The data set is from market research that I did for a client who was developing a device to read how well done a piece of meat was.
This is an excessively wide dataframe with nested column headers, many blank and unneccessary columns, and likert scale results across columns.
Plan of Attack
import data
trim unneccesary columns
separate likert columns in new DF and unify the responses, then join them to remaining columns of first DF
convert columns that are “which of these would you want” types of questions to logical/binary
analysis and conclusions
mydata <- read.csv("https://raw.githubusercontent.com/ajbentley/cuny_ms_ds/master/607/Steak_Rite_Market_Test.csv", header = TRUE, blank.lines.skip = TRUE)
dim(mydata)## [1] 308 98## Respondent.ID Collector.ID Start.Date End.Date IP.Address
## 1 NA NA NA
## 2 6480248644 164398360 10/27/17 6:21 10/27/17 6:25 NA
## 3 6480245815 164398360 10/27/17 6:19 10/27/17 6:22 NA
## 4 6480223781 164398360 10/27/17 6:03 10/27/17 6:06 NA
## 5 6480184966 164398360 10/27/17 5:35 10/27/17 5:38 NA
## 6 6480150943 164398360 10/27/17 5:08 10/27/17 5:13 NA
## Email.Address First.Name Last.Name Custom.Data.1
## 1 NA NA NA
## 2 NA NA NA 6c0ff50b90493d245d05e76820
## 3 NA NA NA a4d0316f64e7241723a4d4d748
## 4 NA NA NA 5313424229a9a797ba2e62dd01
## 5 NA NA NA 75a8e42d1f310a62c339455e54
## 6 NA NA NA ac46d6d26096ecc135d0b809bf
## What.is.your.first.reaction.to.the.product. X X.1
## 1 Very positive Somewhat positive Neutral
## 2 1
## 3 2
## 4 3
## 5 1
## 6 2
## X.2 X.3 How.innovative.is.the.product.
## 1 Somewhat negative Very negative Extremely innovative
## 2 1
## 3 1
## 4
## 5
## 6
## X.4 X.5 X.6
## 1 Very innovative Somewhat innovative Not so innovative
## 2
## 3
## 4 3
## 5 3
## 6 2
## X.7
## 1 Not at all innovative
## 2
## 3
## 4
## 5
## 6
## When.you.think.about.the.product..do.you.think.of.it.as.something.you.need.or.don.t.need.
## 1 Definitely need
## 2
## 3
## 4
## 5
## 6
## X.8 X.9 X.10 X.11
## 1 Probably need Neutral Probably don’t need Definitely don’t need
## 2 2
## 3 2
## 4 5
## 5 3
## 6 4
## If.the.product.were.available.today..how.likely.would.you.be.to.buy.the.product.
## 1 Extremely likely
## 2 1
## 3
## 4
## 5
## 6
## X.12 X.13 X.14 X.15
## 1 Very likely Somewhat likely Not so likely Not at all likely
## 2
## 3 2
## 4 5
## 5 3
## 6 3
## How.likely.is.it.that.you.would.recommend.the.Steak.Rite.to.a.friend.or.colleague.
## 1 NA
## 2 8
## 3 8
## 4 1
## 5 8
## 6 7
## How.much.would.you.expect.to.pay.for.an.item.like.this.
## 1 Open-Ended Response
## 2 25
## 3 15
## 4 10
## 5 15
## 6 13
## What.are.the.most.important.features.for.an.item.like.this.one..choose.at.least.3..
## 1 No batteries
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## X.16 X.17 X.18 X.19 X.20
## 1 Water resistant Easy to clean Easy to read Easy to use High tech
## 2 2 3 4 5 6
## 3 3
## 4 2 3 5
## 5 5
## 6 2 3 4 5
## X.21 X.22 X.23 X.24
## 1 Attractive Easy to hold Doesn't damage food Doesn't lose juices
## 2 8 9 10
## 3
## 4
## 5 9 10
## 6
## X.25 X.26 X.27 X.28
## 1 Quality of materials Usable for poultry Usable for fish Usable for pork
## 2 11
## 3 11
## 4 11
## 5
## 6 11
## What.is.the.single.most.important.feature. X.29 X.30
## 1 No batteries Water resistant Easy to clean
## 2
## 3
## 4
## 5
## 6 1
## X.31 X.32 X.33 X.34 X.35
## 1 Easy to read Easy to use High tech Attractive Easy to hold
## 2
## 3
## 4
## 5
## 6
## X.36 