World Catnip Surveys — Gwern

Google Surveys, as mentioned, doesn’t seem to support any elegant way of repeating a series of questions, so I hardcoded questions for up to 5 cats, producing a ‘wide’ survey format in which each row is a single respondent with 5 sets of age/breed/fur/neuter/sex/personality+catnip/valerian/silvervine/thyme/honeysuckle ratings. For almost all tasks, it’s better to have the survey in a ‘long’ format, where each row is instead a single cat’s set of covariates & ratings, with the owner information (and a unique ID) repeated across the rows for all the cats they provided information on. (This then permits straightforward analyses like regressions of the form Catnip ~ (1|ID) + Sex + Breed etc.) This is complicated enough I couldn’t figure out how to use the usual reshaping libraries to convert wide to long, so I did it by brute force. As well, an additional variable is added to investigate the demand bias, noting whether a cat entry is the “first” cat provided by a user; if there is a demand bias as I hypothesize based on the extremely high reported catnip response rates for first cats, then first vs the rest (second/third/fourth/fifth) should predict catnip responses.

After the data is reshaped, the free response fields need to be cleaned up and similar responses combined (eg. “UK” combined with “United Kingdom”).

catnip <- read.csv("https://gwern.net/doc/cat/psychology/drug/catnip/survey/2017-01-02-catnipsurvey-conveniencesample.csv")

catnip$ID <- 1:nrow(catnip); catnip$Timestamp <- NULL
catnipLong <- data.frame(ID=integer(), Owner.age=integer(), Owner.sex=factor(), Owner.education=factor(),
    Owner.country=factor(), Catnip.types=factor(), Nth.cat=integer(), Cat.age=numeric(), Cat.breed=factor(),
    Cat.fur.color=factor(), Cat.neuter=logical(), Cat.personality=integer(), Cat.sex=factor(),
    Cat.response.Catnip=logical(), Cat.response.Valerian=logical(), Cat.response.Silvervine=logical(),
    Cat.response.Thyme=logical(), Cat.response.Honeysuckle=logical())
for (i in 1:nrow(catnip)) {
  catnipLong <- with(catnip[i,],
      rbind(catnipLong,
      data.frame(ID=ID, Owner.age=Owner.age, Owner.sex=Owner.sex, Owner.education=Owner.education, Owner.country=Owner.country, Catnip.types=Catnip.types,
          Nth.cat=1, Cat.age=Cat1.age, Cat.breed=Cat1.breed, Cat.fur.color=Cat1.fur.color, Cat.neuter=Cat1.neuter, Cat.personality=Cat1.personality,
          Cat.sex=Cat1.sex, Cat.response.Catnip=Cat1.response.Catnip, Cat.response.Valerian=Cat1.response.Valerian, Cat.response.Silvervine=Cat1.response.Silvervine,
          Cat.response.Thyme=Cat1.response.Thyme, Cat.response.Honeysuckle=Cat1.response.Honeysuckle),
      data.frame(ID=ID, Owner.age=Owner.age, Owner.sex=Owner.sex, Owner.education=Owner.education, Owner.country=Owner.country, Catnip.types=Catnip.types,
              Nth.cat=2, Cat.age=Cat2.age, Cat.breed=Cat2.breed, Cat.fur.color=Cat2.fur.color, Cat.neuter=Cat2.neuter, Cat.personality=Cat2.personality,
              Cat.sex=Cat2.sex, Cat.response.Catnip=Cat2.response.Catnip, Cat.response.Valerian=Cat2.response.Valerian,
              Cat.response.Silvervine=Cat2.response.Silvervine, Cat.response.Thyme=Cat2.response.Thyme, Cat.response.Honeysuckle=Cat2.response.Honeysuckle),
      data.frame(ID=ID, Owner.age=Owner.age, Owner.sex=Owner.sex, Owner.education=Owner.education, Owner.country=Owner.country, Catnip.types=Catnip.types,
          Nth.cat=3, Cat.age=Cat3.age, Cat.breed=Cat3.breed, Cat.fur.color=Cat3.fur.color, Cat.neuter=Cat3.neuter, Cat.personality=Cat3.personality,
          Cat.sex=Cat3.sex, Cat.response.Catnip=Cat3.response.Catnip, Cat.response.Valerian=Cat3.response.Valerian, Cat.response.Silvervine=Cat3.response.Silvervine,
          Cat.response.Thyme=Cat3.response.Thyme, Cat.response.Honeysuckle=Cat3.response.Honeysuckle),
      data.frame(ID=ID, Owner.age=Owner.age, Owner.sex=Owner.sex, Owner.education=Owner.education, Owner.country=Owner.country, Catnip.types=Catnip.types,
          Nth.cat=4, Cat.age=Cat4.age, Cat.breed=Cat4.breed, Cat.fur.color=Cat4.fur.color, Cat.neuter=Cat4.neuter, Cat.personality=Cat4.personality, Cat.sex=Cat4.sex,
          Cat.response.Catnip=Cat4.response.Catnip, Cat.response.Valerian=Cat4.response.Valerian, Cat.response.Silvervine=Cat4.response.Silvervine,
          Cat.response.Thyme=Cat4.response.Thyme, Cat.response.Honeysuckle=Cat4.response.Honeysuckle),
      data.frame(ID=ID, Owner.age=Owner.age, Owner.sex=Owner.sex, Owner.education=Owner.education, Owner.country=Owner.country, Catnip.types=Catnip.types,
          Nth.cat=5, Cat.age=Cat5.age, Cat.breed=Cat5.breed, Cat.fur.color=Cat5.fur.color, Cat.neuter=Cat5.neuter, Cat.personality=Cat5.personality,
          Cat.sex=Cat5.sex, Cat.response.Catnip=Cat5.response.Catnip, Cat.response.Valerian=Cat5.response.Valerian, Cat.response.Silvervine=Cat5.response.Silvervine,
          Cat.response.Thyme=Cat5.response.Thyme, Cat.response.Honeysuckle=Cat5.response.Honeysuckle)))
   }

