financial forecasting Mobile Massage Business
Janis Harris
2026-01-09
This R project is a project to see how a mobile massage business data that has been anonymized can turn into less a side hussle and more an actual small business. The data starts with personal identifiers removed to protect private client data, but keep the financial aspect from income and the permissions and consents approved from multiple different consent forms and locals of Corona CA during schedule changes that impacted the business like diminishing availability as progressing through school while working and motivation to work at building clientele and losing clients as well as having to rebuild clients while working part time and advertising on a few sites like Spa Finder, and free business lisings on Yelp and Google.
The business started with its first client in May 2019 prepandemic closures and saw an uptick in services for mobile massage following safety protocols during the pandemic and slightly afterwards for those who spent time and money getting mommy makeover plastic surgery and liposuction and needing lymphatic massages regularly. The school began with prerequisite courses in the summer of 2021 to start September 2021 through September 2024. The consent form was changed some times and lengthy. Most questions were on health and permission or consents to have them recommend business and most were optional other than their removed personal information and consent. Some didn’t upload to the database retrieval and storage online site due to client printing out form and only selecting which options they wanted. There were only a handful of those clients as most went through answering the multiple questions that could take a while to read the 26 item terms of service and sign. Those had to be input manually and some were also not in the database because they were dropped due to putting a different name and manually had to extract the data from their client folder within the clients folder of clients. That process of cleaning and preparing data took hours to do as combining by full name on consent form and income form from personal excel table didn’t match and some dropped, hence manually finding those items using the which() within dataframe to find the matches and not matched and shrink that data of not matched down to zero. However, when combining the 488 transactions for service May 2019 through Jan 2026, some folks have multiple consent forms from gaps in massage treatment and filling out again or erroring in the initial input and those were dropped and one with most information kept unless the consent form had a huge time gap of years or months between each one for same client. But something in this data is still duplicating many more observations at 533 instead of 488 when only a couple of people filled out multiple consent forms with a gap. And some folks used the page on site for consent form but selected a different consent form than the top page ‘new client’ form and scrolled down to the health and/or wellness forms that had different questions. The older form was from 2019-2021 and some intersection between old and new on data selected and answered. Jotform data form company used and lowest cost one for this small business allows 5 forms, so altered and new forms were at times archived and then put back into service. All information to run forecasts and time series analysis on making profits and some filled in optional consents for type of modalities as in types of massage modality like Swedish, acupressure, sports, prenatal, and so on preferred, or massage tools as in are elbows ok, pressure from light to super deep, bundled packages like savings where these were also only offered before and while massage wasn’t too busy. There was a buy 10 lymphatic drainage massages for $45 each but pay upfront $450 and get the credits for each massage, There was the family massage that the price changed structure from $35-50 per family member in 4 hours of massage, some special discounts later off the regular price that started at $60 but around 2023-2024 prices increased to $80/hour and 90 minutes from $80 to $120 per hour. All that information is in the self explanatory feature of the data. Tipping was optional as well but only because prices set and other than expenses kept. Discounts stopped once it was clear that folks mostly bought the mobile massage service for birthdays and special occasions or the packages for bundled options and some referred clients. The return is kept for each client and if they didn’t return yet, no is put in the return feature for that client on last visit. The dates of service, application for consent to massage submission, and date since last massage as well as days and years since last massage. There is a feature for whether like to massage client again and only 3 have a no because they are either too stressful in not allowing parking, flaking, making me feel unsafe or uncomfortable, or giving wrong address. That is there so that if making email mailers, phone calls, text messaging, or mailed in promotional offers that they don’t get put on the list of people to contact. There will be multiple receipts on later transactions with same receipt number because each person is counted and has a consent form and received service at that household at that appointment. You will see amounts paid and remaining features that relate to the income and splits how payment made and the remaining balance might have some errors in it. There are only negative payments when someone bought a package but couldn’t finish it and needed more availability so refunded them the balance if my unavailable schedule was reason why, but tacked on discount to each used service then refunded balance if it was due to client’s unavailability to complete the package as planned. Gratuities were tracked and if you see nothing in the feature it is because there was none given, some folk don’t like to add gratuity and mostly the package deals on lymphatic massage are the folk who didn’t tip and also since prices went up and cost was very low that bundled package was discontinued around the time that chiropractic school started. If you have any questions on the data, the feature column tries to explain as best it can, and many were summarized and shortened, some input manually to answer the optional feature that represents that consent form question more like a survey, and others will have the same input as these were selected as input, some were input text boxes the client could respond with own input. The consent forms are necessary to weed out and exclude creeps, weirdo, criminals, and other sort as crazy folk don’t typically spend 20 minutes of their time and a small non-refundable deposit of 25 USD per hour or 40 USD per 90 minutes plus additional cost on driving outside the 30 minutes to service address and back which increased from 10 USD each way using mapquest or google maps to 20 USD per half hour outside given 30 minutes. When company first started service provider was willing to drive an hour away and back but traffic really upset and influenced choice to not drive beyond the given 30 minutes without monetary compensation as prices low and adding on additonal drive money also validates they keep their appointment as not refunded. There aren’t that many clients but most are quality clients and idea clients for this respectable mobile massage business that is a female owned and operated sole proprietorship and not an Limited Liability Company.
Injury has kept the mobile massage provider from working and getting paid by day job so she must try to increase her income by contacting former clients and offering wellness plans and bundled package discounts to her previous contacts and maybe to encourage them visiting her social media sites to write a good comment to attract more business. Expenses weren’t added because those are tallied up from insurance, licensing, liablility and auto insurance, goDaddy website costs, personalized business website name cost annually, jotform and dropbox hosting online, software like microsoft monthly subscription, Amazon subscription for free delivery of tools replaced or new investments, website name annual cost, cell phone utilities, laundry soap, dryer sheets, fuel, car wear and tear if needed that year, lease cost per year if used, business costs, replacing worn supplies like linens and top cover or massage table, lotion, oil, aromatherapy, additional service tools like hot stone kits, towel warmers, new tools like lipocavitation machine, massage hyperpercussion tool, instrument assisted soft tissue mobilization tools and kits, cupping supplies, physiotherapy tools like resistance bands, foam roller, yoga mat, etc. and so on.
Get file (here)[https://docs.google.com/spreadsheets/d/1KeMR8UpPeQ_iHEhmLB50oRHEEgosgUTf50SM45O0geI/edit?usp=sharing].
library(prophet)## Loading required package: Rcpp## Loading required package: rlanglibrary(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, unionlibrary(ggplot2)Data <- read.csv("combinedBest.csv", sep=',', header=T, na.strings=c('',' ','na','NA'))
str(Data)## 'data.frame': 531 obs. of 73 variables:
## $ SalesReceiptNumber : int 150 196 482 469 181 179 430 482 121 264 ...
## $ dateOfService : chr "11/7/2020" "1/31/2021" "11/8/2025" "6/12/2025" ...
## $ StartTime : chr "8:30 PM" "6:00 PM" "10:00 AM" "7:00 PM" ...
## $ EndTime : chr "9:30 PM" "7:30 PM" "11:00 AM" "8:00 PM" ...
## $ newReturn : chr "new" "new" "return" "new" ...
## $ returned : chr "no" "no" "no" "yes" ...
## $ referred : chr "referred" "referred" NA "referred" ...
## $ DateLastServiceReturned : chr NA NA "6/12/2025" NA ...
## $ daysPassedBetweenServices : int 0 0 149 0 1 0 0 0 0 70 ...
## $ weeksPassedBetweenServices : int 0 0 21 0 0 0 0 0 0 10 ...
## $ todaysDate : chr "1/8/2026" "1/8/2026" "1/8/2026" "1/8/2026" ...
## $ daysSinceTodayAndDateOfThisService : int 1888 1803 61 210 1867 1868 379 61 2036 1671 ...
## $ weeksSinceTodayAndDateOfThisService : int 270 258 9 30 267 267 54 9 291 239 ...
## $ likeToSeeAgain : chr "yes" "yes" "yes" "yes" ...
## $ City : chr "Corona" "Corona" "Corona" "Corona" ...
## $ ZipCode : int 92883 92882 92882 92882 92752 92752 92883 92882 92807 92881 ...
## $ MassagePackage : chr "One and one half hour Customized Massage + 1/2 hour of wife's changed 90 minute massage given to daughter" "One and one half hour Customized Massage" "1 hour swedish regular price" "1 hour swedish regular price" ...
## $ ServiceCost : chr "60.00" "80.00" "80.00" "80.00" ...
## $ driveTimeBeyond30minBothWays : int NA NA NA NA NA NA NA NA NA NA ...
## $ Discount : num NA NA NA NA 30 15 40 NA NA 20 ...
## $ TypeOfDiscount : chr NA NA "regular 1 hour swedish price" "regular 1 hour swedish price" ...
## $ AmountPaid_NotCreditUsed : num 60 100 80 80 30 45 140 80 69 50 ...
## $ CreditPaid_monthlyOrPkg : int NA NA NA NA NA NA NA NA NA NA ...
## $ AmountPaid_As_Income : num 60 100 80 80 30 45 140 80 69 50 ...
## $ clientRemainingCreditBalance : int NA NA NA NA NA NA NA NA NA NA ...
## $ Gratuity : num NA 20 NA NA NA NA 20 NA 9 10 ...
## $ FSA_HSA : num NA NA NA NA NA NA NA NA NA NA ...
## $ SpaFinderGiftCard : int NA NA NA NA NA NA NA NA NA NA ...
## $ otherGiftCard : int NA NA NA NA NA NA NA NA NA NA ...
## $ creditCard : num 60 NA NA 80 NA NA 140 NA 69 NA ...
## $ cash : int NA 100 NA NA NA NA NA NA NA NA ...
## $ zelle : num NA NA 80 NA 30 45 NA 80 NA 50 ...
## $ check : num NA NA NA NA NA NA NA NA NA 10 ...
## $ TypePayment : chr "credit card" "cash" "zelle 80 exactly with brother additiol 80 same receipt" "credit card paid for by daughter after her 90 minute, return client for daughter same household" ...
## $ consentFormSubmissionDate : chr "11/7/2020" "1/31/2021" "6/12/2025" "6/12/2025" ...
## $ consentForm_formType : chr "oldForm" "oldForm" "newForm" "newForm" ...
## $ manualInputMissingInDataBaseSourceOrPrinted : chr "no" "no" "no" "no" ...
## $ receivedMassageFromMe : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Birth.Date : chr "4/2/1997" "1/28/1988" "10/28/1957" "10/28/1957" ...
## $ ageAtSubmission : int 23 33 67 67 50 50 36 64 28 26 ...
## $ termsAgreedTo : chr "Yes" "Yes" "Yes" "Yes" ...
## $ termsSignature : chr "https://www.jotform.com/uploads/janiscorona/91274881154158/4806106762411562587/4806106762411562587_signature_86.png" "https://www.jotform.com/uploads/janiscorona/91274881154158/4879535172718857846/4879535172718857846_signature_86.png" "https://www.jotform.com/uploads/janiscorona/211143470705143/6255850840271681099/6255850840271681099_signature_86.png" "https://www.jotform.com/uploads/janiscorona/211143470705143/6255850840271681099/6255850840271681099_signature_86.png" ...
## $ dateAgreedSigned : chr "11/7/2020" "1/31/2021" "6/12/2025" "6/12/2025" ...
## $ femaleOrMaleTherapistPreference : chr "No preference" "No preference" "female massage therapist" "female massage therapist" ...
## $ lotionOrOilPreference : chr "lotion or oil, no preference" "lotion or oil, no preference" "oil" "oil" ...
## $ describeWhyMobileMassagePreferred : chr NA "Mid back pain" "Back, neck" "Back, neck" ...
## $ painScale1least_10worst : int 10 2 7 7 4 4 4 NA 2 2 ...
## $ healthConditionsPrecautions : chr NA NA NA NA ...
## $ sleep1poor_10BestEver : int 10 10 4 4 5 5 2 8 7 6 ...
## $ stress1none_10extremely : int 10 3 7 7 5 5 9 2 8 6 ...
## $ Pressure : chr "deep tissue (deep layer muscles)" NA NA NA ...
## $ massageModalitiesPreferred : chr "Full spectrum CBD (.oo3 THC) TOPICAL (must be 18 or older)" NA NA NA ...
## $ areasToAvoid : chr "feet" NA NA NA ...
## $ massageStyleToolsPreferred : chr NA NA NA NA ...
## $ massageGoals : chr NA NA NA NA ...
## $ additonalServices : chr NA NA NA NA ...
## $ howHeardAboutCompany : chr NA NA NA NA ...
## $ willingToRecommendCompany : chr NA NA NA NA ...
## $ OKtoContactEmailTextPhone : chr NA NA NA NA ...
## $ BioPsychoSocialModelOfPain_massageExpectations : chr NA NA "I recently had some MAJOR LIFE CHANGES that have stressed me out into changing my diet, sleep schedule, number "| __truncated__ "I recently had some MAJOR LIFE CHANGES that have stressed me out into changing my diet, sleep schedule, number "| __truncated__ ...
