     = Sys.Date(),
                              object_type = "users",
                              metrics     = "all",
                              login       = "agency_login")
# }