r,postgresql,dplyr , How do I turn sets into indicators of set membership?


How do I turn sets into indicators of set membership?

Question:

Tag: r,postgresql,dplyr

The data I have have for each observation a set of "flavors". I would like to turn those sets (which exist as text[] arrays in PostgreSQL) into indicators for the presence of individual flavors, as I'd like to examine how flavors either do or do not go together.

What I have now is working, but I'd actually like to run more complicated variants and I have a hunch that the way I am pulling together the data is far less elegant than it could be. I tried using the tidyr and dplyr packages, but could not see how to apply these.

Is there a better way (using R)?

Here is some sample code:

library("PostgreSQL")

pg <- dbConnect(PostgreSQL())

# Make the data set in the form I have it.
rs <- dbGetQuery(pg, "
     DROP TABLE IF EXISTS icecream ;

     CREATE TABLE icecream (id text, date date, flavours text[]);

     INSERT INTO icecream (id, date, flavours) VALUES 
     ('a', '2013-01-01', ARRAY['Chocolate', 'Vanilla']),
     ('b', '2013-01-01', ARRAY['Strawberry', 'Vanilla']),
     ('b', '2013-02-01', ARRAY['Raspberry', 'Lemon']),
     ('c', '2013-01-01', ARRAY['Raspberry', 'Blueberry']);")

# Get data in an R-friendly format
df <- dbGetQuery(pg, "    
     SELECT id, date, UNNEST(flavours) AS flavour
     FROM icecream;")

rs <- dbDisconnect(pg)

# Rearrange data and look at correlations
library(reshape2)
temp <- dcast(df, id + date ~ flavour, value.var="flavour")
temp[, -c(1,2)] <- !is.na(temp[, -c(1,2)])
cor(temp[, -c(1,2)])

Here is what the data ultimately look like:

  id       date Blueberry Chocolate Lemon Raspberry Strawberry Vanilla
1  a 2013-01-01     FALSE      TRUE FALSE     FALSE      FALSE    TRUE
2  b 2013-01-01     FALSE     FALSE FALSE     FALSE       TRUE    TRUE
3  b 2013-02-01     FALSE     FALSE  TRUE      TRUE      FALSE   FALSE
4  c 2013-01-01      TRUE     FALSE FALSE      TRUE      FALSE   FALSE

And here is an illustration of the kind of analysis I'd like to do:

> cor(temp[, -c(1,2)])
            Blueberry  Chocolate      Lemon  Raspberry Strawberry    Vanilla
Blueberry   1.0000000 -0.3333333 -0.3333333  0.5773503 -0.3333333 -0.5773503
Chocolate  -0.3333333  1.0000000 -0.3333333 -0.5773503 -0.3333333  0.5773503
Lemon      -0.3333333 -0.3333333  1.0000000  0.5773503 -0.3333333 -0.5773503
Raspberry   0.5773503 -0.5773503  0.5773503  1.0000000 -0.5773503 -1.0000000
Strawberry -0.3333333 -0.3333333 -0.3333333 -0.5773503  1.0000000  0.5773503
Vanilla    -0.5773503  0.5773503 -0.5773503 -1.0000000  0.5773503  1.0000000

To skip the PostgreSQL, I guess one could just pull df together using this information. I include the PostgreSQL just in case a more elegant solution uses PostgreSQL more effectively.

dput(df)
structure(list(id = c("a", "a", "b", "b", "b", "b", "c", "c"), 
    date = structure(c(15706, 15706, 15706, 15706, 15737, 15737, 
    15706, 15706), class = "Date"), flavour = c("Chocolate", 
    "Vanilla", "Strawberry", "Vanilla", "Raspberry", "Lemon", 
    "Raspberry", "Blueberry")), .Names = c("id", "date", "flavour"
), row.names = c(NA, 8L), class = "data.frame")

Answer:

Any postgres solution is going to be less elegant. You'd have to use crosstab, which would require column definitions for each of your flavors.

Here's a way with dplyr and tidyr:

library(dplyr)
library(tidyr)
df %>%
    mutate_(indicator=~TRUE) %>%
    spread('flavour', 'indicator', fill=FALSE)

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