## $ lastMassageWhen : chr NA NA "About two months ago" "About two months ago" ...
## $ lastMassageComplaints : chr NA NA "Nothing" "Nothing" ...
## $ lastMassageReceivedWhere : chr NA NA "Depends" "Depends" ...
## $ minorNameIfParentPresentWholeTimeForMinorMassage : logi NA NA NA NA NA NA ...
## $ whyChildMassageAndHasChildEverHadMassageBefore : logi NA NA NA NA NA NA ...
## $ minorDOBifForMinor : chr NA NA "Invalid date" "Invalid date" ...
## $ massagePraisesWhatYouLikeAboutMassageBenefitsYou : chr NA NA "Relief of muscle aches" "Relief of muscle aches" ...
## $ talkingDuringMassageOK_isYes_or_Not_isNo : chr NA NA "yes, but only about answering questions I ask about the technique, training, benefits, or other questions I may"| __truncated__ "yes, but only about answering questions I ask about the technique, training, benefits, or other questions I may"| __truncated__ ...
## $ pressureHealthPrecautionsDescribedNowWhatPressurePreferred: chr NA NA "medium pressure (deeper pressure than light pressure, but able to get into the muscle belly of the top layers o"| __truncated__ "medium pressure (deeper pressure than light pressure, but able to get into the muscle belly of the top layers o"| __truncated__ ...
## $ genderPreferredWarningPrecautionOnMalesFemalesRespect : chr NA NA "male" "male" ...
## $ whySelectThisMassageCompany : chr NA NA NA NA ...
## $ yourGender : chr NA NA "male" "male" ...
## $ bundledMassagePackageSavingsInterestedIn : chr NA NA NA NA ...Change Date into Date columns.
dates <- grep('date',colnames(Data))
Dates <- grep('Date',colnames(Data))
DateColumns <- colnames(Data)[c(dates,Dates)]
DateColumns## [1] "dateOfService" "dateAgreedSigned"
## [3] "DateLastServiceReturned" "todaysDate"
## [5] "daysSinceTodayAndDateOfThisService" "weeksSinceTodayAndDateOfThisService"
## [7] "consentFormSubmissionDate" "Birth.Date"DateColumnsToChange <- DateColumns[-c(5,6)]
DateColumnsToChange## [1] "dateOfService" "dateAgreedSigned"
## [3] "DateLastServiceReturned" "todaysDate"
## [5] "consentFormSubmissionDate" "Birth.Date"Data$dateOfService[1:10]## [1] "11/7/2020" "1/31/2021" "11/8/2025" "6/12/2025" "11/28/2020"
## [6] "11/27/2020" "12/25/2024" "11/8/2025" "6/12/2020" "6/12/2021"Change 4 digit year into 2 digit year to use date features in as.Date function.
Data$dateOfService <- format(as.Date(Data$dateOfService, "%m/%d/%Y"),"%m/%d/%y")
Data$dateOfService[1:10]## [1] "11/07/20" "01/31/21" "11/08/25" "06/12/25" "11/28/20" "11/27/20"
## [7] "12/25/24" "11/08/25" "06/12/20" "06/12/21"Good that it was able to convert the 4 digit year into 2 digits.
Data$dateOfService <- as.Date(Data$dateOfService, "%m/%d/%y")
str(Data$dateOfService)## Date[1:531], format: "2020-11-07" "2021-01-31" "2025-11-08" "2025-06-12" "2020-11-28" ...The years are correct as another try with just above code before converting to 2 digit year gave all the same year.
summary(Data$dateOfService)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2019-05-20" "2021-03-16" "2021-08-08" "2022-04-26" "2023-01-07" "2026-01-07"summary(Data[,c(DateColumnsToChange)])## dateOfService dateAgreedSigned DateLastServiceReturned
## Min. :2019-05-20 Length:531 Length:531
## 1st Qu.:2021-03-16 Class :character Class :character
## Median :2021-08-08 Mode :character Mode :character
## Mean :2022-04-26
## 3rd Qu.:2023-01-07
## Max. :2026-01-07
## todaysDate consentFormSubmissionDate Birth.Date
## Length:531 Length:531 Length:531
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
## Seems like it worked so we will do the same to the other date columns
to change them from character to date objects. “dateAgreedSigned”
[3] “DateLastServiceReturned” “todaysDate”
[5] “consentFormSubmissionDate” “Birth.Date”
Data$dateAgreedSigned <- as.Date(format(as.Date(Data$dateAgreedSigned, "%m/%d/%Y"), "%m/%d/%y"), "%m/%d/%y")
str(Data$dateAgreedSigned)## Date[1:531], format: "2020-11-07" "2021-01-31" "2025-06-12" "2025-06-12" "2020-11-23" ...summary(Data$dateAgreedSigned)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2019-05-19" "2020-11-22" "2021-03-08" "2021-11-30" "2021-12-19" "2025-12-29"
## NA's
## "2"error1 <- Data[is.na(Data$dateAgreedSigned),]
error1## SalesReceiptNumber dateOfService StartTime EndTime newReturn returned
## 215 123 2020-07-30 6:00 PM 7:00 PM new yes
## 216 126 2020-08-23 11:00 AM 12:00 PM return no
## referred DateLastServiceReturned daysPassedBetweenServices
## 215 <NA> <NA> 0
## 216 <NA> 7/30/2020 24
## weeksPassedBetweenServices todaysDate daysSinceTodayAndDateOfThisService
## 215 0 1/8/2026 1988
## 216 3 1/8/2026 1964
## weeksSinceTodayAndDateOfThisService likeToSeeAgain City ZipCode
## 215 284 yes Corona 92882
## 216 281 yes Corona 92882
## MassagePackage ServiceCost driveTimeBeyond30minBothWays
## 215 One Hour Persolized Massage 60.00 NA
## 216 One Hour Persolized Massage 60.00 NA
## Discount TypeOfDiscount AmountPaid_NotCreditUsed CreditPaid_monthlyOrPkg
## 215 NA <NA> 100 NA
## 216 NA <NA> 100 NA
## AmountPaid_As_Income clientRemainingCreditBalance Gratuity FSA_HSA
## 215 100 NA 40 NA
## 216 100 NA 40 NA
## SpaFinderGiftCard otherGiftCard creditCard cash zelle check
## 215 NA NA NA 100 NA NA
## 216 NA NA NA 100 NA NA
## TypePayment consentFormSubmissionDate
## 215 cash 7/30/2020
## 216 cash, paid by husband Anthony 7/30/2020
## consentForm_formType manualInputMissingInDataBaseSourceOrPrinted
## 215 handwritten printed old form yes
## 216 handwritten printed old form yes
## receivedMassageFromMe Birth.Date ageAtSubmission termsAgreedTo
## 215 Yes 7/30/1991 29 Yes
## 216 Yes 7/30/1991 29 Yes
## termsSignature dateAgreedSigned
## 215 sneaky printed out but avoided checking and signing box <NA>
## 216 sneaky printed out but avoided checking and signing box <NA>
## femaleOrMaleTherapistPreference lotionOrOilPreference
## 215 <NA> <NA>
## 216 <NA> <NA>
## describeWhyMobileMassagePreferred painScale1least_10worst
## 215 <NA> NA
## 216 <NA> NA
## healthConditionsPrecautions sleep1poor_10BestEver stress1none_10extremely
## 215 <NA> NA NA
## 216 <NA> NA NA
## Pressure massageModalitiesPreferred areasToAvoid massageStyleToolsPreferred
## 215 medium <NA> <NA> <NA>
## 216 medium <NA> <NA> <NA>
## massageGoals additonalServices howHeardAboutCompany
## 215 <NA> <NA> <NA>
## 216 <NA> <NA> <NA>
## willingToRecommendCompany OKtoContactEmailTextPhone
## 215 <NA> <NA>
## 216 <NA> <NA>
## BioPsychoSocialModelOfPain_massageExpectations lastMassageWhen
## 215 <NA> <NA>
## 216 <NA> <NA>
## lastMassageComplaints lastMassageReceivedWhere
## 215 <NA> <NA>
## 216 <NA> <NA>
## minorNameIfParentPresentWholeTimeForMinorMassage
## 215 NA
## 216 NA
## whyChildMassageAndHasChildEverHadMassageBefore minorDOBifForMinor
## 215 NA <NA>
## 216 NA <NA>
## massagePraisesWhatYouLikeAboutMassageBenefitsYou
## 215 <NA>
## 216 <NA>
## talkingDuringMassageOK_isYes_or_Not_isNo
## 215 <NA>
## 216 <NA>
## pressureHealthPrecautionsDescribedNowWhatPressurePreferred
## 215 <NA>
## 216 <NA>
## genderPreferredWarningPrecautionOnMalesFemalesRespect
## 215 <NA>
## 216 <NA>
## whySelectThisMassageCompany yourGender
## 215 <NA> <NA>
## 216 <NA> <NA>
## bundledMassagePackageSavingsInterestedIn
## 215 <NA>
## 216 <NA>Just checking, because I thought it was the one group but it was the printed handwritten couple, the female didn’t sign but printed it out, very few did this. The husband had a different name than his consent form too. This is fine programmatically, not for reality of mobile massage provider.
We will do each of these individually, because after running this earlier, the Birth.Date field produced DOB into the future.
[3] “DateLastServiceReturned”
Data$DateLastServiceReturned <- as.Date(format(as.Date(Data$DateLastServiceReturned, "%m/%d/%Y"), "%m/%d/%y"), "%m/%d/%y")
summary(Data$DateLastServiceReturned)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2019-05-20" "2021-03-24" "2021-07-20" "2022-01-26" "2022-05-30" "2025-12-29"
## NA's
## "150"This is ok to have 150 NAs in the above field bc those belong to new clients whose history cannot be exact for last massage and is not applicable to last massage with this mobile massage provider.
“todaysDate”
Data$todaysDate <- as.Date(format(as.Date(Data$todaysDate, "%m/%d/%Y"), "%m/%d/%y"), "%m/%d/%y")
summary(Data$todaysDate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2026-01-08" "2026-01-08" "2026-01-08" "2026-01-08" "2026-01-08" "2026-01-08"The date of running this program in Excel is this field’s value. Take note that the ‘todaysDate’ field is the date the program was last opened in Excel before manipulating format in R. But it should actually say the 9th of January because it was opened in Excel today and then opened in R for edits made with currency to numeric done today the 9th.
[5] “consentFormSubmissionDate” “Birth.Date”
Data$consentFormSubmissionDate <- as.Date(format(as.Date(Data$consentFormSubmissionDate, "%m/%d/%Y"), "%m/%d/%y"), "%m/%d/%y")
summary(Data$consentFormSubmissionDate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2019-05-19" "2020-11-22" "2021-03-08" "2021-11-29" "2021-11-24" "2025-12-29"Seems good.