X.37 X.38
## 1 Doesn't damage food Doesn't lose juices Quality of materials
## 2 9
## 3 9
## 4 11
## 5 10
## 6
## X.39 X.40 X.41
## 1 Usable for poultry Usable for fish Usable for pork
## 2
## 3
## 4
## 5
## 6
## Why.do.you.think.someone.would.buy.this.product. X.42
## 1 Use for self / household Birthday gift
## 2 1
## 3 1
## 4 1
## 5 1
## 6
## X.43 X.44 X.45 X.46
## 1 Holiday gift Housewarming gift Other gift For cooking professional
## 2
## 3
## 4
## 5
## 6 4
## What.is.your.age. X.47 X.48 X.49 X.50 What.is.your.gender. X.51
## 1 Under 18 18 - 29 30 - 44 45 - 59 60+ Female Male
## 2 3 2
## 3 3 2
## 4 3 2
## 5 3 2
## 6 3 2
## How.much.total.combined.money.did.all.members.of.your.HOUSEHOLD.earn.last.year.
## 1 $0 to $9,999
## 2
## 3
## 4
## 5
## 6 1
## X.52 X.53 X.54
## 1 $10,000 to $24,999 $25,000 to $49,999 $50,000 to $74,999
## 2 3
## 3
## 4
## 5
## 6
## X.55 X.56 X.57
## 1 $75,000 to $99,999 $100,000 to $124,999 $125,000 to $149,999
## 2
## 3 5
## 4
## 5 5
## 6
## X.58 X.59 X.60
## 1 $150,000 to $174,999 $175,000 to $199,999 $200,000 and up
## 2
## 3
## 4 10
## 5
## 6
## X.61 US.Region X.62 X.63
## 1 Prefer not to answer New England Middle Atlantic East North Central
## 2
## 3 3
## 4
## 5 2
## 6
## X.64 X.65 X.66 X.67
## 1 West North Central South Atlantic East South Central West South Central
## 2 6
## 3
## 4
## 5
## 6
## X.68 X.69 Device.Types X.70
## 1 Mountain Pacific iOS Phone / Tablet Android Phone / Tablet
## 2 2
## 3
## 4 9
## 5
## 6 9
## X.71 X.72 X.73
## 1 Other Phone / Tablet Windows Desktop / Laptop MacOS Desktop / Laptop
## 2
## 3 4
## 4
## 5 4
## 6 4
## X.74
## 1 Other
## 2
## 3
## 4 6
## 5
## 6There are columns here that are used to identify individual responses that are not neccessary. I also don’t need to know when the survey was taken.
Create the initial dataframe
likert <- df[c(2:308),c(2:21)] # create DF from appropriate rows / columns
likert[] <- lapply(likert, function(likert) as.numeric(as.character(likert))) # convert data from factor to numeric
head(likert)## What.is.your.first.reaction.to.the.product. X X.1 X.2 X.3
## 2 1 NA NA NA NA
## 3 NA 2 NA NA NA
## 4 NA NA 3 NA NA
## 5 1 NA NA NA NA
## 6 NA 2 NA NA NA
## 7 1 NA NA NA NA
## How.innovative.is.the.product. X.4 X.5 X.6 X.7
## 2 1 NA NA NA NA
## 3 1 NA NA NA NA
## 4 NA NA 3 NA NA
## 5 NA NA 3 NA NA
## 6 NA 2 NA NA NA
## 7 1 NA NA NA NA
## When.you.think.about.the.product..do.you.think.of.it.as.something.you.need.or.don.t.need.
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## 7 1
## X.8 X.9 X.10 X.11
## 2 2 NA NA NA
## 3 2 NA NA NA
## 4 NA NA NA 5
## 5 NA 3 NA NA
## 6 NA NA 4 NA
## 7 NA NA NA NA
## If.the.product.were.available.today..how.likely.would.you.be.to.buy.the.product.
## 2 1
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## 7 NA
## X.12 X.13 X.14 X.15
## 2 NA NA NA NA
## 3 2 NA NA NA
## 4 NA NA NA 5
## 5 NA 3 NA NA
## 6 NA 3 NA NA
## 7 2 NA NA NACondense the answers into a single row
likert[, "Reaction"] <- apply(likert[, 1:5], 1, max, na.rm=TRUE)
likert[, "Innovative"] <- apply(likert[, 6:10], 1, max, na.rm=TRUE)
likert[, "Need"] <- apply(likert[, 11:15], 1, max, na.rm=TRUE)
likert[, "Likely2Buy"] <- apply(likert[, 16:20], 1, max, na.rm=TRUE)
dim(likert)## [1] 307 24## [1] 308 90Because the main DF has 308 rows and likert only has 307 I have to either insert an artificial row into likert or remove a row from the main df.
