Campaign metadata for all campaigns run for the Customer Journey study. This dataset gives the length of time for which a campaign runs. So, any coupons received as part of a campaign are valid within the dates contained in this dataset.
campaign_descriptionsA data frame with 27 rows and 4 variables
campaign_id: Uniquely identifies each campaign; Ranges 1-27
campaign_type: Type of campaign (Type A, Type B, Type C)
start_date: Start date of campaign
end_date: End date of campaign
84.51°, Customer Journey study, http://www.8451.com/area51/
a tibble
# \donttest{
# full data set
campaign_descriptions
#> # A tibble: 27 × 4
#> campaign_id campaign_type start_date end_date
#> <chr> <ord> <date> <date>
#> 1 1 Type B 2017-03-03 2017-04-09
#> 2 2 Type B 2017-03-08 2017-04-09
#> 3 3 Type C 2017-03-13 2017-05-08
#> 4 4 Type B 2017-03-29 2017-04-30
#> 5 5 Type B 2017-04-03 2017-05-07
#> 6 6 Type C 2017-04-19 2017-05-21
#> 7 7 Type B 2017-04-24 2017-05-28
#> 8 8 Type A 2017-05-08 2017-06-25
#> 9 9 Type B 2017-05-31 2017-07-02
#> 10 10 Type B 2017-06-28 2017-07-30
#> # ℹ 17 more rows
# Join product campaign metadata to campaign_table dataset
require("dplyr")
#> Loading required package: dplyr
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
campaigns %>%
left_join(campaign_descriptions, "campaign_id")
#> # A tibble: 6,589 × 5
#> campaign_id household_id campaign_type start_date end_date
#> <chr> <chr> <ord> <date> <date>
#> 1 1 105 Type B 2017-03-03 2017-04-09
#> 2 1 1238 Type B 2017-03-03 2017-04-09
#> 3 1 1258 Type B 2017-03-03 2017-04-09
#> 4 1 1483 Type B 2017-03-03 2017-04-09
#> 5 1 2200 Type B 2017-03-03 2017-04-09
#> 6 1 293 Type B 2017-03-03 2017-04-09
#> 7 1 529 Type B 2017-03-03 2017-04-09
#> 8 1 536 Type B 2017-03-03 2017-04-09
#> 9 1 568 Type B 2017-03-03 2017-04-09
#> 10 1 630 Type B 2017-03-03 2017-04-09
#> # ℹ 6,579 more rows
# }