“Birth.Date”
Data$Birth.Date## [1] "4/2/1997" "1/28/1988" "10/28/1957" "10/28/1957" "11/16/1970"
## [6] "11/16/1970" "12/27/1987" "11/4/1961" "12/3/1991" "1/15/1995"
## [11] "1/15/1995" "1/15/1995" "1/23/1969" "3/4/1994" "3/4/1994"
## [16] "3/4/1994" "3/4/1994" "3/4/1994" "3/4/1994" "3/4/1994"
## [21] "3/4/1994" "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984"
## [26] "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984"
## [31] "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984"
## [36] "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984" "6/11/1984"
## [41] "6/11/1984" "1/5/1993" "10/17/1984" "3/8/1964" "6/28/1997"
## [46] "3/8/1964" "6/28/1997" "12/6/1986" "6/16/1963" "2/17/1948"
## [51] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [56] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [61] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [66] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [71] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [76] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [81] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [86] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [91] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [96] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [101] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [106] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [111] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [116] "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948" "2/17/1948"
## [121] "6/13/1986" "6/13/1986" "6/13/1986" "6/13/1986" "6/13/1986"
## [126] "6/13/1986" "6/13/1986" "6/13/1986" "6/13/1986" "4/26/1990"
## [131] "11/6/2000" "11/6/2000" "11/6/2000" "11/6/2000" "5/14/1988"
## [136] "6/24/1990" "10/10/1976" "8/31/1994" "8/31/1994" "8/31/1994"
## [141] "8/31/1994" "8/31/1994" "8/31/1994" "8/31/1994" "8/31/1994"
## [146] "8/31/1994" "8/31/1994" "8/31/1994" "8/31/1994" "7/29/1970"
## [151] "7/29/1970" "6/28/1988" "6/28/1988" "6/28/1988" "6/28/1988"
## [156] "7/11/1980" "7/11/1980" "5/25/1988" "12/28/1997" "12/29/1985"
## [161] "4/12/1983" "4/12/1983" "4/12/1983" "4/12/1983" "4/12/1983"
## [166] "9/9/1987" "6/23/1961" "7/12/2000" "9/13/1971" "9/13/1971"
## [171] "7/28/1989" "7/2/1992" "7/2/1992" "7/2/1992" "9/6/1956"
## [176] "10/5/1946" "1/26/1987" "2/11/1965" "6/1/1979" "6/1/1979"
## [181] "6/1/1979" "6/25/1997" "3/6/1962" "4/8/1959" "4/8/1959"
## [186] "2/28/1985" "2/28/1985" "2/28/1985" "2/28/1985" "8/1/1996"
## [191] "8/1/1996" "8/1/1996" "8/1/1996" "8/1/1996" "8/1/1996"
## [196] "8/1/1996" "11/15/1977" "7/7/1990" "7/7/1990" "7/7/1990"
## [201] "7/7/1990" "7/7/1990" "7/7/1990" "7/7/1990" "4/5/1957"
## [206] "4/5/1957" "1/16/1988" "1/16/1988" "2/3/1982" "2/3/1982"
## [211] "9/4/1970" "9/4/1970" "9/4/1970" "9/4/1970" "7/30/1991"
## [216] "7/30/1991" "3/27/1988" "5/25/1986" "5/25/1986" "5/25/1986"
## [221] "5/25/1986" "5/25/1986" "11/21/1963" "1/15/1995" "1/15/1995"
## [226] "1/15/1995" "1/15/1995" "1/15/1995" "1/15/1995" "1/15/1995"
## [231] "1/15/1995" "1/15/1995" "1/15/1995" "1/15/1995" "1/15/1995"
## [236] "1/15/1995" "8/7/1986" "1/4/1947" "6/22/1988" "6/22/1963"
## [241] "6/22/1963" "11/11/1990" "11/11/1990" "11/11/1990" "11/11/1990"
## [246] "11/11/1990" "11/11/1990" "10/16/1966" "1/3/2000" "1/3/2000"
## [251] "1/3/2000" "1/3/2000" "1/3/2000" "1/3/2000" "2/14/1994"
## [256] "6/9/1973" "12/26/1974" "12/14/1983" "12/14/1983" "6/30/1988"
## [261] "3/27/1986" "3/27/1986" "6/10/1992" "6/10/1992" "6/30/1987"
## [266] "7/2/1987" "6/16/1979" "6/16/1979" "6/16/1979" "6/16/1979"
## [271] "6/16/1979" "6/16/1979" "6/16/1979" "6/10/1998" "10/2/1982"
## [276] "1/23/1988" "6/6/2001" "6/6/2001" "8/12/1982" "8/12/1982"
## [281] "8/12/1982" "8/12/1982" "8/12/1982" "8/12/1982" "8/12/1982"
## [286] NA "6/7/1987" "6/7/1987" "6/7/1987" "6/7/1987"
## [291] "6/7/1987" "6/7/1987" "6/7/1987" "6/7/1987" "6/7/1987"
## [296] "6/7/1987" "12/26/1985" "5/1/1974" "5/1/1974" "5/1/1974"
## [301] "5/1/1974" "5/1/1974" "5/1/1974" "5/1/1974" "5/1/1974"
## [306] "5/1/1974" "5/1/1974" "3/25/1998" "3/25/1998" "10/17/1999"
## [311] "10/17/1999" "10/17/1999" "1/10/1952" "8/29/1968" "5/4/1983"
## [316] "2/22/1968" "2/22/1968" "2/22/1968" "2/22/1968" "11/9/1989"
## [321] "11/9/1989" "12/6/1963" "4/11/1954" "6/29/1956" "6/29/1956"
## [326] "6/29/1956" "10/18/1971" "10/18/1971" "10/18/1971" "10/18/1971"
## [331] "10/18/1971" "10/18/1971" "5/9/1986" "5/26/1975" "5/26/1975"
## [336] "5/26/1975" "5/26/1975" "5/26/1975" "5/26/1975" "7/25/1964"
## [341] "7/25/1964" "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987"
## [346] "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987"
## [351] "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987"
## [356] "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987"
## [361] "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987" "7/18/1987"
## [366] "3/12/1999" "2/19/1993" "2/19/1960" "6/24/1986" "6/24/1986"
## [371] "7/31/1986" "7/31/1986" "7/31/1986" "7/31/1986" "7/18/1969"
## [376] "9/30/1976" "10/15/1992" "10/13/1986" "10/13/1986" "10/13/1986"
## [381] "10/13/1986" "10/13/1986" "10/13/1986" "10/13/1986" "10/13/1986"
## [386] "10/29/1965" "11/29/1966" "6/12/1989" "6/24/1998" "1/26/1978"
## [391] "8/5/1995" "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992"
## [396] "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992"
## [401] "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992"
## [406] "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992" "3/8/1992"
## [411] "3/8/1992" "3/8/1992" "12/9/1973" "12/4/1963" "3/1/1954"
## [416] "1/17/1972" "1/17/1972" "1/17/1972" "1/17/1972" "7/24/1996"
## [421] "9/20/1990" "2/20/1993" "9/26/1984" "2/9/1989" "2/9/1989"
## [426] "2/9/1989" "2/9/1989" "2/9/1989" "2/9/1989" "2/9/1989"
## [431] "2/9/1989" "2/9/1989" "5/26/1984" "5/26/1984" "5/26/1984"
## [436] "5/26/1984" "5/26/1984" "4/22/1974" "4/30/1991" "5/8/1994"
## [441] "3/9/1981" "3/9/1981" "3/9/1981" "3/9/1981" "3/9/1981"
## [446] "3/9/1981" "3/9/1981" "3/9/1981" "3/9/1981" "2/2/1983"
## [451] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [456] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [461] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [466] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [471] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [476] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [481] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [486] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [491] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [496] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [501] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983"
## [506] "2/2/1983" "2/2/1983" "2/2/1983" "2/2/1983" "4/7/1989"
## [511] "9/19/1984" "9/11/1980" "2/19/1984" "10/13/1993" "6/2/1990"
## [516] "3/16/1963" "3/16/1963" "3/16/1963" "3/16/1963" "8/25/1986"
## [521] "9/27/1983" "9/27/1983" "9/27/1983" "11/28/1966" "5/19/1965"
## [526] "11/2/1975" "11/2/1975" "11/2/1975" "11/2/1975" "11/2/1975"
## [531] "11/2/1975"After trying the convert to 2 digit year, there was a problem, so we are not changing the birthdate field as we don’t need to use it in our analytics or timeseries forecasting.
Seems like a Y2K dilemma as the formatting is getting closer to older years and some clients are older than 60 years old which seems to be when the problem starts with putting their birthday as 2064 or 2055 and so on. See what I mean when we just take the field or feature and do change the formatting to 2 digit year, there are years in the future for a date of birth. This is not how it should be, so we will leave it alone.
birthday <- Data$Birth.Date
birthday1 <- format(as.Date(birthday, "%m/%d/%Y"), "%m/%d/%y")
birthday2 <- as.Date(birthday1, "%m/%d/%y")
futureDOBerror <- data.frame(DOB = birthday2[1:50], age=Data$ageAtSubmission[1:50])
y2k <- subset(futureDOBerror, age>55)
y2k## DOB age
## 3 2057-10-28 67
## 4 2057-10-28 67
## 8 2061-11-04 64
## 13 1969-01-23 56
## 44 2064-03-08 56
## 46 2064-03-08 56
## 49 2063-06-16 62
## 50 2048-02-17 72You can see what will happen to the DOB of those older than 55 years of age from output above.
Lets NOT try manipulating the year so that if it is in the future beyond 2026, then we convert the year to replace the ‘20’ with ‘19’. We haven’t changed it in the data base yet, and there is already an Excel calculated field for age of client at time of service. But lets see all date field summary statistics.
summary(Data[,c(DateColumnsToChange)])## dateOfService dateAgreedSigned DateLastServiceReturned
## Min. :2019-05-20 Min. :2019-05-19 Min. :2019-05-20
## 1st Qu.:2021-03-16 1st Qu.:2020-11-22 1st Qu.:2021-03-24
## Median :2021-08-08 Median :2021-03-08 Median :2021-07-20
## Mean :2022-04-26 Mean :2021-11-30 Mean :2022-01-26
## 3rd Qu.:2023-01-07 3rd Qu.:2021-12-19 3rd Qu.:2022-05-30
## Max. :2026-01-07 Max. :2025-12-29 Max. :2025-12-29
## NA's :2 NA's :150
## todaysDate consentFormSubmissionDate Birth.Date
## Min. :2026-01-08 Min. :2019-05-19 Length:531
## 1st Qu.:2026-01-08 1st Qu.:2020-11-22 Class :character
## Median :2026-01-08 Median :2021-03-08 Mode :character
## Mean :2026-01-08 Mean :2021-11-29
## 3rd Qu.:2026-01-08 3rd Qu.:2021-11-24
## Max. :2026-01-08 Max. :2025-12-29
## The Birth.Date field is character format because of the as.Date function error in giving future birth dates if the age is greater than 55 or if the double digit is greater than 1969 of January.
We will leave the date fields as they are as still more data manipulation to do before running some time series and other analytics in the exploratory data analysis or EDA of this project. To see who we can target for most income on returns, favorability, idea client, good price, gratuity, and local or willing to pay the extra drive cost beyond 30 minutes.
Lets see if our data keeps the date fields when we write it to csv and read it in for summary statistics.