As I will eventually be cutting a secondary header row out of df I will do that work now and bring likert back in later.
While the likert scale columns needed to be condensed, the columns that ask what the most important features are need to be converted to logical / binary.
features <- df[c(1:308),c(24:37)] # create DF from appropriate rows / columns
fcl <- unlist(features[1,]) # create list to assign as column names
colnames(features) <- fcl # assign fcl as column names to features
features <- features[2:308,] # remove row that had feature names originally (making this a 307 row df)
features[] <- lapply(features, function(features) as.numeric(as.character(features))) # convert data from factor to numeric
feats <- data.frame(is.na(features)) # create new dataframe that gives true/false if na
feats[] <- lapply(feats, function(feats) as.factor(as.logical(feats))) # convert data from factor to numeric
feats <- as.data.frame( # run through all columns in feats to change
lapply(feats, function(x) { # true to 0 and false to 1
levels(x)[levels(x) %in% c(TRUE)] <- 0
levels(x)[levels(x) %in% c(FALSE)] <- 1
x
}))
feats[] <- lapply(feats, function(feats) as.numeric(as.factor(feats))) # need to convert to num in order to join later
head(feats)## No.batteries Water.resistant Easy.to.clean Easy.to.read Easy.to.use
## 1 1 1 1 1 1
## 2 1 2 1 2 2
## 3 1 1 1 2 1
## 4 1 2 2 2 1
## 5 1 1 1 1 1
## 6 2 2 2 1 2
## High.tech Attractive Easy.to.hold Doesn.t.damage.food
## 1 1 2 1 1
## 2 2 2 2 2
## 3 2 2 2 2
## 4 2 2 2 1
## 5 2 2 2 2
## 6 2 2 1 2
## Doesn.t.lose.juices Quality.of.materials Usable.for.poultry
## 1 1 1 2
## 2 2 1 2
## 3 2 1 2
## 4 1 2 2
## 5 2 1 2
## 6 2 2 2
## Usable.for.fish Usable.for.pork
## 1 2 2
## 2 2 2
## 3 2 2
## 4 2 2
## 5 2 2
## 6 1 2For this section there are two ways to think about storing the data.
If you are going to be doing any kind of modeling on it then it is best to keep each of these features in a separate column as they have no relationship to each other.
If you are going to be creating a relational database or just generating summary data it would be better to consolidate them in the same way that was done with the likert scales.
As I will not be modeling on this any time soon, and as it is easy enough to dummy the variables out if I decide to later, I’ll go with consolidation
mif <- df[c(2:308),c(38:51)] # create DF from appropriate rows / columns
mif[] <- lapply(mif, function(mif) as.numeric(as.character(mif))) # convert data from factor to numeric
mif[, "Most_Important_Feature"] <- apply(mif[, 1:14], 1, max, na.rm=TRUE) # consolidate
head(mif)## What.is.the.single.most.important.feature. X.29 X.30 X.31 X.32 X.33 X.34
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA
## 6 1 NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA
## X.35 X.36 X.37 X.38 X.39 X.40 X.41 Most_Important_Feature
## 2 NA 9 NA NA NA NA NA 9
## 3 NA 9 NA NA NA NA NA 9
## 4 NA NA NA 11 NA NA NA 11
## 5 NA NA 10 NA NA NA NA 10
## 6 NA NA NA NA NA NA NA 1
## 7 NA 9 NA NA NA NA NA 9who <- df[c(2:308),c(52:57)] # create DF from appropriate rows / columns
who[] <- lapply(who, function(who) as.numeric(as.character(who))) # convert data from factor to numeric
who[, "Who_Would_Buy"] <- apply(who[, 1:6], 1, max, na.rm=TRUE) # consolidate
head(who)## Why.do.you.think.someone.would.buy.this.product. X.42 X.43 X.44 X.45