## filter out the empty data-frame rows by filtering on `Cat.sex` - potential false positives, but anyone who doesn't even know the gender of their cat can't be a good judge of their responses anyway...
catnipLong <- catnipLong[!is.na(catnipLong$Cat.sex),]
## Test response bias:
catnipLong$First <- catnipLong$Nth.cat==1

catnipLong[!is.na(catnipLong$Cat.fur.color) &
    catnipLong$Cat.fur.color=="Orange and white on left here http://b.robnugen.com/cats/kawasaki/cats_2015-03-26_09.22.26.jpg",]$Cat.fur.color <-
    "Tabby (orange, cream, and buff)"
catnipLong[!is.na(catnipLong$Cat.fur.color) & catnipLong$Cat.fur.color=="Orange and white splotched",]$Cat.fur.color <- "Tabby (orange, cream, and buff)"
catnipLong[!is.na(catnipLong$Cat.fur.color) & catnipLong$Cat.fur.color=="tortoiseshell",]$Cat.fur.color <- "Torbie (tortoiseshell colors with tabby pattern)"
catnipLong[!is.na(catnipLong$Cat.fur.color) & catnipLong$Cat.fur.color=="white",]$Cat.fur.color <- "gray-and-white"
catnipLong[!is.na(catnipLong$Cat.fur.color) & catnipLong$Cat.fur.color=="Orange tabby with paint/large white areas",]$Cat.fur.color <- "Tabby (orange, cream, and buff)"
catnipLong[!is.na(catnipLong$Cat.fur.color) & catnipLong$Cat.fur.color=="Tabby/Tuxedo pattern mix (black and white)",]$Cat.fur.color <- "black-and-white"
catnipLong[!is.na(catnipLong$Cat.fur.color) & catnipLong$Cat.fur.color=="tuxedo black & white longhair",]$Cat.fur.color <- "black-and-white"
levels(catnipLong$Cat.fur.color) <- c(levels(catnipLong$Cat.fur.color), "Other")
usableFurColors <- row.names(sort(table(catnipLong$Cat.fur.color)))[22:29]
catnipLong[!is.na(catnipLong$Cat.fur.color) & !(catnipLong$Cat.fur.color %in% usableFurColors),]$Cat.fur.color <- "Other"

## use only breeds with n>3, lump the rest together:
usableBreeds <- row.names(sort(table(catnipLong$Cat.breed))[26:30])
levels(catnipLong$Cat.breed) <- c(levels(catnipLong$Cat.breed), "Other")
catnipLong[!is.na(catnipLong$Cat.breed) & !(catnipLong$Cat.breed %in% usableBreeds),]$Cat.breed <- "Other"

## Clean up country free responses:
replaceFactor <- function(df, wrong,right) { df[!is.na(df$Owner.country) & df$Owner.country==wrong,]$Owner.country <- right
    return(df) }
catnipLong <- replaceFactor(catnipLong, "Czech republic", "Czech Republic")
catnipLong <- replaceFactor(catnipLong, "Czech Republic ", "Czech Republic")
catnipLong <- replaceFactor(catnipLong, "india", "India")
catnipLong <- replaceFactor(catnipLong, "latvia", "Latvia")
catnipLong <- replaceFactor(catnipLong, "N/A", NA)
catnipLong <- replaceFactor(catnipLong, "Sweden ", "Sweden")
catnipLong <- replaceFactor(catnipLong, "UK", "United Kingdom")

write.csv(catnipLong, file="catnip-long-clean.csv", row.names=FALSE)