write.csv(Data, 'datesdone.csv', row.names=F)Data1 <- read.csv('datesdone.csv', header=T, sep=',', na.strings=c('',' ','na','NA'))
summary(Data1)## SalesReceiptNumber dateOfService StartTime EndTime
## Min. :100.0 Length:531 Length:531 Length:531
## 1st Qu.:209.5 Class :character Class :character Class :character
## Median :307.0 Mode :character Mode :character Mode :character
## Mean :301.9
## 3rd Qu.:395.0
## Max. :493.0
##
## newReturn returned referred
## Length:531 Length:531 Length:531
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## DateLastServiceReturned daysPassedBetweenServices weeksPassedBetweenServices
## Length:531 Min. : 0.00 Min. : 0.000
## Class :character 1st Qu.: 0.00 1st Qu.: 0.000
## Mode :character Median : 7.00 Median : 1.000
## Mean : 54.81 Mean : 7.812
## 3rd Qu.: 28.00 3rd Qu.: 4.000
## Max. :1437.00 Max. :205.000
##
## todaysDate daysSinceTodayAndDateOfThisService
## Length:531 Min. : 1
## Class :character 1st Qu.:1097
## Mode :character Median :1614
## Mean :1353
## 3rd Qu.:1758
## Max. :2425
##
## weeksSinceTodayAndDateOfThisService likeToSeeAgain City
## Min. : 0.0 Length:531 Length:531
## 1st Qu.:157.0 Class :character Class :character
## Median :231.0 Mode :character Mode :character
## Mean :193.2
## 3rd Qu.:251.0
## Max. :346.0
##
## ZipCode MassagePackage ServiceCost
## Min. :91701 Length:531 Length:531
## 1st Qu.:92503 Class :character Class :character
## Median :92879 Mode :character Mode :character
## Mean :92625
## 3rd Qu.:92883
## Max. :92887
##
## driveTimeBeyond30minBothWays Discount TypeOfDiscount
## Min. :10.00 Min. : 5.00 Length:531
## 1st Qu.:15.00 1st Qu.:10.00 Class :character
## Median :20.00 Median :15.00 Mode :character
## Mean :23.33 Mean :21.44
## 3rd Qu.:30.00 3rd Qu.:30.00
## Max. :40.00 Max. :80.00
## NA's :528 NA's :228
## AmountPaid_NotCreditUsed CreditPaid_monthlyOrPkg AmountPaid_As_Income
## Min. :-390.00 Min. :-225.00 Min. :-315.00
## 1st Qu.: 60.00 1st Qu.: 50.00 1st Qu.: 60.00
## Median : 80.00 Median : 50.00 Median : 73.33
## Mean : 84.81 Mean : 82.04 Mean : 74.66
## 3rd Qu.: 95.00 3rd Qu.: 70.00 3rd Qu.: 90.00
## Max. : 450.00 Max. : 450.00 Max. : 200.00
## NA's :102 NA's :450 NA's :11
## clientRemainingCreditBalance Gratuity FSA_HSA SpaFinderGiftCard
## Min. : 1.0 Min. : 2.00 Min. : 80.0 Min. : 25.00
## 1st Qu.:100.0 1st Qu.:10.00 1st Qu.: 97.5 1st Qu.: 50.00
## Median :150.0 Median :15.00 Median :130.0 Median : 55.00
## Mean :170.2 Mean :16.01 Mean :122.2 Mean : 57.96
## 3rd Qu.:225.0 3rd Qu.:20.00 3rd Qu.:145.0 3rd Qu.: 67.00
## Max. :405.0 Max. :65.00 Max. :145.0 Max. :100.00
## NA's :423 NA's :122 NA's :520 NA's :508
## otherGiftCard creditCard cash zelle
## Min. :10.0 Min. :-390.00 Min. : 10.0 Min. : 20.00
## 1st Qu.:27.5 1st Qu.: 60.00 1st Qu.: 10.0 1st Qu.: 58.75
## Median :45.0 Median : 80.00 Median : 20.0 Median : 70.00
## Mean :45.0 Mean : 77.68 Mean : 35.5 Mean : 71.70
## 3rd Qu.:62.5 3rd Qu.: 90.00 3rd Qu.: 50.0 3rd Qu.: 80.00
## Max. :80.0 Max. : 450.00 Max. :160.0 Max. :120.00
## NA's :529 NA's :263 NA's :360 NA's :446
## check TypePayment consentFormSubmissionDate
## Min. : 10.0 Length:531 Length:531
## 1st Qu.:200.0 Class :character Class :character
## Median :200.0 Mode :character Mode :character
## Mean :179.1
## 3rd Qu.:200.0
## Max. :405.0
## NA's :509
## consentForm_formType manualInputMissingInDataBaseSourceOrPrinted
## Length:531 Length:531
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## receivedMassageFromMe Birth.Date ageAtSubmission termsAgreedTo
## Length:531 Length:531 Min. :19.00 Length:531
## Class :character Class :character 1st Qu.:32.00 Class :character
## Mode :character Mode :character Median :38.00 Mode :character
## Mean :42.18
## 3rd Qu.:50.00
## Max. :78.00
## NA's :1
## termsSignature dateAgreedSigned femaleOrMaleTherapistPreference
## Length:531 Length:531 Length:531
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## lotionOrOilPreference describeWhyMobileMassagePreferred
## Length:531 Length:531
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## painScale1least_10worst healthConditionsPrecautions sleep1poor_10BestEver
## Min. : 1.000 Length:531 Min. : 1.0
## 1st Qu.: 3.000 Class :character 1st Qu.: 6.0
## Median : 6.000 Mode :character Median : 7.0
## Mean : 4.829 Mean : 6.7
## 3rd Qu.: 6.000 3rd Qu.: 8.0
## Max. :10.000 Max. :10.0
## NA's :70 NA's :15
## stress1none_10extremely Pressure massageModalitiesPreferred
## Min. : 1.000 Length:531 Length:531
## 1st Qu.: 3.000 Class :character Class :character
## Median : 5.000 Mode :character Mode :character
## Mean : 5.218
## 3rd Qu.: 7.750
## Max. :10.000
## NA's :13
## areasToAvoid massageStyleToolsPreferred massageGoals
## Length:531 Length:531 Length:531
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## additonalServices howHeardAboutCompany willingToRecommendCompany
## Length:531 Length:531 Length:531
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## OKtoContactEmailTextPhone BioPsychoSocialModelOfPain_massageExpectations
## Length:531 Length:531
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## lastMassageWhen lastMassageComplaints lastMassageReceivedWhere
## Length:531 Length:531 Length:531
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## minorNameIfParentPresentWholeTimeForMinorMassage
## Mode:logical
## NA's:531
##
##
##
##
##
## whyChildMassageAndHasChildEverHadMassageBefore minorDOBifForMinor
## Mode:logical Length:531
## NA's:531 Class :character
## Mode :character
##
##
##
##
## massagePraisesWhatYouLikeAboutMassageBenefitsYou
## Length:531
## Class :character
## Mode :character
##
##
##
##
## talkingDuringMassageOK_isYes_or_Not_isNo
## Length:531
## Class :character
## Mode :character
##
##
##
##
## pressureHealthPrecautionsDescribedNowWhatPressurePreferred
## Length:531
## Class :character
## Mode :character
##
##
##
##
## genderPreferredWarningPrecautionOnMalesFemalesRespect
## Length:531
## Class :character
## Mode :character
##
##
##
##
## whySelectThisMassageCompany yourGender
## Length:531 Length:531
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## bundledMassagePackageSavingsInterestedIn
## Length:531
## Class :character
## Mode :character
##
##
##
## Bummer! It doesn’t store the date formatted features to be read back in as date objects. At least we still have the Data in Rstudio environment of objects. Remove the Data1 and other objects not using again.
rm(Data1,df,error1,futureDOBerror,maxDOB, birthday, birthday1, y2k, birthday2,DateColumns, DateColumnsToChange,dates,Dates)## Warning in rm(Data1, df, error1, futureDOBerror, maxDOB, birthday, birthday1, :
## object 'df' not found## Warning in rm(Data1, df, error1, futureDOBerror, maxDOB, birthday, birthday1, :
## object 'maxDOB' not foundls()## [1] "Data"str(Data)## 'data.frame': 531 obs. of 73 variables:
## $ SalesReceiptNumber : int 150 196 482 469 181 179 430 482 121 264 ...
## $ dateOfService : Date, format: "2020-11-07" "2021-01-31" ...
## $ StartTime : chr "8:30 PM" "6:00 PM" "10:00 AM" "7:00 PM" ...
## $ EndTime : chr "9:30 PM" "7:30 PM" "11:00 AM" "8:00 PM" ...
## $ newReturn : chr "new" "new" "return" "new" ...
## $ returned : chr "no" "no" "no" "yes" ...
## $ referred : chr "referred" "referred" NA "referred" ...
## $ DateLastServiceReturned : Date, format: NA NA ...
## $ daysPassedBetweenServices : int 0 0 149 0 1 0 0 0 0 70 ...
## $ weeksPassedBetweenServices : int 0 0 21 0 0 0 0 0 0 10 ...
## $ todaysDate : Date, format: "2026-01-08" "2026-01-08" ...
## $ daysSinceTodayAndDateOfThisService : int 1888 1803 61 210 1867 1868 379 61 2036 1671 ...
## $ weeksSinceTodayAndDateOfThisService : int 270 258 9 30 267 267 54 9 291 239 ...
## $ likeToSeeAgain : chr "yes" "yes" "yes" "yes" ...
## $ City : chr "Corona" "Corona" "Corona" "Corona" ...
## $ ZipCode : int 92883 92882 92882 92882 92752 92752 92883 92882 92807 92881 ...
## $ MassagePackage : chr "One and one half hour Customized Massage + 1/2 hour of wife's changed 90 minute massage given to daughter" "One and one half hour Customized Massage" "1 hour swedish regular price" "1 hour swedish regular price" ...
## $ ServiceCost : chr "60.00" "80.00" "80.00" "80.00" ...
## $ driveTimeBeyond30minBothWays : int NA NA NA NA NA NA NA NA NA NA ...
## $ Discount : num NA NA NA NA 30 15 40 NA NA 20 ...
## $ TypeOfDiscount : chr NA NA "regular 1 hour swedish price" "regular 1 hour swedish price" ...
## $ AmountPaid_NotCreditUsed : num 60 100 80 80 30 45 140 80 69 50 ...
## $ CreditPaid_monthlyOrPkg : int NA NA NA NA NA NA NA NA NA NA ...
## $ AmountPaid_As_Income : num 60 100 80 80 30 45 140 80 69 50 ...
## $ clientRemainingCreditBalance : int NA NA NA NA NA NA NA NA NA NA ...
## $ Gratuity : num NA 20 NA NA NA NA 20 NA 9 10 ...
## $ FSA_HSA : num NA NA NA NA NA NA NA NA NA NA ...
## $ SpaFinderGiftCard : int NA NA NA NA NA NA NA NA NA NA ...
## $ otherGiftCard : int NA NA NA NA NA NA NA NA NA NA ...
## $ creditCard : num 60 NA NA 80 NA NA 140 NA 69 NA ...
## $ cash : int NA 100 NA NA NA NA NA NA NA NA ...
## $ zelle : num NA NA 80 NA 30 45 NA 80 NA 50 ...
## $ check : num NA NA NA NA NA NA NA NA NA 10 ...
## $ TypePayment : chr "credit card" "cash" "zelle 80 exactly with brother additiol 80 same receipt" "credit card paid for by daughter after her 90 minute, return client for daughter same household" ...
## $ consentFormSubmissionDate : Date, format: "2020-11-07" "2021-01-31" ...
## $ consentForm_formType : chr "oldForm" "oldForm" "newForm" "newForm" ...
## $ manualInputMissingInDataBaseSourceOrPrinted : chr "no" "no" "no" "no" ...
## $ receivedMassageFromMe : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Birth.Date : chr "4/2/1997" "1/28/1988" "10/28/1957" "10/28/1957" ...
## $ ageAtSubmission : int 23 33 67 67 50 50 36 64 28 26 ...
## $ termsAgreedTo : chr "Yes" "Yes" "Yes" "Yes" ...
## $ termsSignature : chr "https://www.jotform.com/uploads/janiscorona/91274881154158/4806106762411562587/4806106762411562587_signature_86.png" "https://www.jotform.com/uploads/janiscorona/91274881154158/4879535172718857846/4879535172718857846_signature_86.png" "https://www.jotform.com/uploads/janiscorona/211143470705143/6255850840271681099/6255850840271681099_signature_86.png" "https://www.jotform.com/uploads/janiscorona/211143470705143/6255850840271681099/6255850840271681099_signature_86.png" ...
## $ dateAgreedSigned : Date, format: "2020-11-07" "2021-01-31" ...
## $ femaleOrMaleTherapistPreference : chr "No preference" "No preference" "female massage therapist" "female massage therapist" ...
## $ lotionOrOilPreference : chr "lotion or oil, no preference" "lotion or oil, no preference" "oil" "oil" ...
## $ describeWhyMobileMassagePreferred : chr NA "Mid back pain" "Back, neck" "Back, neck" ...
## $ painScale1least_10worst : int 10 2 7 7 4 4 4 NA 2 2 ...
## $ healthConditionsPrecautions : chr NA NA NA NA ...
## $ sleep1poor_10BestEver : int 10 10 4 4 5 5 2 8 7 6 ...
## $ stress1none_10extremely : int 10 3 7 7 5 5 9 2 8 6 ...
## $ Pressure : chr "deep tissue (deep layer muscles)" NA NA NA ...
## $ massageModalitiesPreferred : chr "Full spectrum CBD (.oo3 THC) TOPICAL (must be 18 or older)" NA NA NA ...
## $ areasToAvoid : chr "feet" NA NA NA ...
## $ massageStyleToolsPreferred : chr NA NA NA NA ...
## $ massageGoals : chr NA NA NA NA ...
## $ additonalServices : chr NA NA NA NA ...
## $ howHeardAboutCompany : chr NA NA NA NA ...
## $ willingToRecommendCompany : chr NA NA NA NA ...
## $ OKtoContactEmailTextPhone : chr NA NA NA NA ...
## $ BioPsychoSocialModelOfPain_massageExpectations : chr NA NA "I recently had some MAJOR LIFE CHANGES that have stressed me out into changing my diet, sleep schedule, number "| __truncated__ "I recently had some MAJOR LIFE CHANGES that have stressed me out into changing my diet, sleep schedule, number "| __truncated__ ...
## $ lastMassageWhen : chr NA NA "About two months ago" "About two months ago" ...
## $ lastMassageComplaints : chr NA NA "Nothing" "Nothing" ...
## $ lastMassageReceivedWhere : chr NA NA "Depends" "Depends" ...
## $ minorNameIfParentPresentWholeTimeForMinorMassage : logi NA NA NA NA NA NA ...
## $ whyChildMassageAndHasChildEverHadMassageBefore : logi NA NA NA NA NA NA ...
## $ minorDOBifForMinor : chr NA NA "Invalid date" "Invalid date" ...
## $ massagePraisesWhatYouLikeAboutMassageBenefitsYou : chr NA NA "Relief of muscle aches" "Relief of muscle aches" ...
## $ talkingDuringMassageOK_isYes_or_Not_isNo : chr NA NA "yes, but only about answering questions I ask about the technique, training, benefits, or other questions I may"| __truncated__ "yes, but only about answering questions I ask about the technique, training, benefits, or other questions I may"| __truncated__ ...
## $ pressureHealthPrecautionsDescribedNowWhatPressurePreferred: chr NA NA "medium pressure (deeper pressure than light pressure, but able to get into the muscle belly of the top layers o"| __truncated__ "medium pressure (deeper pressure than light pressure, but able to get into the muscle belly of the top layers o"| __truncated__ ...