## 2 1 NA NA NA NA
## 3 1 NA NA NA NA
## 4 1 NA NA NA NA
## 5 1 NA NA NA NA
## 6 NA NA NA 4 NA
## 7 NA 2 NA NA NA
## X.46 Who_Would_Buy
## 2 NA 1
## 3 NA 1
## 4 NA 1
## 5 NA 1
## 6 NA 4
## 7 NA 2age <- df[c(2:308),c(58:62)] # create DF from appropriate rows / columns
age[] <- lapply(age, function(age) as.numeric(as.character(age))) # convert data from factor to numeric
age[, "AgeRange"] <- apply(age[, 1:5], 1, max, na.rm=TRUE) # consolidate
head(age)## What.is.your.age. X.47 X.48 X.49 X.50 AgeRange
## 2 NA NA 3 NA NA 3
## 3 NA NA 3 NA NA 3
## 4 NA NA 3 NA NA 3
## 5 NA NA 3 NA NA 3
## 6 NA NA 3 NA NA 3
## 7 NA 2 NA NA NA 2gen <- df[c(2:308),c(63:64)] # create DF from appropriate rows / columns
gen[] <- lapply(gen, function(gen) as.numeric(as.character(gen))) # convert data from factor to numeric
gen[, "Gender"] <- apply(gen[, 1:2], 1, max, na.rm=TRUE) # consolidate
head(gen)## What.is.your.gender. X.51 Gender
## 2 NA 2 2
## 3 NA 2 2
## 4 NA 2 2
## 5 NA 2 2
## 6 NA 2 2
## 7 NA 2 2income <- df[c(2:308),c(65:75)] # create DF from appropriate rows / columns
income[] <- lapply(income, function(income) as.numeric(as.character(income))) # convert data from factor to numeric
income[, "IncomeRange"] <- apply(income[, 1:11], 1, max, na.rm=TRUE) # consolidate
head(income)## How.much.total.combined.money.did.all.members.of.your.HOUSEHOLD.earn.last.year.
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 1
## 7 1
## X.52 X.53 X.54 X.55 X.56 X.57 X.58 X.59 X.60 X.61 IncomeRange
## 2 NA 3 NA NA NA NA NA NA NA NA 3
## 3 NA NA NA 5 NA NA NA NA NA NA 5
## 4 NA NA NA NA NA NA NA NA 10 NA 10
## 5 NA NA NA 5 NA NA NA NA NA NA 5
## 6 NA NA NA NA NA NA NA NA NA NA 1
## 7 NA NA NA NA NA NA NA NA NA NA 1region <- df[c(2:308),c(76:84)] # create DF from appropriate rows / columns
region[] <- lapply(region, function(region) as.numeric(as.character(region))) # convert data from factor to numeric
region[is.na(region)] <- 0 # needed to include this because some didn't fill this out leading to the max being infinity
region[, "Region"] <- apply(region[, 1:9], 1, max, na.rm=TRUE) # consolidate
head(region)## US.Region X.62 X.63 X.64 X.65 X.66 X.67 X.68 X.69 Region
## 2 0 0 0 0 0 6 0 0 0 6
## 3 0 0 3 0 0 0 0 0 0 3
## 4 0 0 0 0 0 0 0 0 9 9
## 5 0 2 0 0 0 0 0 0 0 2
## 6 0 0 0 0 0 0 0 0 9 9
## 7 0 2 0 0 0 0 0 0 0 2device <- df[c(2:308),c(85:90)] # create DF from appropriate rows / columns
device[] <- lapply(device, function(device) as.numeric(as.character(device))) # convert data from factor to numeric
device[, "Device"] <- apply(device[, 1:6], 1, max, na.rm=TRUE) # consolidate
head(device)## Device.Types X.70 X.71 X.72 X.73 X.74 Device
## 2 NA 2 NA NA NA NA 2
## 3 NA NA NA 4 NA NA 4
## 4 NA NA NA NA NA 6 6
## 5 NA NA NA 4 NA NA 4
## 6 NA NA NA 4 NA NA 4
## 7 1 NA NA NA NA NA 1At this point the majority of columns have been processed in such a way that only 307 rows exist. For the 2 remaining I’ll do the same and then reconstitute the dataframe
## How.likely.is.it.that.you.would.recommend.the.Steak.Rite.to.a.friend.or.colleague.
## 2 8
## 3 8
## 4 1
## 5 8
## 6 7
## 7 4
## How.much.would.you.expect.to.pay.for.an.item.like.this.
## 2 25
## 3 15
## 4 10
## 5 15
## 6 13
## 7 53df1 <- data.frame(cbind(likert[1:307,21:24], remain[1:307,1:2], feats[1:307,1:14], mif[1:307,15], who[1:307,7], age[1:307,6], gen[1:307,3], income[1:307,12],region[1:307,10], device[1:307,7]))
