Reputation: 83
I Have the following dataset regarding a digital marketing problem. the size of the audience, no of inactive e mails read mails and the priority of the segment are given
AudienceSize inactiveemails Readmails Importanceof targetgroup
246238 63581 1015 Low
402042 609 2089 Medium
2395 4 12 Medium
10958 76 105 High
120291 1237 707 Medium
65199 0 544 Low
106341 1506 1171 Medium
496986 8501 3139 Medium
293509 4805 2059 Medium
93218 97 814 Medium
246238 63581 1015 Low
402042 609 2089 Medium
2395 4 12 Medium
10958 76 105 High
120291 1237 707 Medium
65199 0 544 Low
106341 1506 1171 Medium
496986 8501 3139 Medium
293509 4805 2059 Medium
93218 97 814 Medium
I need to scale the data. The low priority e mails should be scaled among members low category alone. Similarly for the medium and high category, the scaling should be done using that alone. Is there anyway to achieve this.
Importanceoftargetgroup AudienceSize Readmails Inactivemails
Low .03444 .5366 .7437
Low .03664 .7500 .8000
medium .7665 .4333 .6543
medium .7965 .5533 .7543
Note: DPLYR has helped me subset the data and get means, but I need the scaled versions.
Upvotes: 1
Views: 214
Reputation: 11955
You should get the desired result using
library(dplyr)
df %>%
group_by(Importanceoftargetgroup) %>%
mutate_each(funs(scale), AudienceSize, inactiveemails, Readmails)
Upvotes: 3