## $ genderPreferredWarningPrecautionOnMalesFemalesRespect : chr NA NA "male" "male" ...
## $ whySelectThisMassageCompany : chr NA NA NA NA ...
## $ yourGender : chr NA NA "male" "male" ...
## $ bundledMassagePackageSavingsInterestedIn : chr NA NA NA NA ...colnames(Data)## [1] "SalesReceiptNumber"
## [2] "dateOfService"
## [3] "StartTime"
## [4] "EndTime"
## [5] "newReturn"
## [6] "returned"
## [7] "referred"
## [8] "DateLastServiceReturned"
## [9] "daysPassedBetweenServices"
## [10] "weeksPassedBetweenServices"
## [11] "todaysDate"
## [12] "daysSinceTodayAndDateOfThisService"
## [13] "weeksSinceTodayAndDateOfThisService"
## [14] "likeToSeeAgain"
## [15] "City"
## [16] "ZipCode"
## [17] "MassagePackage"
## [18] "ServiceCost"
## [19] "driveTimeBeyond30minBothWays"
## [20] "Discount"
## [21] "TypeOfDiscount"
## [22] "AmountPaid_NotCreditUsed"
## [23] "CreditPaid_monthlyOrPkg"
## [24] "AmountPaid_As_Income"
## [25] "clientRemainingCreditBalance"
## [26] "Gratuity"
## [27] "FSA_HSA"
## [28] "SpaFinderGiftCard"
## [29] "otherGiftCard"
## [30] "creditCard"
## [31] "cash"
## [32] "zelle"
## [33] "check"
## [34] "TypePayment"
## [35] "consentFormSubmissionDate"
## [36] "consentForm_formType"
## [37] "manualInputMissingInDataBaseSourceOrPrinted"
## [38] "receivedMassageFromMe"
## [39] "Birth.Date"
## [40] "ageAtSubmission"
## [41] "termsAgreedTo"
## [42] "termsSignature"
## [43] "dateAgreedSigned"
## [44] "femaleOrMaleTherapistPreference"
## [45] "lotionOrOilPreference"
## [46] "describeWhyMobileMassagePreferred"
## [47] "painScale1least_10worst"
## [48] "healthConditionsPrecautions"
## [49] "sleep1poor_10BestEver"
## [50] "stress1none_10extremely"
## [51] "Pressure"
## [52] "massageModalitiesPreferred"
## [53] "areasToAvoid"
## [54] "massageStyleToolsPreferred"
## [55] "massageGoals"
## [56] "additonalServices"
## [57] "howHeardAboutCompany"
## [58] "willingToRecommendCompany"
## [59] "OKtoContactEmailTextPhone"
## [60] "BioPsychoSocialModelOfPain_massageExpectations"
## [61] "lastMassageWhen"
## [62] "lastMassageComplaints"
## [63] "lastMassageReceivedWhere"
## [64] "minorNameIfParentPresentWholeTimeForMinorMassage"
## [65] "whyChildMassageAndHasChildEverHadMassageBefore"
## [66] "minorDOBifForMinor"
## [67] "massagePraisesWhatYouLikeAboutMassageBenefitsYou"
## [68] "talkingDuringMassageOK_isYes_or_Not_isNo"
## [69] "pressureHealthPrecautionsDescribedNowWhatPressurePreferred"
## [70] "genderPreferredWarningPrecautionOnMalesFemalesRespect"
## [71] "whySelectThisMassageCompany"
## [72] "yourGender"
## [73] "bundledMassagePackageSavingsInterestedIn"Lets remove the client remaining balance feature because it will throw off timeseries as sparse data input with each service so input changed and this field left blank just calculating off type of payment or retrieving in private data the person and notes there. And also the minor fields because only a few inquired about the minor and those questions were as part of the consent form to give consent to massage their minor as long as they are there and present entire time. Nobody actually had a minor massaged except for the earlier days in the family package that was offered when traveling distance was ok up to an hour. They actually had separate forms for those in the family and only the matriarch was counted and total was for package total that the form was altered later to individual family members and splitting cost and gratuity given in equal parts per member of the couple, family, or group. That is why few decimals but when splitting the cost and gratuity this is why becuase nobody actually tips that way.
Data <- Data[,-c(25,64:66)]
colnames(Data)## [1] "SalesReceiptNumber"
## [2] "dateOfService"
## [3] "StartTime"
## [4] "EndTime"
## [5] "newReturn"
## [6] "returned"
## [7] "referred"
## [8] "DateLastServiceReturned"
## [9] "daysPassedBetweenServices"
## [10] "weeksPassedBetweenServices"
## [11] "todaysDate"
## [12] "daysSinceTodayAndDateOfThisService"
## [13] "weeksSinceTodayAndDateOfThisService"
## [14] "likeToSeeAgain"
## [15] "City"
## [16] "ZipCode"
## [17] "MassagePackage"
## [18] "ServiceCost"
## [19] "driveTimeBeyond30minBothWays"
## [20] "Discount"
## [21] "TypeOfDiscount"
## [22] "AmountPaid_NotCreditUsed"
## [23] "CreditPaid_monthlyOrPkg"
## [24] "AmountPaid_As_Income"
## [25] "Gratuity"
## [26] "FSA_HSA"
## [27] "SpaFinderGiftCard"
## [28] "otherGiftCard"
## [29] "creditCard"
## [30] "cash"
## [31] "zelle"
## [32] "check"
## [33] "TypePayment"
## [34] "consentFormSubmissionDate"
## [35] "consentForm_formType"
## [36] "manualInputMissingInDataBaseSourceOrPrinted"
## [37] "receivedMassageFromMe"
## [38] "Birth.Date"
## [39] "ageAtSubmission"
## [40] "termsAgreedTo"
## [41] "termsSignature"
## [42] "dateAgreedSigned"
## [43] "femaleOrMaleTherapistPreference"
## [44] "lotionOrOilPreference"
## [45] "describeWhyMobileMassagePreferred"
## [46] "painScale1least_10worst"
## [47] "healthConditionsPrecautions"
## [48] "sleep1poor_10BestEver"
## [49] "stress1none_10extremely"
## [50] "Pressure"
## [51] "massageModalitiesPreferred"
## [52] "areasToAvoid"
## [53] "massageStyleToolsPreferred"
## [54] "massageGoals"
## [55] "additonalServices"
## [56] "howHeardAboutCompany"
## [57] "willingToRecommendCompany"
## [58] "OKtoContactEmailTextPhone"
## [59] "BioPsychoSocialModelOfPain_massageExpectations"
## [60] "lastMassageWhen"
## [61] "lastMassageComplaints"
## [62] "lastMassageReceivedWhere"
## [63] "massagePraisesWhatYouLikeAboutMassageBenefitsYou"
## [64] "talkingDuringMassageOK_isYes_or_Not_isNo"
## [65] "pressureHealthPrecautionsDescribedNowWhatPressurePreferred"
## [66] "genderPreferredWarningPrecautionOnMalesFemalesRespect"
## [67] "whySelectThisMassageCompany"
## [68] "yourGender"
## [69] "bundledMassagePackageSavingsInterestedIn"Lets get a summary of those who filled out the optional summary questions on their consent forms. After we change them all to factors.
Data$newReturn <- as.factor(Data$newReturn)
Data$returned <- as.factor(Data$returned )
Data$referred <- as.factor(Data$referred)
Data$likeToSeeAgain <- as.factor(Data$likeToSeeAgain)
Data$City <- as.factor(Data$City)
Data$ZipCode <- as.factor(Data$ZipCode)
Data$MassagePackage <- as.factor(Data$MassagePackage)
Data$TypeOfDiscount <- as.factor(Data$TypeOfDiscount)
Data$consentForm_formType <- as.factor(Data$consentForm_formType)
Data$manualInputMissingInDataBaseSourceOrPrinted <- as.factor(Data$manualInputMissingInDataBaseSourceOrPrinted)
Data$receivedMassageFromMe <- as.factor(Data$receivedMassageFromMe)
Data$femaleOrMaleTherapistPreference <- as.factor(Data$femaleOrMaleTherapistPreference)
Data$lotionOrOilPreference <- as.factor(Data$lotionOrOilPreference)
Data$describeWhyMobileMassagePreferred <- as.factor(Data$describeWhyMobileMassagePreferred)
Data$painScale1least_10worst <- as.factor(Data$painScale1least_10worst)
Data$healthConditionsPrecautions <- as.factor(Data$healthConditionsPrecautions)
Data$sleep1poor_10BestEver <- as.factor(Data$sleep1poor_10BestEver)
Data$stress1none_10extremely <- as.factor(Data$stress1none_10extremely)
Data$Pressure <- as.factor(Data$Pressure)
Data$massageModalitiesPreferred <- as.factor(Data$massageModalitiesPreferred)
Data$areasToAvoid <- as.factor(Data$areasToAvoid)
Data$massageStyleToolsPreferred <- as.factor(Data$massageStyleToolsPreferred)
Data$massageGoals <- as.factor(Data$massageGoals)
Data$additonalServices <- as.factor(Data$additonalServices)
Data$howHeardAboutCompany <- as.factor(Data$howHeardAboutCompany)
Data$willingToRecommendCompany <- as.factor(Data$willingToRecommendCompany)
Data$OKtoContactEmailTextPhone <- as.factor(Data$OKtoContactEmailTextPhone)
Data$BioPsychoSocialModelOfPain_massageExpectations <- as.factor(Data$BioPsychoSocialModelOfPain_massageExpectations)
Data$lastMassageWhen <- as.factor(Data$lastMassageWhen)
Data$lastMassageComplaints <- as.factor(Data$lastMassageComplaints)
Data$lastMassageReceivedWhere <- as.factor(Data$lastMassageReceivedWhere)
Data$massagePraisesWhatYouLikeAboutMassageBenefitsYou <- as.factor(Data$massagePraisesWhatYouLikeAboutMassageBenefitsYou)
Data$talkingDuringMassageOK_isYes_or_Not_isNo <- as.factor(Data$talkingDuringMassageOK_isYes_or_Not_isNo)
Data$pressureHealthPrecautionsDescribedNowWhatPressurePreferred <- as.factor(Data$pressureHealthPrecautionsDescribedNowWhatPressurePreferred)
Data$genderPreferredWarningPrecautionOnMalesFemalesRespect <- as.factor(Data$genderPreferredWarningPrecautionOnMalesFemalesRespect)
Data$whySelectThisMassageCompany <- as.factor(Data$whySelectThisMassageCompany)
Data$yourGender <- as.factor(Data$yourGender)
Data$bundledMassagePackageSavingsInterestedIn <- as.factor(Data$bundledMassagePackageSavingsInterestedIn) Since the receipt included multiple people if in a couple or family or group, lets make that a factor as well so that the receipts can be used by their household total for that date.
Data$SalesReceiptNumber <- as.factor(Data$SalesReceiptNumber)We should see factor summary stats now, so lets do a summary of these features.