head(df1)## Reaction Innovative Need Likely2Buy
## 2 1 1 2 1
## 3 2 1 2 2
## 4 3 3 5 5
## 5 1 3 3 3
## 6 2 2 4 3
## 7 1 1 1 2
## How.likely.is.it.that.you.would.recommend.the.Steak.Rite.to.a.friend.or.colleague.
## 2 8
## 3 8
## 4 1
## 5 8
## 6 7
## 7 4
## How.much.would.you.expect.to.pay.for.an.item.like.this. No.batteries
## 2 25 1
## 3 15 1
## 4 10 1
## 5 15 1
## 6 13 1
## 7 53 2
## Water.resistant Easy.to.clean Easy.to.read Easy.to.use High.tech
## 2 1 1 1 1 1
## 3 2 1 2 2 2
## 4 1 1 2 1 2
## 5 2 2 2 1 2
## 6 1 1 1 1 2
## 7 2 2 1 2 2
## Attractive Easy.to.hold Doesn.t.damage.food Doesn.t.lose.juices
## 2 2 1 1 1
## 3 2 2 2 2
## 4 2 2 2 2
## 5 2 2 1 1
## 6 2 2 2 2
## 7 2 1 2 2
## Quality.of.materials Usable.for.poultry Usable.for.fish Usable.for.pork
## 2 1 2 2 2
## 3 1 2 2 2
## 4 1 2 2 2
## 5 2 2 2 2
## 6 1 2 2 2
## 7 2 2 1 2
## mif.1.307..15. who.1.307..7. age.1.307..6. gen.1.307..3.
## 2 9 1 3 2
## 3 9 1 3 2
## 4 11 1 3 2
## 5 10 1 3 2
## 6 1 4 3 2
## 7 9 2 2 2
## income.1.307..12. region.1.307..10. device.1.307..7.
## 2 3 6 2
## 3 5 3 4
## 4 10 9 6
## 5 5 2 4
## 6 1 9 4
## 7 1 2 1We now have a solid dataframe we can use for analysis, but there are a few tweaks to make.
Many column headings need to be cleaned
Codes in many columns can be replaced by their values
Clean Column Names
colnames(df1)[5:6] <- c("Recommend",'ExpPrice')
colnames(df1)[21:27] <- c('MostImpFeat', 'WhyBuy', 'AgeRange', 'Gender','IncomeRange', 'Region', 'DeviceUsed')
colnames(df1)## [1] "Reaction" "Innovative" "Need"
## [4] "Likely2Buy" "Recommend" "ExpPrice"
## [7] "No.batteries" "Water.resistant" "Easy.to.clean"
## [10] "Easy.to.read" "Easy.to.use" "High.tech"
## [13] "Attractive" "Easy.to.hold" "Doesn.t.damage.food"
## [16] "Doesn.t.lose.juices" "Quality.of.materials" "Usable.for.poultry"
## [19] "Usable.for.fish" "Usable.for.pork" "MostImpFeat"
## [22] "WhyBuy" "AgeRange" "Gender"
## [25] "IncomeRange" "Region" "DeviceUsed"## Reaction Innovative Need Likely2Buy Recommend ExpPrice No.batteries
## 2 1 1 2 1 8 25 1
## 3 2 1 2 2 8 15 1
## 4 3 3 5 5 1 10 1
## 5 1 3 3 3 8 15 1
## 6 2 2 4 3 7 13 1
## 7 1 1 1 2 4 53 2
## Water.resistant Easy.to.clean Easy.to.read Easy.to.use High.tech
## 2 1 1 1 1 1
## 3 2 1 2 2 2
## 4 1 1 2 1 2
## 5 2 2 2 1 2
## 6 1 1 1 1 2
## 7 2 2 1 2 2
## Attractive Easy.to.hold Doesn.t.damage.food Doesn.t.lose.juices
## 2 2 1 1 1
## 3 2 2 2 2
## 4 2 2 2 2
## 5 2 2 1 1
## 6 2 2 2 2
## 7 2 1 2 2
## Quality.of.materials Usable.for.poultry Usable.for.fish Usable.for.pork
## 2 1 2 2 2
## 3 1 2 2 2
## 4 1 2 2 2
## 5 2 2 2 2
## 6 1 2 2 2
## 7 2 2 1 2
## MostImpFeat WhyBuy AgeRange Gender IncomeRange Region DeviceUsed
## 2 9 1 3 2 3 6 2
## 3 9 1 3 2 5 3 4
## 4 11 1 3 2 10 9 6
## 5 10 1 3 2 5 2 4
## 6 1 4 3 2 1 9 4
## 7 9 2 2 2 1 2 1Replace number keys with values
df1[,21:27]<-lapply(df1[,21:27], factor) # convert numeric to factor in the appropriate columns
df1$MostImpFeat <- revalue(df1$MostImpFeat, c('1' = 'No batteries',
'2' = 'Water resistant',
'3' = 'Easy to clean',
'4' = 'Easy to read',
'5' = 'Easy to use',
'6' = 'High tech',
'7' = 'Attractive',
'8' = 'Easy to hold',
'9' = 'Doesn\'t damage food',
'10' = 'Doesn\'t lose juices',