summary(Data)## SalesReceiptNumber dateOfService StartTime EndTime
## 142 : 4 Min. :2019-05-20 Length:531 Length:531
## 218 : 4 1st Qu.:2021-03-16 Class :character Class :character
## 308 : 4 Median :2021-08-08 Mode :character Mode :character
## 344 : 4 Mean :2022-04-26
## 345 : 4 3rd Qu.:2023-01-07
## 348 : 4 Max. :2026-01-07
## (Other):507
## newReturn returned referred DateLastServiceReturned
## new :150 no :147 no : 17 Min. :2019-05-20
## return:381 yes:384 referred: 57 1st Qu.:2021-03-24
## yes : 1 Median :2021-07-20
## NA's :456 Mean :2022-01-26
## 3rd Qu.:2022-05-30
## Max. :2025-12-29
## NA's :150
## daysPassedBetweenServices weeksPassedBetweenServices todaysDate
## Min. : 0.00 Min. : 0.000 Min. :2026-01-08
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:2026-01-08
## Median : 7.00 Median : 1.000 Median :2026-01-08
## Mean : 54.81 Mean : 7.812 Mean :2026-01-08
## 3rd Qu.: 28.00 3rd Qu.: 4.000 3rd Qu.:2026-01-08
## Max. :1437.00 Max. :205.000 Max. :2026-01-08
##
## daysSinceTodayAndDateOfThisService weeksSinceTodayAndDateOfThisService
## Min. : 1 Min. : 0.0
## 1st Qu.:1097 1st Qu.:157.0
## Median :1614 Median :231.0
## Mean :1353 Mean :193.2
## 3rd Qu.:1758 3rd Qu.:251.0
## Max. :2425 Max. :346.0
##
## likeToSeeAgain City ZipCode
## no : 15 Corona :205 92883 :109
## yes:516 Norco : 72 92860 : 72
## Yorba Linda: 55 92887 : 55
## Riverside : 50 92503 : 40
## Eastvale : 22 91752 : 37
## Chino Hills: 20 92882 : 35
## (Other) :107 (Other):183
## MassagePackage
## One Hour Persolized Massage :155
## One and one half hour Splitting an hour credit in half of missed weekly massage: 46
## One and one half hour Customized Massage : 32
## One Hour Couples Massage : 27
## One hour MLD 10 pkg $45 each : 24
## (Other) :244
## NA's : 3
## ServiceCost driveTimeBeyond30minBothWays Discount
## Length:531 Min. :10.00 Min. : 5.00
## Class :character 1st Qu.:15.00 1st Qu.:10.00
## Mode :character Median :20.00 Median :15.00
## Mean :23.33 Mean :21.44
## 3rd Qu.:30.00 3rd Qu.:30.00
## Max. :40.00 Max. :80.00
## NA's :528 NA's :228
## TypeOfDiscount
## manual lymphathic draige (MLD) Oct/Nov special : 43
## monthly membership paid in June/weekly $50 rate : 39
## n/a : 20
## MLD 10 prepaid : 19
## monthly membership paid for November 90 minute weekly this month only: 19
## (Other) :209
## NA's :182
## AmountPaid_NotCreditUsed CreditPaid_monthlyOrPkg AmountPaid_As_Income
## Min. :-390.00 Min. :-225.00 Min. :-315.00
## 1st Qu.: 60.00 1st Qu.: 50.00 1st Qu.: 60.00
## Median : 80.00 Median : 50.00 Median : 73.33
## Mean : 84.81 Mean : 82.04 Mean : 74.66
## 3rd Qu.: 95.00 3rd Qu.: 70.00 3rd Qu.: 90.00
## Max. : 450.00 Max. : 450.00 Max. : 200.00
## NA's :102 NA's :450 NA's :11
## Gratuity FSA_HSA SpaFinderGiftCard otherGiftCard
## Min. : 2.00 Min. : 80.0 Min. : 25.00 Min. :10.0
## 1st Qu.:10.00 1st Qu.: 97.5 1st Qu.: 50.00 1st Qu.:27.5
## Median :15.00 Median :130.0 Median : 55.00 Median :45.0
## Mean :16.01 Mean :122.2 Mean : 57.96 Mean :45.0
## 3rd Qu.:20.00 3rd Qu.:145.0 3rd Qu.: 67.00 3rd Qu.:62.5
## Max. :65.00 Max. :145.0 Max. :100.00 Max. :80.0
## NA's :122 NA's :520 NA's :508 NA's :529
## creditCard cash zelle check
## Min. :-390.00 Min. : 10.0 Min. : 20.00 Min. : 10.0
## 1st Qu.: 60.00 1st Qu.: 10.0 1st Qu.: 58.75 1st Qu.:200.0
## Median : 80.00 Median : 20.0 Median : 70.00 Median :200.0
## Mean : 77.68 Mean : 35.5 Mean : 71.70 Mean :179.1
## 3rd Qu.: 90.00 3rd Qu.: 50.0 3rd Qu.: 80.00 3rd Qu.:200.0
## Max. : 450.00 Max. :160.0 Max. :120.00 Max. :405.0
## NA's :263 NA's :360 NA's :446 NA's :509
## TypePayment consentFormSubmissionDate
## Length:531 Min. :2019-05-19
## Class :character 1st Qu.:2020-11-22
## Mode :character Median :2021-03-08
## Mean :2021-11-29
## 3rd Qu.:2021-11-24
## Max. :2025-12-29
##
## consentForm_formType
## group event form : 1
## handwritten printed old form: 3
## health history wellness form: 1
## HealthIntake : 3
## newForm :181
## oldForm :342
##
## manualInputMissingInDataBaseSourceOrPrinted receivedMassageFromMe
## no :515 Yes:531
## yes: 16
##
##
##
##
##
## Birth.Date ageAtSubmission termsAgreedTo termsSignature
## Length:531 Min. :19.00 Length:531 Length:531
## Class :character 1st Qu.:32.00 Class :character Class :character
## Mode :character Median :38.00 Mode :character Mode :character
## Mean :42.18
## 3rd Qu.:50.00
## Max. :78.00
## NA's :1
## dateAgreedSigned femaleOrMaleTherapistPreference
## Min. :2019-05-19 female massage therapist:393
## 1st Qu.:2020-11-22 male massage therapist : 12
## Median :2021-03-08 No preference : 92
## Mean :2021-11-30 NA's : 34
## 3rd Qu.:2021-12-19
## Max. :2025-12-29
## NA's :2
## lotionOrOilPreference
## lotion : 5
## lotion or oil, no preference:342
## oil :150
## NA's : 34
##
##
##
## describeWhyMobileMassagePreferred
## deep tissue all over arthritis pain chronic : 71
## Back is ticklish but I think with deep slow pressure it's ok. \n\nPark in carport Unit E. Sometimes iphones take you to Unit C. I'm in Unit E. Park next to the garage in the carport. The gate will be open to the backyard to go inside the unit.: 30
## I'm interested in a lymphatic massage and maybe a deep tissue. : 30
## Lower back pain.\nLower back and shoulders W/ medium pressure. Swedish. please avoid shins : 24
## Lymphatic drainage massage : 22
## (Other) :291
## NA's : 63
## painScale1least_10worst
## 6 :135
## 1 : 76
## 7 : 57
## 4 : 47
## 5 : 42
## (Other):104
## NA's : 70
## healthConditionsPrecautions
## High or Low Blood Pressure\nknee replacement : 71
## Bruise easily\nCold hands and feet : 30
## Skin Abrasions : 20
## High or Low Blood Pressure : 17
## Recent Surgeries (limbs, neck, low back, shoulders, etc.): 13
## (Other) :142
## NA's :238
## sleep1poor_10BestEver stress1none_10extremely
## 6 :135 5 :127
## 8 :117 1 : 82
## 7 : 79 8 : 75
## 5 : 66 6 : 62
## 10 : 49 3 : 39
## (Other): 70 (Other):133
## NA's : 15 NA's : 13
## Pressure
## medium (just reaching middle level muscles, not too painful) : 53
## firm (broader pressure) : 35
## deep tissue (deep layer muscles) : 14
## deep tissue (deep layer muscles)\nsuper deep tissue (very dense muscle layers need more pressure): 13
## Light (only superficial muscles reached) : 9
## (Other) : 37
## NA's :370
## massageModalitiesPreferred
## Variable Speed Sports Massage Tool (good for precise tendon and muscle adhesion pressure and percussion, or as the larger, flat, broad application with longer movements towards the heart for lymphatic drainage)\nSports (stretching involved, can vary in speed of massage from slow and elongated strokes of fibers in muscle or fast to stimulate circulation and prepare body for events)\nStretches (not the entire session, but for region specific areas such as to increase range of motion around the hips, neck, knees, elbows, and ankles)\nLymphatic drainage (skin rolling and light rhythmic strokes to eliminate edema. CONTRAINDICATED CONDITIONS: liver disease, ascites, hypothyroidism untreated, diabetes untreated, CHF or other heart disease, kidney disease, other endocrine disease like pancreatitis, atherosclerosis, stroke, arterial dissection, deep vein thrombosis, peripheral embolism, contagious skin disorder/infection, Chronic Obstructive Pulmonary Disease COPD, emphysema, other pulmonary diseases, lymph node(s) removal a local contraindication to removal site, on open wounds)\nReflexology of hands and feet (this is relaxing and great for people who cannot withstand a moderate amount of pressure during massage, this is also good for calming the nervous system, but not recommended if you have feet that get uncomfortably ticklish): 30
## Swedish (speed and pressure of application vary and don't have to be fixed, such as deep tissue with strong application and slowly to the back AND limbs)\nAccupressure (deep tissue massage with thumbs or elbows along long muscles of body and the deeper muscle layers of body) : 25
## Lymphatic drainage (skin rolling and light rhythmic strokes to eliminate edema) : 17
## Swedish (speed and pressure of application vary and don't have to be fixed, such as deep tissue with strong application and slowly to the back AND limbs)\nLymphatic drainage (skin rolling and light rhythmic strokes to eliminate edema) : 10
## Accupressure (deep tissue massage with thumbs or elbows along long muscles of body and the deeper muscle layers of body) : 9
## (Other) : 71
## NA's :369
## areasToAvoid
## abdomen (digestion/lymph drainage) : 14
## hair\nface\nfeet\nglutial muscles\nquadriceps (front thighs)\nknees: 10
## face : 3
## feet : 2
## glutial muscles\npectoralis major (chest) : 2
## (Other) : 11
## NA's :489
## massageStyleToolsPreferred
## Open to trying anything that works : 18
## thumbs (common)\nknuckles (also common if MT has long nails)\nfists ( for gathering tissue layers and rolling out the adhesions in the layers)\npalms of hands (for firm, light, and medium pressure)\nelbows (for deeper pressure)\nforearms (for deeper medium pressure, but not deep pressure unless the sides of body like the Tendons of the legs)\nHot Stones\nChinese Cups: 13
## thumbs (common)\nknuckles (also common if MT has long nails)\nfists ( for gathering tissue layers and rolling out the adhesions in the layers)\npalms of hands (for firm, light, and medium pressure)\nelbows (for deeper pressure)\nforearms (for deeper medium pressure, but not deep pressure unless the sides of body like the Tendons of the legs)\nHot Stones : 12
## thumbs (common)\nknuckles (also common if MT has long nails)\nfists ( for gathering tissue layers and rolling out the adhesions in the layers)\npalms of hands (for firm, light, and medium pressure)\n : 10
## thumbs (common)\npalms of hands (for firm, light, and medium pressure)\nfeet (for accupressure of large muscle areas like upper back, thighs, hamstrings, and hips)\nHot Stones\nsound therapy : 9
## (Other) : 43
## NA's :426
## massageGoals
## relaxed state of mind\nless to no pain in areas of my body that ache\nless inflammation from stress : 28
## relaxed state of mind\nless fluid build up in areas of my body that have edema\nless inflammation from stress\nSleep better : 10
## relaxed state of mind\nless to no pain in areas of my body that ache\nless inflammation from stress\nless tension\nless anxiety: 9
## relaxed state of mind : 8
## less fluid build up in areas of my body that have edema\nSleep better : 7
## (Other) : 57
## NA's :412
## additonalServices
## aromatherapy\nhot stone massage : 11
## variable speed massage for lymph drainage\nDynamic cupping for lymph drainage, myofascial massage, and/or promoting immunity and recovery: 7
## variable speed massage for lymph drainage : 4
## aromatherapy : 2
## aromatherapy\nhot stone massage\nkinesiology taping with sports balm/gel for pain relief : 2
## (Other) : 7
## NA's :498
## howHeardAboutCompany willingToRecommendCompany
## Yelp : 55 Maybe: 24
## Internet: 29 yes : 2
## Family : 27 Yes : 82
## Friend : 6 NA's :423
## Google : 5
## (Other) : 8
## NA's :401
## OKtoContactEmailTextPhone
## text me promotional offers : 30
## email promotional deals : 26
## email promotional deals\ntext me promotional offers : 10
## send me my assessment and suggestions for next massage service and detailed receipt: 3
## email promotional deals\ncall me about promotional offers : 2
## (Other) : 6
## NA's :454
## BioPsychoSocialModelOfPain_massageExpectations
## My body is in the best shape of its life and I am only getting a massage to help my health. : 48
## I have had a recent ache that feels BETTER with stretching, massage, and exercising. : 27
## I have pain that is fine most times of the day, but when I am CONSTANTLY on my feet or sitting too long the old aches from a previous injury or surgery RETURN and make me want to sit down and rest. : 7
## I recently had some MAJOR LIFE CHANGES that have stressed me out into changing my diet, sleep schedule, number of hours of sleep, lost a loved one even a pet counts, had no time to grieve a loved one, changed jobs, added a new overwhelming task to my daily activities such as watching over kids or elderly parents. : 7
## I recently had some MAJOR LIFE CHANGES that have stressed me out into changing my diet, sleep schedule, number of hours of sleep, lost a loved one even a pet counts, had no time to grieve a loved one, changed jobs, added a new overwhelming task to my daily activities such as watching over kids or elderly parents.\nI have had a recent ache that feels BETTER with stretching, massage, and exercising.: 5
## (Other) : 13
## NA's :424
## lastMassageWhen
## 12/21/2021 : 30
## 2 months : 7
## 6 months ago : 7
## Haven’t had one since my surgery: 6
## 4-6 months ago : 5
## (Other) : 51
## NA's :425
## lastMassageComplaints
## Nothing. : 30
## No results : 6
## Didn’t work the muscles as much as I would like, didn’t stretch out the muscles/move my body while pressing on the muscles: 5
## worked out neck and shoulder pain : 4
## None : 3
## (Other) : 43
## NA's :440
## lastMassageReceivedWhere
## 23968 Old Pomegranate Road, Yorba Linda, CA: 30
## I don’t recall : 6
## Red Rouge : 5
## Spa in SF : 5
## you : 5
## (Other) : 50
## NA's :430
## massagePraisesWhatYouLikeAboutMassageBenefitsYou
## Relief of muscle tightness : 30
## Lipo recovery : 6
## all of the above : 5
## All of the above : 5
## Stretch out tight muscles in neck, upper back, and glutes Relief of pain in those areas: 5
## (Other) : 55
## NA's :425
## talkingDuringMassageOK_isYes_or_Not_isNo
## no : 9
## yes : 40
## yes, but only about answering questions I ask about the technique, training, benefits, or other questions I may have during my massage that wasn't addressed before the massage was scheduled.: 55
## NA's :427
##
##
##
## pressureHealthPrecautionsDescribedNowWhatPressurePreferred
## deep pressure (enough pressure to dig into the deep muscles of the back that uses less than firm broad application, applied slower than light, medium, or firm to dig through those top and middle layers of muscle or tight fascia.) : 27
## firm pressure (a little bit deeper than medium pressure but a broader surface and wants to spread the pressure among the middle layers of muscles and muscle fascia to pull apart muscle and myofascial adhesions) : 17
## good strength, deep pressure : 1
## light pressure (top surface for reaching the systemic circulation and lymphatic vessels, or the top layers of muscles in back, abdomen, feet, hands. : 8
## medium pressure (deeper pressure than light pressure, but able to get into the muscle belly of the top layers of muscles in the back, legs, arms, and glutes) : 48
## super deep pressure (this is if you have very tight muscle fascia and need someone to dig into your back, glutes, forearms, calves, or pectoralis muscles by the shoulder girdle. If you need deep pressure in the hands or feet, we recommend visiting the biopsychosocial section of this form as those areas that are in need of more pressure than deep have little muscle depth and could be due to allodynia or pain fiber crossing with nociceptive and proprioceptive fibers that aren't passing on the pressure or depth of pressure to thalamus. If you request more than deep pressure, more often you will not get super deep pressure. Massage therapists get labral tears from attempting to satisfy disrupted corticothalamic sensory to a client that has allodynia. We do not provide super deep pressure.): 3
## NA's :427
## genderPreferredWarningPrecautionOnMalesFemalesRespect
## female: 48
## male : 59
## NA's :424
##
##
##
##
## whySelectThisMassageCompany
## I am recovering from surgery and need a massage to improve healing outcomes and decrease healing time : 7
## This is a gift from a loved one : 5
## I am recovering from surgery and need a massage to improve healing outcomes and decrease healing time\nI am changing my lifestyle for the better and want to help my health by doing regular massage in combination with my diet, exercising, stretching, sleep, lowering stress, and other wellness additions to my health: 4
## I don't want to drive to and from a massage provider but am used to regular massages : 3
## I have had massages before and am too busy to visit or schedule in a massage at any of the local massage providers near me\nI don't want to drive to and from a massage provider but am used to regular massages : 2
## (Other) : 2
## NA's :508
## yourGender
## female: 88
## male : 35
## NA's :408
##
##
##
##
## bundledMassagePackageSavingsInterestedIn
## family massage special (4 hours of massage for as many family members to split time between for $145) : 1
## family massage special (4 hours of massage for as many family members to split time between for $145)\nmanual lymphatic drainage (ten 1-hour sessions $45 each, $450 prepaid)\nmonthly membership (four 1-hour massage sessions per month, $200): 1
## family massage special (4 hours of massage for as many family members to split time between for $200)\nmonthly membership (four 1-hour massage sessions per month, $200) : 2
## manual lymphatic drainage (ten 1-hour sessions $45 each, $450 prepaid) : 7
## monthly membership (four 1-hour massage sessions per month, $200) : 5
## NA's :515
## Well there is a lot more to infer from the summary stats on these factors to predict the best audience by most money per household earned and return client. Keep in mind there were actually only 488 actual transactions per person but not per household, the data when merging with consent data by full name added more observations. But you can eliminate duplicates by looking at the start and end time per transaction and see that one person cannot massage multiple people at the same time so those are duplicates, and rarely were there more than 4 family members per household so there may rarely be more than 4 observations per receipt number but some may be duplicates.
We cannot store these modifications to the data as it is because when written out as csv it loses those properties, and is read in exactly how it was to begin with, so to get the data to manipulate you need to run this script and then do the analysis and predictive analytics.
Lets look at the receipts since they are factors now to see how many per receipt or service address show up.
table(Data$SalesReceiptNumber)##
## 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 117 118 119 120
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 1 1 1 1 1 2 1 1 1 1 2 1 3 1 2 3 1 1 1 1
## 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 1 4 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 2 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 3 1 1
## 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
## 2 1 1 1 1 2 1 1 2 2 1 1 1 2 1 1 1 4 2 1
## 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 2 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 2
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
## 1 1 1 2 1 1 1 2 1 2 2 1 1 1 2 1 1 1 1 1
## 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280
## 2 1 1 2 2 1 1 1 2 2 2 1 1 1 2 1 2 1 1 1
## 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300
## 2 1 1 1 1 1 1 2 1 2 1 1 1 2 2 1 1 2 2 1
## 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 3 1 1 2 1 2 1 4 1 2 2 1 1 2 2 2 2 1 1 1
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340
## 2 2 2 1 2 2 2 2 2 1 2 1 2 1 1 2 2 1 1 2
## 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360
## 1 2 1 4 4 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1
## 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380
## 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1
## 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 2 2 1 1 1 1 1 1 2 1 1 1 4 1 4 1 2 1 1 1
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420
## 1 1 1 1 1 1 2 2 1 2 2 2 1 1 1 2 1 1 1 1
## 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440
## 1 1 1 2 1 1 2 1 2 1 2 2 1 2 1 2 1 1 2 1
## 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460
## 2 1 1 2 1 1 2 2 1 1 1 1 2 1 2 1 1 1 1 1
## 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 1 2 2 2 1 1 1 1 2 1 1 1 2 2 2 2 1 1 1 1
## 481 482 483 484 485 486 487 488 489 490 491 492 493
## 1 2 1 1 4 1 1 1 1 1 1 1 1None have more than 2 because some of the family members paid separately as most were couples so they had their own receipt. Lets look at total earned by massage package. df %>% group_by(category, another_column) %>% summarise(total_value = sum(value, na.rm = TRUE)) %>% ungroup()
summary_tbl <- Data %>% group_by(MassagePackage) %>% summarise(totalByPackage=sum(AmountPaid_As_Income, na.rm=TRUE))
summT <- summary_tbl[order(summary_tbl$totalByPackage, decreasing =T)[1:10],]
summT## # A tibble: 10 × 2
## MassagePackage totalByPackage
## <fct> <dbl>
## 1 One Hour Persolized Massage 10007.
## 2 One and one half hour Splitting an hour credit in half of mis… 2860
## 3 One and one half hour Customized Massage 2836.
## 4 One and one half hour Couples Massage 1742.
## 5 One Hour Couples Massage 1655
## 6 90 minutes regular price $120 1461
## 7 90 minutes $75 regularly $120 1330
## 8 Family Massage Special 1274.
## 9 1 hour swedish regular price 1070
## 10 One hour MLD 10 pkg $45 each 890The one hour personalized massage seems to be the most with 150 or more entries and around $10,000 over the years as a side gig from 2019-2025. The next ones for earnings per package are the 90 minutes for $120 no discountsLets look at those factors again, so that we can see if some can be combined, because we have 84 types of massage packages.
summT## # A tibble: 10 × 2
## MassagePackage totalByPackage
## <fct> <dbl>
## 1 One Hour Persolized Massage 10007.
## 2 One and one half hour Splitting an hour credit in half of mis… 2860
## 3 One and one half hour Customized Massage 2836.
## 4 One and one half hour Couples Massage 1742.
## 5 One Hour Couples Massage 1655
## 6 90 minutes regular price $120 1461
## 7 90 minutes $75 regularly $120 1330
## 8 Family Massage Special 1274.
## 9 1 hour swedish regular price 1070
## 10 One hour MLD 10 pkg $45 each 890ggplot(summT, aes(x = MassagePackage, y = totalByPackage)) +
geom_bar(stat = "identity", fill = "blue") +
theme(
axis.text.x = element_text(
angle = 90, # Rotate text 90 degrees
)) +
labs(title = "Total Income per Massage Package",
x = "Massage Package Type",
y = "Total Per Massage Package") +
theme_minimal()
sum(Data$AmountPaid_As_Income, na.rm=TRUE)## [1] 38825.5The total sum for this side gig with bloated entries of 533 entries instead of 488 says the total earnings before expenses was 38,825.5 USD. That is about 7k a year from 2019-2025.
summary(Data$AmountPaid_As_Income)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -315.00 60.00 73.33 74.66 90.00 200.00 11Lets look at earnings per year if we can.
Data %>% group_by(format(dateOfService,"%y")) %>% summarise(totalByYear=sum(AmountPaid_As_Income, na.rm=TRUE))## # A tibble: 8 × 2
## `format(dateOfService, "%y")` totalByYear
## <chr> <dbl>
## 1 19 2062.
## 2 20 6327.
## 3 21 14205
## 4 22 4362.
## 5 23 1190
## 6 24 2625
## 7 25 7855
## 8 26 200Make a barplot of this information of Earnings per Year.
summary_tbl <- Data %>% group_by(Year=format(dateOfService,"%y")) %>% summarise(totalByYear = sum(AmountPaid_As_Income, na.rm=TRUE))
ggplot(summary_tbl, aes(x = Year, y = totalByYear)) +
geom_bar(stat = "identity", fill = "plum") +
theme(
axis.text.x = element_text(
angle = 90, # Rotate text 90 degrees
)) +
labs(title = "Total Income per Year",
x = "Year",
y = "Total Per Year") +
theme_minimal()
Looks like the early post pandemic years were the better years and those were a time when the lymphatic drainage massages were sold in bundles. But then again some of this data is duplicated as 533-488 is a bloated 45 more entries than it should have. Lets see if we can see any of these duplicate entries because duplicated() won’t work on this type of data and I tried earlier after the merger of income with consent form information.
I want to return entries where the receipts as factors are more than 2 entries per receipt. df %>% count(group) %>% slice_max(n, n = 1, with_ties = TRUE)
Data %>% count(SalesReceiptNumber) %>% slice_max(n,n=1,with_ties=TRUE)## SalesReceiptNumber n
## 1 142 4
## 2 218 4
## 3 308 4
## 4 344 4
## 5 345 4
## 6 348 4
## 7 379 4
## 8 393 4
## 9 395 4
## 10 485 4It says the receipts for 142,218,308,344,345,348,379,393,395, and 485 are at the max per house receipt of 4. I know I massaged some families with four people but not that many that paid on same receipt. Lets see the data on those sales receipts.
topReceipts <- Data[Data$SalesReceiptNumber %in% c(142,218,308,344,345,348,379,393,395,485),]
topReceipts[,c(1:4)]## SalesReceiptNumber dateOfService StartTime EndTime
## 12 218 2021-04-03 4:10 PM 5:25 PM
## 44 348 2022-01-15 6:00 PM 7:30 PM
## 45 348 2022-01-15 6:00 PM 7:30 PM
## 136 348 2022-01-15 7:30 PM 8:30 PPM
## 139 345 2022-01-02 7:30 PM 8:30 PM
## 141 393 2022-12-28 6:00 PM 7:30 PM
## 146 308 2021-08-08 8:00 PM 9:00 PM
## 166 142 2020-10-24 3:20 PM 4:05 PM
## 180 142 2020-10-24 5:15 PM 6:45 PM
## 186 344 2021-12-31 12:00 PM 1:00 PM
## 187 344 2021-12-31 12:00 PM 1:00 PM
## 188 485 2025-12-07 12:00 PM 1:00 PM
## 189 485 2025-12-07 12:00 PM 1:00 PM
## 197 142 2020-10-24 4:15 PM 5:05 PM
## 228 393 2022-12-28 7:30 PM 9:00 PM
## 229 308 2021-08-08 7:00 PM 8:00 PM
## 235 345 2022-01-02 6:30 PM 7:30 PM
## 239 218 2021-04-03 6:55 PM 8:05 PM
## 241 348 2022-01-15 8:30 PM 9:30 PM
## 249 393 2022-12-28 10:00 PM 11:00 PM
## 250 345 2022-01-02 9:30 PM 10:30 PM
## 253 308 2021-08-08 6:00 PM 7:00 PM
## 310 393 2022-12-28 9:00 PM 10:00 PM
## 311 308 2021-08-08 5:00 PM 6:00 PM
## 312 345 2022-01-02 8:30 PM 9:30 PM
## 316 379 2022-09-16 1:00 PM 2:00 PM
## 317 379 2022-09-20 10:00 AM 11:00 AM
## 318 379 2022-09-27 10:30 AM 11:30 AM
## 319 379 2022-09-26 6:00 PM 7:00 PM
## 326 218 2021-04-03 5:35 PM 6:45 PM
## 366 142 2020-10-24 7:00 PM 7:45 PM
## 368 395 2023-01-07 7:00 PM 8:00 PM
## 371 485 2025-12-07 1:00 PM 2:00 PM
## 372 485 2025-12-07 1:00 PM 2:00 PM
## 373 344 2021-12-31 1:00 PM 2:00 PM
## 374 344 2021-12-31 1:00 PM 2:00 PM
## 377 395 2023-01-07 9:00 PM 10:00 PM
## 386 395 2023-01-07 8:00 PM 9:00 PM
## 388 395 2023-01-07 6:00 PM 7:00 PM
## 510 218 2021-04-03 8:10 PM 9:20 PMWe can see already that there are duplicate time stamps at row 44 and 45 as well as rows 188 and 189. If the start time and date are the same, then its a bloated added entry from the merge of the consent forms to each income transaction guest. Actually there is different error because the dates should all be the same per receipt and for some receipts the dates are different. Like rows 316 through 319 have different dates from 1/2/2022 trhough 9/26/2022. This could be a discrepancy with the date feature again or an initial error within the data after the 2 data frames were merged together.