'11' = 'Quality of materials',
'12' = 'Usable for poultry',
'13' = 'Usable for fish',
'14' = 'Usable for pork'))
df1$WhyBuy <- revalue(df1$WhyBuy, c('1' = 'Use for self / household',
'2' = 'Birthday gift',
'3' = 'Holiday gift',
'4' = 'Housewarming gift',
'5' = 'Other gift',
'6' = 'For cooking professional'))
df1$AgeRange <- revalue(df1$AgeRange, c('1' = 'Under 18',
'2' = '18 - 29',
'3' = '30 - 44',
'4' = '45 - 59',
'5' = '60+'))## The following `from` values were not present in `x`: 1df1$Gender <- revalue(df1$Gender, c('1' = 'Female', '2' = 'Male'))
df1$IncomeRange <- revalue(df1$IncomeRange, c('1' = '$0 to $9,999',
'2' = '$10,000 to $24,999',
'3' = '$25,000 to $49,999',
'4' = '$50,000 to $74,999',
'5' = '$75,000 to $99,999',
'6' = '$100,000 to $124,999',
'7' = '$125,000 to $149,999',
'8' = '$150,000 to $174,999',
'9' = '$175,000 to $199,999',
'10' = '$200,000 and up',
'11' = 'Prefer not to answer'))
df1$Region <- revalue(df1$Region, c('1' = 'New England',
'2' = 'Middle Atlantic',
'3' = 'East North Central',
'4' = 'West North Central',
'5' = 'South Atlantic',
'6' = 'East South Central',
'7' = 'West South Central',
'8' = 'Mountain',
'9' = 'Pacific'))
df1$DeviceUsed <- revalue(df1$DeviceUsed, c('1' = 'iOS Phone / Tablet',
'2' = 'Android Phone / Tablet',
'3' = 'Other Phone / Tablet',
'4' = 'Windows Desktop / Laptop',
'5' = 'MacOS Desktop / Laptop',
'6' = 'Other'))## The following `from` values were not present in `x`: 3df1[,7:20]<- df1[,7:20]-1 # This is just adjusting the binary numerics in these columns from 1 and 2 to
# 0 and 1--a personal preference1 - Pull averages for Reaction, Innovative, Need, Likely to Buy, Would Recommend, and Expected price
## Reaction Innovative Need Likely2Buy Recommend ExpPrice
## 1 2.228013 2.34202 3.175896 3.254072 5.504886 NAReaction through Likely2Buy 1 is best, 5 is worst.
Recommend 10 is best 1 is worst
Reaction was positive and people saw the product as innovative, but not overwhelmingly so in either case.
They do not perceive the product as something they need, nor are they likely to buy it and they aren’t eager to recommend it.
The client had hoped to sell the item for around $20-$25 so the expected price isn’t far off.
2 - What are the most important features and who would buy this?
## # A tibble: 14 x 2
## MostImpFeat no_rows
## <fct> <int>
## 1 No batteries 30
## 2 Water resistant 8
## 3 Easy to clean 25
## 4 Easy to read 28
## 5 Easy to use 84
## 6 High tech 8
## 7 Attractive 2
## 8 Easy to hold 13
## 9 Doesn't damage food 38
## 10 Doesn't lose juices 31
## 11 Quality of materials 20
## 12 Usable for poultry 12
## 13 Usable for fish 3
## 14 Usable for pork 5The most important thing, by far, is that the unit is easy to use. People also like that it wouldn’t damage the food or let juices out, as well as that it doesn’t need batteries.
3 - What person would someone buy this for?
## # A tibble: 6 x 2
## WhyBuy no_rows
## <fct> <int>
## 1 Use for self / household 143
## 2 Birthday gift 22
## 3 Holiday gift 42
## 4 Housewarming gift 29
## 5 Other gift 25
## 6 For cooking professional 46Most folks think that this is something that someone would buy for themselves, not as a gift.