Lets look at the sales by zipcode and do a barplot of it.
summary_tbl <- Data %>% group_by(ZipCode) %>% summarise(totalByZipCode = sum(AmountPaid_As_Income, na.rm=TRUE))According to this data made more money in South Corona with ZipCode 92883, next best was 92860 which is Norco that is because I had a regular a few years that did the weekly massage at $50/massage for almost 2 years, very nice lady, after some surgeries she stopped getting them and found out she was doing OK but that was it.
Lets see if we can plot this tibble data as a barplot by zipcode of amount per zipcode earned. Internet feedback on plotting tibble results:
#library(ggplot2)
summT10 <- summary_tbl[order(summary_tbl$totalByZipCode, decreasing=T)[1:10],]
ggplot(summT10, aes(x = ZipCode, y = totalByZipCode)) +
geom_bar(stat = "identity", fill = "tomato") +
theme(
axis.text.x = element_text(
angle = 90, # Rotate text 90 degrees
)) +
labs(title = "Total Income per ZipCode",
x = "Zip Code",
y = "Total Per Zip Code") +
theme_minimal()
Now lets plot a table of count returns by age after making a tibble for it.
summary_tbl <- Data %>% group_by(ageAtSubmission) %>% summarise(totalReturns=sum(returned=="yes", na.rm=TRUE))
summary_tbl## # A tibble: 49 × 2
## ageAtSubmission totalReturns
## <int> <int>
## 1 19 1
## 2 20 3
## 3 21 8
## 4 22 0
## 5 23 0
## 6 24 6
## 7 25 13
## 8 26 21
## 9 27 0
## 10 28 25
## # ℹ 39 more rowsThe most returns are from my regular over almost 2 years and the next best client that returns by age is age 38 and then age 42. So it seems my idea demographic could be between 38 and 42 years of age or young middle aged adults and finding regulars that are retired and focused on weekly massages they benefit from and can afford. Overall the table says between 26 to 42 years of age are the best return clients for this mobile massage provider who is currently 43 but started company at age 37.
Now lets plot this as a bar plot.
summary_tbl <- Data %>% group_by(ageAtSubmission) %>% summarise(totalReturns=sum(returned=="yes", na.rm=TRUE))
summT <- summary_tbl[order(summary_tbl$totalReturns, decreasing=T)[1:10],]
ggplot(summT, aes(x = ageAtSubmission, y = totalReturns)) +
geom_bar(stat = "identity", fill = "aquamarine") +
theme(
axis.text.x = element_text(
angle = 90, # Rotate text 90 degrees
)) +
labs(title = "Total Returns per Age Group",
x = "Age Group",
y = "Total Returns Per Age Group") +
theme_minimal()
Lets look at those clients who pay the most based on pressure they
receive. We are looking at Amount earned per group of pressure requested
on consent form after we look at the return rate by those requesting
different types of pressure.
summary_tbl <- Data %>% group_by(Pressure) %>% summarise(totalReturns=sum(returned=="yes", na.rm=TRUE))
summT <- summary_tbl[order(summary_tbl$totalReturns, decreasing=T)[1:10],]
summT## # A tibble: 10 × 2
## Pressure totalReturns
## <fct> <int>
## 1 <NA> 271
## 2 "medium (just reaching middle level muscles, not too painful)" 45
## 3 "firm (broader pressure)" 26
## 4 "deep tissue (deep layer muscles)\nsuper deep tissue (very dens… 11
## 5 "deep tissue (deep layer muscles)" 7
## 6 "Light\nmedium" 7
## 7 "Light (only superficial muscles reached)" 6
## 8 "deep tissue" 3
## 9 "Light (only superficial muscles reached)\nmedium (just reachin… 3
## 10 "medium\ndeep tissue" 3The top 10 pressure groups that return again by count want medium, firm, or deep pressure. With most wanting medium pressure.
ggplot(summT, aes(x = Pressure, y = totalReturns)) +
geom_bar(stat = "identity", fill = "aquamarine") +
theme(
axis.text.x = element_text(
angle = 90, # Rotate text 90 degrees
)) +
labs(title = "Total Returns per Pressure Preference",
x = "Return Clients per Pressure preferred",
y = "Total Returns Per Pressure Preference") +
theme_minimal()
Plot not so great on pressure due to labels being worded long and the
axis not turning labels vertical like code was described as doing off
internet. But its ok, because we can see the tall bar is the NAs that
showed in the plot even though they were to be removed in the summarise
function with na.rm=T.
Now we will look at the amount earned by groups of pressure requested or filled in on consent form.
summary_tbl <- Data %>% group_by(Pressure) %>% summarise(PressureEarnings=sum(AmountPaid_As_Income, na.rm=TRUE))
summTen <- summary_tbl[order(summary_tbl$PressureEarnings, decreasing=T)[1:10],]
summTen## # A tibble: 10 × 2
## Pressure PressureEarnings
## <fct> <dbl>
## 1 <NA> 25533.
## 2 "medium (just reaching middle level muscles, not too painfu… 3740
## 3 "firm (broader pressure)" 2987.
## 4 "Light\nmedium" 1464.
## 5 "deep tissue (deep layer muscles)" 1142.
## 6 "deep tissue (deep layer muscles)\nsuper deep tissue (very … 968.
## 7 "deep tissue" 791
## 8 "Light (only superficial muscles reached)" 591
## 9 "medium\ndeep tissue" 344
## 10 "medium" 290More earnings from the medium, firm, and light categories of pressure even though more return clientele from the deep pressure prefered group than the light pressure preferred group.
Lets plot this information of earnings per group of pressure.
ggplot(summTen, aes(x = Pressure, y = PressureEarnings)) +
geom_bar(stat = "identity", fill = "purple") +
theme(
axis.text.x = element_text(
angle = 90, # Rotate text 90 degrees
)) +
labs(title = "Total Earnings per Pressure Preference",
x = "Earnings per Type of Pressure preferred",
y = "Total Earnings Per Pressure Preference") +
theme_minimal()
Lets now plot by sum of all payment methods a bar chart for amount earned from type of payment.
matrix <- as.matrix(Data[,26:32])
matrixColSums <- colSums(matrix,na.rm=TRUE)
matrixColSums## FSA_HSA SpaFinderGiftCard otherGiftCard creditCard
## 1344.29 1333.00 90.00 20818.14
## cash zelle check
## 6070.00 6094.32 3940.00barplot(matrixColSums, beside=TRUE,
col = c("skyblue","orange","lightgreen","plum","aquamarine","tomato","maroon"),
legend.text=colnames(matrix),
args.legend = list(x="topright"),
main = "Earnings by Payment Type",
ylab = "Earnings Value")
Lets use the library prophet to run some forecasting with the
information we have. We want to predict our earnings based on a group of
people that are 26 to 42 years old, like medium to firm pressure, are
local but preferrably in the south Corona Zip code of 92883 region. We
want respectable people, that keep returning for massage, have families
or are with a significant other as the returns are by multiple in a
household by receipt number count of people and earnings per receipt of
those receipts with more than one person getting a massage and most
people like one hour massages. That isn’t to say nobody else outside
that demographic is an idea client it just means more in this
demographic will continue purchasing massage services and pay adequate
pricing in earnings.
Lets load the prophet library if you haven’t done it earlier because we will use it now to forecast date of service to earnings over next year.
Now that we are doing forecasting with our times series we should use the date field and get returns per date and group of people that are 26-42, within Corona areas or top 3 zip codes from earlier information, use credit card and/or zelle or cash but nobody books an appointment for new client without a small deposit.
qplot(dateOfService, AmountPaid_As_Income, data=Data )## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
df <- data.frame(ds = Data$dateOfService, y= Data$AmountPaid_As_Income)
m <- prophet(df)## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.future <- make_future_dataframe(m, periods=365)
tail(future)## ds
## 693 2027-01-02
## 694 2027-01-03
## 695 2027-01-04
## 696 2027-01-05
## 697 2027-01-06
## 698 2027-01-07forecast <- predict(m, future)
tail(forecast)## ds trend additive_terms additive_terms_lower
## 693 2027-01-02 105.9893 -3.538267 -3.538267
## 694 2027-01-03 106.0101 6.385643 6.385643
## 695 2027-01-04 106.0309 2.042684 2.042684
## 696 2027-01-05 106.0517 -11.797776 -11.797776
## 697 2027-01-06 106.0725 12.685530 12.685530
## 698 2027-01-07 106.0933 2.773395 2.773395
## additive_terms_upper weekly weekly_lower weekly_upper yearly
## 693 -3.538267 -5.387751 -5.387751 -5.387751 1.8494842
## 694 6.385643 5.129927 5.129927 5.129927 1.2557159
## 695 2.042684 1.375216 1.375216 1.375216 0.6674682
## 696 -11.797776 -11.897978 -11.897978 -11.897978 0.1002013
## 697 12.685530 13.117604 13.117604 13.117604 -0.4320743
## 698 2.773395 3.690534 3.690534 3.690534 -0.9171397
## yearly_lower yearly_upper multiplicative_terms multiplicative_terms_lower
## 693 1.8494842 1.8494842 0 0
## 694 1.2557159 1.2557159 0 0
## 695 0.6674682 0.6674682 0 0
## 696 0.1002013 0.1002013 0 0
## 697 -0.4320743 -0.4320743 0 0
## 698 -0.9171397 -0.9171397 0 0
## multiplicative_terms_upper yhat_lower yhat_upper trend_lower trend_upper
## 693 0 60.51621 142.3970 104.2803 107.5726
## 694 0 73.37389 154.8079 104.2967 107.6017
## 695 0 67.50180 149.0343 104.3113 107.6309
## 696 0 54.57378 137.0957 104.3226 107.6601
## 697 0 77.33702 158.0876 104.3357 107.6890
## 698 0 66.76423 149.7973 104.3502 107.7184
## yhat
## 693 102.45101
## 694 112.39573
## 695 108.07358
## 696 94.25393
## 697 118.75805
## 698 108.86673Well if I get my target idea client to do more returns, in zip codes of local areas with higher earnings and age group and pressure group of those who want around medium to firm pressure are aged 26-42 years, and life in Corona, then I can expect to make next year 80% more than this year from a lower end of 61 USD to 143 USD next year after New Year’s day, and 65 to 148 USD on the 7th of January next year if target group and goals met.
tail(forecast[c("ds","yhat","yhat_lower", "yhat_upper")])## ds yhat yhat_lower yhat_upper
## 693 2027-01-02 102.45101 60.51621 142.3970
## 694 2027-01-03 112.39573 73.37389 154.8079
## 695 2027-01-04 108.07358 67.50180 149.0343
## 696 2027-01-05 94.25393 54.57378 137.0957
## 697 2027-01-06 118.75805 77.33702 158.0876
## 698 2027-01-07 108.86673 66.76423 149.7973plot(m,forecast)
prophet_plot_components(m,forecast)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the prophet package.
## Please report the issue at <https://github.com/facebook/prophet/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Following notes on code from the youtube video by Dr. Bharatendra Rai
Time Series Forecasting in R, his videos are very informative and
detailed, highly recommend watching them to learn more information:
ds <- data\(date y <- log(data\)count) df <- data.frame(ds,y) qplot(ds,y,data=df) #large plot not so obvious for seasonality and 4 rows removed with NA library(prophet) #forecasting with prophet m <- prophet(df) m #details on model # go to help and type in prophet to understand those arguments #prediciton with prophet future <- make_future_dataframe(m, periods = 365) tail(future) forecast <- predict(m, future) #compared to original of 1094 the future has 1454 data points tail(forecast) #values per feature is forecast in year 2016 tail(forecast[c(‘ds’,‘yhat’,‘yhat_lower’,‘y_hat_upper’)]) #since log take exp exp(8.239266) #on 2016-12-29 in prediction #3786.76 is actual prediction plot(m, forecast) # plot actual vs predicted #forecast with confidence interval over actual data points, the lines are the predictions prophet_plot_components(m, forecast)
Well, that is great news. The trend is forecasted by date of service and earnings without targeting my demographics to be upward. There was a dip when starting medical school or from discounts that got the business owner more business but not more earnings as availability declined. Tuesdays don’t seem to be getting much activity, but Sundays, Mondays, and Wednesdays seem to be better active days for mobile massage. And this is entirely dependent on the provider’s availability throughout this time period as she has worked at least one job and been in school for the earlier years.
Thanks for reading and check back for more.